Data Analysis: Python

Why Python?

Why Python?

Python is a popular open-source, general purpose programming language. Python is free to download and use. There are many resources online for those learning Python.

Where to Find Python?

You can download Python for use on your own laptop. Python can also be installed by users on all computers in the Data & GIS Lab in Geisel Library and in some ETS labs.

For an interactive programming and data analysis experience, also download Jupyter Lab (formerly Notebook).

Trouble installing? The Carpentries organization has a detailed guide for installing a version of Python on Windows, Mac, and Linux.

Learn Python

Plotting and Programming in Python
This set of lessons from Software Carpentry are a good introduction to programming in Python for beginners.
Tutorials for Non-Programmers
Large list (with links) to tutorials for those new to programming.
Python for Data Science
Tutorial assumes no prior experience with Python. In addition to general introduction, the tutorial walks through an example, complete with plots.

Getting started with Jupyter notebooks
This guide is an introduction to the Jupyter notebook interface for beginners. Topics covered include: how to open Jupyter, creating and working with notebooks, working with notebook cells, and adding comments and text.

Useful Python packages

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.

If using the conda 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 analysis Python libraries:

NumPy
NumPy is a fundamental library, used for any scientific computing
NumPy quick start
NumPy basic examples
NumPy cheat sheet
SciPy
SciPy is another fundamental library for scientific computing, often used in conjunction with NumPy
SciPy documentation and user guide
SciPy tutorial
pandas
pandas is a library for working with/organizing data (data wrangling)
10 minute to pandas
pandas cookbook
pandas cheat sheet
Statsmodel
Statsmodels is a library for statistical analysis
Statsmodels examples
Lots of Statsmodels tutorials
scikit-learn
Scikit-learn is a library for machine learning
scikit-learn quick start
scikit-learn algorithm cheat sheet
NLTK
NLTK (Natural Language Toolkit) is a library for Natural Language Processing (NLP)
NLTK ebook tutorial
hands-on NLTK tutorial
NLTK cheat sheet
matplotlib
Matplotlib is a core data visualization library
matplotlib tutorials (intro, intermediate, and advanced)
Matplotlib at Python Graph Gallery
seaborn
Seaborn is a statistical data visualization library based on matplotlib
Seaborn tutorials
Seaborn examples
Seaborn example
Bokeh
Bokeh is used for making interactive plots/graphs
Bokeh tutorial

Featured Books

Python for Data Analysis by Wes McKinney
Call Number: Online; UCSD restricted

ISBN: 9781491957660

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python by Daniel J. Denis
ISBN: 9781119578185

Publication Date: 2021-07-14

A practical, "how-to" reference for anyone performing essential statistical analyses and data management tasks in Python. Includes a review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors.
An Introduction to Statistics with Python by Thomas Haslwanter
ISBN: 3319283162

Publication Date: 2016-07-20

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics.

UC Library Search: subject = Python (Computer Programming Language)