LibGuides: Research Data Services: Python

Getting Started

There are three popular options for installing Python on your computer:

Download Anaconda. Anaconda comes with many useful packages including integrated development environments (Jupyter, etc.), libraries for analytics and scientific computing (NumPy, SciPy, pandas, etc.), libraries for visualization (matplotlib, bokeh, etc.), and libraries for machine learning (such as scikit-learn).
Download WinPython. WinPython is similar to Anaconda; it has the added benefit that it can be run off a USB stick if you are using a public computer and can't install new programs but contains fewer packages than Anaconda.
Download directly from Python. Comes with standard library, but will need to install libraries such as NumPy, SciPy, matplotlib, etc.

Python Resources

Python 3 Documentation
Tutorials, standard library reference, installation instructions, and more from the Python Software Foundation.
Automate the Boring Stuff
An excellent introduction to Python for those who are new to programming. Each chapter comes with accompanying YouTube videos and practice problems.
A Byte of Python
Python tutorials for beginners.
Python for Everyone
A tutorial with exercises for new programmers. Note that the examples may use syntax from Python 2.x rather than the current Python 3.x versions, but there are footnotes with corresponding Python 3 syntax.
Python for Ecologists
A tutorial covering everything from introductory Python concepts to pandas data analysis to matplotlib visualization. The entire lesson set is meant to be covered in a day, so this is a quick guide for those who wish to begin using Python for data analysis immediately.
Python Tutor
A step-by-step visualization tool to help you understand how Python executes any piece of code.

Data Manipulation & Analysis

Numpy (and Scipy) Documentation
User guides and reference guides for each version of NumPy (and SciPy) for scientific computing in Python.
Wes McKinney's Python for Data Analysis eBook
A practical textbook on using NumPy and pandas written by the main developer of pandas, Wes McKinney.
Brandon Rhodes's PyCon 2015 Pandas Tutorial
An excellent tutorial on using pandas for data manipulation and analysis. The GitHub page includes several exercises and an embedded YouTube video of Brandon conducting the tutorial.

Data Visualization

Matplotlib Documentation
User's guide to matplotlib, a popular and well-established plotting library in Python. Check out the pyplot tutorial for a good overview of the main plotting module.
Nicolas Rougier's Matplotlib Tutorial
An introduction to plotting with matplotlib with some nice examples (including examples of animated figures).
Visualization with Matplotlib
A chapter on visualization from Jake VanderPlas's Python Data Science Handbook. This excerpt takes an in-depth look into matplotlib's functionalities and gives a brief overview of seaborn, a visualization library built on top of matplotlib.
Seaborn Documentation
Reference, gallery, tutorial, and more for the seaborn visualization library, which allows for convenient creation of "attractive statistical graphics."

Machine Learning

Scikit-learn Documentation
User guide, tutorials, API reference, and more for the scikit-learn machine learning library. Check out the user guide for detailed descriptions of algorithms and how to implement them.
SciPy 2017 Scikit-learn Tutorial
A broad overview of how scikit-learn can be used for machine learning topics including classification, regression, clustering, text feature extraction, cross-validation, evaluation metrics, and much more. Take a look at the files under the "notebooks" folder and/or watch the YouTube videos of Andreas Mueller and Alex Gramfort conducting the tutorial.

Getting Started with Python

Learning Python by Mark Lutz
ISBN: 9781449355739
Publication Date: 2013-07-06
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3-- the latest releases in the 3.X and 2.X lines--plus all other releases in common use today. You'll also learn some advanced language features that recently have become more common in Python code. Explore Python's major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python's general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python's object-oriented programming tool for structuring code Write large programs with Python's exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
Python Cookbook by David Beazley; Brian K. Jones
ISBN: 1449340377
Publication Date: 2013-06-11
If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, you'll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works. Topics include: Data Structures and Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions