Development And Installation Statsmodels Statsmodels Deepwiki

Leo Migdal

-Dec 4, 2025, 7:11 AM

development and installation statsmodels statsmodels deepwiki

This document explains how to install, set up a development environment, and contribute to the Statsmodels project. It covers installation methods, development setup, building and testing procedures, and documentation generation. For information about using the installed package, see the corresponding user documentation. Statsmodels can be installed in several ways depending on your needs: Sources: docs/source/install.rst1-97 INSTALL.txt69-107 Sources: docs/source/install.rst128-165 INSTALL.txt1-67 requirements.txt1-8 setup.py46-71

The build process is managed by setuptools with custom enhancements for Cython extensions. The build configuration is defined in: The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing from PyPI, source or a development version are also provided. statsmodels supports Python 3.9, 3.10, 3.11, 3.12, and 3.13.

statsmodels is available through conda provided by Anaconda. The latest release can be installed using: To obtain the latest released version of statsmodels using pip: Installing Statsmodels takes just a few commands, but the process varies slightly depending on your operating system and Python setup. The library supports Python 3.9 through 3.14, so you’ll need one of these versions installed before starting. I recommend using pip for most installations.

Conda works well if you’re managing complex scientific computing environments. Both methods handle dependencies automatically, installing NumPy, SciPy, Pandas, and Patsy alongside Statsmodels. Your system needs Python 3.9 or newer. Check your version by opening a terminal and running: You should see something like Python 3.12.3 or similar. If your version is older than 3.9, upgrade Python first.

You also need pip (Python’s package installer) or conda (if you’re using Anaconda). Most Python installations include pip by default. Verify it’s installed: Statsmodels is a Python library that enables us to estimate and analyze various statistical models. It is built on numeric and scientific libraries like NumPy and SciPy. It provides classes & functions for the estimation of many different statistical models.

Before installing Statsmodels, ensure that you have: The easiest way to install Statsmodels is by using pip. Run the following command in your terminal or command prompt: python -m venv env .\env\Scripts\activatepip install statsmodels This will automatically install Statsmodels with its dependencies including NumPy, SciPy, Pandas, and Patsy. Patsy is used for handling formulas in statistical models.

Python ecosystem is equipped with many tools and libraries which primarily focus on prediction or machine learning. For example, scikit-learn focuses on predictive modeling and machine learning and does not provide statistical summaries (like p-values, confidence intervals, R² adj.). SciPy.statsfocuses on Individual statistical tests and distributions but has no modeling framework (like OLS or GLM). Other libraries like linearmodels , PyMC / Bambi , Pingouin have their own limitations. Statsmodels was developed to fill the gap created by these existing tools.

Statsmodels is a Python library for statistical models and quantitative analysis. It provides a comprehensive suite of tools for model estimation, statistical tests, and data exploration. The library emphasizes statistical computation, model inspection, and rigorous statistical methods rather than machine learning or predictive modeling. This overview introduces the core architecture, main model families, and common usage patterns in statsmodels. For more specific information about individual model types, see the following wiki pages: Statsmodels is organized around several model families that share common base classes and interfaces.

At the highest level, models inherit from the base Model class, with specialized models extending this foundation to implement specific statistical techniques. Sources: statsmodels/base/model.py65-188 statsmodels/regression/linear_model.py193-449), statsmodels/discrete/discrete_model.py173-931 statsmodels/genmod/generalized_linear_model.py82-292 statsmodels/tsa/base/tsa_model.py98-135 The core architecture follows several key principles: 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 available 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 statsmodels.org. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Here is a simple example using ordinary least squares: You can also use numpy arrays instead of formulas: Have a look at dir(results) to see available results.

Attributes are described in results.__doc__ and results methods have their own docstrings. Please use following citation to cite statsmodels in scientific publications: Last modified: Jan 21, 2025 By Alexander Williams Statsmodels is a powerful Python library for statistical modeling. It is widely used for data analysis and visualization. This guide will help you install it easily.

Before installing Statsmodels, ensure you have Python installed. You can check this by running python --version in your terminal. If Python is not installed, download it from the official website. Also, ensure you have pip, Python's package installer. The easiest way to install Statsmodels is using pip. Open your terminal and run the following command:

Statsmodels is a powerful Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and data exploration. It is particularly used in econometrics and involves tools for linear regression, time series analysis, and data visualization. This article will guide you through the installation and initial setup of Statsmodels, so you can begin your statistical analysis with Python. The easiest way to install Statsmodels is using pip, a package manager for Python. It manages Python packages, allowing you to install and maintain them with simplicity. To install Statsmodels, ensure that Python is already installed on your system, and open your command prompt or terminal.

Alternatively, if you are working in a Jupyter notebook, you can use the following command within a code cell: If you encounter any issues with pip, you can also use Anaconda, which is another package manager suitable for scientific computing. Open your Anaconda prompt and execute: Once you have installed Statsmodels, you can verify the installation by importing it in a Python shell or script. Run the following code in your Python environment to ensure the installation was successful:

Development And Installation Statsmodels Statsmodels Deepwiki

People Also Search

This Document Explains How To Install, Set Up A Development

The Build Process Is Managed By Setuptools With Custom Enhancements

Statsmodels Is Available Through Conda Provided By Anaconda. The Latest

Conda Works Well If You’re Managing Complex Scientific Computing Environments.

You Also Need Pip (Python’s Package Installer) Or Conda (if