Datasets:
File size: 3,697 Bytes
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# Impoting required packages
import pandas as pd
import numpy as np
import zipfile
# setting default option
pd.set_option("mode.copy_on_write", True)
# ------------------------------------------
# reading the files
z = zipfile.ZipFile(".01_Data/01_Raw/raw_mortality.zip")
fips = pd.read_csv(".01_Data/01_Raw/county_fips.csv")
abbreviations = pd.read_csv(".01_Data/01_Raw/state_abbreviations.csv")
# extracting list of files from Zip folder to read
# using files starting with "Underlying" so as to ignore system files
file_list = sorted([f for f in z.namelist() if f.startswith("Underlying")])
# ------------------------------------------
# read data selected files and append to list
df_list = []
for file in file_list:
# read individual files
df_temp = pd.read_csv(z.open(file), sep="\t")
# drop the notes columns and remove rows with null values in County column
df_temp.drop(columns=["Notes"], inplace=True)
df_temp.dropna(subset=["County"], inplace=True)
# add the cleaned temp Df to the main list
df_list.append(df_temp)
# ------------------------------------------
# create the dataframe
df = pd.concat(df_list, ignore_index=True)
# ------------------------------------------
# Correcting Data Types for columns
df2 = df.copy()
# Pad county code with 0 for consistency with other data sets
df2["County Code"] = df2["County Code"].astype(int).astype(str).str.zfill(5)
# padding fips to have consistency
fips["countyfips"] = fips["countyfips"].astype(str).str.zfill(5)
# Convert Year to Int
df2["Year"] = df2["Year"].astype(int)
# Convert Deaths to Int
df2["Deaths"] = df2["Deaths"].replace("Missing", np.nan)
df2["Deaths"] = (
df2["Deaths"].astype(float).astype("Int64")
) # making it as int64 so that we retain null values for later analysis
# ------------------------------------------
# Store only the rows related drugs, modify this list later if required
required_causes = [
"Drug poisonings (overdose) Unintentional (X40-X44)",
"All other drug-induced causes",
"Drug poisonings (overdose) Homicide (X85)",
"Drug poisonings (overdose) Suicide (X60-X64)",
"Drug poisonings (overdose) Undetermined (Y10-Y14)",
]
# ------------------------------------------------------
# create and optimize subset data
df3 = df2[df2["Drug/Alcohol Induced Cause"].isin(required_causes)]
# remove extra columns
df3.drop(columns=["Year Code", "Drug/Alcohol Induced Cause Code"], inplace=True)
# renaming columns
df3.rename(
columns={"Drug/Alcohol Induced Cause": "Cause", "County Code": "County_Code"},
inplace=True,
)
# ------------------------------------------------------
# mapping with fips for proper county names and state name
df4 = pd.merge(
df3,
fips,
how="left",
left_on="County_Code",
right_on="countyfips",
validate="m:1",
indicator=True,
)
# --------------------------------------------------------
# Prepare final DF for saving
# select required colums
df5 = df4[["BUYER_STATE", "BUYER_COUNTY", "County_Code", "Year", "Cause", "Deaths"]]
# rename columns
df5 = df5.rename(columns={"BUYER_COUNTY": "County", "BUYER_STATE": "State_Code"})
abbreviations = abbreviations.rename(
columns={
"state": "State",
"code": "State_Code",
}
)
# merge with abbreviations
df6 = pd.merge(
df5,
abbreviations[["State", "State_Code"]],
how="left",
on="State_Code",
validate="m:1",
)
# reorder columns to match population data
df6 = df6[["State", "State_Code", "County", "County_Code", "Year", "Cause", "Deaths"]]
# ------------------------------------------
# Writing to Parquet
df6.to_parquet(".01_Data/02_Processed/02_Mortality.parquet", index=False)
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