If you're a student in the Data Science major, you'll be learning Python through your coursework. The resources here are meant to supplement that learning, as well as provide avenues for you to pursue your more specific interests (e.g., machine learning, web scraping, etc.).
If you are not a Data Science student, these resources are still useful! Learning a programming language can help automate your research, whether you're working in biology, physics, social science, or some other domain. For those new to programming in general, the "Introductory Python tutorials" section is the place to start.
Download Python
First things first, you'll need to download Python, which is free. You can
download Python by itself from the Python Software Foundation
.
This guide appeals to your intelligence and ability to solve practical problems, while gently teaching the most recent revision of the programming language Python. You can learn solid software design skills and accomplish practical programming tasks, like extending applications and automating everyday processes, even if you have no programming experience at all.
We have quite a few advanced Python books available through the library. Some of these are only accessible via a physical book copy, but many are available as e-books. Try searching the library catalog
UC Library Search
for "python" to see our entire collection.
In the meantime, these books may be useful.
Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python.
This practical guide provides recipes to help you solve machine learning challenges you may encounter in your daily work. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models.
The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter.
One of the main benefits of Python is the vast array of pre-existing packages (also called libraries), written by other Python users and available for installation. You can find Python packages on
PyPI
, the Python Package Index.
This
overview
of popular Python libraries provides a starting point for finding applicable libraries. For more advanced users, this
comprehensive list
of packages by topic includes links to further resources.
If using the Anaconda distribution of Python, many libraries come pre-installed. This
tutorial
covers the steps needed to install additional packages.
Here are some resources for popular data science Python libraries:
NumPy is a fundamental library, used for any scientific computing
NumPy quick start
NumPy basic examples
NumPy cheat sheet
SciPy is another fundamental library for scientific computing, often used in conjunction with NumPy
SciPy documentation and user guide
SciPy tutorial
pandas is a library for working with/organizing data (data wrangling)
10 minute to pandas
pandas cookbook
pandas cheat sheet
NLTK (Natural Language Toolkit) is a library for Natural Language Processing (NLP)
NLTK ebook tutorial
hands-on NLTK tutorial
NLTK cheat sheet
Matplotlib is a core data visualization library
matplotlib tutorials (intro, intermediate, and advanced)
Matplotlib at Python Graph Gallery
Seaborn is a statistical data visualization library based on matplotlib
Seaborn tutorials
Seaborn examples
Seaborn example
Scrapy is a package for web scraping and crawling web sites
Scrapy overview
Scrapy tutorial
Scrapy tutorial with examples
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