Research Guides: Data Science: Working with Python

Getting started with Python

If you're a student in Data Science, 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 here. Alternatively, you can install the Anaconda distribution of Python. Anaconda includes Python, Jupyter notebooks capability, many pre-installed Python packages, and more, making it easy to get started quickly.

Introductory Python tutorials

Plotting and Programming in Python (Software Carpentry)
This series of lessons from Software Carpentry is a great resource for beginners. The focus is on working with one data set from start to end. Highlighted packages include pandas and matplotlib.

Python Crash Course, 3rd Edition by Eric Matthes
ISBN: 9781718502703
Publication Date: 2023-01-10
Since its initial debut in 2015, this critically acclaimed quick-start guide to programming has taught millions of people all over the world to write clean code, solve problems, and build custom applications in the popular language of Python. The highly anticipated third edition of Python Crash Course has been completely revised with updated code, practices, and projects-making it the ultimate launchpad for beginners to start their engines and code in Python 3!
Head First Python by Paul Barry
ISBN: 9781492051299
Publication Date: 2023-09-12
What will you learn from this book? Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you'll quickly grasp Python's fundamentals by working with built-in data structures and functions. You'll build your very own web app, which--once it's ready for prime time--runs in the cloud. You'll learn how to wrangle data with Python, scrape data from the web, feed data to pandas, and interact with databases.

Advanced Python tutorials

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.

In the meantime, these books may be useful.

Learn More Python 3 the Hard Way by Zed Shaw
ISBN: 9780134123486
Publication Date: 2017-09-13
The focus is on practice projects, so this is a great way to apply your Python knowledge to something more complex.
Python Data Science Handbook by Jake VanderPlas
ISBN: 9781491912058
Publication Date: 2017-01-03
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.
Python for Data Analysis by William McKinney
ISBN: 9781491957660
Publication Date: 2017-11-14
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.
Machine Learning with Python Cookbook by Chris Albon
ISBN: 9781491989388
Publication Date: 2018-04-17
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.
Web Scraping with Python by Ryan Mitchell
ISBN: 9781491985571
Publication Date: 2018-05-08
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.

Python Libraries

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
NumPy is a fundamental library, used for any scientific computing
SciPy
SciPy is another fundamental library for scientific computing, often used in conjunction with NumPy
pandas
pandas is a library for working with/organizing data (data wrangling)
Statsmodel
Statsmodels is a library for statistical analysis
scikit-learn
Scikit-learn is a library for machine learning
Keras
Keras is a package for neural networks (deep learning)
TensorFlow
TensorFlow is a machine learning/deep learning library
NLTK
NLTK (Natural Language Toolkit) is a library for Natural Language Processing (NLP)
matplotlib
Matplotlib is a core data visualization library
seaborn
Seaborn is a statistical data visualization library based on matplotlib
Bokeh
Bokeh is used for making interactive plots/graphs
Scrapy
Scrapy is a package for web scraping and crawling web sites

Read here for an overview of some of the other data science-related packages, including wget (downloading files from browsers), FlashText (cleaning text for natural language processing), PyFlux (time series data), and more.