Upload MissingnessAudit.py

MissingnessAudit.py

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+# -*- coding: utf-8 -*-
+"""MissingnessAudit.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10ktqYR6Cv7gMByA9WSlIqMg-NWywhRU_
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+file_path = "DigitalNomadPolicyDataSet.xlsx"
+xls = pd.ExcelFile(file_path)
+print(xls.sheet_names)
+
+data = {
+    sheet: pd.read_excel(file_path, sheet_name=sheet)
+    for sheet in xls.sheet_names
+}
+
+# Variable-level missingness
+for sheet_name, df in data.items():
+    missing_summary = pd.DataFrame({
+        "Variable": df.columns,
+        "Missing_Count": df.isna().sum().values,
+        "Missing_Percent": np.round(df.isna().mean().values * 100, 2)
+    })
+    missing_summary = missing_summary.sort_values(
+        "Missing_Percent",
+        ascending=False
+    )
+    print("\nTop variables with missing values:")
+    print(missing_summary.head(20))
+    missing_summary.to_csv(
+        f"{sheet_name}_missingness_summary.csv",
+        index=False
+    )
+
+#Dataset-level missingness
+for sheet_name, df in data.items():
+    total_cells = np.prod(df.shape)
+    missing_cells = df.isna().sum().sum()
+    print(f"\n{sheet_name}")
+    print(f"Rows: {df.shape[0]:,}")
+    print(f"Columns: {df.shape[1]:,}")
+    print(f"Total Missing Cells: {missing_cells:,}")
+    print(f"Overall Missingness: {100*missing_cells/total_cells:.2f}%")
+
+# Country-level missingness
+for sheet_name, df in data.items():
+    if 'iso3' in df.columns:
+        country_missing = (
+            df.groupby('iso3')
+            .apply(lambda x: x.isna().mean().mean()*100)
+            .reset_index(name='Missing_Percent')
+            .sort_values('Missing_Percent', ascending=False)
+        )
+        country_missing.to_csv(
+            f"{sheet_name}_country_missingness.csv",
+            index=False
+        )
+
+        print(f"\nTop countries with missing data ({sheet_name})")
+        print(country_missing.head(10))
+
+# 5. Year-level missingness
+
+for sheet_name, df in data.items():
+
+    if 'year' in df.columns:
+
+        yearly_missing = (
+            df.groupby('year')
+            .apply(lambda x: x.isna().mean().mean()*100)
+            .reset_index(name='Missing_Percent')
+        )
+
+        yearly_missing.to_csv(
+            f"{sheet_name}_year_missingness.csv",
+            index=False
+        )
+
+        plt.figure(figsize=(10,5))
+        sns.lineplot(
+            data=yearly_missing,
+            x='year',
+            y='Missing_Percent'
+        )
+        plt.title(f"Missingness by Year: {sheet_name}")
+        plt.ylabel("% Missing")
+        plt.tight_layout()
+        plt.savefig(
+            f"{sheet_name}_yearly_missingness.png",
+            dpi=300
+        )
+        plt.close()
+
+# --------------------------------------------
+# 6. Missingness Heatmap
+
+for sheet_name, df in data.items():
+
+    plt.figure(figsize=(14,8))
+
+    sns.heatmap(
+        df.isna(),
+        cbar=True,
+        yticklabels=False
+    )
+
+    plt.title(f"Missing Data Pattern: {sheet_name}")
+
+    plt.tight_layout()
+
+    plt.savefig(
+        f"{sheet_name}_missingness_heatmap.png",
+        dpi=300
+    )
+
+    plt.close()
+
+# Data Descriptor Table
+
+for sheet_name, df in data.items():
+
+    audit_table = pd.DataFrame({
+        "Variable": df.columns,
+        "Data_Type": df.dtypes.astype(str),
+        "Missing_Count": df.isna().sum(),
+        "Missing_Percent": round(df.isna().mean()*100,2),
+        "Unique_Values": df.nunique()
+    })
+
+    audit_table.to_excel(
+        f"{sheet_name}_audit_table.xlsx",
+        index=False
+    )