Crandi

Statsmodels Documentation Devdocs

Leo Migdal
-
statsmodels documentation devdocs

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: There was an error while loading.

Please reload this page. pip install statsmodels Copy PIP instructions Statistical computations and models for Python statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at

From bottom of this page: http://statsmodels.sourceforge.net/stable/index.html Every tutorial you read shows a different way to import Statsmodels. One guide starts with import statsmodels.api as sm. Another uses from statsmodels.formula.api import ols. A third imports directly from submodules like from statsmodels.regression.linear_model import OLS. Which approach should you use?

The confusion stems from a deliberate design choice. Statsmodels offers multiple import paths because different users need different things. Researchers writing academic papers want one workflow. Data scientists doing quick exploratory analysis want another. Understanding these three approaches will save you from blindly copying code that doesn’t match your actual needs. The statsmodels.api module serves as your main gateway to the library.

When you import sm, you get access to the most commonly used models and functions through a clean namespace. Ordinary Least Squares becomes sm.OLS. Logistic regression becomes sm.Logit. The add_constant function becomes sm.add_constant. The statsmodels.formula.api module gives you R-style formula syntax. Instead of manually separating your endog and exog variables, you write a formula string that describes the relationship.

The lowercase function names (ols instead of OLS) signal that you’re using the formula interface. Direct imports pull specific classes or functions from their exact location in the library structure. You import only what you need, nothing more. The main statsmodels API is split into models: statsmodels.api: Cross-sectional models and methods. Canonically imported using import statsmodels.api as sm.

statsmodels.tsa.api: Time-series models and methods. Canonically imported using import statsmodels.tsa.api as tsa. statsmodels.formula.api: A convenience interface for specifying models using formula strings and DataFrames. This API directly exposes the from_formula class method of models that support the formula API. Canonically imported using import statsmodels.formula.api as smf The API focuses on models and the most frequently used statistical test, and tools.

Import Paths and Structure explains the design of the two API modules and how importing from the API differs from directly importing from the module where the model is defined. See the detailed topic pages in the User Guide for a complete list of available models, statistics, and tools.

People Also Search

Statsmodels Is A Python Module That Provides Classes And Functions

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 ...

Here Is A Simple Example Using Ordinary Least Squares: You

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: There was an error while loading.

Please Reload This Page. Pip Install Statsmodels Copy PIP Instructions

Please reload this page. pip install statsmodels Copy PIP instructions Statistical computations and models for Python statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at

From Bottom Of This Page: Http://statsmodels.sourceforge.net/stable/index.html Every Tutorial You Read

From bottom of this page: http://statsmodels.sourceforge.net/stable/index.html Every tutorial you read shows a different way to import Statsmodels. One guide starts with import statsmodels.api as sm. Another uses from statsmodels.formula.api import ols. A third imports directly from submodules like from statsmodels.regression.linear_model import OLS. Which approach should you use?

The Confusion Stems From A Deliberate Design Choice. Statsmodels Offers

The confusion stems from a deliberate design choice. Statsmodels offers multiple import paths because different users need different things. Researchers writing academic papers want one workflow. Data scientists doing quick exploratory analysis want another. Understanding these three approaches will save you from blindly copying code that doesn’t match your actual needs. The statsmodels.api module...