Skip to content

Statsmodels

Data & AnalyticsData SciencePython
Official docs ↗GitHub ↗

What it is

Statsmodels is a Python library that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration.

Statsmodels supports regression models (linear, logistic, etc.), time series analysis, generalized linear models, and more. It provides detailed statistical output, making it ideal for rigorous analysis and reporting.

Installation

pip install statsmodels

Getting started

The smallest useful thing you can do with it, and what each part means.

Linear Regression
import statsmodels.api as sm
import numpy as np
X = np.array([1,2,3,4,5])
y = np.array([2,4,5,4,5])
X = sm.add_constant(X)  # add intercept
model = sm.OLS(y, X).fit()
print(model.summary())
Performs ordinary least squares (OLS) linear regression and prints a detailed statistical summary.
Logistic Regression
import statsmodels.api as sm
import numpy as np
X = np.array([[1],[2],[3],[4],[5]])
y = np.array([0,0,0,1,1])
X = sm.add_constant(X)
model = sm.Logit(y, X).fit()
print(model.summary())
Fits a logistic regression model for binary outcome data and outputs statistical details.

Advanced usage

Where the library earns its place over a simpler alternative.

Time Series ARIMA Model
import statsmodels.api as sm
import pandas as pd
data = pd.Series([1,2,3,4,5,6,7,8,9,10])
model = sm.tsa.ARIMA(data, order=(1,1,0))
results = model.fit()
print(results.summary())
Fits an ARIMA model to a time series and outputs coefficients and diagnostics.
Generalized Linear Models (GLM)
import statsmodels.api as sm
import numpy as np
X = np.array([1,2,3,4,5])
y = np.array([2,3,5,4,6])
X = sm.add_constant(X)
model = sm.GLM(y, X, family=sm.families.Poisson()).fit()
print(model.summary())
Fits a Poisson regression model using GLM and prints the summary.
Hypothesis Testing
from statsmodels.stats.weightstats import ztest
import numpy as np
data1 = np.array([1,2,3,4,5])
data2 = np.array([2,3,4,5,6])
z_stat, p_val = ztest(data1, data2)
print(f'Z-statistic: {z_stat}, p-value: {p_val}')
Performs a Z-test to compare two samples.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

LinAlgError: Singular matrix
Check for multicollinearity or duplicate columns in the predictor matrix.
ValueError: endog and exog matrices are not aligned
Ensure that response (`y`) and predictor (`X`) arrays have compatible dimensions.
PerfectSeparationError
Occurs in logistic regression if one predictor perfectly predicts the outcome; consider removing or regularizing variables.

Best practices

  • Always inspect `model.summary()` for statistical diagnostics and model fit.
  • Preprocess data (standardize, handle missing values) before modeling.
  • Choose appropriate model type based on data distribution and research question.
  • Use statistical tests to validate assumptions (normality, heteroscedasticity, etc.).
  • Combine with Pandas for data manipulation and cleaning prior to modeling.

Background

Why it exists, and what it was reacting to.

Statsmodels was created by Skipper Seabold and Josef Perktold to provide a Python package for classical statistics and econometrics. It complements libraries like NumPy, SciPy, and Pandas, enabling researchers and analysts to fit statistical models, perform hypothesis testing, and explore data efficiently.

Related libraries

Others in Data & Analytics for Python.

← All Python librariesPython guide