---
title: statsmodels Reviews
meta_title: 'statsmodels Reviews 2026: Details, Pricing, & Features | G2'
meta_description: Filter reviews by the users' company size, role or industry to find
  out how statsmodels works for a business like yours.
aggregate_rating:
  rating_value: 4.5
  review_count: 3
  scale: '5'
date_modified: '2025-10-31'
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---

# statsmodels Reviews
**Vendor:** statsmodels  
**Category:** [Component Libraries Software](https://www.g2.com/categories/component-libraries)  
**Average Rating:** 4.5/5.0  
**Total Reviews:** 3
## About statsmodels
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




## statsmodels Reviews
  ### 1. Statsmodels

**Rating:** 4.0/5.0 stars

**Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.)

**Reviewed Date:** February 06, 2019

**What do you like best about statsmodels?**

A comprehensive collection of advanced or special-use statistical and regression functions. Having access to models and statistical tests related to those models in the same package is very handy. Additionally, for regression models such as ARMA/ARIMA/ARIMAX/SARIMAX, the output already contains a lot of relevant information like AIC and BIC score.

**What do you dislike about statsmodels?**

Output type is inconsistent and can be a hurdle at times. For example some methods will output a list, others a dictionary, etc, which can make using a statsmodels output as an input somewhere else a little tricky. Documentation is also inconsistent.

**Recommendations to others considering statsmodels:**

Just be aware that outputs from very similar functions aren't necessarily consistent with one another. Some processing is often necessary for use with more standard libraries numpy, pandas, etc.

**What problems is statsmodels solving and how is that benefiting you?**

I primarily use statsmodels for time series analysis and regression modeling. Having tests such as augmented dickey-fuller and ACF/PACF functions built in are very useful when prepping data for an ARIMA model.

  ### 2. Statsmodels

**Rating:** 5.0/5.0 stars

**Reviewed by:** Verified User in Mining & Metals | Mid-Market (51-1000 emp.)

**Reviewed Date:** October 18, 2018

**What do you like best about statsmodels?**

Statsmodel gives many advanced statistical functions which are not present in pandas or numpy. Scikit-learn also has many of the statistical functions, but their functionalities are limited. For advanced operations, statsmodel is the way to go.

**What do you dislike about statsmodels?**

It is a relatively new package,  the quantity and quality of documentation available is very poor. It is less pythonic than scikit-learn.

**What problems is statsmodels solving and how is that benefiting you?**

We use stastsmodel for regression problems over large datasets. In summary, if the requirement is for machine learning, scikit-learn has far more features available than statsmodels but if you intend to use them for statistical analysis then you should go for statsmodels.

  ### 3. great for running econometric models!

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Education Management | Small-Business (50 or fewer emp.)

**Reviewed Date:** May 03, 2019

**What do you like best about statsmodels?**

easy and intuitive to use - works for solving a variety of problems

**What do you dislike about statsmodels?**

at times there seems like there aren't enough online resources to help dictate

**What problems is statsmodels solving and how is that benefiting you?**

working on running a variety of different regression specifications



- [View statsmodels pricing details and edition comparison](https://www.g2.com/products/statsmodels/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-25+21%3A09%3A08+-0500&secure%5Bsession_id%5D=fb319f83-ea48-4f24-8098-d6cd427798df&secure%5Btoken%5D=4a065042950bb8218d549836854565b5d8c72e7d34d517f00fc7b5b45b1df77c&format=llm_user)

## statsmodels Features
**Functionality**
- Language Contingency
- Component Library
- Unlocked Components

**Management**
- Framework Integration
- Repository Management
- Support

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