Unexpected NaN Values in Pivot Table When Aggregating with Custom Function in Pandas
I've searched everywhere and can't find a clear answer. I tried several approaches but none seem to work. I'm trying to create a pivot table from a DataFrame using a custom aggregation function, but I'm working with unexpected NaN values in the resulting table. I'm using Pandas version 1.4.2 and here's the relevant code snippet: ```python import pandas as pd import numpy as np # Sample DataFrame data = { 'Category': ['A', 'A', 'B', 'B', 'C', 'C', 'C'], 'Values': [10, 20, 30, 40, np.nan, 60, 70], 'Date': pd.date_range(start='2023-01-01', periods=7) } df = pd.DataFrame(data) # Custom aggregation function def custom_agg(values): return np.sum(values) / len(values) pivot_table = df.pivot_table( index='Category', values='Values', aggfunc=custom_agg ) print(pivot_table) ``` When I run the above code, I expect to see the average values for each category, but instead, I'm getting NaN for Category 'A': ``` Category A NaN B 35.0 C 62.5 ``` Upon checking the DataFrame, it seems Category 'A' has two valid values (10 and 20), so I should see 15.0 instead of NaN. I've also tried using built-in aggregation functions like `np.mean`, and they work fine. I suspect that my custom function might be the scenario, but I'm unsure how to properly handle NaNs in this context since I want to keep the function generic. Can anyone guide to understand what's going wrong with my aggregation, or how to modify it to handle NaNs properly? For context: I'm using Python on Ubuntu. Thanks in advance! I'm using Python 3.11 in this project. Any ideas how to fix this?