A collection of resources for pandas (Python) and related subjects. Pull requests are very welcome!

Contents: This is an unofficial collection of resources for learning pandas, an open source Python library for data analysis. Here you will find videos, cheat-sheets, tutorials and books / papers. The curated list is divided into three parts:

1. pandas resources - A collection of videos, cheat-sheets, tutorials and books directly related to pandas.
2. Data analysis with Python resources - Material related to adjacent Python libraries and software such as NumPy, scipy, matplotlib, seaborn, statsmodels and Jupyter.
3. Miscellaneous related resources - Resources related to general data analysis, Python programming, algorithms, computer science, machine learning, statistics, etc.
4. Packages - Python packages for helping to work with Pandas.

The videos below were collected in July of 2018. They are all directly related to pandas, and the Level of a video is quantified roughly as follows:

• 😃 : Beginner - requires little knowledge to jump into, elementary topics.
• 😅 : Intermediate - some prior knowledge needed, more technical.
• 😱 : Advanced - very technical, or discusses advanced topics.
• ⭐ : Recommended video - high quality video and audio, great presentation.

Know of a recent, good video? Send a pull request! 👍

• Data Wrangling with pandas
• The pandas DataFrame Object
• Python For Data Science - pandas Basics
• Python For Data Science - pandas

• ⭐ 10 Minutes to pandas
• ⭐ pandas_exercises
• ⭐ pycon-pandas-tutorial [Video: Pandas From The Ground Up]
• ⭐ Learn-Pandas
• ⭐ pandas-cookbook
• ⭐ Modern pandas. Parts: 1, 2, 3, 4, 5, 6 and 7.
• pandas-tutorial [Video: Pandas & Advanced Pandas]
• pandas_tutorial [Video: Analyzing and Manipulating Data with Pandas]
• scipy-2017-tutorial-pandas [Video: Pandas for Data Analysis]
• Pandas-Tutorial
• sklearn_pandas_tutorial
• pandas_basics
• first-python-notebook
• Learn Pandas
• Pandas practice website

• [amazon] McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2 edition. O’Reilly Media, 2017.
• [amazon] VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. 1 edition. O’Reilly Media, 2016.
• [manning] Lerner, Reuven. 50 exercises that will strengthen your pandas skills to a level of automatic fluency. 1 edition. Manning Publications, 2021.
• [manning] Paskhaver, Boris. This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.. 1 edition. Manning Publications, 2021.

• Numpy Cheat Sheet
• Python For Data Science - Scikit-Learn

• Varoquaux, Gael, Valentin Haenel, Emmanuelle Gouillart, Zbigniew Jędrzejewski-Szmek, Ralf Gommers, Fabian Pedregosa, Olav Vahtras, et al. Scipy Lecture Notes. Zenodo, September 28, 2015. https://doi.org/10.5281/zenodo.31521.
• [amazon] Nunez-Iglesias, Juan, Stéfan van der Walt, and Harriet Dashnow. Elegant SciPy: The Art of Scientific Python. 1 edition. O’Reilly Media, 2017.
• Rougier, Nicolas P. From Python to Numpy Zenodo, December 31, 2016. https://doi.org/10.5281/zenodo.225783.
• [amazon] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 1 edition. O’Reilly Media, 2017.

• Python 3 Cheat Sheet
• Python Cheat Sheet

• [amazon] Slatkin, Brett. Effective Python: 59 Specific Ways to Write Better Python. 1 edition. Addison-Wesley Professional, 2015.
• [amazon] Ramalho, Luciano. Fluent Python. 1st edition. O’Reilly, 2015.
• [pdf] P Rougier, Nicolas, Michael Droettboom, and Philip Bourne. "Ten Simple Rules for Better Figures." PLoS Computational Biology 10 (September 1, 2014): e1003833. https://doi.org/10.1371/journal.pcbi.1003833.
• [pdf] Tidy Data | Wickham | Journal of Statistical Software. Accessed December 31, 2017. https://doi.org/10.18637/jss.v059.i10.
• [amazon] [online] Chacon, Scott, and Ben Straub. Pro Git. 2nd ed. edition. New York, NY: Apress, 2014.

The books below are perhaps of an even more general nature.

• [amazon] Dasgupta, Sanjoy, Christos H. . Papadimitriou, and Umesh Virkumar. Vazirani. Algorithms. Boston, Mass: McGraw Hill, 2008.
• [amazon] Lloyd N. Trefethen. Numerical Linear Algebra. Society for Industrial and Applied Mathematics, 1997.
• [amazon] Gene H. Golub. Matrix Computations. 4th ed. Johns Hopkins Studies in the Mathematical Sciences. Baltimore: Johns Hopkins University Press, 2013.

Every video is below.

• datatest - Tools for test driven data-wrangling and data validation (DataFrame, Series, Index, MultiIndex).
• pandera - A light-weight, flexible, and expressive data validation library for dataframes.
• pandas-vet - A plugin for Flake8 that checks pandas code.