👀 Views: 86 💬 Answers: 1 📅 Created: 2025-06-11

pandas dataframe groupby Python

I'm sure I'm missing something obvious here, but I've been banging my head against this for hours. I'm encountering a puzzling issue while using Pandas version 1.5.0. When performing a `groupby` operation followed by multiple aggregate functions, the resulting DataFrame has inconsistent row counts for different aggregation methods. Here's what I've tried: I have a DataFrame with sales data structured like this: ```python import pandas as pd data = { 'store': ['A', 'A', 'B', 'B', 'C', 'C'], 'sales': [100, 150, 200, 250, 300, 350], 'profit': [10, 15, 20, 25, 30, 35] } df = pd.DataFrame(data) ``` Now, I'm attempting to group by `store` and compute both the sum of `sales` and the average `profit`: ```python result = df.groupby('store').agg({'sales': 'sum', 'profit': 'mean'}) print(result) ``` The output looks correct, but when I try to add another aggregation to get the count: ```python result = df.groupby('store').agg({'sales': 'sum', 'profit': 'mean', 'profit_count': 'count'}) print(result) ``` I get a DataFrame with the same index but the `profit_count` column is not aligned properly, leading to an unexpected shape: ``` sales profit profit_count store A 250 12.5 2 B 450 22.5 2 C 650 32.5 2 ``` The count seems to be working fine, but it doesn't give me the total number of rows per store, as I expected. Instead, I anticipated that the row count would align correctly with the grouped indices. I was also wondering if there’s a way to ensure all aggregations return the same number of rows or if I should expect some discrepancies when dealing with different aggregation functions. Any thoughts or solutions would be greatly appreciated! I'm working on a CLI tool that needs to handle this. Has anyone else encountered this? I'm working in a Ubuntu 20.04 environment. I appreciate any insights!