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"""Contains helper functions and variables."""
from functools import reduce
import numpy as np
import pandas as pd
REGION_MAPPING = {
**dict.fromkeys(["CT", "ME", "MA", "NH", "RI", "VT", "NJ", "NY", "PA"], "North East"),
**dict.fromkeys(
["IL", "IN", "MI", "OH", "WI", "IA", "KS", "MN", "MO", "NE", "ND", "SD"], "Mid West" # codespell:ignore
),
**dict.fromkeys(
["DE", "FL", "GA", "MD", "NC", "SC", "VA", "WV", "DC", "AL", "KY", "MS", "TN", "AR", "LA"], "South"
),
**dict.fromkeys(["AZ", "NM", "OK", "TX"], "South West"),
**dict.fromkeys(["CO", "ID", "MT", "NV", "UT", "WY", "AK", "CA", "HI", "OR", "WA"], "West"),
**dict.fromkeys(["UM", "PR", "AP", "VI", "AE", "AS", "GU", "FM", "PW", "MP"], "Other"),
}
def fill_na_with_random(df, column):
"""Fills missing values in a column with random values from the same column."""
non_na_values = df[column].dropna().values
df[column] = df[column].apply(lambda x: np.random.choice(non_na_values) if pd.isna(x) else x)
return df[column]
def clean_data_and_add_columns(data: pd.DataFrame):
"""Tidies the original data set, adds new columns, and changes cell values for the purpose of this example."""
data = data.rename(
columns={
"Date Sumbited": "Date Submitted",
"Submitted via": "Channel",
"Company response to consumer": "Company response - detailed",
},
)
# Clean cell values and/or assign different values for the purpose of this example
data["Company response - detailed"] = data["Company response - detailed"].replace("Closed", "Closed without relief")
data["State"] = data["State"].replace("UNITED STATES MINOR OUTLYING ISLANDS", "UM")
data["State"] = fill_na_with_random(data, "State")
data["Consumer disputed?"] = data["Consumer disputed?"].fillna("No")
# Convert to correct data type
data["Date Received"] = pd.to_datetime(data["Date Received"], format="%m/%d/%y").dt.strftime("%Y-%m-%d")
# Create additional columns
data["Month"] = pd.to_datetime(data["Date Received"], format="%Y-%m-%d").dt.strftime("%m")
data["Year"] = pd.to_datetime(data["Date Received"], format="%Y-%m-%d").dt.strftime("%Y")
data["Region"] = data["State"].map(REGION_MAPPING)
data["Company response"] = np.where(
data["Company response - detailed"].str.contains("Closed"), "Closed", data["Company response - detailed"]
)
data["Company response - Closed"] = np.where(
data["Company response - detailed"].str.contains("Closed"), data["Company response - detailed"], "Not closed"
)
# Filter 2018 and 2019 only
data = data[(data["Year"].isin(["2018", "2019"]))]
return data
def create_data_for_kpi_cards(data):
"""Formats and aggregates the data for the KPI cards."""
total_complaints = (
data.groupby("Year")
.agg({"Complaint ID": "count"})
.rename(columns={"Complaint ID": "Total Complaints"})
.reset_index()
)
closed_complaints = (
data[data["Company response"] == "Closed"]
.groupby("Year")
.agg({"Complaint ID": "count"})
.rename(columns={"Complaint ID": "Closed Complaints"})
.reset_index()
)
timely_response = (
data[data["Timely response?"] == "Yes"]
.groupby("Year")
.agg({"Complaint ID": "count"})
.rename(columns={"Complaint ID": "Timely response"})
.reset_index()
)
closed_without_cost = (
data[data["Company response - Closed"] != "Closed with monetary relief"]
.groupby("Year")
.agg({"Complaint ID": "count"})
.rename(columns={"Complaint ID": "Closed w/o cost"})
.reset_index()
)
consumer_disputed = (
data[data["Consumer disputed?"] == "Yes"]
.groupby("Year")
.agg({"Complaint ID": "count"})
.rename(columns={"Complaint ID": "Consumer disputed"})
.reset_index()
)
# Merge all data frames into one
dfs_to_merge = [total_complaints, closed_complaints, timely_response, closed_without_cost, consumer_disputed]
df_kpi = reduce(lambda left, right: pd.merge(left, right, on="Year", how="outer"), dfs_to_merge)
# Calculate percentages
df_kpi.fillna(0, inplace=True)
df_kpi["Closed Complaints"] = df_kpi["Closed Complaints"] / df_kpi["Total Complaints"] * 100
df_kpi["Open Complaints"] = 100 - df_kpi["Closed Complaints"]
df_kpi["Timely response"] = df_kpi["Timely response"] / df_kpi["Total Complaints"] * 100
df_kpi["Closed w/o cost"] = df_kpi["Closed w/o cost"] / df_kpi["Total Complaints"] * 100
df_kpi["Consumer disputed"] = df_kpi["Consumer disputed"] / df_kpi["Total Complaints"] * 100
# Pivot the dataframe and flatten
df_kpi["index"] = 0
df_kpi = df_kpi.pivot(
index="index",
columns="Year",
values=[
"Total Complaints",
"Closed Complaints",
"Open Complaints",
"Timely response",
"Closed w/o cost",
"Consumer disputed",
],
)
df_kpi.columns = [f"{kpi}_{year}" for kpi, year in df_kpi.columns]
return df_kpi
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