Pandas: GroupBy with Multiple Aggregations Returning NaN for Certain Groups
I've been researching this but I'm working with an scenario when using the `groupby()` function in Pandas with multiple aggregation methods. I'm trying to aggregate a DataFrame by multiple columns and apply both sum and mean aggregations, but I'm getting NaN values for some groups unexpectedly. Here's a simplified version of my DataFrame: ```python import pandas as pd data = { 'group': ['A', 'A', 'B', 'B', 'C', 'C'], 'value1': [1, 2, 3, None, 5, 6], 'value2': [4, None, 6, 7, 8, 9] } df = pd.DataFrame(data) ``` I want to group by the `group` column and get the sum of `value1` and the mean of `value2`: ```python result = df.groupby('group').agg({'value1': 'sum', 'value2': 'mean'}) ``` However, the resulting DataFrame contains NaNs for `value1` in group 'B', which I find confusing since one of the values is `None` but should be ignored. The output looks like this: ``` value1 value2 group A 3.0 4.0 B NaN 6.5 C 11.0 8.5 ``` What am I missing here? I expected the sum for 'B' to be 3.0, but instead, it's returning NaN. I've tried using `fillna(0)` on `value1` before the aggregation, but that led to incorrect total sums. Is there a better way to handle this aggregation without losing data integrity? I'm using Pandas version 1.4.1. This is happening in both development and production on macOS. What's the best practice here? This is for a CLI tool running on Windows 11.