Top 10 Python Data Science Libraries

Python Data Science Libraries

Today, Python is the most widely used programming language – it’s open-source, easy to learn, and easy to debug.

Another key benefit of using Python is the Python libraries – incredible collections of related modules. Having these bundles of code, that can be repeatedly used in a wide range of different modules, makes Python programming faster and more straightforward, removing the need to write the same bits of code over and over again.

But, which Python libraries are best for data science applications?

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Here’s our rundown of the top 10 Python data science libraries in 2023:


The TensorFlow library features approximately 35,000 comments on GitHub and a community of 1,500 contributors. Used across a variety of scientific fields, this Python data science library acts as a framework for computations involving tensors.

This open-source Python library was built by the Google Brain Team to provide a diverse range of tools, libraries, and resources for creating machine learning-based applications. It is useful for speech and image recognition, time-series analysis, and video detection, as well as text-based applications.

Key features include:

  • Parallel computing to execute complete models
  • Improved computational graph visualizations
  • Deep neural networks and machine learning principles are well supported
  • Reduced errors in neural machine learning.


Created by Wes McKinney, Pandas is the most popular and widely used Python library for data science. The library has around 17,000 comments on GitHub and a community of 1,200 contributors. Offering fast, flexible data structures, it is widely used for data analysis and cleaning, ETL jobs for data transformation, academic and commercial applications, and time-series-specific functionality.

Key features include:

  • High-level data structures and manipulation tools
  • Ability to create your own function and run it across a series of data
  • High-level abstraction
  • Eloquent syntax
  • Rich functionality
  • Data sets can be reshaped and pivoted.


Created in 2015, this open-source Python library includes linear algebra, Fourier transform, and matrix calculation functions. It is mainly used for applications requiring performance and resources and provides the foundation for other data science libraries, including SciPy, Matplotlib, and Pandas.

Key features:

  • Can be one-dimensional or multidimensional
  • Can perform functions on generic data types
  • Broadcasts the shape of smaller arrays based on the geometry of larger ones.


Part of the TensorFlow ecosystem, Keras is a fast, modular, and user-friendly API designed to help people become more proficient in machine learning. This open-source library covers all stages of the machine learning workflow, supporting convolutional and recurrent neural networks, as well as conventional utility layers.

Key features:

  • Vast pre-labeled data sets that can be used to directly import and load
  • Implemented layers and parameters for construction, configuration, training, and evaluation of neural networks
  • Comprehensive documentation and tutorials.


A numerical extension of NumPy, Matplotlib is used to provide a free, open-source alternative for MatLab. It is a plotting library with around 26,000 comments on GitHub and a community of 700 contributors.

Key features:

  • Create quality plots of data
  • Create various charts
  • Make interactive figures capable of zooming in and out, panning, and updating
  • Export to different file formats
  • Low memory consumption
  • Can be used on any operating system.


Scrapy is one of the most popular open-source web frameworks written in Python. It is used to extract data, either as a web crawler or through APIs, using self-contained crawlers called spiders.

Key features:

  • Supports the building of crawling programs capable of retrieving structured data from across the web
  • Can be used to gather data from APIs
  • Follows a ‘Don’t Repeat Yourself’ principle to influence users to write universal codes that can be reused for building large-scale crawlers.


PyTorch is a scientific computing package that helps developers progress from development and research to training and development, using the power of graphics processing units.

This open-source, machine learning framework is one of the most popular Python platforms for deep learning research.

Key features:

  • Uses python integrations and data science stack
  • Simple to use API
  • Dynamic computing graphics, which can be modified during execution
  • Hybrid front-end for ease of use
  • Well-supported on major cloud platforms.


Scikit-Learnis an accessible, open-source package built on NumPy, SciPy, and Matplotlib. It provides enhanced functionality for basic machine learning algorithms, including regression, classification, clustering, dimensionality reduction, and model selection.

Key features:

  • Inbuilt datasets such as the iris dataset, house prices dataset, etc
  • Datasets can be split for training and testing
  • Linear and logistic regression.

Beautiful Soup

Beautiful Soup is a popular Python library for data science. Best known for web crawling and data scraping, it allows users to collect data from some websites, without an API. The platform can then scrape and arrange the collected data into the required format. What’s more, it does it all quickly, potentially saving users days of work.

Key features:

  • Use Pythonic idioms to navigate, search, and modify parse trees
  • Automatically converts incoming documents to Unicode
  • Automatically converts outgoing documents to UTF-8
  • Sits on top of popular Python parsers, including lxml and html5lib.


SciPy is a free, open-source Python library for data science. Widely used for high-level computations, it has around 19,000 comments on GitHub and a community of around 600 contributors. This high-level library gives developers the ability to solve mathematical problems and scientific calculations quickly.

SciPy is commonly used in applications such as multi-dimensional image operations, optimization algorithms, and linear algebra.

Key features:

  • High-level commands for data manipulation and visualization
  • Multi-dimensional image processing
  • Built-in functions for solving different equations.


In the end, it’s really up to you based on the needs of the project and prioritization of features needed that will help you determine which Python Library is right for your project.

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