Create clean_admission_data.py

src/clean_admission_data.py

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+
+def clean_admission_data(input_file_path, output_csv_path):
+    """
+    Reads raw admission data from an Excel file, cleans it, combines minority and non-minority
+    sanctioned/admitted numbers, calculates vacancies, and saves to a new CSV.
+
+    Args:
+        input_file_path (str): Path to the raw input Excel file (e.g., 'Admission 24-07-2025.xlsx').
+        output_csv_path (str): Path where the cleaned CSV file will be saved.
+    """
+    try:
+        # Read the raw Excel file, skipping the initial header rows.
+        # Based on the file snippet, data starts from the 6th row (index 5).
+        # Using pd.read_excel for .xlsx files.
+        df_raw = pd.read_excel(input_file_path, skiprows=4)
+
+        # Define the column names for the raw DataFrame after skipping rows.
+        # This list must match the exact number of columns in your data (12 columns).
+        # These names are derived from the structure of your input Excel snippet.
+        df_raw.columns = [
+            'S.No', 'District', 'Institution Name',
+            'V Minorities Sanctioned', 'V Minorities Admitted',
+            'V NonMinorities Sanctioned', 'V NonMinorities Admitted',
+            'Course',
+            'Inter Minorities Sanctioned', 'Inter Minorities Admitted',
+            'Inter NonMinorities Sanctioned', 'Inter NonMinorities Admitted'
+        ]
+
+        # Clean 'Institution Name': remove text within brackets and replace 'Boys'/'Girls'.
+        # Convert to string first to handle potential non-string types.
+        #df_raw['Institution Name'] = df_raw['Institution Name'].astype(str).apply(
+        #    lambda x: re.sub(r'\([^)]*\)', '', x).replace('Boys', 'B').replace('Girls', 'G').strip()
+        #)
+
+
+         # --- ADDON CODE: Remove the last row of the input data ---
+        # This is useful if the last row contains summary statistics or unwanted data.
+        if not df_raw.empty:
+            df_raw = df_raw.iloc[:-1]
+            print("Last row of the raw data has been removed.")
+        else:
+            print("Raw DataFrame is empty, no row to remove.")
+        # --- END ADDON CODE ---
+
+
+
+        numeric_cols = [
+            'V Minorities Sanctioned', 'V Minorities Admitted',
+            'V NonMinorities Sanctioned', 'V NonMinorities Admitted',
+            'Inter Minorities Sanctioned', 'Inter Minorities Admitted',
+            'Inter NonMinorities Sanctioned', 'Inter NonMinorities Admitted'
+        ]
+
+        for col in numeric_cols:
+            df_raw[col] = pd.to_numeric(df_raw[col], errors='coerce').fillna(0).astype(int)
+
+        # --- Process Class V data ---
+        # Select relevant columns for Class V and create a copy to avoid SettingWithCopyWarning.
+        df_v = df_raw[['S.No', 'District', 'Institution Name',
+                       'V Minorities Sanctioned', 'V Minorities Admitted',
+                       'V NonMinorities Sanctioned', 'V NonMinorities Admitted']].copy()
+        df_v['Class'] = 'V' # Assign 'V' to the 'Class' column for these rows
+
+        # Calculate combined 'Sanctioned' and 'Admitted' for Class V (Minorities + Non-Minorities)
+        df_v['Sanctioned'] = df_v['V Minorities Sanctioned'] + df_v['V NonMinorities Sanctioned']
+        df_v['Admitted'] = df_v['V Minorities Admitted'] + df_v['V NonMinorities Admitted']
+
+        # --- Process Inter 1st Year data ---
+        # Select relevant columns for Inter 1st Year and create a copy.
+        df_inter = df_raw[['S.No', 'District', 'Institution Name',
+                           'Inter Minorities Sanctioned', 'Inter Minorities Admitted',
+                           'Inter NonMinorities Sanctioned', 'Inter NonMinorities Admitted']].copy()
+        df_inter['Class'] = 'Inter 1st Year' # Assign 'Inter 1st Year' to the 'Class' column
+
+        # Calculate combined 'Sanctioned' and 'Admitted' for Inter (Minorities + Non-Minorities)
+        df_inter['Sanctioned'] = df_inter['Inter Minorities Sanctioned'] + df_inter['Inter NonMinorities Sanctioned']
+        df_inter['Admitted'] = df_inter['Inter Minorities Admitted'] + df_inter['Inter NonMinorities Admitted']
+
+        # Concatenate the Class V and Inter DataFrames to form the final DataFrame
+        df_final = pd.concat([df_v, df_inter], ignore_index=True)
+
+        # Calculate 'Vacancies'
+        df_final['Vacancies'] = df_final['Sanctioned'] - df_final['Admitted']
+
+        # Reorder and select final columns as per the desired output format
+        # The 'S.No' from the raw data is kept as the final S.No.
+        df_final = df_final[['S.No', 'District', 'Institution Name', 'Class', 'Sanctioned', 'Admitted', 'Vacancies']]
+
+        # Rename 'Institution Name' to 'name of the tmr institute' to match the example output
+        #df_final.rename(columns={'Institution Name': 'name of the tmr institute'}, inplace=True)
+
+        # Save the cleaned DataFrame to a CSV file
+        df_final.to_csv(output_csv_path, index=False)
+
+       
+    except FileNotFoundError:
+        print(f"Error: The input file '{input_file_path}' was not found. Please ensure it's in the correct directory.")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")