Python实现循环修改DataFrame列名：后缀数字转月份

Here's a clean, scalable way to achieve your goal using Python's calendar module and pandas' column mapping:

Step 1: Import Required Modules #

First, make sure you have pandas and the built-in calendar module imported:

import pandas as pd
import calendar

Step 2: Define the Renaming Function #

Create a helper function that checks each column name, extracts the trailing digit (if present), and maps it to the corresponding month name:

def convert_to_month(col_name):
    # Split the column name by underscores
    name_parts = col_name.split('_')
    # Check if the last segment is a numeric digit
    if name_parts[-1].isdigit():
        # Convert the string digit to an integer
        month_number = int(name_parts[-1])
        # Use calendar.month_name to get the full month name (1-indexed)
        return calendar.month_name[month_number]
    # For columns like 'Total', return the original name
    else:
        return col_name

Step 3: Apply the Function to Your DataFrame Columns #

Use pandas' map() method to apply the function to all column names:

# Example DataFrame with your original columns
df = pd.DataFrame(columns=['MCA036_LIFETIME_1', 'MCA036_LIFETIME_2', 'MCA036_LIFETIME_3', 'Total'])

# Rename the columns
df.columns = df.columns.map(convert_to_month)

# Verify the result
print(list(df.columns))
# Output: ['January', 'February', 'March', 'Total']

Alternative: Manual Month Mapping (No Calendar Module) #

If you prefer not to use the calendar module, you can create a direct dictionary mapping for the digits you need:

month_mapping = {
    '1': 'January',
    '2': 'February',
    '3': 'March',
    # Add more entries for months 4-12 if needed
}

def convert_to_month_manual(col_name):
    name_parts = col_name.split('_')
    return month_mapping.get(name_parts[-1], col_name)

# Apply the same way
df.columns = df.columns.map(convert_to_month_manual)

Key Notes #

• The calendar.month_name list is 1-indexed (so calendar.month_name[1] returns 'January'), which aligns perfectly with your column naming pattern.
• This solution automatically handles any number of month columns (not just 3) as long as they follow the *_digit format.
• Columns that don't match the pattern (like 'Total') are left unchanged, so you don't have to worry about excluding them manually.

内容的提问来源于stack exchange，提问作者Michael Ryan