Statistical Data Analysis in Python

Introductory Tutorial, SciPy 2013, 25 June 2013

Christopher Fonnesbeck - Vanderbilt University School of Medicine

Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia.

Description

This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

The target audience for the tutorial includes all new Python users, though we recommend that users also attend the NumPy and IPython session in the introductory track.

Student Instructions

For students familiar with Git, you may simply clone this repository to obtain all the materials (iPython notebooks and data) for the tutorial. Alternatively, you may download a zip file containing the materials. A third option is to simply view static notebooks by clicking on the titles of each section below.

Outline

Introduction to Pandas

Importing data
Series and DataFrame objects
Indexing, data selection and subsetting
Hierarchical indexing
Reading and writing files
Sorting and ranking
Missing data
Data summarization

Data Wrangling with Pandas

Date/time types
Merging and joining DataFrame objects
Concatenation
Reshaping DataFrame objects
Pivoting
Data transformation
Permutation and sampling
Data aggregation and GroupBy operations

Plotting and Visualization

Plotting in Pandas vs Matplotlib
Bar plots
Histograms
Box plots
Grouped plots
Scatterplots
Trellis plots

Statistical Data Modeling

Statistical modeling
Fitting data to probability distributions
Fitting regression models
Model selection
Bootstrapping

Required Packages

Python 2.7 or higher (including Python 3)
pandas >= 0.11.1 and its dependencies
NumPy >= 1.6.1
matplotlib >= 1.0.0
pytz
IPython >= 0.12
pyzmq
tornado

Optional: statsmodels, xlrd and openpyxl

For students running the latest version of Mac OS X (10.8), the easiest way to obtain all the packages is to install the Scipy Superpack which works with Python 2.7.2 that ships with OS X.

Otherwise, another easy way to install all the necessary packages is to use Continuum Analytics' Anaconda.

Statistical Reading List

The Ecological Detective: Confronting Models with Data, Ray Hilborn and Marc Mangel

Though targeted to ecologists, Mangel and Hilborn identify key methods that scientists can use to build useful and credible models for their data. They don't shy away from the math, but the book is very readable and example-laden.