What are Python Frameworks that are used in Data Science?

The second most renowned programming language in the year 2022 is Python. Python is a programming language that was created in the year 1991 by Guido van Rossum, who was a dutch programmer. This language is not like other complex languages like c or c++, it’s a very user-friendly language. The features of python say all about itself, it is an interpreter meaning it will execute the code one by one. It is an object-oriented programming language meaning it uses all the objects and classes in one go and last but not least it is a high-level programming language making it user-friendly. 

Data Science is the latest study that is disrupting all industries. The studying of massive data using new technology and the new tools to find the answers to all the unanswered data is known as data science. The study of data science needs knowledge of computer science and information technology. It requires the real and new methods of artificial intelligence, machine learning, deep learning, and software development. As earlier described that data science is all about data and data holds maths, so yeah we need all kinds of maths, statistics, and probability in it. For this, you also need domains and business knowledge with traditional research.  

As of now we have to know that python is the easiest programming language of all and it is the one and only user-friendly language. It has many features all in one and is suitable for many things altogether. Whereas we have Data Science that’s causing a big change in all the industries we have all come across. Now, the thing is industries are taking the help of python in creating data science. We are having trillions of libraries and packages in python to work on many new things. So, today I am going to make you come through all the Python Frameworks that will be used in Data Science in the future.


NumPy is the one and only package of python that is defined as the most important package for scientific computation. The latest version of Numpy is NumPy 1.22.0. Talking about its features, so yeah NumPy is very easy to use. It has a very high-level syntax that is actually very easy to access and understand. It is an open-source package.

It has a powerful N-dimensional array which means it is very fast and compatible with all the programmers and it fits better in today’s array computing world. As described NumPy is used for scientific computation, so it has numeric computing tools that enable many mathematical functions, algebra, statistics, etc. It is Interoperable meaning it supports many varieties of hardware and computing platforms. It is performant also that helps in flexibility in the compilation speed. 


SciPy is a python package that is used for the basic algorithms that are used in scientific computation. Version SciPy 1.8.0 is the latest version of SciPy that is released in the year 2022. It provides fundamental algorithms that are used for various mathematical concepts such as optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and many other types of problems. It is broadly applicable in all algorithms and data structures. It has many tools for array computing and all the machine learning algorithms like sparse matrices and k-dimensional trees.  It is performant that helps in flexibility in the compilation speed. It is easy to use and an open-source package.


TensorFlow is a machine learning platform that is created by a group of Google Brain Team. They use this platform mainly for research and production. It is totally free and an open-source software. It helps in the easy model building using high-level APIs. It helps in easy training and deploying models in the cloud or on the browser or on a device. It best fits for powerful experimentation for research. 


Keras is a platform that is exclusively designed for humans and not for any kind of machine. It has good structured Python-based APIs. It can run TensorFlow, Theano, and CNTK. It mainly works for deep learning. It is a user-friendly platform to work on simple API. It makes it easy to debug and find out all the errors. It is extensible. You can add many things like modules, classes, and functions for research purposes.

Must Read: Mathematical Knowledge required for Data Science

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