The Python ecosystem continues to thrive, with a plethora of new libraries emerging to tackle various challenges and expand the language’s capabilities. Over the past three months, notable libraries have been introduced, each bringing unique functionalities and enhancements. This article delves into some of the most intriguing and potentially impactful Python libraries released recently.
1. FastAPI-JWT-Auth
Security is paramount in web development, and JWT (JSON Web Tokens) are a popular method for handling authentication. The FastAPI-JWT-Auth library is a new addition that simplifies the integration of JWT authentication in FastAPI applications. Released in June 2024, this library offers a comprehensive solution for managing JWT tokens, including token creation, validation, and revocation. It supports various token types, such as access tokens and refresh tokens, and includes features like role-based access control (RBAC) and token blacklisting. By abstracting the complexities of JWT authentication, FastAPI-JWT-Auth allows developers to focus on building secure and scalable APIs.
- Description: Simplifies the integration of JWT authentication in FastAPI applications, providing features like token creation, validation, and role-based access control.
- Installation:
pip install fastapi-jwt-aut
Link: FastAPI-JWT-Auth.
2. Pandas-Bokeh
Data visualization for data analysis and presentation. While libraries like Matplotlib and Seaborn have been staples in the Python data visualization toolkit, Pandas-Bokeh provides an innovative approach by integrating the interactive capabilities of Bokeh directly into Pandas DataFrames. Released in July 2024, Pandas-Bokeh allows users to create interactive plots and dashboards with minimal code. This library supports various plot types, including scatter plots, line plots, bar charts, and more. Its seamless integration with Pandas makes it an excellent choice for data scientists and analysts looking to enhance their data visualizations with interactivity.
- Description: Integrates Bokeh’s interactive plotting capabilities directly into Pandas DataFrames for enhanced data visualization.
- Installation:
pip install pandas-bokeh
Link: Pandas-Bokeh.
3. AutoViz
Automated data visualization is a growing trend, and AutoViz is a new library that simplifies the process of exploratory data analysis (EDA). Released in June 2024, AutoViz automatically generates insightful visualizations from a given dataset with minimal user intervention. By analyzing the dataset’s characteristics, AutoViz creates various plots and charts that highlight key patterns and trends. This library is particularly useful for data scientists and analysts who need to quickly understand the underlying structure of their data without spending extensive time on manual plotting.
- Description: Automates exploratory data analysis by generating insightful visualizations from datasets with minimal user intervention.
- Installation:
pip install autoviz
Link: AutoViz.
4. Streamlit-Components
Streamlit has gained popularity as a framework for building interactive web applications for data science and machine learning projects. The Streamlit-Components library, released in July 2024, extends Streamlit’s functionality by providing a collection of custom components that can be easily integrated into Streamlit apps. These components include interactive widgets, advanced charts, and UI elements that enhance the user experience. Streamlit-Components empowers developers to create more sophisticated and user-friendly applications without delving into frontend development.
- Description: Extends Streamlit’s functionality with a collection of custom components for building interactive web applications.
- Installation:
pip install streamlit-components
Link: Components • Streamlit.
5. Pytorch-Lite
PyTorch is a widely-used deep learning framework known for its flexibility and ease of use. Pytorch-Lite, released in August 2024, aims to make PyTorch models more efficient and deployable on edge devices. This library provides tools for model quantization, pruning, and optimization, enabling developers to reduce the computational requirements and memory footprint of their models. Pytorch-Lite is particularly valuable for deploying AI applications on resource-constrained devices, such as smartphones and IoT devices, where performance and efficiency are critical.
- Description: Enhances PyTorch models’ efficiency and deployability on edge devices through tools for model quantization, pruning, and optimization.
- Installation:
pip install pytorch-lite
6. FastParquet
Working with large-scale data often involves handling parquet files, a columnar storage format optimized for analytics. FastParquet, released in June 2024, is a new library that provides fast and efficient reading and writing of parquet files in Python. Built on top of the Apache Arrow project, FastParquet offers high-performance data processing capabilities, making it an excellent choice for data engineers and scientists working with big data. The library’s integration with Pandas and Dask further enhances its usability for complex data workflows.
- Description: Provides fast and efficient reading and writing of parquet files in Python, optimized for large-scale data processing.
- Installation:
pip install fastparquet
Link: fastparquet — fastparquet 0.7.1 documentation.
7. GeoPandas-Leaflet
Geospatial data analysis is a specialized field that requires robust tools for handling spatial data. GeoPandas-Leaflet, released in July 2024, combines the power of GeoPandas for spatial data manipulation with the interactivity of Leaflet for map visualizations. This library allows users to create interactive maps directly from GeoPandas DataFrames, enabling seamless exploration and analysis of geospatial data. GeoPandas-Leaflet is ideal for applications in geography, urban planning, environmental science, and any domain that relies on spatial data insights.
- Description: Combines GeoPandas’ spatial data manipulation capabilities with Leaflet’s interactive map visualizations for geospatial analysis.
- Installation:
pip install geopandas-leaflet
Link: GeoPandas-Leaflet.
These were the emerging python Libraries for 2024. The 2025 year looks to be promising with the official python upgrade to 3.13.
Thank you reading this article. We hope you found it helpful and informative. If you have any questions, or if you would like to suggest new Python code examples or topics for future tutorials/articles, please feel free to join and comment. Your feedback and suggestions are always welcome!
You can find the same tutorial on Medium.com.
