👀 Views: 1084 💬 Answers: 1 📅 Created: 2025-06-12

pandas pivot-table dataframe aggregation Python

I'm trying to figure out I'm working on a project and hit a roadblock. I'm currently working with a DataFrame containing sales data for different products across various regions, and I'm trying to create a pivot table to summarize the total sales and average price. However, I'm working with unexpected NaN values in my results, even though the data seems complete. Here's a snippet of my DataFrame: ```python import pandas as pd data = { 'Region': ['North', 'South', 'East', 'West', 'North', 'South'], 'Product': ['A', 'A', 'B', 'B', 'A', 'B'], 'Sales': [100, 200, 150, 300, 250, 100], 'Price': [10, 15, 20, 25, 10, 20] } df = pd.DataFrame(data) ``` I want to create a pivot table that shows the total sales and average price for each product by region like this: ```python pivot_df = df.pivot_table(index='Region', columns='Product', values=['Sales', 'Price'], aggfunc={'Sales': 'sum', 'Price': 'mean'}) ``` After executing the above code, I get a pivot table, but some entries are showing up as NaN, which doesn't make sense because I have data for those regions and products. Here’s the output I see: ``` Sales A B Region East NaN 150.0 North 350.0 NaN South 200.0 100.0 West NaN 300.0 Price A B Region East NaN 20.0 North 10.0 NaN South 15.0 20.0 West NaN 25.0 ``` I’ve double-checked the input data, and there are products in those regions that should be reflected. I suspect it might be related to how I'm aggregating multiple columns, but I need to pinpoint the scenario. I've tried using different aggregation functions and also simplifying the DataFrame, but the NaNs continue. Any insights into why this is happening and how I can resolve it would be greatly appreciated! My team is using Python for this service. Thanks, I really appreciate it! What's the correct way to implement this?