Python is the most robust tool, that is widely used for data science. Python offers versatile libraries that can improve the experience of carrying out various data science tasks, for the data scientists and developers.

The different data science tasks are carried out by different Python libraries, and as the requirement arises, so available are the libraries.

This article is targeted to briefly explain the various libraries sorted under the sub-headings of the subparts of a complete data science task.


  1. Visualization of Data
  • Matplotlib

The Matplotlib library is a powerful library that allows 2-D plotting for Python. The pie charts, histograms, line plots, area plots, contour plots, spectrograms, etc. are built easily using the Matplotlib library. The Matplotlib generated plots are flawlessly compatible with various environments.


The Matplotlib library is a MATLAB resembling powerful tool for Python. The library offers object-oriented API that simplifies the plotting procedures for Python language.

The drawback of this library is that for plotting advanced data sets, the programmer needs to dive into more complex and lengthier part of coding.



  1. Tools for Data Modelling and Processing
  • Pandas

The Pandas library for Python is framed on two data structures, the series, and the data frames. This excellent open-source package is used for efficient data frames conversion and manipulation.

Pandas are useful in data analytics tasks, that can be utilized for the solutions to the problems involving real-world data sets.


Pandas also comes with wonderful plotting techniques, that can be used for data visualization. The operations performed, using Pandas, are high-speed and have a high degree of accuracy.


  • NumPy

The advanced array operations are simplified by NumPy (Numerical Python) library.

The NumPy allows the data scientists to perform the scientific computation in a much simpler way, and also, carries out Fourier transforms in a faster manner.


Having a sound knowledge of NumPy is extremely necessary for developers who are also involved in Python-based machine learning projects.


  • SciPy

The statistics, integration and other scientific calculations are made easy by the SciPy library.

Based on the NumPy library, SciPy works on arrays, which store similar types of data.


The SciPy package has in-built tools that are required for carrying out any computing problem including calculus, linear algebra, and processing of signals.


  • TensorFlow

TensorFlow is an extremely useful library for executing machine learning and deep learning tasks.

The highly futuristic goals of speech recognition and identification of objects are made easy using the TensorFlow library.


The TensorFlow library is used for artificial neural networks too, and also, to solve complicated numerical problems that involve advanced mathematical computations.




  • Keras

The Keras library is neural network model-centric, whereas TensorFlow is built to aid various ML/AI projects.


The Keras library uses Microsoft integrated CNTK, TensorFlow, and Theano for backend support.

The Keras’ approach of building graphs with the help of backend tools, and utilizing the same for computational purposes, makes its performance slower, as compared to other libraries.

But, since the library is developed for Python, it is one of the most popular and easier choice for building neural network projects.



  1. Data Mining
  • Scrapy

One of the most reliable tools for web crawling and data scraping, Scrapy is a library that helps to build powerful crawler programs.


The Scrapy library is used for collecting useful API data, for example, contact details and URLs.