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

pandas dataframe pivot-table fillna Python

I'm trying to pivot a DataFrame using `pd.pivot_table`, but I'm encountering issues when there are missing combinations for my MultiIndex columns. Specifically, I'm working with a DataFrame that contains sales data for multiple products across different regions, and I want to summarize the total sales by product and region. However, I notice that when a product is not sold in a particular region, the resulting pivot table shows `NaN` for those combinations, which I want to replace with zeros instead. Here’s the DataFrame I’m starting with: ```python import pandas as pd import numpy as np data = { 'Product': ['A', 'A', 'B', 'B', 'C'], 'Region': ['North', 'South', 'North', 'West', 'South'], 'Sales': [100, 150, 200, 250, 300] } df = pd.DataFrame(data) ``` When I use the `pivot_table` method to create my summary: ```python pivot_df = df.pivot_table(index='Product', columns='Region', values='Sales', aggfunc='sum') ``` This gives me a pivot table that looks like this: ``` Region North South West Product A 100.0 150.0 NaN B 200.0 NaN 250.0 C NaN 300.0 NaN ``` As you can see, there are `NaN` values where no sales were recorded for certain combinations of products and regions. I want to replace these `NaN` values with zeros. I tried using the `fillna` method like this: ```python pivot_df.fillna(0, inplace=True) ``` But I still get a `SettingWithCopyWarning`, which makes me question if I'm doing it correctly. Here’s the full line to create and fill the pivot table: ```python pivot_df = df.pivot_table(index='Product', columns='Region', values='Sales', aggfunc='sum').fillna(0) ``` How can I create this pivot table without encountering the `SettingWithCopyWarning`, and ensure all `NaN` values are replaced with zeros? What’s the best practice for handling this scenario in pandas version 1.5.0? For context: I'm using Python on Linux. Am I missing something obvious?