Pandas, or the Python Data Analysis Library, was created by Wes McKinney in 2008. It’s primary use to manipulate data in DataFrames or 2-dimensional labeled data structure with columns of potentially different types. The insertion, manipulation, and transformation of DataFrames are of significant use to Analysts using Python. Featuring many of the aspects that Excel and other data analysis tools possess, but able to process much larger datasets, Pandas use has grown significantly and is one of the most used libraries for Analysts, Scientists, and Data Engineers.

Pandas has core features which include the following:

• DataFrames & Series objects
• Reading & Writing Data
• Aggregating & Grouping Data
• Pivoting Tables
• Time Series Analysis
• Visualizations in Pandas
• Merging & Joining data

For more on Pandas see our extensive post on its history, usage, and support within the analytics community.