Statistical Data Analysis in Python
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.
By Christopher Fonnesbeck, Vanderbilt University School of Medicine.
Editor's note: This tutorial was originally published as course instructional material, and may contain outofcontext references to other courses therein; this takes nothing away from the validity or usefulness of the material.
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 2part 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 nonlinear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve handson 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
 Importing data
 Series and DataFrame objects
 Indexing, data selection and subsetting
 Hierarchical indexing
 Reading and writing files
 Sorting and ranking
 Missing data
 Data summarization
 Date/time types
 Merging and joining DataFrame objects
 Concatenation
 Reshaping DataFrame objects
 Pivoting
 Data transformation
 Permutation and sampling
 Data aggregation and GroupBy operations
 Plotting in Pandas vs Matplotlib
 Bar plots
 Histograms
 Box plots
 Grouped plots
 Scatterplots
 Trellis plots
 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 exampleladen.
Data Analysis Using Regression and Multilevel/Hierarchical Models, Andrew Gelman and Jennifer Hill
The goto reference for applied hierarchical modeling.
The Elements of Statistical Learning, Hastie, Tibshirani and Friedman
A comprehensive machine learning guide for statisticians.
A First Course in Bayesian Statistical Methods, Peter Hoff
An excellent, approachable book to get started with Bayesian methods.
Regression Modeling Strategies, Frank Harrell
Frank Harrell's bag of tricks for regression modeling. I pull this off the shelf every week.
Bio: Christopher Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, metaanalysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia.
Original. Reposted with permission.
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