👀 Views: 426 💬 Answers: 1 📅 Created: 2025-06-04

pandas dataframe groupby aggregation Python

I'm upgrading from an older version and I'm maintaining legacy code that I'm currently working with an scenario while using Pandas for data aggregation... I have a DataFrame containing sales data that I want to group by 'category' and calculate the total sales for each category. However, after applying the `groupby` and `agg` functions, I end up with an empty DataFrame. Here’s a snippet of the DataFrame I’m working with: ```python import pandas as pd data = { 'category': ['A', 'B', 'A', 'C', 'B', 'C'], 'sales': [100, 200, 150, 300, 250, 100] } df = pd.DataFrame(data) ``` Now, I’m trying to group by 'category' and sum the 'sales': ```python result = df.groupby('category')['sales'].agg('sum') print(result) ``` Instead of getting a grouped DataFrame with total sales per category, I’m receiving an empty DataFrame: ``` Empty DataFrame Columns: [sales] Index: [] ``` I verified that the DataFrame `df` contains data before the aggregation, and the 'category' column is of type `object`. I also checked for any leading/trailing spaces in the column names, which seem to be correct. I’ve tried different aggregation functions like `mean` and `count`, but I still get the same empty DataFrame. Am I missing something in my approach, or is there a specific caveat related to the version of Pandas I am using (currently 1.3.3)? Any insights would be greatly appreciated! I'm developing on CentOS with Python. This is my first time working with Python 3.10. What would be the recommended way to handle this?