Best Python Libraries for Data Science In 2021


Python is an interpreted, interactive, portable, and object-oriented programming language. This all-purpose open source language runs on many flavors of Unix, including Linux and MacOS, as well as Windows. Python has applications in hacking, computer vision, data visualization, 3D machine learning, and robotics and is a favorite with developers around the world.

The following are the ten most commonly used Python libraries for data science:


Developed by the Google Brain team, TensorFlow is an open source library used for deep learning applications. Originally developed for numerical compilations, it provides a comprehensive and flexible ecosystem of tools, libraries, and community resources that developers can use to build and deploy ML-based applications. First released in 2015, the Google Brain team recently released its latest version, TensorFlow 2.5.0, with more features. It supports Python 3.9.

To learn more, click here.


NumPy or Numerical Python was developed by Travis Oliphant in 2015 and is a basic library for mathematical and scientific calculations. The open source software has functions for linear algebra, Fourier transform and matrix calculations and is mainly used for applications where speed and resources are important. NumPy aims to serve array objects 50 times faster than traditional Python lists.

Data science libraries such as SciPy, Matplotlib, Pandas, Scikit-Learn and Statsmodels are based on NumPy.

To learn more, click here.


SciPy or Scientific Python is used for complex math, science, and engineering problems. It is based on the NumPy extension and enables developers to edit and visualize data.

SciPy offers user-friendly and efficient numerical routines for linear algebra, statistics, integration and optimization. Its applications include multidimensional image processing, Fourier transform solving, and differential equations.

To learn more, click here.


Developed by John Hunter, Matplotlib is one of the most common libraries in the Python community. It is used to create static, animated, and interactive data visualizations. Matplotlib offers endless customizations and diagrams. Developers can use histograms to scatter, adjust, and configure charts. The open source library offers an object-oriented API for integrating plots into applications.

To learn more, click here.


Pandas was developed by Wes McKinney and is used for data manipulation and analysis. It offers fast, flexible and expressive data structures as well as functions such as handling missing data, fancy indexing and data alignment.

Pandas provides fast, flexible, and expressive data structures that developers can use to work with labeled and relational data. It is based on two main data structures – series and frames.

To learn more, click here.


Keras open source software library provides an interface for the TensorFlow library and enables rapid experimentation with deep neural networks. It was developed by Francois Chollet and first published in 2015.

Keras provides utilities for compiling models, visualizing charts, and analyzing data sets. In addition, it offers pre-labeled data sets that can be imported and loaded directly. It’s easy to use, versatile, and suitable for creative research.

To learn more, click here.


SciKit-Learn offers classification, regression and clustering algorithms including DBSCAN, gradient enhancement, vector machine support and random forests. David Cournapeau built the library on top of SciPy, NumPy and Matplotlib to handle standard machine learning and data mining applications.

SciKit-Learn is an effective tool for predictive data analysis.

See also

To learn more, click here.

Statistical models

Statsmodels is part of Python’s scientific stack focused on data science, data analysis, and statistics. It is based on NumPy and SciPy and is integrated into pandas for data processing. With statsmodels, users can explore data, estimate statistical models, and perform statistical tests.

To learn more, click here.


Plotly is a collaborative, web-based analysis and graphics platform. It is one of the most powerful libraries for ML, data science, and AI-related operations. Plotly is publication ready and immersive and is used for data visualization.

Plotly can easily import data into charts so developers can easily create slide decks and dashboards. It is used for developing tools like Dash and Chart Studio.

To learn more, click here.


Seaborn is Python’s most widely used statistical data visualization library, used for heat maps and visualizations that summarize data and show distributions. It is based on Matplotlib and can be used for both data frames and arrays.

Seaborn is used for basic presentations – bar charts, line charts, and pie charts.

To learn more, click here.

Join our telegram group. Be part of a dedicated online community. Join here.

Subscribe to our newsletter

Get the latest updates and relevant offers by sharing your email address.

Source link


Comments are closed.