Handling NaN Values in Pandas with Custom Aggregation Function on Grouped Data
I'm working with a DataFrame in Pandas where I need to group the data by a specific column and then apply a custom aggregation function. However, I'm running into issues when my groups contain NaN values. My custom function is supposed to return the mean of the values while ignoring NaNs, but sometimes it throws an behavior or returns an incorrect result. Here’s a simplified version of my DataFrame: ```python import pandas as pd import numpy as np data = { 'group': ['A', 'A', 'B', 'B', 'C', 'C', 'C'], 'value': [1, 2, np.nan, 4, 5, np.nan, 7] } df = pd.DataFrame(data) ``` I want to group by the 'group' column and calculate the mean of the 'value' column, skipping NaNs in the aggregation. ```python def custom_mean(series): return series.mean() result = df.groupby('group')['value'].agg(custom_mean) print(result) ``` However, I get an unexpected result where the mean for group 'C' includes NaN. The output I receive is: ``` group A 1.5 B 4.0 C 6.0 Name: value, dtype: float64 ``` I expected the mean for group 'C' to be calculated as (5 + 7) / 2, which should be 6.0, but when I included NaN, the behavior seemed off. I’ve also tried using `skipna=True` explicitly in my mean function, but it didn’t resolve the scenario. Any insights on how to correctly handle NaN values in this context or if there’s a better way to achieve this? I’m using Pandas version 1.3.3. For context: I'm using Python on Windows.