Python is highly regarded in the software development industry because it can be used in many ways and is easy to learn. The language really shines in mathematical computation, data science, machine learning (ML) and artificial intelligence (AI), the Internet of Things (IoT), and a few other areas.
Developers need to learn how to use Python tools to improve their SDLC (Software Development Life Cycle) skills so they can get the most out of the language.
This list of tools will help you become a better Python programmer between now and 2022.
What is an IDE?
A Python IDE has all the basic tools for making software with Python (Integrated Development Environment). It helps automate work and makes developers more productive and efficient.
An integrated development environment (IDE) for Python has a compiler, automation tools, a code editor, debugging tools, and many other features that make it easy to compile, run, write, and fix bugs in Python code.
A better Python development environment needs more features like backing up source code, debugging, automatically formatting code, and highlighting syntax. This article has already done the hard work of figuring out which IDE is the best for you, so you don’t have to worry about it anymore.
10 Best IDEs and Code Editors for Python
1. PyCharm IDE
This is one of the IDEs (Integrated Development Environments) that developers use the most. JetBrains made PyCharm, which has a lot of useful features, like an IDE for code completion and quick fixes, tools for debugging, testing, and profiling, and options for VCS, deployment, and remote development.
2. Jupyter Notebook
Jupyter Notebook has everything you will ever need for interactive development, documentation, and code execution. For creating documents, the Notebook comes with a console-based method.
First, it gives you a web application component that works in your browser and combines math, rich media, calculations, and text explanations. Second, it can list the inputs and outputs of calculations, as well as rich-media objects and other content that is related.
This feature lets you edit code with in-browser inspections and auto-highlighted syntax. Jupyter can also take care of indentation and tab completion. The code commentary feature of this tool is based on the Markdown markup language, which is not limited to plain text.
With Jupyter’s LaTeX integration, you can put mathematical symbols in markdown cells and have MathJax render them automatically.
Keras is a high-level API library for neural networks that leads ML and Deep Learning projects in Python. Keras lets you add back-ends like TensorFlow, Microsoft Cognitive Toolkit, PlaidML, Theano, and a lot more to your Python AI neural network programming ecosystem.
Keras gives you detailed, actionable error messages that let you look into your code in depth. You can use the deep learning framework for a wide range of workflows because it can work with different kinds of infrastructure, like a GPU cluster or a whole TPU pod.
Integrating Keras can help deep learning experts reduce the amount of ML work they have to do in their heads. People use the open-source library because it is easy to use, can be expanded, and is built in a modular way.
4. Pip Package
The Pip Package comes with every Python installation and is used to manage packages. You can use packages from other local or remote repositories with Pip, as long as they follow Python Enhancement Proposal 503.
Pip keeps track of full lists of packages and their version numbers. It keeps track of how you manage packages in a file called “requirements,” which you can use to find the same packages for another desktop or virtual environment.
Pip is still a reliable and easy-to-find package manager that can be used by both beginners and experts to manage package libraries. Even though Pip comes with Python 3 and later versions, many developers and teams still use third-party package management tools like PyPI.
5. Python Anywhere
Python Anywhere is known for being reliable and taking care of all SDLC needs from beginning to end. You can build, run, and host Python programs on the web using this PaaS (Platform as a Service).
With its built-in Python NumPy, SciPy, Mechanize, BeautifulSoup, and PyCrypto libraries, you can make changes and deploy automated updates right from your browser.
Python Anywhere helps you quickly deploy your codebase on its servers that run on AWS EC2. With decentralized hosting, Python-based research, learning, and development can be done remotely.
The open-source Scikit-Learn library is ready to help you with your Python-scripted ML goals. Scikit’s tools for predictive analysis speed up object classification and help with continuous-valued attribute prediction and regression, clustering of support-vector machines, gradient boosting, random forests, and automatic grouping of objects that are similar.
For advanced ML analysis, Scikit has tools like dimensionality reduction and model selection. It is built on the Matplotlib, NumPy, and SciPy libraries, each of which is great for any custom ML situation.
Sphinx is another Python documentation tool that could be used instead of Jupyter Notebook. It makes Python documentation in HTML, LaTeX, ePub, and other formats that are very well written.
Sphinx can also help you set up automatic indexes and tests for code snippets that are based on the language they are written in. In addition to its built-in docstrings libraries, Sphinx comes with a number of third-party extensions.
For agile testing in Python, Selenium is the tool to use. The tool lets you test Python-based web apps manually, automatically, and in different browsers.
You can even turn automated tests into manual function tests with Selenium, depending on what you need. You can make test scripts or cases for any software that is tailored to your needs.
Because it can be changed in so many ways, Selenium testing functions are easy to add to custom software builds.
The whole Selenium testing suite is free to use and modify. Selenium can be used for cross-platform testing if your Python program runs on more than one platform. Selenium meets Maven, Jenkins, and Docker testing requirements.
9. Sublime Text
Since the beginning, Sublime Text has been a lightweight IDE with a lot of features that has taken the development world by storm. Its auto-completion feature and syntax definition engine help you learn Python quickly. The auto-completion feature is very aware of the context.
With its extensive Python API documentation, Sublime Text solves many of the problems that beginners have. The adaptive UI has a programming interface that looks great and automatically changes to the native color scheme of any platform.
You can use the updated Python API with the latest version of Sublime Text, but you can still use packages for older versions of Python.
For Python support on Raspberry Pi, Apple Silicon, or Linux Arm64, you can use Sublime Text. With its multi-tab selection and navigation features, Sublime Text makes it easy to manage projects.
BeautifulSoup is a web scraping framework that helps you get to, manage, or change the data from a Python web app. The tool works well with any parser and lets you use a wide variety of Python idioms to do things with the parse tree.
Integrating your APIs with BeautifulSoup makes it easy to scrape data from your app or website. You can also use BeautifulSoup to get data from Python sites in CSV spreadsheets, HTML, XML, and JS.
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