Pandas Primer

Pandas is one of, if not the, most widely-used and relied-upon libraries in the Python ecosystem. Pandas is often the first stop for data scientists for data processing, analysis, and manipulation.

Do you have tabular data you want to process? There is basically not way around using Pandas, and nor should you look for one. Pandas is rich in functionality, is incredibly powerful, and provides robust flexibility. Want to inspect data? Pandas can help. Need to query data? Pandas has you covered. Have to prepare tabular data for machine learning? Pandas is here for you.

KDnuggets' Abid Ali Awan further describes Pandas as follows:

Pandas is a flexible and easy-to-use tool for performing data analysis and data manipulation. It is widely used among data scientists for preparing data, cleaning data, and running data science experiments. Pandas is an open-source library that helps you solve complex statistical problems with simple and easy-to-use syntax.

Do you know how to leverage Pandas in your projects? You really should! There are plenty of resources to help with this, but getting right to work and dirtying your hands is always a great idea. But where do you turn for a quick reference?

To help, KDnuggets has put together this fantastic Pandas primer, which covers some of the important first steps in your Pandas journey.

You can download the cheatsheet here.

Getting Started with Pandas Cheatsheet

 
This quick reference cheatsheet guide will provide you with the basic Pandas operations needed to start querying and modifying DataFrames, the basic data structure of the library. It will show you how to create DataFrames, import and export data to and from them, inspect the DataFrames, as well subset, query, and reshape the DataFrames. Once you master these introductory operations, you should be ready for more advanced Pandas tasks.

Learning Pandas is worth the effort. Beginners are often discouraged by the breadth of operations and the at-first intimidating syntax. But by taking it step by step, mastering the basics, and keeping a reference handy while you practice (like, say, this cheatsheet), you will be making progress with Python's most data processing ubiquitous library in no time.