Unexpected behavior when using pd.pivot_table with NaN values in aggregation
I've been struggling with this for a few days now and could really use some help. I've searched everywhere and can't find a clear answer. I'm stuck on something that should probably be simple. I'm trying to create a pivot table using `pandas` version 1.3.3, but I am encountering unexpected results when my DataFrame contains NaN values in the aggregation columns. Here's a snippet of my DataFrame: ```python import pandas as pd data = { 'date': ['2023-01-01', '2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'], 'category': ['A', 'B', 'A', 'B', 'A'], 'value': [10, None, 20, 30, None] } df = pd.DataFrame(data) df['date'] = pd.to_datetime(df['date']) ``` When I create a pivot table to sum the values for each category per date, I expect it to treat NaN as zero, but instead, I get NaN as the result for the categories that contain NaN values. Here’s the code I used to generate the pivot table: ```python pivot = df.pivot_table(index='date', columns='category', values='value', aggfunc='sum') print(pivot) ``` The output I get is: ``` category A B date 2023-01-01 30.0 NaN 2023-01-02 NaN 30.0 ``` I was expecting the result to be: ``` category A B date 2023-01-01 30.0 0.0 2023-01-02 0.0 30.0 ``` I tried using `fillna(0)` on the pivot table after creating it, but it still seems a bit inefficient. Here’s what I attempted: ```python pivot = df.pivot_table(index='date', columns='category', values='value', aggfunc='sum').fillna(0) print(pivot) ``` While this works as a workaround, I’d like to understand if there’s a way to handle NaN values directly in the `pivot_table` call itself. Is there a best practice for this? Am I missing a parameter that allows me to treat NaNs as zeros during the aggregation? Any help would be appreciated! I'm working on a application that needs to handle this. What's the best practice here? My development environment is Ubuntu. I'm working with Python in a Docker container on Debian. Is this even possible?