This paper is a cheat sheet talking about Pandas. The various illustrations provides an easy way to understand the syntax. Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

If you like Pandas, you will probably like this cheatsheet where you will get a syntax summary to reshape, subset, summarize, creating new columns, combining data sets.

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. In this tutorial, we will leverage Python Pandas and NumPy libraries to clean data.

This is a short introduction to pandas, geared mainly for new users.

This pdf is a complete 12 pages cheatsheet on Pandas dataframe

This document is a jupyter note-book talking about Python functions used to simplify the utilisation of SQL queries. The author uses the mysql.connector and pandas to query into function (the functions are display) and how to use them at the end. With such functions it become super easy to query your database..

Working with Dates in Pandas is a 13 pages Jupyter note-book showing you lot of tips on how to work with dates in Pandas (dates, datetime, formats, filters, methods...)

This e-book is a 172 p talking about pandas. Lot of things are explained like using regex, using the .query() method, merge dataframe, read Json, store dataframe in HDFStore file, handle missing data…

When you are using SQL for many years and try to code in Pandas, you try to make the link between the 2 syntax. This PDF compare how you can do the majority of SQL queries in Pandas.