👀 Views: 69 💬 Answers: 1 📅 Created: 2025-06-06

pandas dataframe groupby python

I recently switched to Hey everyone, I'm running into an issue that's driving me crazy... I'm trying to perform a group-by operation on a DataFrame and return a custom aggregation that includes both the mean and the count of observations. However, I am getting unexpected results where the mean values seem to be calculated incorrectly for some groups. Here's a simplified version of my DataFrame and the code I'm using: ```python import pandas as pd data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C', 'A', 'B'], 'Values': [10, 20, 30, 40, 50, 60, 70, 80]} df = pd.DataFrame(data) ``` I'm trying to achieve the following aggregation: ```python result = df.groupby('Category').agg( mean_value=('Values', 'mean'), count=('Values', 'count') ) ``` When I run this, I expect the following output: ``` mean_value count Category A 33.33 4 B 50.00 3 C 55.00 2 ``` Instead, I'm getting: ``` mean_value count Category A 25.00 4 B 50.00 3 C 55.00 2 ``` It seems that the mean for Category A is incorrect. I've checked the data types and ensured that there are no NaN values in the 'Values' column. Additionally, I even tried resetting the index before performing the group by, but the scenario continues. ```python df['Values'] = df['Values'].astype(float) result = df.groupby('Category').agg( mean_value=('Values', 'mean'), count=('Values', 'count') ) print(result) ``` I’m using Pandas version 1.5.3. Can anyone guide to understand why the mean value for Category A is not reflecting the correct average of the values 10, 20, and 70? Any insights would be greatly appreciated! This is part of a larger web app I'm building. Has anyone else encountered this? I'm developing on Windows 10 with Python. I'm open to any suggestions.