A Brief Tour of Grouping and Aggregating in Pandas
If you work with data in Python, chances are that you’ve heard of the pandas data manipulation library. You can think of pandas as a way to programmatically interact with spreadsheets. It works well with huge datasets, unlike its desktop counterparts like Google Sheets and Microsoft Excel, and implements a number of common database operations like merging, pivoting, and grouping. Moreover, being backed by numpy and efficient algorithm implementations makes it fast and easily integrated with other tools in the vast Python data science landscape.
