按ID分组为各日期列生成Min和Max列

To achieve your desired output, you can use pandas' groupby combined with agg() to specify multiple aggregation functions (min and max) for each date column. Here's a step-by-step implementation:

Step 1: Prepare the DataFrame #

First, create your sample DataFrame (or load your actual data):

import pandas as pd

data = {
    'ID': [1,1,1,2,2,2],
    'DateMade': ['01/01/2020', '01/01/2020', '01/01/2020', '03/04/2020', '05/06/2020', '01/01/2021'],
    'DelDate': ['05/06/2020', '07/06/2020', '07/06/2020', '07/08/2020', '23/08/2020', '31/08/2020'],
    'ExpDate': ['06/05/2022', '07/05/2022', '09/09/2022', '15/12/2022', '31/12/2022', '09/01/2023']
}

df = pd.DataFrame(data)

Step 2: Convert Date Columns to Datetime #

To ensure accurate min/max calculations (string-based date comparison can fail), convert the date columns to datetime objects:

date_columns = ['DateMade', 'DelDate', 'ExpDate']
df[date_columns] = df[date_columns].apply(pd.to_datetime, dayfirst=True)

Step 3: Group by ID and Aggregate Min/Max #

Use groupby('ID') and agg() to apply both min and max to each date column:

# Perform grouping and aggregation
grouped_df = df.groupby('ID').agg({
    'DateMade': ['min', 'max'],
    'DelDate': ['min', 'max'],
    'ExpDate': ['min', 'max']
})

# Flatten the multi-level column names (e.g., ('DateMade', 'min') becomes 'DateMade_Min')
grouped_df.columns = ['_'.join(col).capitalize() for col in grouped_df.columns]

# Reset index to make ID a regular column instead of the index
result_df = grouped_df.reset_index()

Step 4: Optional - Convert Dates Back to String Format #

If you need the dates in the original DD/MM/YYYY string format:

result_df.iloc[:, 1:] = result_df.iloc[:, 1:].apply(lambda x: x.dt.strftime('%d/%m/%Y'))

Final Result #

The resulting DataFrame will match your desired output:

Key Notes #

• Using agg() with a dictionary lets you specify different aggregation functions per column, which is perfect for your multi-column scenario.
• Converting dates to datetime ensures that min/max calculations are correct (e.g., "31/08/2020" is recognized as later than "07/08/2020", which wouldn't happen with string comparison).

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