How to group data in Python using Pandas
The Pandas groupby function lets you split data into groups based on some criteria. Pandas DataFrames can be split on either axis, ie., row or column.
To see how to group data in Python, let’s imagine ourselves as the director of a highschool. We can see how the students performed by comparing their grades for different classes or lectures, and perhaps give a raise to the teachers of those classes that performed well.
If we have a large CSV file containing all the grades for all the students for all their lectures, simply iterating through this DataFrame one by one and checking all the data would be too much work. Instead, we can use Pandas’ groupby function to group the data into a Report_Card DataFrame we can more easily work with.
We’ll start with a multi-level grouping example, which uses more than one argument for the groupby function and returns an iterable groupby-object that we can work on:
Given this more structured and manageable dataset, we can now use the following code snippet to check which classes performed well:
The result of this line of code is as follows:
But it wouldn’t be fair to base the performances of different teachers on just class grade average (which in our contrived example only contains one student each), so let’s look at lecture grade averages, as well:
This code groups the Report_Card DataFrame on the Lectures column, and applies a mean function to the Grades column in order to return the average of the numerical values. We can now see at a glance the average grade for all students in each lecture, giving us a better impression of how well each of the teachers performed.
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