👀 Views: 2 💬 Answers: 1 📅 Created: 2025-06-11

pandas dataframe pivot-table Python

I'm relatively new to this, so bear with me. I'm currently working with a question with `pandas` version 1.3.3 when using the `pivot_table` function on a DataFrame that has a multi-index on the rows and columns. After aggregating, I’m getting unexpected NaN values where I thought there would be data. Here’s the DataFrame I’m working with: ```python import pandas as pd import numpy as np # Sample data with multi-index index = pd.MultiIndex.from_tuples([('A', 'foo'), ('A', 'bar'), ('B', 'foo'), ('B', 'bar')], names=['first', 'second']) columns = ['value'] data = [[1], [2], [np.nan], [4]] df = pd.DataFrame(data, index=index, columns=columns) print(df) ``` When I try to create a pivot table with the following command: ```python pivot = df.pivot_table(values='value', index='first', columns='second', aggfunc='sum') print(pivot) ``` I get this output: ``` second bar foo first A 2.0 1.0 B 4.0 NaN ``` I expected to see a 0.0 for the B foo entry instead of a NaN. I’ve also tried using `fill_value=0` in the `pivot_table` method: ```python pivot = df.pivot_table(values='value', index='first', columns='second', aggfunc='sum', fill_value=0) ``` This still doesn’t change the NaN to 0. I’ve confirmed that there are indeed no entries in my original DataFrame for B foo with non-null values, but I thought that providing a fill value should override these NaNs. Is there something I’m misunderstanding about how `pivot_table` aggregates data in conjunction with multi-indexing? Any insights on this would be greatly appreciated! Is there a better approach?