statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are avalable for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at sourceforge.
Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models. Here is a simple example using ordinary least squares:
import numpy as np import pandas as pd import statsmodels.formula.api as smf # Load data url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv' dat = pd.read_csv(url) # Fit regression model (using the natural log of one of the regressors) results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit() # Inspect the results print results.summary()
You can also use numpy arrays instead of formulas:
import numpy as np import statsmodels.api as sm # Generate artificial data (2 regressors + constant) nobs = 100 X = np.random.random((nobs, 2)) X = sm.add_constant(X) beta = [1, .1, .5] e = np.random.random(nobs) y = np.dot(X, beta) + e # Fit regression model results = sm.OLS(y, X).fit() # Inspect the results print results.summary()
Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings.
Information about the structure and development of statsmodels: