Python is one of the most popular programming languages which is used by more than 80% of the developers. It offers some amazing features like easy to learn and understand, enhanced security and performance. It consists of modules, packages, and libraries that play a major role in keeping a project sorted and fueling up different processes. Modules are python files that contain functions saved with a .py extension. Packages are collections of modules. Libraries are a collection of codes having the same functionality.
Yet, not clear? We’ll make it more clear, Suppose, we have a file that has a function about addition operation saved with .py extension. This file is called a module. A collection of files having separate functions in each is called packages. A single file in which we have functions for various operations is called a library. There are a lot of packages in python that make the complex process easy and hassle-free.
In this blog, we will discuss the Top 10 Python Packages to Learn in 2022. So let’s get started.
1. Pandas
Pandas stand for “Python Data Analysis Library”. If you’re a person who is into data science, machine learning, data analysis you must use this package as it is used extensively in data manipulation and analysis. It has an extensive set of features that handle large data efficiently. It is well suited for different kinds of data be it Tabular, SQL or Excel, JSON and allows importing data. This is one of the amazing Python packages to learn in 2022. To get more in-depth information about this package click here.
2. NumPy
NumPy is the fundamental package for scientific computing and array processing. Libraries like TensorFlow uses NumPy at the backend for manipulating tensors. It handles linear algebra, random numbers, and Fourier Transforms very efficiently. It also serves as a multi-dimensional container for any generic data that is in any datatype. The key features of NumPy are:
- Powerful N-dimensional array object.
- Out-of-box tools to integrate C/C++ and Fortran code.
- Easy to learn and use.
3. TensorFlow
TensorFlow is also called a “Google” product as it is developed by Google. It provides a collection of workflows to develop and train models using Python, and to easily deploy in the cloud, on-prem, in the browser, or on-device no matter what language you use. There are three steps to act – Load and preprocesses data, Build, train and reuse models, and Deployment. It is highly scalable, compatible, and also helps in building models easily.
4. Pywin32
Pywin32 is a must-have package, particularly for Windows Python programming. It provides access to many of the native Windows API functions, allowing you to do things like interact with the Windows registry, use the Windows clipboard, and much more. It enables you to access the Win32 application programming interface (API) on Python. It works only on Windows systems having 32-bit processors.
5. PyTest
This package has a variety of modules used for testing new code which includes small unit tests and complex functional tests for applications. It helps in writing simple and scalable test cases for APIs, databases, and UIs. Pytest is a great tool for testing your programs. Some amazing features of this framework are:
- Built-in support for test discovery.
- Extensive plugin architecture.
- Modular fixtures for test setup.
To get more information about this python package click here.
6. Requests
It allows you to exchange requests on the web in a more responsive and user-friendly manner using HTTP(HyperText Transfer Protocol). HTTP works as a request-response between client and server. The intuitive JSON method offered by this package helps you avoid manually adding query strings to URLs. You can inspect, configure, authorize and configure HTTP requests, upload multiple files at the same time and decompress data automatically. You get to use various methods like – POST, DELETE, PATCH, PUT, GET, HEAD. 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 draw attractive statistical graphics using the Seaborn package. You can easily create complex heatmaps, violin plots, joint plots, multi-plot grids, and many other types of plots with this library. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of the matplotlib library and is closely integrated into the data structures from pandas.
8. MoviePy
This package provides a range of functionality for common tasks associated with importing, modifying, and exporting video files. It helps in inserting titles into videos or rotating videos to 90 degrees. This package aims to make visualization the central part of exploring and understanding data. It provides dataset-oriented APIs so that we can switch between different visual representations for the same variables for a better understanding of the dataset.
9. Pendulum
When you’re dealing with date and time and don’t want a complex code, you can use this package. It manages time zones automatically and is designed to be a drop-in replacement for DateTime. It is a cleaner and easier way to use API. It simplifies the issue of complex date manipulations involving timezones that are not handled correctly in native DateTime instances.
10. Matplotlib
Matplotlib is the foundation of every other visualization library. This can be used to create basic graphs like line plots, histograms, scatter plots, bar charts, and pie charts. Creating animated and interactive visualizations can be made easy using this library. It offers a great deal of flexibility about formatting and styling plots. It allows you to freely choose how to display labels, grids, legends, etc. However, to create complex and visually appealing plots, you’ll need to write quite a lot of code.
