semeval_id
int32 0
319
| question
stringlengths 23
161
| dataset
stringclasses 16
values | split
stringclasses 1
value | predicted_type
stringclasses 5
values | predicted_columns
stringlengths 6
161
| phase
stringclasses 1
value | content
stringlengths 2.99k
55.1k
| update_timestamp
stringclasses 15
values |
|---|---|---|---|---|---|---|---|---|
200 | Are all transactions IDs unique? | 060_Bakery | dev | boolean | [Transaction] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are all transactions IDs unique?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are all transactions IDs unique?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is boolean, this will be checked.
return | 20250129013723 |
201 | Is there any transaction that took place during the night? | 060_Bakery | dev | boolean | [period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Is there any transaction that took place during the night?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Is there any transaction that took place during the night?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is boolean, this will be checked.
return | 20250129013723 |
202 | Do all items have transactions recorded on weekdays? | 060_Bakery | dev | boolean | [Item, weekday_weekend] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Do all items have transactions recorded on weekdays?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Do all items have transactions recorded on weekdays?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is boolean, this will be checked.
return | 20250129013723 |
203 | Are there any transactions recorded in the evening on weekends? | 060_Bakery | dev | boolean | [period_day, weekday_weekend] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any transactions recorded in the evening on weekends?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any transactions recorded in the evening on weekends?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is boolean, this will be checked.
return | 20250129013723 |
204 | How many unique items are there in the dataset? | 060_Bakery | dev | number | [Item] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many unique items are there in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many unique items are there in the dataset?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is number, this will be checked.
return | 20250129013723 |
205 | On how many different days were transactions recorded? | 060_Bakery | dev | number | [date_time] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: On how many different days were transactions recorded?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: On how many different days were transactions recorded?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is number, this will be checked.
return | 20250129013723 |
206 | What's the highest transaction number? | 060_Bakery | dev | number | [Transaction] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What's the highest transaction number?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What's the highest transaction number?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is number, this will be checked.
return | 20250129013723 |
207 | How many different IDs are there in the transactions that were made during the afternoon? | 060_Bakery | dev | number | [Transaction, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many different IDs are there in the transactions that were made during the afternoon?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many different IDs are there in the transactions that were made during the afternoon?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is number, this will be checked.
return | 20250129013723 |
208 | Which day period has the highest number of unique transaction IDs? | 060_Bakery | dev | category | [period_day, Transaction] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Which day period has the highest number of unique transaction IDs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Which day period has the highest number of unique transaction IDs?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is category, this will be checked.
return | 20250129013723 |
209 | On weekdays, what's the most commonly bought item? | 060_Bakery | dev | category | [Item, weekday_weekend] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: On weekdays, what's the most commonly bought item?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: On weekdays, what's the most commonly bought item?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is category, this will be checked.
return | 20250129013723 |
210 | What's the least popular item bought during weekdays? | 060_Bakery | dev | category | [Item, weekday_weekend] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What's the least popular item bought during weekdays?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What's the least popular item bought during weekdays?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is category, this will be checked.
return | 20250129013723 |
211 | During which period of the day is Brownie" most frequently bought?" | 060_Bakery | dev | category | [Item, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: During which period of the day is Brownie" most frequently bought?"
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: During which period of the day is Brownie" most frequently bought?"
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is category, this will be checked.
return | 20250129013723 |
212 | List the top 3 items most frequently bought in the morning. | 060_Bakery | dev | list[category] | [Item, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: List the top 3 items most frequently bought in the morning.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: List the top 3 items most frequently bought in the morning.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[category], this will be checked.
return | 20250129013723 |
213 | Name the top 2 most purchased during the afternoon. | 060_Bakery | dev | list[category] | [Item, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Name the top 2 most purchased during the afternoon.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Name the top 2 most purchased during the afternoon.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[category], this will be checked.
return | 20250129013723 |
214 | Identify the top 2 items sold on weekends. If a tie then sort alphabetical. | 060_Bakery | dev | list[category] | [Item, weekday_weekend] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Identify the top 2 items sold on weekends. If a tie then sort alphabetical.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Identify the top 2 items sold on weekends. If a tie then sort alphabetical.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[category], this will be checked.
return | 20250129013723 |
215 | What are the 4 items that were bought two times in the evening? | 060_Bakery | dev | list[category] | [Item, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the 4 items that were bought two times in the evening?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the 4 items that were bought two times in the evening?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[category], this will be checked.
return | 20250129013723 |
216 | Which are the 4 transaction numbers with the most items bought? | 060_Bakery | dev | list[number] | [Transaction] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Which are the 4 transaction numbers with the most items bought?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Which are the 4 transaction numbers with the most items bought?
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[number], this will be checked.
return | 20250129013723 |
217 | Identify the highest 5 transaction numbers. | 060_Bakery | dev | list[number] | [Transaction] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Identify the highest 5 transaction numbers.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Identify the highest 5 transaction numbers.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[number], this will be checked.
return | 20250129013723 |
218 | List the highest 4 transaction numbers during which 'Bread' was purchased. | 060_Bakery | dev | list[number] | [Transaction, Item] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: List the highest 4 transaction numbers during which 'Bread' was purchased.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: List the highest 4 transaction numbers during which 'Bread' was purchased.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
219 | Name the lowest 2 transaction numbers where purchases were made in the morning. | 060_Bakery | dev | list[number] | [Transaction, period_day] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Transaction",
"type": "uint16"
},
{
"name": "Item",
"type": "category"
},
{
"name": "date_time",
"type": "category"
},
{
"name": "period_day",
"type": "category"
},
{
"name": "weekday_weekend",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Transaction":20507.0
},
"mean":{
"Transaction":4976.2023699225
},
"std":{
"Transaction":2796.203001082
},
"min":{
"Transaction":1.0
},
"25%":{
"Transaction":2552.0
},
"50%":{
"Transaction":5137.0
},
"75%":{
"Transaction":7357.0
},
"max":{
"Transaction":9684.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Name the lowest 2 transaction numbers where purchases were made in the morning.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Name the lowest 2 transaction numbers where purchases were made in the morning.
df.columns = ['Transaction', 'Item', 'date_time', 'period_day', 'weekday_weekend']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
220 | Are all the reviews from Australia positive (rating > 3)? | 061_Disneyland | dev | boolean | ['Reviewer_Location', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are all the reviews from Australia positive (rating > 3)?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are all the reviews from Australia positive (rating > 3)?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
221 | Is Disneyland_HongKong the most reviewed branch? | 061_Disneyland | dev | boolean | ['Branch'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Is Disneyland_HongKong the most reviewed branch?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Is Disneyland_HongKong the most reviewed branch?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
222 | Are there any reviews with a rating of 1? | 061_Disneyland | dev | boolean | ['Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any reviews with a rating of 1?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any reviews with a rating of 1?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
223 | Does every reviewer location have at least one review with a rating of 5? | 061_Disneyland | dev | boolean | ['Reviewer_Location', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Does every reviewer location have at least one review with a rating of 5?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Does every reviewer location have at least one review with a rating of 5?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
224 | How many unique reviewer locations are there? | 061_Disneyland | dev | number | ['Reviewer_Location'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many unique reviewer locations are there?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many unique reviewer locations are there?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
225 | What is the average rating for Disneyland_HongKong? | 061_Disneyland | dev | number | ['Branch', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the average rating for Disneyland_HongKong?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the average rating for Disneyland_HongKong?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
226 | What is the maximum review ID? If there is a tie then pick the highest ID. | 061_Disneyland | dev | number | ['Review_ID'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the maximum review ID? If there is a tie then pick the highest ID.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the maximum review ID? If there is a tie then pick the highest ID.
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
227 | How many reviews were made in 2019? | 061_Disneyland | dev | number | ['Year_Month'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many reviews were made in 2019?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many reviews were made in 2019?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
228 | What is the most common reviewer location? | 061_Disneyland | dev | category | ['Reviewer_Location'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the most common reviewer location?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the most common reviewer location?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
229 | What is the branch with the lowest average rating? | 061_Disneyland | dev | category | ['Branch', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the branch with the lowest average rating?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the branch with the lowest average rating?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
230 | In which date was the first most negative review (rating=1) made? | 061_Disneyland | dev | category | ['Year_Month', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: In which date was the first most negative review (rating=1) made?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: In which date was the first most negative review (rating=1) made?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
231 | What is the reviewer location with the highest average rating? If there is a tie then pick the first one alphabetically | 061_Disneyland | dev | category | ['Reviewer_Location', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the reviewer location with the highest average rating? If there is a tie then pick the first one alphabetically
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the reviewer location with the highest average rating? If there is a tie then pick the first one alphabetically
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
232 | What are the top 3 reviewer locations with the most reviews? | 061_Disneyland | dev | list[category] | ['Reviewer_Location'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 3 reviewer locations with the most reviews?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 3 reviewer locations with the most reviews?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
233 | What are the bottom 2 branches in terms of average rating? | 061_Disneyland | dev | list[category] | ['Branch', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 2 branches in terms of average rating?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 2 branches in terms of average rating?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
234 | What are the 4 dates with the most reviews? Include those missing. | 061_Disneyland | dev | list[category] | ['Year_Month'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the 4 dates with the most reviews? Include those missing.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the 4 dates with the most reviews? Include those missing.
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
235 | What are the bottom 3 reviewer locations in terms of average rating? | 061_Disneyland | dev | list[category] | ['Reviewer_Location', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 3 reviewer locations in terms of average rating?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 3 reviewer locations in terms of average rating?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
236 | What are the highest 5 review IDs in terms of rating? If you find a tie then keep the highest IDs. | 061_Disneyland | dev | list[number] | ['Review_ID', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the highest 5 review IDs in terms of rating? If you find a tie then keep the highest IDs.
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the highest 5 review IDs in terms of rating? If you find a tie then keep the highest IDs.
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
237 | What are the lowest 4 review IDs in terms of rating? If there are more than four with the same rating keep the lowest IDs | 061_Disneyland | dev | list[number] | ['Review_ID', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the lowest 4 review IDs in terms of rating? If there are more than four with the same rating keep the lowest IDs
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the lowest 4 review IDs in terms of rating? If there are more than four with the same rating keep the lowest IDs
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
238 | What are the top 3 (not necessarily unique) ratings given by reviewers from Australia? | 061_Disneyland | dev | list[number] | ['Reviewer_Location', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 3 (not necessarily unique) ratings given by reviewers from Australia?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 3 (not necessarily unique) ratings given by reviewers from Australia?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
239 | What are the lowest 2 ratings given for Disneyland_HongKong? | 061_Disneyland | dev | list[number] | ['Branch', 'Rating'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "Review_ID",
"type": "uint32"
},
{
"name": "Rating",
"type": "uint8"
},
{
"name": "Year_Month",
"type": "category"
},
{
"name": "Reviewer_Location",
"type": "category"
},
{
"name": "Review_Text",
"type": "object"
},
{
"name": "Branch",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"Review_ID":42656.0,
"Rating":42656.0
},
"mean":{
"Review_ID":318855269.8283711672,
"Rating":4.2176950488
},
"std":{
"Review_ID":165709224.260283798,
"Rating":1.0633710689
},
"min":{
"Review_ID":1398724.0,
"Rating":1.0
},
"25%":{
"Review_ID":174327393.0,
"Rating":4.0
},
"50%":{
"Review_ID":290758265.0,
"Rating":5.0
},
"75%":{
"Review_ID":448957905.75,
"Rating":5.0
},
"max":{
"Review_ID":670801367.0,
"Rating":5.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the lowest 2 ratings given for Disneyland_HongKong?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the lowest 2 ratings given for Disneyland_HongKong?
df.columns = ['Review_ID', 'Rating', 'Year_Month', 'Reviewer_Location', 'Review_Text', 'Branch']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
240 | Are all the tweets in English? | 062_Trump | dev | boolean | ['lang'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are all the tweets in English?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are all the tweets in English?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
241 | Has the author ever been retweeted? | 062_Trump | dev | boolean | ['retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Has the author ever been retweeted?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Has the author ever been retweeted?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
242 | Are there any tweets with more than 10000 retweets? | 062_Trump | dev | boolean | ['retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any tweets with more than 10000 retweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any tweets with more than 10000 retweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
243 | Have any of the tweets been favorited more than 50000 times? | 062_Trump | dev | boolean | ['favorites'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Have any of the tweets been favorited more than 50000 times?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Have any of the tweets been favorited more than 50000 times?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
244 | How many unique authors are there? | 062_Trump | dev | number | ['author_name'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many unique authors are there?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many unique authors are there?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
245 | What is the average number of retweets? | 062_Trump | dev | number | ['retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the average number of retweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the average number of retweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
246 | What is the maximum number of favorites received for a single tweet? | 062_Trump | dev | number | ['favorites'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the maximum number of favorites received for a single tweet?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the maximum number of favorites received for a single tweet?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
247 | How many tweets were posted in 2018? | 062_Trump | dev | number | ['date'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many tweets were posted in 2018?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many tweets were posted in 2018?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
248 | What is the most common author name? | 062_Trump | dev | category | ['author_name'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the most common author name?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the most common author name?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
249 | What is the tweet with the most favorites? | 062_Trump | dev | category | ['text', 'favorites'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the tweet with the most favorites?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the tweet with the most favorites?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
250 | In which language is the tweet with the most retweets written? | 062_Trump | dev | category | ['lang', 'retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: In which language is the tweet with the most retweets written?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: In which language is the tweet with the most retweets written?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
251 | What is the most common language of the tweets? | 062_Trump | dev | category | ['lang'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the most common language of the tweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the most common language of the tweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
252 | What are the top 3 author handlers with the most tweets? | 062_Trump | dev | list[category] | ['author_handler'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 3 author handlers with the most tweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 3 author handlers with the most tweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
253 | What are the bottom 2 languages in terms of tweet count? If there are more than two give priority to those starting with the letter p | 062_Trump | dev | list[category] | ['lang'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 2 languages in terms of tweet count? If there are more than two give priority to those starting with the letter p
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 2 languages in terms of tweet count? If there are more than two give priority to those starting with the letter p
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
254 | What are the top 4 mentioned names in the tweets? | 062_Trump | dev | list[category] | ['mention_names'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 4 mentioned names in the tweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 4 mentioned names in the tweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
255 | What are the bottom 3 author names in terms of tweet count? | 062_Trump | dev | list[category] | ['author_name'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 3 author names in terms of tweet count?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 3 author names in terms of tweet count?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
256 | What are the top 5 tweet IDs in terms of retweet count? | 062_Trump | dev | list[number] | ['id', 'retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 5 tweet IDs in terms of retweet count?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 5 tweet IDs in terms of retweet count?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
257 | What are the bottom 4 tweet IDs in terms of favorite count? | 062_Trump | dev | list[number] | ['id', 'favorites'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 4 tweet IDs in terms of favorite count?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 4 tweet IDs in terms of favorite count?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
258 | What are the top 6 favorite counts of the tweets? | 062_Trump | dev | list[number] | ['favorites'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 6 favorite counts of the tweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 6 favorite counts of the tweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
259 | What are the bottom 3 retweet counts of the tweets? | 062_Trump | dev | list[number] | ['retweets'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "int64"
},
{
"name": "author_id",
"type": "uint32"
},
{
"name": "author_name",
"type": "category"
},
{
"name": "author_handler",
"type": "category"
},
{
"name": "author_avatar",
"type": "category"
},
{
"name": "lang",
"type": "category"
},
{
"name": "text",
"type": "object"
},
{
"name": "date",
"type": "datetime64[ns, UTC]"
},
{
"name": "mention_ids",
"type": "object"
},
{
"name": "mention_names",
"type": "object"
},
{
"name": "retweets",
"type": "uint32"
},
{
"name": "favorites",
"type": "uint32"
},
{
"name": "links",
"type": "object"
},
{
"name": "links_first",
"type": "category"
},
{
"name": "image_links",
"type": "object"
},
{
"name": "image_links_first",
"type": "category"
},
{
"name": "tweet_link",
"type": "category"
},
{
"name": "rp_user_id",
"type": "float64"
},
{
"name": "rp_user_name",
"type": "category"
},
{
"name": "location",
"type": "category"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":15039.0,
"author_id":15039.0,
"retweets":15039.0,
"favorites":15039.0,
"rp_user_id":38.0
},
"mean":{
"id":7.708974475e+17,
"author_id":25073877.0,
"retweets":8774.5204468382,
"favorites":34438.8956712547,
"rp_user_id":409658793.0
},
"std":{
"id":1.524628348e+17,
"author_id":0.0,
"retweets":11471.4259073933,
"favorites":43738.3620217327,
"rp_user_id":973036746.0114293098
},
"min":{
"id":5.755855984e+17,
"author_id":25073877.0,
"retweets":1.0,
"favorites":1.0,
"rp_user_id":1457641.0
},
"25%":{
"id":6.452230056e+17,
"author_id":25073877.0,
"retweets":702.5,
"favorites":1522.0,
"rp_user_id":25073877.0
},
"50%":{
"id":7.232668993e+17,
"author_id":25073877.0,
"retweets":4216.0,
"favorites":11854.0,
"rp_user_id":25073877.0
},
"75%":{
"id":9.053052425e+17,
"author_id":25073877.0,
"retweets":14414.5,
"favorites":63094.5,
"rp_user_id":99623321.25
},
"max":{
"id":1.075621116e+18,
"author_id":25073877.0,
"retweets":334538.0,
"favorites":589793.0,
"rp_user_id":3412873425.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 3 retweet counts of the tweets?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 3 retweet counts of the tweets?
df.columns = ['id', 'author_id', 'author_name', 'author_handler', 'author_avatar', 'lang', 'text', 'date', 'mention_ids', 'mention_names', 'retweets', 'favorites', 'links', 'links_first', 'image_links', 'image_links_first', 'tweet_link', 'rp_user_id', 'rp_user_name', 'location']
# Expected output type: is list[number], this will be checked.
return | 20250129013724 |
260 | Are there any organizations in the dataset? | 063_Influencers | dev | boolean | ['is_organization'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any organizations in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any organizations in the dataset?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
261 | Are there any individuals (non-organizations) in the dataset? | 063_Influencers | dev | boolean | ['is_organization'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any individuals (non-organizations) in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any individuals (non-organizations) in the dataset?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
262 | Do all entities have a picture? | 063_Influencers | dev | boolean | ['pic'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Do all entities have a picture?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Do all entities have a picture?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
263 | Are there any entities with a weight greater than 500? | 063_Influencers | dev | boolean | ['weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any entities with a weight greater than 500?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any entities with a weight greater than 500?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is boolean, this will be checked.
return | 20250129013724 |
264 | How many unique communities are there? | 063_Influencers | dev | number | ['community'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many unique communities are there?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many unique communities are there?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
265 | What is the average page rank norm? | 063_Influencers | dev | number | ['page_rank_norm'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the average page rank norm?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the average page rank norm?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
266 | What is the maximum weight of an entity? | 063_Influencers | dev | number | ['weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the maximum weight of an entity?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the maximum weight of an entity?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
267 | How many entities have a community identifier of 16744206? | 063_Influencers | dev | number | ['community'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many entities have a community identifier of 16744206?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many entities have a community identifier of 16744206?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is number, this will be checked.
return | 20250129013724 |
268 | What is the most common name? | 063_Influencers | dev | category | ['name'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the most common name?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the most common name?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
269 | Which entity has the highest page rank norm? | 063_Influencers | dev | category | ['name', 'page_rank_norm'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Which entity has the highest page rank norm?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Which entity has the highest page rank norm?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
270 | What is the picture URL of the entity with the maximum weight? | 063_Influencers | dev | category | ['pic', 'weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the picture URL of the entity with the maximum weight?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the picture URL of the entity with the maximum weight?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
271 | Which entity has the highest y-coordinate? | 063_Influencers | dev | category | ['name', 'y'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Which entity has the highest y-coordinate?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Which entity has the highest y-coordinate?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is category, this will be checked.
return | 20250129013724 |
272 | What are the top 3 entity names with the highest weights? | 063_Influencers | dev | list[category] | ['name', 'weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 3 entity names with the highest weights?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 3 entity names with the highest weights?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[category], this will be checked.
return | 20250129013724 |
273 | What are the bottom 2 entities in terms of page rank norm? | 063_Influencers | dev | list[category] | ['name', 'page_rank_norm'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 2 entities in terms of page rank norm?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 2 entities in terms of page rank norm?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
274 | What are the top 4 entities with the highest x-coordinates? | 063_Influencers | dev | list[category] | ['name', 'x'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 4 entities with the highest x-coordinates?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 4 entities with the highest x-coordinates?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
275 | What are the bottom 3 entities in terms of y-coordinates? | 063_Influencers | dev | list[category] | ['name', 'y'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 3 entities in terms of y-coordinates?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 3 entities in terms of y-coordinates?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
276 | What are the top 5 entity IDs in terms of weight? | 063_Influencers | dev | list[number] | ['id', 'weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 5 entity IDs in terms of weight?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 5 entity IDs in terms of weight?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
277 | What are the bottom 4 entity IDs in terms of page rank norm? | 063_Influencers | dev | list[number] | ['id', 'page_rank_norm'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 4 entity IDs in terms of page rank norm?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 4 entity IDs in terms of page rank norm?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
278 | What are the top 6 page rank norms of the entities? | 063_Influencers | dev | list[number] | ['page_rank_norm'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 6 page rank norms of the entities?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 6 page rank norms of the entities?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
279 | What are the bottom 3 weights of the entities? | 063_Influencers | dev | list[number] | ['weight'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "id",
"type": "uint32"
},
{
"name": "name",
"type": "category"
},
{
"name": "pic",
"type": "category"
},
{
"name": "is_organization",
"type": "bool"
},
{
"name": "community",
"type": "uint32"
},
{
"name": "page_rank_norm",
"type": "float64"
},
{
"name": "weight",
"type": "float64"
},
{
"name": "x",
"type": "float64"
},
{
"name": "y",
"type": "float64"
},
{
"name": "twitter_profile_id",
"type": "int64"
},
{
"name": "gx_link_target",
"type": "object"
},
{
"name": "gx_link_weight",
"type": "object"
},
{
"name": "gx_link_reciprocal",
"type": "object"
},
{
"name": "gx_link_should",
"type": "object"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"id":1039.0,
"community":1039.0,
"page_rank_norm":1039.0,
"weight":1039.0,
"x":1039.0,
"y":1039.0,
"twitter_profile_id":1039.0
},
"mean":{
"id":23047.0644850818,
"community":11681769.4696823861,
"page_rank_norm":0.0884803326,
"weight":147.2675649663,
"x":540.3537380859,
"y":394.8444226516,
"twitter_profile_id":2.008029257e+16
},
"std":{
"id":39075.4047414455,
"community":6854727.7264998332,
"page_rank_norm":0.1000416103,
"weight":118.9822209357,
"x":714.4689670341,
"y":690.4519130739,
"twitter_profile_id":1.345815525e+17
},
"min":{
"id":101.0,
"community":2062260.0,
"page_rank_norm":0.0,
"weight":1.0,
"x":-1367.0425072672,
"y":-1543.3173448435,
"twitter_profile_id":633.0
},
"25%":{
"id":467.5,
"community":2062260.0,
"page_rank_norm":0.0161780641,
"weight":54.0,
"x":8.4145058529,
"y":-37.4390967754,
"twitter_profile_id":17274921.5
},
"50%":{
"id":7209.0,
"community":16744206.0,
"page_rank_norm":0.0557416047,
"weight":120.5,
"x":607.9121194347,
"y":394.4469887335,
"twitter_profile_id":82890309.0
},
"75%":{
"id":35014.5,
"community":16744206.0,
"page_rank_norm":0.1264938658,
"weight":215.0,
"x":1100.2234176144,
"y":891.8761373502,
"twitter_profile_id":412977526.0
},
"max":{
"id":204406.0,
"community":16744206.0,
"page_rank_norm":1.0,
"weight":770.5,
"x":2252.6290667868,
"y":2101.5421596499,
"twitter_profile_id":1.086037534e+18
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 3 weights of the entities?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 3 weights of the entities?
df.columns = ['id', 'name', 'pic', 'is_organization', 'community', 'page_rank_norm', 'weight', 'x', 'y', 'twitter_profile_id', 'gx_link_target', 'gx_link_weight', 'gx_link_reciprocal', 'gx_link_should']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
280 | Are there any animals with feathers in the dataset? | 064_Clustering | dev | boolean | ['feathers'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any animals with feathers in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any animals with feathers in the dataset?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is boolean, this will be checked.
return | 20250129013725 |
281 | Are there any venomous animals in the dataset? | 064_Clustering | dev | boolean | ['venomous'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any venomous animals in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any venomous animals in the dataset?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is boolean, this will be checked.
return | 20250129013725 |
282 | Do all animals breathe? | 064_Clustering | dev | boolean | ['breathes'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Do all animals breathe?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Do all animals breathe?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is boolean, this will be checked.
return | 20250129013725 |
283 | Are there any domesticated animals in the dataset? | 064_Clustering | dev | boolean | ['domestic'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: Are there any domesticated animals in the dataset?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: Are there any domesticated animals in the dataset?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is boolean, this will be checked.
return | 20250129013725 |
284 | How many unique types of animals are there? | 064_Clustering | dev | number | ['class_type'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many unique types of animals are there?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many unique types of animals are there?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is number, this will be checked.
return | 20250129013725 |
285 | What is the average number of legs? | 064_Clustering | dev | number | ['legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the average number of legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the average number of legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is number, this will be checked.
return | 20250129013725 |
286 | What is the maximum number of legs an animal has? | 064_Clustering | dev | number | ['legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the maximum number of legs an animal has?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the maximum number of legs an animal has?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is number, this will be checked.
return | 20250129013725 |
287 | How many animals are there with 2 legs? | 064_Clustering | dev | number | ['legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: How many animals are there with 2 legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: How many animals are there with 2 legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is number, this will be checked.
return | 20250129013725 |
288 | What is the most common class type? | 064_Clustering | dev | category | ['class_type'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the most common class type?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the most common class type?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is category, this will be checked.
return | 20250129013725 |
289 | What is the name of the first animal with 8 legs? | 064_Clustering | dev | category | ['animal_name', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the name of the first animal with 8 legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the name of the first animal with 8 legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is category, this will be checked.
return | 20250129013725 |
290 | What is the class type of the animals with the most legs? | 064_Clustering | dev | category | ['class_type', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the class type of the animals with the most legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the class type of the animals with the most legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is category, this will be checked.
return | 20250129013725 |
291 | What is the name of the first animal in the dataset that is venomous? | 064_Clustering | dev | category | ['animal_name', 'venomous'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What is the name of the first animal in the dataset that is venomous?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What is the name of the first animal in the dataset that is venomous?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is category, this will be checked.
return | 20250129013725 |
292 | What are the top 3 animal names with the most legs? If there are more than two with the lowest number go with alphabetical order | 064_Clustering | dev | list[category] | ['animal_name', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the top 3 animal names with the most legs? If there are more than two with the lowest number go with alphabetical order
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the top 3 animal names with the most legs? If there are more than two with the lowest number go with alphabetical order
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
293 | What are the bottom 2 animal names in terms of the number of legs? If there are more than two with the lowest number go with alphabetical order | 064_Clustering | dev | list[category] | ['animal_name', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 2 animal names in terms of the number of legs? If there are more than two with the lowest number go with alphabetical order
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 2 animal names in terms of the number of legs? If there are more than two with the lowest number go with alphabetical order
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
294 | What are the most common 4 class types with the most animals? | 064_Clustering | dev | list[category] | ['class_type'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the most common 4 class types with the most animals?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the most common 4 class types with the most animals?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
295 | What are the least common 3 class types with the least animals? | 064_Clustering | dev | list[category] | ['class_type'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the least common 3 class types with the least animals?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the least common 3 class types with the least animals?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[category], this will be checked.
return | 20250129013725 |
296 | What are the most common 5 class types with the most combined total legs? | 064_Clustering | dev | list[number] | ['class_type', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the most common 5 class types with the most combined total legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the most common 5 class types with the most combined total legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
297 | What are the bottom 4 class types with the least combined total legs? | 064_Clustering | dev | list[number] | ['class_type', 'legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the bottom 4 class types with the least combined total legs?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the bottom 4 class types with the least combined total legs?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[number], this will be checked.
return | 20250129013725 |
298 | What are the most common 4 numbers of legs that animals have? | 064_Clustering | dev | list[number] | ['legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the most common 4 numbers of legs that animals have?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the most common 4 numbers of legs that animals have?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[number], this will be checked.
return | 20250129013726 |
299 | What are the least common 3 numbers of legs that animals have? | 064_Clustering | dev | list[number] | ['legs'] | dev | # Instructions: Generate ONLY python code. Do not include explanations.
# you can use pandas and numpy. Use the meta data information from df_schema, df_descprtion.
import pandas as pd
import numpy as np
# Description of dataframe schema.
df_schema = {
"columns": [
{
"name": "animal_name",
"type": "category"
},
{
"name": "hair",
"type": "uint8"
},
{
"name": "feathers",
"type": "uint8"
},
{
"name": "eggs",
"type": "uint8"
},
{
"name": "milk",
"type": "uint8"
},
{
"name": "airborne",
"type": "uint8"
},
{
"name": "aquatic",
"type": "uint8"
},
{
"name": "predator",
"type": "uint8"
},
{
"name": "toothed",
"type": "uint8"
},
{
"name": "backbone",
"type": "uint8"
},
{
"name": "breathes",
"type": "uint8"
},
{
"name": "venomous",
"type": "uint8"
},
{
"name": "fins",
"type": "uint8"
},
{
"name": "legs",
"type": "uint8"
},
{
"name": "tail",
"type": "uint8"
},
{
"name": "domestic",
"type": "uint8"
},
{
"name": "catsize",
"type": "uint8"
},
{
"name": "class_type",
"type": "uint8"
}
]
}
# Description of dataframe columns.
df_descrption = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
# Randome sample of 10 rows from the dataframe.
df_random_sample = {
"count":{
"hair":101.0,
"feathers":101.0,
"eggs":101.0,
"milk":101.0,
"airborne":101.0,
"aquatic":101.0,
"predator":101.0,
"toothed":101.0,
"backbone":101.0,
"breathes":101.0,
"venomous":101.0,
"fins":101.0,
"legs":101.0,
"tail":101.0,
"domestic":101.0,
"catsize":101.0,
"class_type":101.0
},
"mean":{
"hair":0.4257425743,
"feathers":0.198019802,
"eggs":0.5841584158,
"milk":0.4059405941,
"airborne":0.2376237624,
"aquatic":0.3564356436,
"predator":0.5544554455,
"toothed":0.603960396,
"backbone":0.8217821782,
"breathes":0.7920792079,
"venomous":0.0792079208,
"fins":0.1683168317,
"legs":2.8415841584,
"tail":0.7425742574,
"domestic":0.1287128713,
"catsize":0.4356435644,
"class_type":2.8316831683
},
"std":{
"hair":0.4969212141,
"feathers":0.4004947435,
"eggs":0.495324676,
"milk":0.4935223971,
"airborne":0.4277502741,
"aquatic":0.4813347778,
"predator":0.4995047052,
"toothed":0.4915121142,
"backbone":0.3846047219,
"breathes":0.4078438839,
"venomous":0.2714099599,
"fins":0.376013482,
"legs":2.0333847313,
"tail":0.4393965259,
"domestic":0.3365521159,
"catsize":0.4983139891,
"class_type":2.1027092378
},
"min":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":0.0,
"breathes":0.0,
"venomous":0.0,
"fins":0.0,
"legs":0.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"25%":{
"hair":0.0,
"feathers":0.0,
"eggs":0.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":0.0,
"toothed":0.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":2.0,
"tail":0.0,
"domestic":0.0,
"catsize":0.0,
"class_type":1.0
},
"50%":{
"hair":0.0,
"feathers":0.0,
"eggs":1.0,
"milk":0.0,
"airborne":0.0,
"aquatic":0.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":0.0,
"class_type":2.0
},
"75%":{
"hair":1.0,
"feathers":0.0,
"eggs":1.0,
"milk":1.0,
"airborne":0.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":0.0,
"fins":0.0,
"legs":4.0,
"tail":1.0,
"domestic":0.0,
"catsize":1.0,
"class_type":4.0
},
"max":{
"hair":1.0,
"feathers":1.0,
"eggs":1.0,
"milk":1.0,
"airborne":1.0,
"aquatic":1.0,
"predator":1.0,
"toothed":1.0,
"backbone":1.0,
"breathes":1.0,
"venomous":1.0,
"fins":1.0,
"legs":8.0,
"tail":1.0,
"domestic":1.0,
"catsize":1.0,
"class_type":7.0
}
}
'''
The question categories are:
- boolean: Valid answers include True/False, Y/N, Yes/No (all case insensitive).
- category: A value from a cell (or a substring of a cell) in the dataset.
- number: A numerical value from a cell in the dataset, which may represent a computed statistic (e.g., average, maximum, minimum).
- list[category]: A list containing a fixed number of categories. The expected format is: "['cat', 'dog']". Pay attention to the wording of the question to determine if uniqueness is required or if repeated values are allowed.
- list[number]: Similar to list[category], but with numbers as its elements.
'''
# TODO: complete the following function in one line, by completing the return statement. It should give the answer to: How many rows are there in this dataframe?
def example(df: pd.DataFrame):
df.columns=["A"]
# Expected output type: is number, this will be checked.
return df.shape[0]
# It should give the answer to: What are the least common 3 numbers of legs that animals have?
# The answer should only contain python code, you are not allowed leave any TODO undone.
def answer(df: pd.DataFrame):
# Use df to answer: What are the least common 3 numbers of legs that animals have?
df.columns = ['animal_name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class_type']
# Expected output type: is list[number], this will be checked.
return | 20250129013726 |