Ten high-quality Python libraries you need to master in 2022

Python within the programming language offers some amazing features such as ease of learning and understanding, enhanced security, and performance. It includes modules, packages, and libraries, and thus plays an important role in keeping the project organized and powering different processes. Modules are python files containing functions saved with a .py extension. A package is a collection of modules. A function library is a collection of code that has the same functionality.

#Not clear yet? So, suppose, we have a file that has a function about addition. py extension. This file is called a component. A collection of files each with independent functionality is called a package. A separate file containing functions for various operations is called a function library. There are many packages in python that make complex processes simple and hassle-free.

We will discuss the top 10 Python libraries to learn in 2022. let's start.

1.Pandas

Pandas stands for "Python Data Analysis Library". If you are someone who loves data science, machine learning, data analysis you must use this package as it is widely used for data manipulation and analysis. It has an extensive set of features that can handle large amounts of data efficiently. It is ideal for different kinds of data tables, SQL or Excel, JSON and allows importing data.

2.NumPy

NumPy is the basic library for scientific computing and array processing. Libraries like TensorFlow use NumPy on the backend to operate. It handles linear algebra, random numbers, and Fourier transforms very efficiently. It also acts as a multi-dimensional container for data of any data type. The main features of NumPy are:

• Powerful N-dimensional array objects.
• Ready-made tools to integrate C/C and Fortran code.
• Easy to learn and use.

3.TensorFlow

TensorFlow is also called a "Google" product because it was developed by Google. It provides a set of workflow computer programming languages ​​for developing and training models, and can be easily deployed in the cloud, on-premises, in the browser, or on your device, regardless of the language you use. The action has three steps: loading and preprocessing data, building, training and reusing the model, and deploying. It is highly scalable, compatible, and helps in building models easily.

4.Pywin32

Pywin32 is an essential software package, especially for Windows Python programming. It provides access to many native Windows API functions, allowing you to perform operations such as interacting with the Windows registry, using the Windows clipboard, and more. It enables you to access the Win32 application programming interface (API) computer programming language. It only runs on Windows systems with 32-bit processors.

5.PyTest

This package has various modules for testing new code, including small unit tests and complex functional tests of applications. It helps in writing simple and scalable test cases for API, database and UI. Pytest is a great tool for testing your programs. Some of the features of the framework are:

• Built-in support for test discovery.
• Extensive plug-in architecture.
• Combined fixture for test device.

6.Requests

It allows you to exchange requests HTTP (Hypertext Transfer Protocol) on the web in a more responsive and user-friendly way. HTTP acts as a link between client and server. request-response. The intuitive JSON methods provided by this package can help you avoid manually adding query strings to URLs. You can inspect, configure, authorize and configure HTTP requests, upload multiple files simultaneously, and automatically decompress data. You can use various methods like publish, delete, patch, put, get, title. In short, if your application sends any data over HTTP, Request is a must-have package.

7.Seaborn

With just a few lines of code, you can use Seaborn. You can easily create complex heat maps, violin plots, joint plots, multi-plot grids, and many other types of plots. It provides beautiful default styles and color palettes to make your charts more attractive. It builds on top of the matplotlib library and is tightly integrated into Pandas.

8.MoviePy

This library ties together common tasks like importing, modifying, and exporting video files. You can insert a title into the video or rotate the video 90 degrees. MoviePy aims to make visualization a core part of exploring and understanding data. A dataset-oriented API is provided so we can switch between different visual representations of the same variables to better understand the dataset.

9.Pendulum

You can use this library when you want to deal with dates and times and don't want complicated code. It automatically manages time zones and is designed to replace DateTime. It is a cleaner and easier way to use the API. It simplifies complex date manipulation problems involving time zones that are not handled correctly in native DateTime instances.

10.Matplotlib

Matplotlib is the base visualization library for everything else. This can be used to create things like line charts, histograms, scatter plots, bar charts, and pie charts. Animations and interactive visualizations can be easily created using this library. It provides a lot of flexibility for formatting and styling drawings. It allows you to freely choose how to display labels, grids, legends, etc. However, to create complex and visually appealing plots, you need to write a lot of code.

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