Delete clean_mshr19_NV.py

clean_mshr19_NV.py

@@ -1,51 +0,0 @@
-import pandas as pd
-import glob
-import os
-from datetime import datetime
-
-# Prompt the user for a valid date in DD/MM/YYYY format
-while True:
-    date_str = input("Please input the date in YYYY/MM/DD format: ")
-    try:
-        # Validate format
-        date_obj = datetime.strptime(date_str, "%Y-%m-%d")
-        break
-    except ValueError:
-        print("Invalid date format. Please try again (YYYY/MM/DD).")
-
-# Collect all .xls and .xlsx files in the current directory
-excel_files = glob.glob("*.xls") + glob.glob("*.xlsx")
-
-for file_path in excel_files:
-    print(f"Processing: {file_path}")
-    # Read the first sheet with no header; treat all rows as data
-    df = pd.read_excel(file_path, sheet_name=0, header=None)
-    
-    # 1. Remove the first 9 rows
-    df = df.iloc[9:, :]
-    
-    # 2. Remove rows where column A has "TỔNG"
-    #    (Here, column A = df.iloc[:,0])
-    df = df[df.iloc[:, 0] != "TỔNG"]
-    
-    # 3. Add a new column (the DataFrame column name here won't matter 
-    #    because we'll save with header=False)
-    df["temp_col"] = ""
-    
-    # If there is at least one row, set the *top* row of that new column to "getDate"
-    if not df.empty:
-        df.iloc[0, df.columns.get_loc("temp_col")] = "getDate"
-    
-    # If there's more than one row, fill the rest of that column with the user-entered date
-    if df.shape[0] > 1:
-        df.iloc[1:, df.columns.get_loc("temp_col")] = date_str
-    
-    # Construct output file name
-    base_name = os.path.splitext(file_path)[0]
-    output_file = f"{base_name}_cleaned.xlsx"
-    
-    # 4. Save the result WITHOUT a pandas header row
-    #    So the first row of df remains the first row in Excel
-    df.to_excel(output_file, index=False, header=False)
-    
-    print(f" --> Cleaned file saved as: {output_file}")