Python Data Science  Libraries

Top 10


Developed and released by Facebook’s AI research group in 2016, PyTorch is one of the most popular open-source Python data science libraries. Being highly compatible with the Python programming style, PyTorch is one of the go-to Python data science libraries for implementing complex neural network use cases. Developers use PyTorch for designing complex and high-end deep learning models because of its fast and flexible experimentation feature.


Developed by Google in 2015, TensorFlow is an open-source and most popular Python data science library for deep learning and artificial intelligence applications. With TensorFlow, you can quickly solve any complex numerical computations and implement large-scale machine learning models, including handwritten digit classification, image recognition, text classification, and recommendation systems.


Keras is an open-source and easy-to-use Python data science library for machine learning and deep learning operations. Incorporating multiple neural network models like CNN and RNN in its backend, Keras helps you build high-end and complex deep learning models in less time. Since Keras is beginner-friendly and fast during model deployments, users can develop high-end deep learning models with minimum codes in less time.

Sci-Kit Learn

SciKit-Learn, also called sklearn, is one of the most popular Python data science libraries that comprises a variety of supervised and unsupervised algorithms for building machine learning models. SciKit-Learn has a rich set of functions and modules that allow you to seamlessly perform all machine learning-related tasks, from loading the dataset to model building to evaluating the metrics.


Originally developed by Intel, OpenCV is the most popular Python artificial intelligence library for building real-time computer vision, machine learning, and image processing applications. Being one of the widely used libraries, OpenCV allows you to implement computer vision applications like video processing, image recognition, object detection, motion tracking, and much more.


Seaborn is one of the most popular Python libraries for data visualization and exploratory data analysis. With Seaborn, you can create high-level and attractive statistical plots in different styles and colors. Seaborn eases the process of data visualization where you just need to pass your dataset into the seaborn function to instantly get insights into your dataset.


Pandas is one of the most straightforward yet powerful Python data science libraries for performing data analysis. It is also one of the most popular open-source libraries for implementing data manipulation and wrangling operations. With Pandas, you can perform data-related operations for data that are represented in the 2D tabular format i.e., rows and columns..


Numpy stands for Numerical Python, which allows you to perform logical and mathematical operations on arrays. Since Numpy comprises pre-defined and high-level mathematical functions, you can quickly solve complex math problems without writing a single line of code. Additionally, when combined with Scipy, a scientific library, and Matplotlib, a visualization library, Numpy can effectively replace MatLab, a technical computing software.


NLTK stands for Natural Language ToolKit, which is an open-source Python library that allows performing Natural Language Processing (NLP) operations on human language data. In other words, NLTK is a suite that contains libraries and programs for performing language processing operations on text data, including tokenization, stemming, and lemmatization.

Beautiful Soup

Beautiful Soup is one of the open-source Python data science libraries for performing web scraping operations using Python. In other words, this library is used to pull required data out of HTML and XML files. With Beautiful Soup, you can seamlessly extract all data or filter only specific elements of interest from the respective website.



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