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Top Python Libraries That Are Ruling The Data Universe

In the fast-moving, humongous, and dynamic technological arena, python libraries have earned their place as a sophisticated staple. But before we attempt to decipher Python libraries, let’s first take a quick overview of Python. Python is the most popular computer programming language in machine learning and data science. Today, most data scientists and data analysts are judiciously using this language for accomplishing a variety of tasks such as building machine learning algorithms, website development, software development, data analysis, and creating data visualization.

What are Python Libraries?

Python libraries refer to a large chunk of reusable codes that you can include in your projects at any given time. The term library here refers to a collection of core modules. Python libraries are like the backbone of developing data science, machine learning, data visualization, data manipulation application, etc. Dating back to the 80s, Python is the brainchild of a prolific Dutch programmer, Guido Van Rossum who started working on this programming project as a hobby during the Christmas holidays and he wished to do something productive around this time. According to research by SlashData, there are over 8 million developers of Python.

Did you know that currently, there are around 137,000 python libraries that are working tirelessly to eradicate the need for writing codes? If this sounds intriguing, let’s delve into the world of the most commonly used python libraries.

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Popularly Used Python Libraries


Touted as the most widely used open-source, interactive and user-friendly library, NumPy also known as Numerical Python focuses on scientific computation. With built-in mathematical functions for computations, this library supports huge matrices and multidimensional data. NumPy can be used in linear algebra, as a random generator or a multi-dimensional container for generic data. Arcsin(), arccos(), tan(), radians(), etc are NumPy’s important functions.

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This numerical calculation open-source library employs both deep learning and machine learning algorithms. Coined by researchers at Google Brain, which is a part of Google Ai, Tensorflow is widely used by machine learning researchers to decode complex mathematical equations. Unlike NumPy, visualizing each part of the graph is easier with Tensorflow. Another feature that sets this apart is its adaptability in machine learning models which enables to the creation and highlighting of different sections. You can also train several GPUs and neural networks simultaneously. It is used in Google Photos and Google Voice Search.


Extensively used in the data science field, Pandas is a BSD (Berkeley Software Distribution) an open-source library mainly used for manipulation, cleaning, data analysis, etc by data scientists. With Pandas, you can perform a variety of tasks such as slicing the data frame, joining and merging the data frame, changing the index values, changing the headers, data conversions, etc. From simplifying and implementing complex mathematical equations and easing the process of coding, NumPy can also be used to represent sound waves, images, and binary raw streams.

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It is an open-source neural network library that enables to experiment with deep neural networks. With the rising popularity of deep learning, Keras seems like a viable option according to its creators who consider it as an API (Application Programming Interface) more apt for humans than machines. Keras supports most neural networks including convolutional, pooling, fully connected, recurrent, embedding, etc. all of these models can be merged to create a more sophisticated version. Its Python-based framework makes it easier to not just debug but also explore different projects. With its expressive modular design, Keras is an appropriate fit in the research domain. Companies including Netflix, Uber, Yelp, and Square are already implementing Keras-powered features.

Python is constantly reinventing itself to blend with the emerging technological trend. It is safe to say that with python on our side, brands are sure to make a poised leap in the world of technology while offering a far-reaching impact in the years to come.

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