Phuong's Pull Request

Mini-Project/Arrest Data Clean .py

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+import pandas as pd
+import os
+
+# ----------------------------
+# 1. Load the Excel file
+# ----------------------------
+df = pd.read_excel('/Users/phuong/Desktop/Mini-Project/Arrests.xlsx')
+
+# ----------------------------
+# 2. Keep only selected columns
+# ----------------------------
+columns_to_keep = [
+    "Apprehension Date",
+    "Apprehension State",
+    "Apprehension AOR",
+    "Apprehension Criminality",
+    "Final Order Yes No",
+    "Citizenship Country",
+    "Gender"
+]
+
+df_edited = df[columns_to_keep]
+
+# ----------------------------
+# 3. Save new Excel file in the same folder as the original
+# ----------------------------
+folder = os.path.dirname('/Users/phuong/Desktop/Mini-Project/Arrests.xlsx')
+output_path = os.path.join(folder, "Arrests_Edited.xlsx")
+df_edited.to_excel(output_path, index=False)
+
+print("File saved successfully as: Arrests_Edited.xlsx")
+
+
+
+# --------------------------------------------------------
+# Drop Missing Data Not Recovered
+# --------------------------------------------------------
+import pandas as pd
+
+# Load your edited arrests file
+df = pd.read_excel('/Users/phuong/Desktop/Mini-Project/Arrests_Edited.xlsx')
+
+# ----------------------------
+# 1. Remove rows where BOTH 'Apprehension State' and 'Apprehension AOR' are missing
+# ----------------------------
+df_cleaned = df.dropna(subset=['Apprehension State', 'Apprehension AOR'], how='all')
+
+# ----------------------------
+# 2. List unique 'Apprehension AOR' values where 'Apprehension State' is missing
+# ----------------------------
+missing_state_aors = df_cleaned.loc[df_cleaned['Apprehension State'].isna(), 'Apprehension AOR'].unique()
+
+# ----------------------------
+# Optional: save the cleaned file back
+# ----------------------------
+output_path = '/Users/phuong/Desktop/Mini-Project/Arrests_Edited_Cleaned.xlsx'
+df_cleaned.to_excel(output_path, index=False)
+
+# ----------------------------
+# Print the results
+# ----------------------------
+print(f"Cleaned file saved as: {output_path}")
+print("Unique 'Apprehension AOR' missing 'Apprehension State':")
+print(missing_state_aors)
+
+# --------------------------------------------------------
+# Fill In The Missing Data
+# --------------------------------------------------------
+
+
+import pandas as pd
+
+# Load the cleaned arrests file
+df = pd.read_excel('/Users/phuong/Desktop/Mini-Project/Arrests_Edited_Cleaned.xlsx')
+
+# ----------------------------
+# 1. Define the mapping dictionary
+# ----------------------------
+aor_to_state = {
+    'Atlanta Area of Responsibility': 'GEORGIA',
+    'San Francisco Area of Responsibility': 'CALIFORNIA',
+    'El Paso Area of Responsibility': 'TEXAS',
+    'Salt Lake City Area of Responsibility': 'UTAH',
+    'Phoenix Area of Responsibility': 'ARIZONA',
+    'Houston Area of Responsibility': 'TEXAS',
+    'Baltimore Area of Responsibility': 'MARYLAND',
+    'Philadelphia Area of Responsibility': 'PENNSYLVANIA',
+    'Dallas Area of Responsibility': 'TEXAS',
+    'Newark Area of Responsibility': 'NEW JERSEY',
+    'Miami Area of Responsibility': 'FLORIDA',
+    'Boston Area of Responsibility': 'MASSACHUSETTS',
+    'Harlingen Area of Responsibility': 'TEXAS',
+    'Los Angeles Area of Responsibility': 'CALIFORNIA',
+    'Detroit Area of Responsibility': 'MICHIGAN',
+    'Denver Area of Responsibility': 'COLORADO',
+    'Chicago Area of Responsibility': 'ILLINOIS',
+    'St. Paul Area of Responsibility': 'MINNESOTA',
+    'Seattle Area of Responsibility': 'WASHINGTON',
+    'New Orleans Area of Responsibility': 'LOUISIANA',
+    'Washington Area of Responsibility': 'DISTRICT OF COLUMBIA',
+    'San Diego Area of Responsibility': 'CALIFORNIA',
+    'San Antonio Area of Responsibility': 'TEXAS',
+    'New York City Area of Responsibility': 'NEW YORK',
+    'HQ Area of Responsibility': 'VIRGINIA',
+    'Buffalo Area of Responsibility': 'NEW YORK'
+}
+
+# ----------------------------
+# 2. Fill missing 'Apprehension State' values using the dictionary
+# ----------------------------
+df['Apprehension State'] = df.apply(
+    lambda row: aor_to_state[row['Apprehension AOR']] if pd.isna(row['Apprehension State']) else row['Apprehension State'],
+    axis=1
+)
+
+# ----------------------------
+# 3. Save the updated file
+# ----------------------------
+output_path = '/Users/phuong/Desktop/Mini-Project/Arrests_Edited_Filled.xlsx'
+df.to_excel(output_path, index=False)
+
+print("Missing 'Apprehension State' values filled and file saved as:", output_path)
+
+# --------------------------------------------------------
+# Fill In The Year
+# --------------------------------------------------------
+
+import pandas as pd
+
+# Load the filled arrests file
+df = pd.read_excel('/Users/phuong/Desktop/Mini-Project/Arrests_Edited_Filled.xlsx')
+
+# Ensure 'Apprehension Date' is datetime
+df['Apprehension Date'] = pd.to_datetime(df['Apprehension Date'], errors='coerce')
+
+# Create 'Apprehension Year'
+df['Apprehension Year'] = df['Apprehension Date'].dt.year
+
+# Reorder columns: move 'Apprehension Year' right after 'Apprehension Date'
+cols = df.columns.tolist()
+date_idx = cols.index('Apprehension Date')
+# Remove 'Apprehension Year' from current position
+cols.remove('Apprehension Year')
+# Insert it right after 'Apprehension Date'
+cols.insert(date_idx + 1, 'Apprehension Year')
+df = df[cols]
+
+# Save the updated file
+output_path = '/Users/phuong/Desktop/Mini-Project/Arrests_Edited_With_Year.xlsx'
+df.to_excel(output_path, index=False)
+
+print("New column 'Apprehension Year' added after 'Apprehension Date' and file saved as:", output_path)
+
+
+
+
+
+import pandas as pd
+
+# Load file
+df = pd.read_excel('/Users/phuong/Desktop/Mini-Project/Arrests_Edited_With_Year.xlsx')
+
+# Ensure date column is datetime
+df['Apprehension Date'] = pd.to_datetime(df['Apprehension Date'], errors='coerce')
+
+# Extract year and month (if not already present)
+df['Year'] = df['Apprehension Date'].dt.year
+df['Month'] = df['Apprehension Date'].dt.month
+
+# Filter Jan–Oct (months 1–10)
+jan_oct = df[df['Month'].between(1, 10)]
+
+# Count arrests for each year
+arrests_2024 = jan_oct[jan_oct['Year'] == 2024].shape[0]
+arrests_2025 = jan_oct[jan_oct['Year'] == 2025].shape[0]
+
+print("Total arrests Jan–Oct 2024:", arrests_2024)
+print("Total arrests Jan–Oct 2025:", arrests_2025)
+
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