👀 Views: 67 💬 Answers: 1 📅 Created: 2025-07-05

pandas groupby dataframe aggregation Python

I need some guidance on I'm working with an scenario while trying to perform a custom aggregation on a DataFrame using the `groupby` method in Pandas. Specifically, I'm trying to calculate a weighted average for different groups based on two columns: `group` and `value`. I want to apply a custom function that takes both the `value` and a `weight` column into account. My DataFrame looks like this: ```python import pandas as pd data = { 'group': ['A', 'A', 'B', 'B', 'B'], 'value': [10, 20, 30, 40, 50], 'weight': [1, 2, 1, 3, 1] } df = pd.DataFrame(data) ``` When I attempt to use `groupby` with my custom aggregation function, I encounter an behavior. Here's what I tried: ```python def weighted_average(x): return (x['value'] * x['weight']).sum() / x['weight'].sum() result = df.groupby('group').apply(weighted_average) ``` This results in a `TypeError` indicating that the `x` passed to `weighted_average` is a `Series` instead of a `DataFrame`. I tried using `apply` with `groupby` and specifying `as_index=False`, but that didn’t solve the scenario. I'd like to know how to correctly implement a custom aggregation function in this context. Why is my approach failing, and what adjustments should I make to properly calculate the weighted average for each group? I'm using Pandas version 1.5.0. The project is a desktop app built with Python. Any advice would be much appreciated.