Pandas DataFrame ValueError when applying custom function to grouped object with NaN values
I'm relatively new to this, so bear with me... This might be a silly question, but I'm working with a `ValueError` when trying to apply a custom function to a grouped DataFrame that contains NaN values. I'm using pandas version 1.3.0 and I want to compute the mean of a specific column after grouping by another column. However, my custom function is supposed to handle NaN values, and I thought it would work seamlessly. Here’s a simplified version of my code: ```python import pandas as pd import numpy as np # Sample data data = { 'group': ['A', 'A', 'B', 'B', 'C', 'C'], 'value': [1, 2, np.nan, 4, 5, np.nan] } df = pd.DataFrame(data) # Custom function to compute mean, ignoring NaNs def custom_mean(x): return np.mean(x) # Applying the function after grouping result = df.groupby('group')['value'].apply(custom_mean) print(result) ``` When I run this code, I get an behavior: ``` ValueError: want to mask with array of incompatible shape ``` I’ve tried using `dropna()` before applying the function, but that doesn’t solve the scenario as I still want to retain groups even if they contain NaN values. I also attempted to modify the function to handle NaNs directly, but it doesn't seem to have any effect. Has anyone experienced this scenario and found a way to resolve it? Any insights on how to correctly apply a function to a grouped DataFrame with NaN values would be greatly appreciated! Any ideas what could be causing this? This is part of a larger API I'm building.