Python groupby by two or more columns

The groupby function in Python is a powerful tool when it comes to data manipulation and analysis. It allows you to group data based on one or more columns and then perform various operations on each group. In this tutorial, we'll explore how to use the groupby function with multiple columns in Python, using the pandas library.
Make sure you have the pandas library installed. If not, you can install it using:
Let's start by creating a sample DataFrame to work with:
This DataFrame contains three columns: 'Category', 'Subcategory', and 'Value'.
Now, grouped_two_columns is a DataFrameGroupBy object, and we can apply various aggregation functions to it.
This code groups the DataFrame by 'Category' and 'Subcategory', then calculates the sum of 'Value' for each group and resets the index to make the result a DataFrame.
You can extend the groupby function to include more columns as needed.
This code groups the DataFrame by 'Category', 'Subcategory', and 'Value', then calculates the size (count) of each group and resets the index.
The groupby function in Python, especially when used with multiple columns, is a valuable tool for data analysis. It allows you to efficiently organize and analyze your data based on different criteria. Experiment with different aggregation functions and column combinations to gain insights into your datasets.
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