Notebooks

Data management and analysis in Python

This is a collection of IPython notebooks demonstrating a variety of techniques for data management and analysis.

Language and library basics

This section contains notebooks that are not tied to specific data sets.

• Indexing and slicing

US 2012 Medicare payment data

The data can be obtained here. The first script below can be used to produce state-level data files that are used in the subsequent scripts.

• Subset data by state

• Drill-down: identify providers receiving very high payments

• Compare provider types by state

• Associations between provider type and service type

Comparison of gene expression in ulcerative colitis and Chron's disease patients

The data are available from the NCBI Gene Expression Omnibus (GEO) here.

• [Differential expression analysis] (http://nbviewer.ipython.org/github/kshedden/python-workshop/blob/master/GDS1615/differential_expression.ipynb)

Analysis of nation-level fertility trends

The data are available from the World Bank here.

• [PCA] (http://nbviewer.ipython.org/github/kshedden/python-workshop/blob/master/WB-Fertility/WB_fertility_factor.ipynb)

National Health and Nutrition Examination Survey (NHANES)

The data are available from the NHANES web site: http://www.cdc.gov/nchs/nhanes.htm

• Scatterplot smoothing

• Linear regression

Births and deaths of notable individuals

The raw data are available from the supplementary materials of this article.

• Small data conversion script

• Several analyses

Global Surface Network weather data

The raw data are available here.

• Script to generate plots of annual series of climate variables