updated

.gitattributes

@@ -47,7 +47,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Image files - uncompressed
 *.bmp filter=lfs diff=lfs merge=lfs -text
 *.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
+#*.png filter=lfs diff=lfs merge=lfs -text
 *.tiff filter=lfs diff=lfs merge=lfs -text
 # Image files - compressed
 *.jpg filter=lfs diff=lfs merge=lfs -text

02_Codes/06_master_script.py

@@ -0,0 +1,172 @@
+"""
+To impute the missing death data based on the state level mortality.
+
+Saves final dataset as data.parquet
+
+refer to the 05_master_eda.ipynb for EDA and other details like how the data was imputed.
+"""
+
+# importing libraries and setting default option
+import pandas as pd
+import numpy as np
+
+pd.set_option("mode.copy_on_write", True)
+
+# reading the data files
+df = pd.read_parquet(".01_Data/02_Processed/02_Mortality.parquet")
+population = pd.read_parquet(".01_Data/02_Processed/01_Population.parquet")
+
+# ------------------------------------------
+# initial Cleaning
+df = df[df["State"] != "AK"]  # dropping ALASKA since it is Out of Scope (OOS)
+
+# We will consider only unintentional drug related deaths since other have very few values
+df = df[df["Cause"] == "Drug poisonings (overdose) Unintentional (X40-X44)"]
+
+df = (
+    df.dropna()
+)  # dropping rows with missing values since they are very few and will be imputed later
+
+# ------------------------------------------
+# Merge with population data
+combined = pd.merge(
+    df,
+    population,
+    on=["State", "State_Code", "County", "County_Code", "Year"],
+    how="left",
+    validate="1:1",
+    indicator=True,
+)
+
+# ------------------------------------------
+# Clean the Merged Data
+df2 = combined[
+    [
+        "State",
+        "State_Code",
+        "County",
+        "County_Code",
+        "Year",
+        "Population",
+        "Deaths",
+    ]
+]
+
+# ------------------------------------------
+# calculating Mortality Rate (County Level)
+df3 = df2.copy()
+df3["Mortality_Rate"] = df3["Deaths"] / df3["Population"]
+
+# ------------------------------------------
+# Mortality Rate (State Level)
+
+# aggregate at state-cause level
+df4 = (
+    df3.groupby(["State", "Year"])
+    .agg({"Deaths": "sum", "Population": "sum"})
+    .reset_index()
+)
+
+# clacualting mortality rate
+df4["State_Mortality_Rate"] = df4["Deaths"] / df4["Population"]
+
+# ------------------------------------------
+# Creating a list of State-Counties from the POPULATION dataset
+st_county = population[
+    ["State", "State_Code", "County", "County_Code", "Year"]
+].drop_duplicates()
+
+# ------------------------------------------
+# Merging State Mortality Rate with State-County list
+master = pd.merge(st_county, df4, on=["State", "Year"], how="left", indicator=True)
+
+# dropping NA rows since we have no state level data for them
+master = master[master["_merge"] == "both"]
+
+# Cleaning the merged data
+master_2 = master[
+    [
+        "State",
+        "State_Code",
+        "County",
+        "County_Code",
+        "Year",
+        "State_Mortality_Rate",
+    ]
+]
+
+# ------------------------------------------
+# merge with the county level data
+df5 = pd.merge(
+    master_2,
+    df3,
+    on=["State", "State_Code", "County", "County_Code", "Year"],
+    how="left",
+    indicator=True,
+    validate="1:1",
+)
+
+# adding a new flag to identify if original data or not
+df5["Original"] = df5["_merge"] == "both"
+
+# ------------------------------------------
+# Remap with population data to get county population
+df6 = pd.merge(
+    df5,
+    population[["County_Code", "Year", "Population"]],
+    on=["County_Code", "Year"],
+    how="left",
+    validate="1:1",
+    indicator="merge2",
+)
+
+
+# ------------------------------------------
+def new_death(row):
+    """Function to Calcuate the deaths in county using the State Mortality Rate and County Population
+    if the deaths are missing in the original dataset.
+    Max value is limited to 9 since we know that it can't be 10 or more"""
+
+    if pd.isna(row["Deaths"]):
+        return min(int(row["Population_y"] * row["State_Mortality_Rate"]), 9)
+    else:
+        return row["Deaths"]
+
+
+# ------------------------------------------
+# calautating the Final Deaths by using the new_death function
+df7 = df6.copy()
+df7["Deaths_2"] = df7.apply(new_death, axis=1)
+
+# ------------------------------------------
+# Cleaning the dataset
+df8 = df7[
+    [
+        "State",
+        "State_Code",
+        "County",
+        "County_Code",
+        "Year",
+        "Population_y",
+        "Deaths_2",
+        "Original",
+        "State_Mortality_Rate",
+    ]
+]
+
+# Renaming columns
+df8 = df8.rename(columns={"Population_y": "Population", "Deaths_2": "Deaths"})
+
+# ------------------------------------------
+# Calculating Mortality Rate for each county, if population is 0 then mortality rate is 0
+df8["County_Mortality_Rate"] = np.where(
+    df8["Population"] == 0, 0, df8["Deaths"] / df8["Population"]
+)
+
+# sorting the rows
+df9 = df8.sort_values(by=["State", "County", "Year"]).reset_index(drop=True)
+
+
+# ------------------------------------------
+# Saving the Final Dataset
+df9.to_parquet("data.parquet", index=False)