Getting started

This very simple case-study is designed to get you up-and-running quickly with statsmodels. Starting from raw data, we will show the steps needed to estimate a statistical model and to draw a diagnostic plot. We will only use functions provided by statsmodels or its pandas and patsy dependencies.

Loading modules and functions

After installing statsmodels and its dependencies, we load a few modules and functions:

In [1]: from __future__ import print_function

In [2]: import statsmodels.api as sm

In [3]: import pandas

In [4]: from patsy import dmatrices

pandas builds on numpy arrays to provide rich data structures and data analysis tools. The pandas.DataFrame function provides labelled arrays of (potentially heterogenous) data, similar to the R “data.frame”. The pandas.read_csv function can be used to convert a comma-separated values file to a DataFrame object.

patsy is a Python library for describing statistical models and building Design Matrices using R-like formulas.

Data

We download the Guerry dataset, a collection of historical data used in support of Andre-Michel Guerry’s 1833 Essay on the Moral Statistics of France. The data set is hosted online in comma-separated values format (CSV) by the Rdatasets repository. We could download the file locally and then load it using read_csv, but pandas takes care of all of this automatically for us:

In [5]: df = sm.datasets.get_rdataset("Guerry", "HistData").data

The Input/Output doc page shows how to import from various other formats.

We select the variables of interest and look at the bottom 5 rows:

In [6]: vars = ['Department', 'Lottery', 'Literacy', 'Wealth', 'Region']

In [7]: df = df[vars]

In [8]: df[-5:]
Out[8]: 
      Department  Lottery  Literacy  Wealth Region
81        Vienne       40        25      68      W
82  Haute-Vienne       55        13      67      C
83        Vosges       14        62      82      E
84         Yonne       51        47      30      C
85         Corse       83        49      37    NaN

Notice that there is one missing observation in the Region column. We eliminate it using a DataFrame method provided by pandas:

In [9]: df = df.dropna()

In [10]: df[-5:]
Out[10]: 
      Department  Lottery  Literacy  Wealth Region
80        Vendee       68        28      56      W
81        Vienne       40        25      68      W
82  Haute-Vienne       55        13      67      C
83        Vosges       14        62      82      E
84         Yonne       51        47      30      C

Substantive motivation and model

We want to know whether literacy rates in the 86 French departments are associated with per capita wagers on the Royal Lottery in the 1820s. We need to control for the level of wealth in each department, and we also want to include a series of dummy variables on the right-hand side of our regression equation to control for unobserved heterogeneity due to regional effects. The model is estimated using ordinary least squares regression (OLS).

Design matrices (endog & exog)

To fit most of the models covered by statsmodels, you will need to create two design matrices. The first is a matrix of endogenous variable(s) (i.e. dependent, response, regressand, etc.). The second is a matrix of exogenous variable(s) (i.e. independent, predictor, regressor, etc.). The OLS coefficient estimates are calculated as usual:

where \(y\) is an \(N \times 1\) column of data on lottery wagers per capita (Lottery). \(X\) is \(N \times 7\) with an intercept, the Literacy and Wealth variables, and 4 region binary variables.

The patsy module provides a convenient function to prepare design matrices using R-like formulas. You can find more information here.

We use patsy’s dmatrices function to create design matrices:

In [11]: y, X = dmatrices('Lottery ~ Literacy + Wealth + Region', data=df, return_type='dataframe')

The resulting matrices/data frames look like this:

In [12]: y[:3]
Out[12]: 
   Lottery
0     41.0
1     38.0
2     66.0

In [13]: X[:3]