👀 Views: 2 💬 Answers: 1 📅 Created: 2025-06-05

pandas dataframe pivot Python

This might be a silly question, but I've looked through the documentation and I'm still confused about I'm refactoring my project and I'm upgrading from an older version and I've been struggling with this for a few days now and could really use some help....... I tried several approaches but none seem to work. I'm currently working with a DataFrame in pandas version 1.3.3, and I'm encountering unexpected NaN values when trying to pivot my data. My DataFrame contains sales data with the following structure: ```python import pandas as pd data = { 'Date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'], 'Product': ['A', 'B', 'A', 'B'], 'Sales': [100, 150, 200, 250], } df = pd.DataFrame(data) ``` I want to pivot this DataFrame to summarize the total sales for each product by date, so I used the following code: ```python pivot_df = df.pivot(index='Date', columns='Product', values='Sales') ``` However, when I run this, I get a pivot table that looks like this: ``` Product A B Date 2021-01-01 100.0 150.0 2021-01-02 200.0 NaN ``` The issue is that I expected to see the total sales for both products on both dates, but I'm getting NaN for Product B on '2021-01-02'. I confirmed that there are no missing values in the original DataFrame, so I’m puzzled about why this is happening. I even tried using the `pivot_table` method with `aggfunc='sum'` in hopes that it would resolve the issue: ```python pivot_table_df = df.pivot_table(index='Date', columns='Product', values='Sales', aggfunc='sum') ``` But the result is the same, with NaN values persisting for Product B. I would appreciate any insights into why this is happening and how I can get the expected results without NaNs. Is there a best practice I am missing or something about the data structure that I need to address? Thanks in advance! My development environment is macOS. I'm working on a application that needs to handle this. Thanks in advance! This is part of a larger desktop app I'm building. Am I missing something obvious? This issue appeared after updating to Python LTS. Is this even possible? This is for a microservice running on CentOS.