Top Python Libraries used in Data Science 2021


  • Introduction
  • Here are the top Python Libraries used in Data Science
  • NumPy
  • Pandas
  • Matplotlib
  • SciPy
  • Scikit
  • Statsmodels
  • Seaborn
  • Bokeh
  • SymPy
  • Dash
  • H2O(Hadoop 2
  • Apache Spark
  • Tensorflow
  • NLTK (Natural Language ToolKit)
  • PyBrain
  • PySpark
  •  PySM
  • PyQtGraph
  • PyCall Graph
  •  Python
  • Blaze
  • Rpy2
  • PyShp
  • PyMC
  • Cython
  • PyQGIS
  • PIPI
  • Scikit
  • Gradio
  • PyGTS
  • PySAL
  • Pydap
  • Conclusion


Python is a widely used open-source, high-level programming language that supports multiple programming paradigms, including object-oriented, imperative and functional. In Data Science, Python provides easy-to-use data structures and inbuilt libraries that enable users to perform many tasks with less coding. 

Many tools were developed on Python, such as pandas, matplotlib, NumPy, scikit-learn and statsmodels, which are used in data analysis to solve different tasks. 

Python is also preferred as a first language for learners who want to take up Data science courses. 

In this blog, you will learn a bit about the top python libraries used in data science.


Here are the top Python Libraries used in Data Science


  1. NumPy is the fundamental package required for scientific computing and implements array-oriented calculations. It provides efficient ways to operate on arrays, matrices and multi-dimensional data sets. It has advanced functions which enable high-level mathematical functions such as linear algebra, Fourier transform, random number generation etc.
  2. Pandas is one of the most popular python libraries used to operate on tabular data sets and implements high-level data analysis functions to perform tasks such as merging, appending, transforming and reshaping datasets. |
  1. Matplotlib: It is a 2D plotting library that provides a high-level interface to generate different types of plots, from line plots to bar charts and heat maps.
  2. SciPy is a library of scientific tools for Python, and it includes an array programming package, a statistics module, optimisation routines and other functions for integration with third-party libraries such as plotting libraries etc. It provides high magnitude mathematical and scientific functions to operate on different types of data. It performs tasks such as integration, interpolation, and numerical solutions.
  1. Scikit-Learn provides machine learning algorithms. It is used for statistical modelling. It is built over NumPy, Scipy and matplotlib.
  2. Statsmodels are used to perform statistical data examination and tests, which include estimation, hypothesis testing etc., and can be used to build regression models efficiently.
  3. Seaborn is a data visualisation library that provides a high-level interface to implement statistical graphics plotting.
  4. Bokeh provides interactive visualisation of large datasets. It also can control the visualisation during interaction by adding or removing attributes.
  1. SymPy is a computer algebra system that provides symbolic mathematics methods and computes mathematical expressions efficiently during data analysis. It also performs calculus, discrete dynamical systems etc.
  2. Dash (Dashing Python library) helps to build modern, responsive and interactive web applications.
  1. H2O(Hadoop 2) is a scalable machine learning platform that enables users to run algorithms over big data sets distributed across computer clusters.
  1. Apache Spark is an open-source, fast and general engine for large-scale data processing and provides in-memory speed, enabling analysing massive data sets in real-time.
  2. Tensorflow is an open-source machine learning library built over the concepts of tensors and dynamic neural networks.
  1. NLTK (Natural Language ToolKit) is a leading platform for building Python programs to work with human language data and perform tasks such as automatic tagging, parsing, etc.
  1. PyBrain provides an extensive collection of machine learning algorithms built over NumPy, SciPy and matplotlib.
  1. PySpark is an interface to use Apache Spark from Python and allows users to submit jobs on a cluster through its IPython interface.
  1. PySM is an interface to use Stata from Python and allows users to perform statistical analysis on a dataset in real-time.
  2. PyQtGraph is a plotting and visualisation toolkit for scientific purposes built over the Qt framework without using any other dependencies.
  3. PyCall Graph (matplotlib) is a simple interface to call matplotlib from Python without importing it into the script.
  1. Python (scikit image) provides an interface to use SciKit Image for image processing and segmentation tasks within Python scripts and allows users to perform image analysis efficiently.
  1. Blaze is a NumPy interface that uses C, C++ and Fortran code within Python scripts and provides data structures optimised for parallel computation with OpenMP or MPI on supercomputers.
  1. Rpy2 provides an interface to call R functions from within Python scripts and allows users to efficiently perform statistical analysis over big data sets.
  1. PyShp provides an interface to read and write ESRI Shapefile format within Python scripts.
  1. PyMC is a Bayesian statistical framework that allows users to build probabilistic models, evaluate them and work with hierarchical models.
  2. Cython is a tool to write Python code in C, and it creates C files from the Python scripts, which can be compiled using standard tools to run very fast.
  3. PyQGIS provides an interface to use QGIS from Python and allows developers to download vector, raster and database layers from the QGIS application.
  1. PIPI provides an interface to use matplotlib, NumPy and Scipy within the IPython notebook.
  2. Scikit Flow is a machine learning package that allows users to easily manipulate, visualise, and transform data sets in Python.
  3. Gradio (graph drawing) is a simple Python package to draw graphs and network layouts.
  1. PyGTS is an interface to use the Generic Mapping Tools to perform GIS-related work in Python.
  1. PySAL provides an interface for spatial analysis algorithms in Python using the Scientific Applications Library in C++.
  1. Pydap provides an interface to access data servers and provides the necessary abstractions to deal with common tasks such as sessions, uploading, downloading files etc



The blog provides 32 of the best libraries used for data science projects. Most of these libraries are used by top companies to develop high-performance data science applications such as Uber, Facebook, etc.

Here is where you can earn something new. If you have any questions regarding Python Libraries, comment below.