Top Python Libraries You Should Know in 2020

Python is one of the most popular programming languages. Many developers choose Python because it’s easy to learn and good for varies task including data science, machine learning, data analysis and visualization, and web or desktop app development. The reason Python can be used in so many different types of programming is its large number of libraries.

Top Python Data Manipulation Libraries

1. Pandas 

Pandas provide high-performing data structures that make working with data easy, fast, and intuitive. The library’s primary data structures – series (one-dimensional) and DataFrame (two-dimensional) – are used very often in finance, statistical computing, social science, and engineering. Pandas is used in conjunction with other libraries, such as NumPy, SciPy, and Matplotlib.

2. NumPy 

It is an open-source tool designed for efficient numerical computing. NumPy provides high-performance multidimensional arrays and matrices and the tools to operate on them. It also contains helpful functions for linear algebra, Fourier transform, and random numbers.

Top Python Data Visualization Libraries

1. SciPy

SciPy is an open-source library designed for scientific computing. It contains functions that facilitate linear algebra, integration, image processing, and optimization. It is a good tool for a wide variety of scientific, mathematical, and engineering tasks that require some manipulation of numbers.

2. Matplotlib

Matplotlib is the most popular open-source library for data visualization. It can generate many types of plots – including bar charts, scatter plots, and histograms – with just a few lines of code.

This library also delivers an API for embedding plots into applications. Matplotlib allows programmers to visualize huge amounts of data and produce high-quality images in a range of formats. 

3. Seaborn

Seaborn is an open-source library that’s designed for data visualization. This library is based on Matplotlib, but it gives users an easier and simpler way to create complicated plots. Seaborn has a lot of built-in styles, which means you can change the look of charts very quickly. (It also integrates well with Pandas data structures.) Seaborn provides automatic estimation and plotting of linear regression models.

Top Python Deployment Libraries

1. Scrapy 

Scrapy is an open-source Python framework that’s designed for crawling websites and extracting their data. These tasks are simple and fast with Scrapy; it’s also easy to plug in new functionality to this framework. Thanks to this flexibility, Scrapy can also be used for data mining, automated testing, and information processing.

2. Flask

Flask is another very popular Python framework. It is used in deploying data science models. This open-source tool is lightweight and designed to deploy complex applications easily and quickly. Since it is a microframework, it does not require particular libraries or tools. And you can add more functionality by way of its many extensions.

Conclusion

There are many more libraries to explore in Python dedicated to various business areas. This vast number of specialist tools is one of the reasons Python is so popular.