24 best Python Statistics libraries in 2025

Python has quickly gone up the ranks to become the most sought-after language for statistics and data science. It is a high-level, object-oriented language.

We also have a thriving open-source Python community that keeps developing various unique libraries for maths, data analysis, mining, exploration, and visualization.

Keeping that in mind, here are some of the best Python libraries helpful for implementing statistical data. Pandas is a high-performance Python package with easy-to-grasp and expressive data structures. It is designed for rapid data manipulation and visualization and is the best tool when it comes to data munging or wrangling. With this 30k stars+ Github repository, you also get time series-specific functionality. Seaborn is essentially an extension of the Matplotlib plotting library with various advanced features and shorter syntax. With Seaborn, you can determine relationships between various variables, observe and determine aggregate statistics, and plot high-level and multi-plot grids. We also have Prophet, which is a forecasting procedure developed using Python and R. It’s quick and offers automated forecasts for time series data to be used by analysts.

pandas:  

• Pandas offers robust structures like DataFrames for easy storage and manipulation of data.  
• Efficient tools for aligning and managing data, simplifying data cleaning and preparation.  
• Provides diverse functions for flexible data manipulation and analysis.  

prophet:  

• Specialized in predicting future values in time series data.  
• Can handle missing data and outliers effectively for reliable forecasting.  
• Captures recurring patterns in data, especially those tied to seasons or cycles.  

seaborn:  

• Simplifies the creation of statistical graphics for a better understanding of data.  
• Seamlessly works with Pandas DataFrames for easy data visualization.  
• Allows users to tailor plots for a visually appealing presentation.  

statsmodels:  

• Offers a variety of statistical models and hypothesis tests.  
• Well-suited for economic and financial data analysis.  
• Provides tools to visualize and summarize statistical information.

altair:  

• Enables concise and declarative creation of interactive visualizations.  
• Leverages a powerful JSON specification for describing visualizations.  
• Emphasizes simplicity and minimal code for creating sophisticated visualizations.  

pymc3:  

• Allows expressing complex statistical models using a probabilistic programming approach.  
• Focuses on Bayesian statistical methods for uncertainty estimation.  
• Integrates with Aesara for efficient symbolic mathematical expressions.  

imbalanced-learn:  

• Tools for addressing imbalances in class distribution within machine learning datasets.  
• Integrates smoothly with Pandas DataFrames for preprocessing imbalanced data.  
• Offers flexibility through customizable algorithms for imbalanced data handling.  

sktime:  

• Specializes in analyzing and forecasting time series data.  
• Provides a modular framework for easy extension and customization.  
• Seamlessly integrates with other machine learning and deep learning libraries.  

httpstat:  

• Visualizes statistics related to HTTP requests made with the curl tool.  
• Implemented as a compact Python script for simplicity.  
• Works seamlessly with Python 3 for compatibility with the latest Python environments. 

darts:  

• Tools for manipulating time series data facilitating data preprocessing.  
• Specialized in making predictions on time series data.  
• Integrates with deep learning frameworks for advanced forecasting using neural networks.  

gluon-ts:  

• Focuses on modeling uncertainty in time series predictions.  
• Integrates with Apache MXNet for efficient deep learning capabilities.  
• Allows users to experiment with various modeling approaches and customize their models. 

selfspy:  

• Monitors and logs personal data continuously for self-analysis.  
• Compatible with various platforms for versatility in data tracking.  
• Aids in tracking and analyzing personal habits and activities for self-improvement.

stumpy:  

• Implements algorithms for efficient time series analysis using matrix profiles.  
• Identifies recurring patterns or motifs in time series data.  
• Utilizes parallel computing for faster and more efficient computations.  

gitinspector:  

• Analyzes and provides insights into Git repositories.  
• Features an interactive command-line interface for user-friendly exploration.  
• Allows users to customize analysis output format.  

Mycodo:  

• Logs data from sensors for environmental monitoring.  
• Provides a user-friendly interface accessible through a web browser.  
• Enables automation and control of devices based on collected sensor data.  

pyFlux:  

• Implements models for probabilistic time series analysis.  
• Scales efficiently for large datasets and complex models.  
• Provides tools for diagnosing and evaluating the performance of statistical models.  

sweetviz:  

• Automates the process of exploring and analyzing datasets.  
• Allows for easy comparison of two datasets to identify differences.  
• Provides flexibility in generating and customizing analysis reports.  

vectorbt:  

• Enables efficient backtesting of trading strategies using vectorized operations.  
• Provides tools for analyzing and visualizing trading strategy performance.  
• Allows for flexible management of investment portfolios. 

gitStats:  

• Analyzes and presents historical metrics related to code development.  
• Generates visual representations of code-related metrics.  
• Includes metrics related to code contributor diversity.  

pmdarima:  

• Automatically selects suitable ARIMA models for time series data.  
• Decomposes time series data into seasonal components for analysis.  
• Integrates with the scikit-learn library for seamless machine learning workflows.

covid-19:  

• Provides up-to-date information on the COVID-19 pandemic.  
• Offers data at both global and country-specific levels.  
• Presents COVID-19 data in a visual format for better understanding.  

spacy-models:  

• Includes pre-trained natural language processing models for various tasks.  
• Supports multiple languages for broader applicability.  
• Allows users to customize and fine-tune models for specific tasks.

nba_py:  

• Retrieves data related to the National Basketball Association (NBA).  
• Integrates seamlessly with NBA APIs for data access.  
• Provides tools for analyzing and interpreting statistical aspects of NBA data.  

pingouin:  

• Offers a library for conducting various statistical analyses.  
• Includes tools for analysis of variance (ANOVA) and regression analysis.  
• Provides measures for quantifying the magnitude of observed effects in statistical tests.  

FAQ

1. What makes Pandas a valuable tool for data manipulation and visualization?  

Pandas is a high-performance Python package with expressive data structures. It carries out rapid data manipulation and visualization. Its design and specialized time series functions make it ideal for data munging.  

2. How does Seaborn extend the functionality of the Matplotlib plotting library?  

Seaborn is an extension of Matplot lib, offering advanced features and shorter syntax. It enables users to determine relationships between variables, observe statistics, and plot high-level. This provides a more streamlined approach to data visualization.  

3. What unique features does Seaborn bring to data visualization?  

Seaborn provides advanced features for statistical data visualization. This includes 

• the ability to determine relationships between variables, 
• observe aggregate statistics, and 
• easily create high-level and multi-plot grids. 

Its syntax is designed for simplicity and efficiency in plotting.  

4. What is the role of Prophet in time series forecasting, and why is it notable?  

Prophet is a forecasting procedure developed in Python and R. It offers quick and automated forecasts for time series data. It is user-friendly for analysts and generates accurate forecasts. It does not require extensive manual intervention.  

5. How can the Python community contribute to developing and improving these libraries?  

The Python community can contribute to library development. Contribute by participating in open-source projects, submitting bug reports, and engaging in discussions. Contributing code, documentation, or insights in forums continuously enhances these libraries.