This is a guide to many pandas tutorials by the community, geared mainly for new users.
The goal of this 2015 cookbook (by Julia Evans) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that entails. For the table of contents, see the pandas-cookbook GitHub repository.
A set of lesson for new pandas users: https://bitbucket.org/hrojas/learn-pandas
This guide is an introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. There are four sections covering selected topics as munging data, aggregating data, visualizing data and time series.
Practice your skills with real data sets and exercises. For more resources, please visit the main repository.
Tutorial series written in 2016 by Tom Augspurger. The source may be found in the GitHub repository TomAugspurger/effective-pandas.
Modern Pandas
Method Chaining
Indexes
Performance
Tidy Data
Visualization
Timeseries
Using Pandas and XlsxWriter to create Excel charts
Pandas From The Ground Up (2015) (2:24) GitHub repo
Introduction Into Pandas (2016) (1:28) GitHub repo
Pandas: .head() to .tail() (2016) (1:26) GitHub repo
Data analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook
Best practices with pandas (2018) GitHub repo and Jupyter Notebook
Wes McKinney’s (pandas BDFL) blog
Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson
Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013
Financial analysis in Python, by Thomas Wiecki
Intro to pandas data structures, by Greg Reda
Pandas and Python: Top 10, by Manish Amde
Pandas DataFrames Tutorial, by Karlijn Willems
A concise tutorial with real life examples