👀 Views: 13 💬 Answers: 1 📅 Created: 2025-06-11

pandas dataframe groupby transform python

I'm performance testing and I'm following best practices but I'm a bit lost with Hey everyone, I'm running into an issue that's driving me crazy... I'm encountering an issue where applying a transformation function to a grouped DataFrame results in unexpected NaN values in some of my columns. I have a dataset where I group by a 'category' column and then apply a custom function to calculate the mean of another column, but some groups are returning NaNs instead of the expected values. Here’s a simplified version of what I’m doing: ```python import pandas as pd # Sample data data = { 'category': ['A', 'A', 'B', 'B', 'C', 'C'], 'value': [10, 20, None, 30, 5, None] } df = pd.DataFrame(data) # Trying to group by 'category' and calculate mean grouped = df.groupby('category') result = grouped['value'].transform(lambda x: x.mean()) df['mean_value'] = result print(df) ``` I expected the output to replace `None` values in 'value' with the mean of their respective groups. However, after running the code, the entries for category 'A' and 'C' have NaN values in the 'mean_value' column. The output shows: ``` category value mean_value 0 A 10.0 15.0 1 A 20.0 15.0 2 B NaN 30.0 3 B 30.0 30.0 4 C 5.0 NaN 5 C NaN NaN ``` The mean for category 'B' is calculated correctly, but why is category 'C' producing NaN for the mean value? I’ve also tried replacing `None` with 0 before the transformation, but that didn’t help. Any guidance on how to handle this situation appropriately would be appreciated. I'm using pandas version 1.4.3. Thanks in advance! For context: I'm using Python on Linux. Any ideas what could be causing this? Thanks for any help you can provide! I'm working with Python in a Docker container on macOS.