16 best Python Table libraries in 2025

Add functionalities such as building tables, data grids, and interactive data visualizations into your application easily and in a visually appealing way with the Python table libraries.

Python table libraries are highly useful in advanced applications with data management functions such as analytics, data science, and machine learning. Using these libraries, you can represent data in an organized manner while controlling and customizing various aspects of a table. These include:

• width and column padding
• text alignment
• data sorting
• table borders, and more.

These libraries also provide a large number of predefined formats that allows you to publish tables in different ways. Also, you can format the tables to publish on popular project management software such as Jira and GitHub.

Explore the top and trending Python table libraries to add table formatting features in your applications:

prettytable  

• Supports custom table formatting and styling.  
• Allows adding, deleting, and rearranging columns easily.  
• Provides a simple way to display tabular data in a text-based interface.  

pytablewriter  

• Supports various file formats for table export (CSV, Excel, JSON, etc.).  
• Allows fine-grained control over cell alignment and formatting.  
• Offers an intuitive and extensible API for creating and writing tables.  

python-tabulate  

• Provides multiple table styles and formatting options.  
• Supports data alignment, numbering, and various output formats.  
• Can render tables as plain text or HTML for different use cases.  

dataset  

• Offers a simplified interface for working with databases.  
• Supports data loading, querying, and manipulation with minimal code.  
• Provides a consistent API across different database backends.  

camelot  

• Specialized for extracting tables from PDF documents.  
• Supports both text-based and image-based table extraction.  
• Offers advanced table configuration options for precise extraction.  

pdftabextract  

• Designed for OCR-based table extraction from scanned PDFs.  
• Provides tools for layout analysis and structured table data extraction.  
• Useful for digitizing tabular information from documents.  

django-tables2  

• Integrates seamlessly with Django web applications.  
• Offers a declarative syntax for defining table structures.  
• Supports sorting, pagination, and customization of table rendering.  

django-watson  

• A full-text search library for Django applications.  
• Provides efficient search functionality across multiple models.  
• Supports stemming, ranking, and result highlighting.  

petl  

• A powerful library for ETL (Extract, Transform, Load) operations on tabular data.  
• Offers a wide range of data manipulation functions.  
• Integrates well with other data processing libraries like pandas.  

csvtotable  

• Converts CSV data into an interactive HTML table.  
• Supports data sorting, filtering, and pagination in the web interface.  
• Suitable for quickly visualizing and exploring CSV data.  

textfsm  

• A template-based text parsing library.  
• Used for extracting structured data from unstructured text.  
• Commonly used for parsing network device configuration files.  

rows  

• A minimalistic library for working with tabular data.  
• Provides basic data manipulation and serialization capabilities.  
• Lightweight and easy to use for simple table operations.  

image2csv  

• Converts images containing tabular data into CSV format.  
• Useful for digitizing data from scanned documents or images.  
• Supports customization of image preprocessing and OCR options.  

csvtomd  

• Converts CSV data into Markdown tables for documentation.  
• Supports alignment and column formatting in Markdown.  
• Useful for generating human-readable documentation from CSV data.  

Pylsy  

• Creates pretty ASCII tables with cell alignment.  
• Offers easy customization of table styling and formatting.  
• Suitable for displaying tabular data in console applications.  

jupyter_pivottablejs  

• Integrates with Jupyter Notebook for interactive pivot table creation.  
• Allows users to explore and analyze data with a graphical interface.  
• Supports aggregation, filtering, and dynamic table manipulation within Jupyter notebooks.  

FAQ 

1. What Python libraries are available for data visualization?  

Python boasts a plethora of libraries for data visualization. Some popular ones include Matplotlib, Seaborn, Plotly, and Bokeh. Each tool has different features and abilities. Analysts and scientists use this to make charts and graphs and study data better.  

2. How can a Python data visualization library help someone in a Data Science Career?  

Employing a Python data visualization library is instrumental in a Data Science career. It enables professionals to convey complex data findings in a visually comprehensible manner. This helps with decision-making using data and sharing insights to improve business results.  

3. Which software do you need to run the PrettyTable library?  

You do not need additional software to use the PrettyTable library in Python. This Python library can be easily added to Python scripts or projects. It doesn't require any external dependencies.  

4. Is there a machine learning library available for Python?  

Python has many machine learning libraries, and scikit-learn is a popular one. Python is a flexible platform for machine learning. It has powerful libraries like TensorFlow, PyTorch, and Keras. These libraries cater to deep learning and neural networks.  

5. How does one learn Python, and which libraries should they focus on first?  

Learning Python begins with grasping the fundamentals of the language. To become a programmer, start by learning libraries like NumPy and Pandas. These will help you manipulate data. Once you feel comfortable, you can use Matplotlib and Seaborn to visualize data. To delve into machine learning, starting with scikit-learn is advisable. Online tutorials, courses, and hands-on projects help you learn Python and its libraries.