Have you ever coded with Python?
Isn’t it really fun to code in Python??
Most of you will agree with this point !!
In this world of Data Science, Python is one of the most popular programming languages in the world of programming. It is a fundamental and simple programming language that is highly versatile. It comes with a wide range of packages and in-built libraries developed by the Python community.
Table of Contents
Why Do We Need Python Libraries?
Python comes with various advantages, just like its free of cost, most easy to use programming language, very compatible, and comes with a vast range of open-source libraries.
So, the main question is – Why do we need Python libraries?
So, Python libraries help you to actually code like a professional coder without wasting any extra sort of time with a confusing and complex syntax.
Well, now you will be having a good idea about Python libraries and what is the need for it?
Now, you might be confused about which library is the best for you as per your need !!!
If you also have the same question, then your hunt is over right here!
Top 5 Python Libraries Of 2021 For Data Science
Before going ahead towards the top 5 Python libraries of 2021 for Data Science, let’s first discuss –
What is Data Science?
Why choose Python libraries for Data Science?
- Python requires less coding.
- It is a very easy to use programming language.
- This programming language supports a wide variety of libraries for Data Science
- It is a platform-independent language
- It has a vast community of developers
Well, there is a vast number of Python libraries for Data Science out there in the market that offers a variety of features and functionalities.
To save you precious time, we have come up with a list of the top 5 Python libraries of 2021 for Data Science.
So, what are you waiting for??
Let’s get started !!!
Numpy is also known as Numerical Python, is basically a linear algebra library that has been developed in Python language. Most Data Scientists prefer Numpy over other Python Libraries.
It also includes functions for dealing with complex mathematical operations such as Fourier transformation, linear algebra, random number generation, and functionality for working with matrices and n-arrays in this programming language.
This Python library can also perform scientific calculations. Numpy is commonly used in the processing of sound waves, pictures, and other binary functions.
Official Documentation of Numpy – Link
Github Repository of Numpy – Link
Plotly is one of the most well-known Python data science libraries. This library allows you to design visualisation models using a variety of APIs that are supported by multiple programming languages such as Python.
Besides that, you can conveniently use interactive graphics and a variety of robust resources accessible through its main website Plotly.
To use Plotly in your working model, you must properly configure the available API keys. The accessible graphics are processed on the server-side and, if successful, appear on your browser screen.
Official Documentation of Plotly – Link
Github Repository of Plotly – Link
TensorFlow is one of the best Python data science libraries for performing high-end mathematical calculations. It is also regarded as one of the best Python libraries for machine learning.
Deep neural networks for NLP (Natural Language Processing), recurrent neural networks, image recognition, word embedding, handwritten digit classification, and PDE are all supported (Partial Differential Equation).
TensorFlow library offers excellent design support, allowing for quick computation deployments across a number of platforms, including desktops, databases, and mobile devices.
Official Documentation of TensorFlow – Link
Github Repository of TensorFlow – Link
4. Beautiful Soup
Beautiful Soup is a Python library for extracting data from markup languages such as HTML, XML, and others. It is a web scraping application that allows you to clean up and parse the documents you have downloaded from the internet.
Furthermore, this library allows you to extract specific content from a website, uninstall the HTML markup, and save the data. For example, you might have discovered several web pages that show data related to your studies, such as date or address information, but do not allow you to download the data directly.
The Beautiful Soup documentation will show you how this library will assist you with everything from isolating titles and links to removing all of the text from the HTML from changing the HTML tags inside the document you’re dealing with.
Official Documentation of Beautiful Soup – Link
Github Repository of Beautiful Soup – Link
Scrapy is one of the finest Python data science libraries. Scrapy, also known as spider bots, is software that crawls programs and retrieves structured data from web applications. This programming language is used to write this open-source library. It was made for scraping, as the name implies. It is a complete system with the ability to collect data through APIs and function as a crawler.
This Python library allows users to write code, reuse universal programs, and build portable crawlers for their applications. Furthermore, it is generated through the Spider class, which includes crawler instructions.
Scrapy produces feed exports in JSON, CSV, and XML formats. It is crawler-based and allows for automated data extraction from web pages. It includes support for XPath and CSS expressions for collecting and extracting data from various sources.
Official Documentation of Scrapy – Link
Github Repository of Scrapy – Link
Top 5 Python Libraries Of 2021 For Machine Learning
Before going ahead towards the top 5 Python libraries of 2021 for Machine Learning, let’s first discuss –
- What is Machine Learning?
- Why we need Python libraries for Machine Learning?
What is Machine Learning?
Machine Learning is the technology that helps to teach the computer how to act like humans. With the use of Machine Learning, we can embed data and information in the computer in the form of real-world interactions. Machine Learning is completely based on algorithms.
Why choose Python libraries for Machine Learning?
Now, the question that arrives here is – Why choose Python libraries for Machine Learning?
Let’s answer this query!
According to most professional developers, this programming language is the ideal choice and the most preferred programming language when we talk about Machine Learning.
Python comes with a wide variety of libraries and open-source tools, which offers the best features and functionalities to ML developers. Many developers have stated that it increases the productivity of their code along with the quality. The extensive python libraries help to reduce your workload.
Now, let’s move ahead and dig into some of the primary reasons due to which most of the professional ML developers choose this programming language over several other programming languages for Machine Learning.
- Python is a community-friendly programming language that is completely free and open-source as well.
- The extensive libraries of Python help to ease your efforts and workload.
- It reduces the coding time and debugging to increase your productivity.
- This programming language can also be used for NLP and Soft Computing as well.
As you know that there is a large number of Python libraries for Machine Learning available in the market, but you need to sort out the one which is compatible with your requirements.
Well, nothing to worry about when we are here !!!
To minimise your workload, we have compiled a list of the top 5 Python libraries of 2021 for Machine Learning.
So, without wasting even a second !!
Let’s hop on our list !!
The SciPy library includes modules for linear algebra, image optimisation, integration interpolation, specific functions, Fast Fourier transform, signal and image optimisation, Ordinary Differential Equation (ODE) solving, and other scientific and analytical computations.
SciPy’s underlying data structure is a multi-dimensional array given by the NumPy module. The array manipulation subroutines in SciPy are provided by NumPy.
SciPy was designed to work with NumPy arrays while still providing user-friendly and effective numerical functions. Many developers are developing python libraries for machine learning, especially for scientific and analytical computing.
Official Documentation of SciPy – Link
Github Repository of SciPy – Link
Pandas is rapidly becoming the most popular Python data analysis library, with support for easy, scalable, and expressive data structures designed to operate on both relational and labelled data. Pandas is now an unavoidable Python library for solving realistic, real-world data analysis problems.
Pandas is extremely stable and provides highly optimised efficiency. The backend code is written entirely in C or Python. Pandas use two different types of data structures, i.e., Series data structure (for 1 Dimension) and DataFrame data structure (for 2 Dimension).
These two types of data structures can manage the vast majority of data specifications and use cases from most fields, including scientific, statistics, social, and economics, and for analytics and other areas of engineering.
Official Documentation of Pandas – Link
Github Repository of Pandas – Link
Matplotlib is basically a data visualisation library for 2D plotting that generates publication-quality image plots and figures in a number of formats.
With only a few lines of code, the library can produce histograms, plots, error charts, scatter plots, and bar charts.
It has a MATLAB-like GUI and is very user-friendly. It operates by offering an object-oriented API that enables programmers to integrate graphs and plots into their applications using common GUI toolkits such as GTK+, wxPython, Tkinter, or Qt.
Official Documentation of Matplotlib – Link
Github Repository of Matplotlib – Link
PyTorch has a number of resources and libraries for computer vision, machine learning, and natural language processing. PyTorch is an open-source library that is based on the Torch library. The simplicity with which the PyTorch library can be learned and used is the most important benefit.
PyTorch integrates seamlessly with the Python data science framework, including NumPy. There isn’t much of a distinction between NumPy and PyTorch. PyTorch also enables developers to perform Tensor computations.
PyTorch has a solid architecture for building and changing computational graphs in real-time. PyTorch also has multi GPU support, streamlined preprocessors, and custom data loaders.
Official Documentation of PyTorch – Link
Github Repository of PyTorch – Link
Scikit-learn is the most popular Python machine learning library for designing machine learning algorithms, developed on top of two Python libraries – NumPy and SciPy.
Scikit-Learn provides a diverse collection of supervised and unsupervised learning algorithms through a consistent framework. The library can also be used to mine and evaluate data. The Scikit-learn library can handle the following machine learning functions: grouping, regression, clustering, dimensionality reduction, model collection, and preprocessing.
Official Documentation of Scikit-Learn – Link
Github Repository of Scikit-Learn – Link
Wrapping It Up
In today’s world, business data has become as important as money. There is no question that we are living in the Big Data age, and we are processing a significant amount of data every second. Furthermore, major companies depend heavily on this data to grow in the industry.
As a result, we have compiled this collection of Python libraries for Data Science and Machine Learning. We hope this collection assists you in selecting the best Python library for you. This programming language has a vibrant culture in which most developers build libraries for their own use before releasing them to the public.
At last, don’t forget to mention your own favourite library in the comment section below.