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Enumerate the bottom 3 URLs with the highest rankings. | ['https://www.obviously.ai/', 'https://www.obviously.ai/', 'https://venturebeat.com/2021/10/12/no-code-ai-startup-obviously-ai-raises-4-7m/'] | list[category] | ['Ranking', 'URLs'] | ['number[uint8]', 'url'] | [https://www.obviously.ai/, https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses, https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/] | 025_Data |
What are the lowest 4 rankings associated with the keyword 'no code data science'? | [10, 9, 8, 7] | list[number] | ['Keyword', 'Ranking'] | ['category', 'number[uint8]'] | [10, 9, 6, 4] | 025_Data |
List the bottom 3 average monthly searches for URLs with medium competition. | [50, 50, 50] | list[number] | ['Competition', 'Avg. monthly searches'] | ['category', 'number[uint8]'] | [50, 50, 50] | 025_Data |
Provide the lowest 5 rankings of URLs with low competition (if any). | [11, 10, 10, 9, 9] | list[number] | ['Competition', 'Ranking'] | ['category', 'number[uint8]'] | [11, 8, 6, 6, 5] | 025_Data |
Specify the bottom 2 average monthly searches for URLs with the highest rankings. | [50, 50] | list[number] | ['Ranking', 'Avg. monthly searches'] | ['number[uint8]', 'number[uint8]'] | [50] | 025_Data |
Is there any wine with a quality rating of 10? | False | boolean | ['quality'] | ['number[uint8]'] | False | 026_Predicting |
Are there any wines with residual sugar above 15g/dm^3? | True | boolean | ['residual sugar'] | ['number[double]'] | False | 026_Predicting |
Is the highest alcohol content wine also the one with the highest quality rating? | False | boolean | ['alcohol', 'quality'] | ['number[double]', 'number[uint8]'] | False | 026_Predicting |
Does any wine have a pH level below 2.5? | False | boolean | ['pH'] | ['number[double]'] | False | 026_Predicting |
How many unique quality ratings are there in the dataset? | 6 | number | ['quality'] | ['number[uint8]'] | 5 | 026_Predicting |
What is the maximum fixed acidity level found in the dataset? | 15.9 | number | ['fixed acidity'] | ['number[double]'] | 10.7 | 026_Predicting |
What is the minimum volatile acidity level in the dataset? | 0.12 | number | ['volatile acidity'] | ['number[double]'] | 0.28 | 026_Predicting |
How many wines have free sulfur dioxide above 50 mg/dm^3? | 16 | number | ['free sulfur dioxide'] | ['number[UInt8]'] | 0 | 026_Predicting |
What is the quality rating of the wine with the highest alcohol content? | 5 | category | ['alcohol', 'quality'] | ['number[double]', 'number[uint8]'] | 7.0 | 026_Predicting |
What is the quality rating of the wine with the highest fixed acidity? | 5 | category | ['fixed acidity', 'quality'] | ['number[double]', 'number[uint8]'] | 6.0 | 026_Predicting |
What is the quality rating of the wine with the lowest volatile acidity? | 7 | category | ['volatile acidity', 'quality'] | ['number[double]', 'number[uint8]'] | 7.0 | 026_Predicting |
What is the quality rating of the wine with the highest pH level? | 6 | category | ['pH', 'quality'] | ['number[double]', 'number[uint8]'] | 6.0 | 026_Predicting |
List the quality ratings of the top 3 wines with the highest alcohol content. | ['5', '6', '6'] | list[category] | ['alcohol', 'quality'] | ['number[double]', 'number[uint8]'] | [7, 7, 8] | 026_Predicting |
Enumerate the quality ratings of the bottom 2 wines with the lowest residual sugar. | ['6', '6'] | list[category] | ['residual sugar', 'quality'] | ['number[double]', 'number[uint8]'] | [5, 5] | 026_Predicting |
Which are the quality ratings of the top 5 wines with the highest density? | ['6', '6', '7', '5', '5'] | list[category] | ['density', 'quality'] | ['number[double]', 'number[uint8]'] | [6, 7, 5, 7, 6] | 026_Predicting |
List the quality ratings of the bottom 4 wines with the lowest pH level. | ['4', '6', '6', '8'] | list[category] | ['pH', 'quality'] | ['number[double]', 'number[uint8]'] | [7, 8, 5, 7] | 026_Predicting |
What are the alcohol contents of the top 4 wines with the highest quality ratings? | ['12.8', '12.6', '12.9', '9.8'] | list[number] | ['quality', 'alcohol'] | ['number[uint8]', 'number[double]'] | [11.7, 11.8, 12.3, 10.0] | 026_Predicting |
List the volatile acidity levels of the bottom 3 wines with the lowest quality ratings. | ['0.58', '0.61', '1.185'] | list[number] | ['quality', 'volatile acidity'] | ['number[uint8]', 'number[double]'] | [0.76, 0.5, 0.46] | 026_Predicting |
Enumerate the fixed acidity levels of the top 5 wines with the highest quality ratings. | ['7.9', '10.3', '5.6', '12.6', '11.3'] | list[number] | ['quality', 'fixed acidity'] | ['number[uint8]', 'number[double]'] | [9.4, 10.5, 8.9, 10.1, 7.7] | 026_Predicting |
Provide the residual sugar levels of the bottom 2 wines with the lowest quality ratings. | ['2.2', '2.1'] | list[number] | ['quality', 'residual sugar'] | ['number[uint8]', 'number[double]'] | [1.8, 1.6] | 026_Predicting |
Is there any purchase with a total cost above 1000? | True | boolean | ['Total'] | ['number[double]'] | False | 027_Supermarket |
Are there any customers who made a purchase using cash? | True | boolean | ['Payment'] | ['category'] | True | 027_Supermarket |
Is the customer with the highest total purchase cost a 'Member'? | True | boolean | ['Total', 'Customer type'] | ['number[double]', 'category'] | False | 027_Supermarket |
Does any customer with a rating strictly above 9 use 'Ewallet' as their payment method? | True | boolean | ['Rating', 'Payment'] | ['number[double]', 'category'] | False | 027_Supermarket |
How many unique branches are there in the dataset? | 3 | number | ['Branch'] | ['category'] | 3 | 027_Supermarket |
What is the maximum quantity of products bought in a single purchase? | 10 | number | ['Quantity'] | ['number[uint8]'] | 10 | 027_Supermarket |
What is the minimum total cost of a purchase in the dataset? | 10.6785 | number | ['Total'] | ['number[double]'] | 45.927 | 027_Supermarket |
How many purchases were made in Yangon city? | 340 | number | ['City'] | ['category'] | 11 | 027_Supermarket |
What is the payment method used for the purchase with the highest total cost? | Credit card | category | ['Total', 'Payment'] | ['number[double]', 'category'] | Credit card | 027_Supermarket |
What is the product line of the purchase with the highest total cost? | Fashion accessories | category | ['Total', 'Product line'] | ['number[double]', 'category'] | Electronic accessories | 027_Supermarket |
What is the customer type of the purchase with the lowest total cost? | Member | category | ['Total', 'Customer type'] | ['number[double]', 'category'] | Normal | 027_Supermarket |
What is the gender of the customer with the highest total purchase cost? | Female | category | ['Total', 'Gender'] | ['number[double]', 'category'] | Male | 027_Supermarket |
List the payment methods of the top 3 purchases with the highest total cost. | ['Credit card', 'Credit card', 'Ewallet'] | list[category] | ['Total', 'Payment'] | ['number[double]', 'category'] | ['Credit card', 'Cash', 'Ewallet'] | 027_Supermarket |
Enumerate the product lines of the bottom 2 purchases with the lowest total cost. | ['Sports and travel', 'Fashion accessories'] | list[category] | ['Total', 'Product line'] | ['number[double]', 'category'] | ['Sports and travel', 'Sports and travel'] | 027_Supermarket |
Which are the customer types of the top 5 purchases with the highest total cost? | ['Member', 'Normal', 'Member', 'Normal', 'Normal'] | list[category] | ['Total', 'Customer type'] | ['number[double]', 'category'] | ['Normal', 'Normal', 'Normal', 'Normal', 'Normal'] | 027_Supermarket |
List the genders of the bottom 4 purchases with the lowest total cost. | ['Male', 'Female', 'Female', 'Male'] | list[category] | ['Total', 'Gender'] | ['number[double]', 'category'] | ['Male', 'Male', 'Female', 'Female'] | 027_Supermarket |
What are the quantities of products bought in the top 4 purchases with the highest total cost? | [10, 10, 10, 10] | list[number] | ['Total', 'Quantity'] | ['number[double]', 'number[uint8]'] | [10, 7, 10, 10] | 027_Supermarket |
List the unit prices of the bottom 3 purchases with the lowest total cost. | [10.17, 12.09, 12.54] | list[number] | ['Total', 'Unit price'] | ['number[double]', 'number[double]'] | [21.87, 60.87, 42.97] | 027_Supermarket |
Enumerate the ratings of the top 5 purchases with the highest total cost. | [6.6, 8.7, 4.5, 8.0, 4.4] | list[number] | ['Total', 'Rating'] | ['number[double]', 'number[double]'] | [4.2, 7.6, 8.1, 9.0, 6.4] | 027_Supermarket |
Provide the gross incomes of the bottom 2 purchases with the lowest total cost. | [0.5085, 0.6045] | list[number] | ['Total', 'gross income'] | ['number[double]', 'number[double]'] | [2.187, 6.087] | 027_Supermarket |
Are there any individuals in the dataset who are above 60 years of age? | False | boolean | ['Age'] | ['number[uint8]'] | True | 028_Predict |
Does anyone have a Diabetes Pedigree Function score above 2.5? | True | boolean | ['DiabetesPedigreeFunction'] | ['number[double]'] | False | 028_Predict |
Does the person with the highest glucose level also have diabetes? | True | boolean | ['Glucose', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | True | 028_Predict |
Is there anyone who has zero pregnancies and is diabetic? | True | boolean | ['Pregnancies', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | True | 028_Predict |
What is the maximum number of pregnancies recorded in the dataset? | 17 | number | ['Pregnancies'] | ['number[uint8]'] | 10 | 028_Predict |
What is the minimum blood pressure level recorded in the dataset? | 0 | number | ['BloodPressure'] | ['number[uint8]'] | 0 | 028_Predict |
What is the average BMI recorded in the dataset? | 31.992578124999998 | number | ['BMI'] | ['number[double]'] | 31.910000000000004 | 028_Predict |
How many individuals have an insulin level above 150? | 187 | number | ['Insulin'] | ['number[uint16]'] | 4 | 028_Predict |
What is the diabetes outcome for the person with the highest BMI? | 1 | category | ['BMI', 'Outcome'] | ['number[double]', 'number[uint8]'] | 1 | 028_Predict |
What is the diabetes outcome for the person with the lowest blood pressure? | 0 | category | ['BloodPressure', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | 0 | 028_Predict |
What is the diabetes outcome for the person with the highest insulin level? | 1 | category | ['Insulin', 'Outcome'] | ['number[uint16]', 'number[uint8]'] | 1 | 028_Predict |
What is the diabetes outcome for the person with the lowest glucose level? | 0 | category | ['Glucose', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | 0 | 028_Predict |
List the diabetes outcomes of the top 3 individuals with the highest number of pregnancies. | [1, 1, 1] | list[category] | ['Pregnancies', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | [1, 0, 0] | 028_Predict |
List the diabetes outcomes of the bottom 2 individuals with the lowest BMI. | [0, 0] | list[category] | ['BMI', 'Outcome'] | ['number[double]', 'number[uint8]'] | [0, 0] | 028_Predict |
List the diabetes outcomes of the top 5 individuals with the highest insulin levels. | [1, 1, 1, 1, 1] | list[category] | ['Insulin', 'Outcome'] | ['number[uint16]', 'number[uint8]'] | [1, 0, 0, 1, 1] | 028_Predict |
List the diabetes outcomes of the bottom 4 individuals with the lowest blood pressure. | [0, 0, 0, 0] | list[category] | ['BloodPressure', 'Outcome'] | ['number[uint8]', 'number[uint8]'] | [0, 1, 0, 1] | 028_Predict |
What are the ages of the top 4 individuals with the highest number of pregnancies? | [51, 67, 67, 67] | list[number] | ['Pregnancies', 'Age'] | ['number[uint8]', 'number[uint8]'] | [40, 34, 50, 60] | 028_Predict |
List the BMI of the bottom 3 individuals with the lowest glucose levels. | [32.0, 32.0, 32.0] | list[number] | ['Glucose', 'BMI'] | ['number[uint8]', 'number[double]'] | [20.4, 37.2, 30.2] | 028_Predict |
Enumerate the blood pressure levels of the top 5 individuals with the highest Diabetes Pedigree Function scores. | [0, 0, 0, 0, 0] | list[number] | ['DiabetesPedigreeFunction', 'BloodPressure'] | ['number[double]', 'number[uint8]'] | [74, 50, 0, 80, 58] | 028_Predict |
Provide the glucose levels of the 2 individuals with the highest insulin levels. | [189, 197] | list[number] | ['Insulin', 'Glucose'] | ['number[uint16]', 'number[uint8]'] | [318, 190] | 028_Predict |
Are there any articles that have the material type 'Op-Ed'? | False | boolean | ['material_type'] | ['category'] | False | 029_NYTimes |
Does the article with the longest headline contain the keyword 'United States Politics and Government'? | False | boolean | ['headline', 'keywords'] | ['text', 'list[category]'] | False | 029_NYTimes |
Is there any article published on '2021-01-05'? | False | boolean | ['date'] | ['date[ns, UTC]'] | False | 029_NYTimes |
Does any article contain more than 10 keywords? | True | boolean | ['keywords'] | ['list[category]'] | False | 029_NYTimes |
How many unique material types are there in the dataset? | 4 | number | ['material_type'] | ['category'] | 5 | 029_NYTimes |
What is the longest length of a headline in the dataset? | 96 | number | ['headline'] | ['text'] | 110 | 029_NYTimes |
How many articles were published on '2021-01-02'? | 52 | number | ['date'] | ['date[ns, UTC]'] | 0 | 029_NYTimes |
What is the highest number of keywords associated with a single article? | 8 | number | ['keywords'] | ['list[category]'] | 8 | 029_NYTimes |
What is the material type of the article with the longest headline? | News | category | ['headline', 'material_type'] | ['text', 'category'] | News | 029_NYTimes |
What is the material type of the article with the shortest headline? | News | category | ['headline', 'material_type'] | ['text', 'category'] | Editorial | 029_NYTimes |
What is the material type of the article with the most number of keywords? | News | category | ['keywords', 'material_type'] | ['list[category]', 'category'] | News | 029_NYTimes |
What is the material type of the article with the least number of keywords? | News | category | ['keywords', 'material_type'] | ['list[category]', 'category'] | News | 029_NYTimes |
List the material types of the top 3 articles with the longest headlines. | ['News', 'News', 'News'] | list[category] | ['headline', 'material_type'] | ['text', 'category'] | ['News', 'Interactive Feature', 'News'] | 029_NYTimes |
List the material types of the bottom 2 articles with the shortest headlines. | ['News', 'News'] | list[category] | ['headline', 'material_type'] | ['text', 'category'] | ['Editorial', 'News'] | 029_NYTimes |
List the material types of the top 5 articles with the most number of keywords. | ['News', 'News', 'News', 'News', 'News'] | list[category] | ['keywords', 'material_type'] | ['list[category]', 'category'] | ['News', 'Editorial', 'News', 'Review', 'News'] | 029_NYTimes |
List the material types of the bottom 4 articles with the least number of keywords. | ['News', 'News', 'News', 'News'] | list[category] | ['keywords', 'material_type'] | ['list[category]', 'category'] | ['News', 'Interactive Feature', 'News', 'News'] | 029_NYTimes |
What are the lengths of the headlines of the top 4 articles with the most number of keywords? | [86, 85, 84, 84] | list[number] | ['keywords', 'headline'] | ['list[category]', 'text'] | [73, 20, 69, 62] | 029_NYTimes |
List the number of keywords in the bottom 3 articles with the shortest headlines. | [1, 1, 1] | list[number] | ['headline', 'keywords'] | ['text', 'list[category]'] | [8, 1, 2] | 029_NYTimes |
Enumerate the lengths of the headlines of the top 5 articles with the longest headlines. | [96, 96, 95, 95, 95] | list[number] | ['headline'] | ['text'] | [110, 94, 92, 73, 73] | 029_NYTimes |
Provide the number of keywords in the bottom 2 articles with the least number of keywords. | [1, 1] | list[number] | ['keywords'] | ['list[category]'] | [1, 2] | 029_NYTimes |
Is the 'USA' the most common entry in the 'Geographies' column? | False | boolean | ['Geographies'] | ['list[category]'] | True | 030_Professionals |
Are there any participants who are unemployed with a bachelor's degree from Africa? | False | boolean | ['Labeled Clusters', 'Geographies'] | ['category', 'list[category]'] | False | 030_Professionals |
Do all participants recommend Python as the first programming language? | False | boolean | ['What programming language would you recommend an aspiring data scientist to learn first?'] | ['category'] | False | 030_Professionals |
Are there more than 1000 participants who hope to become familiar with AWS in the next 2 years? | False | boolean | ['Which of the following cloud computing platforms do you hope to become more familiar with in the next 2 years?'] | ['list[category]'] | False | 030_Professionals |
How many unique job titles are represented in the dataset? | 14 | number | ['Select the title most similar to your current role (or most recent title if retired)'] | ['category'] | 7 | 030_Professionals |
What's the median of the average number of years participants have used machine learning methods? | 1.5 | number | ['(Average) For how many years have you used machine learning methods?'] | ['number[double]'] | 0.5 | 030_Professionals |
How many participants are from the United Kingdom? | 450 | number | ['In which country do you currently reside?'] | ['category'] | 1 | 030_Professionals |
What is the most common number of programming languages used by participants on a regular basis? | 2 | number | ['What programming languages do you use on a regular basis?'] | ['list[category]'] | 3.0 | 030_Professionals |
What's the most common computing platform used for data science projects? | A laptop | category | ['What type of computing platform do you use most often for your data science projects?'] | ['category'] | A laptop | 030_Professionals |
What's the most common programming language used on a regular basis? | Python | category | ['What programming languages do you use on a regular basis?'] | ['list[category]'] | Python | 030_Professionals |
Which country has the second highest number of participants? | United States of America | category | ['In which country do you currently reside?'] | ['category'] | India | 030_Professionals |
Which title is the most common among participants? | Data Scientist | category | ['Select the title most similar to your current role (or most recent title if retired)'] | ['category'] | Data Scientist | 030_Professionals |
What are the top 4 geographies represented in the dataset? | ['India', 'USA', 'Western Europe', 'China - Japan - Korea'] | list[category] | ['Geographies'] | ['list[category]'] | ['USA', 'India', 'Other', 'Russia'] | 030_Professionals |
Name the top 3 general segments of participants. | ['Analysts', 'Data Scientists', 'Academics'] | list[category] | ['General Segments'] | ['list[category]'] | ['Academics', 'Data Scientists', 'Analysts'] | 030_Professionals |
list the top 4 most common job titles. | ['Data Scientist', 'Software Engineer', 'Other', 'Data Analyst'] | list[category] | ['Select the title most similar to your current role (or most recent title if retired)'] | ['category'] | ['Currently not employed', 'Data Scientist', 'Other', 'Data Analyst'] | 030_Professionals |