Month: January 2021

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

Building on the basic aggregation guide, in this guide we will look at some more advanced ways we can aggregate data using pandas. We are going to cover three techniques:

1. Aggregating using different methods at the same time, for example, summing one column and taking the average of another.
2. Defining and using custom aggregation functions which we can use to calculate aggregates that are not available “out of the box”.
3. The transform method which can be used to do some very useful things with aggregated values.

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

A fundamental tool for working in pandas and with tabular data more generally is the ability to aggregate data across rows. Thankfully pandas gives us some easy-to-use methods for aggregation, which includes a range of summary statistics such as sums, min and max values, means and medians, variances and standard deviations, or even quantiles. In this guide we will walk through the basics of aggregation in pandas, hopefully giving you the basic building blocks to go on to more complex aggregations.