👀 Views: 115 💬 Answers: 1 📅 Created: 2025-06-10

pandas dataframe pivot-table Python

After trying multiple solutions online, I still can't figure this out. Hey everyone, I'm running into an issue that's driving me crazy. Hey everyone, I'm running into an issue that's driving me crazy. I am trying to create a pivot table using Pandas to summarize sales data, but I'm encountering unexpected behavior with multi-index columns. I have a DataFrame structured like this: ```python import pandas as pd data = { 'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'], 'Product': ['A', 'B', 'A', 'B'], 'Sales': [100, 200, 150, 250], 'Returns': [5, 15, 10, 20] } df = pd.DataFrame(data) df['Date'] = pd.to_datetime(df['Date']) ``` When I try to pivot the table using: ```python pivot_df = pd.pivot_table(df, values=['Sales', 'Returns'], index='Date', columns='Product', aggfunc='sum') ``` I expect to see a multi-index DataFrame with 'Sales' and 'Returns' as the first level and 'A' and 'B' as the second level. However, the result looks flat: ```python Product A B Date 2023-01-01 100 200 2023-01-02 150 250 ``` I need to achieve a structure like this: ``` Product A B Sales Returns Sales Returns Date 2023-01-01 100 5 200 15 2023-01-02 150 10 250 20 ``` I have also tried adjusting the `columns` parameter to include both values like this: ```python pivot_df = pd.pivot_table(df, values=['Sales', 'Returns'], index='Date', columns=['Product'], aggfunc='sum') ``` But it still does not provide the multi-level structure I am looking for. Instead, it gives a similar flat output. I checked the version of Pandas I’m using, which is 1.5.1. Is there a way to achieve the desired multi-index format with the pivot table? Any insights or suggestions would be greatly appreciated! Has anyone else encountered this? For context: I'm using Python on macOS. Has anyone else encountered this? My development environment is Linux. Any help would be greatly appreciated!