10 Useful Tools for Python Developers
Python is revered in the software development industry for its versatility, usability, and ease of access. The true value of language shines in the areas of mathematical computation, data science, ML (machine learning) and AI, IoT (internet of things), and a few other miscellaneous applications.
To get the most out of the language, developers should familiarize themselves with Python tools to improve their SDLC (Software Development Life Cycle) skills.
This list of tools will help you become a more complete Python programmer in 2022 and beyond.
1. PyCharm IDE
PyCharm, as an IDE, offers intuitive auto-completion, hinting, PEP8 checks, and other code quality-enhancing features. You can also rely on it for smart automated code refactoring, testing assistance, and code inspections, among other features.
PyCharm supports some of the best known frameworks like Django, Flask, web2py, etc. The IDE has a few built-in databases and developer tools for debugging and profiling, each integrating seamlessly with Visual Studio Code for more functionality.
Its NumPy and Matplotlib library integrations extend its scientific computing capabilities. Remote development, interoperability between technologies, and PyCharm’s built-in test facilities complete the overall feature set of this IDE.
To download: PyCharmName
2. Jupyter Notebook
Jupyter Notebook has everything you’ll need for interactive development, documentation, and code execution. The Notebook is equipped with a console-based approach to document creation.
First, it provides you with a browser-based web application component that combines math, rich media, calculations, and textual explanations. Second, it can list inputs and outputs used in calculations, as well as rich media objects and other related content.
This feature helps you edit code with in-browser inspections and validate automatic syntax highlighting. You can also manage indentation and tab completion with Jupyter. This tool uses the Markdown markup language, which is not limited to plain text for its code commenting function.
With Jupyter’s LaTeX integration, you can insert math notations into markdown cells and render them natively with MathJax.
To download: Jupyter notebook
Keras is a high-level neural network API library at the head of Python ML and Deep Learning projects. Keras helps you extend your Python AI neural network programming ecosystem with back-ends like TensorFlow, Microsoft Cognitive Toolkit, PlaidML, Theano and many more.
Keras generates detailed and actionable error messages to allow you to troubleshoot your code in depth. You can adapt the deep learning framework for an unlimited number of workflow use cases, given its ability to integrate with various infrastructure setups, be it a GPU cluster or full TPU pod.
Integrating Keras can help deep learning specialists reduce their ML cognitive load. The open-source library is widely adopted for its user-friendliness, extensibility, and modular programming approach.
To download: Keras
4. Pip Package
Python’s Pip Package is a default package manager for Python and comes with every installation. Pip helps you take advantage of packages in other local or remote repositories, as long as they comply with the Python 503 enhancement proposal.
Pip maintains complete package lists with corresponding package version numbers. It saves your package management in a requirements to help you catalog the same packages for a different workstation or virtual environment.
Pip remains a trusty and readily available package manager suitable for managing package libraries for both beginners and advanced. However, many developers and teams rely on third-party package management solutions such as PyPL because Pip is available with Python 3 and later.
To download: Pip Package
5. Python Anywhere
Python Anywhere has earned its reputation for its reliability in supporting end-to-end SDLC needs. This PaaS (Platform as a Service) allows you to develop, run and host Python programs on the web.
With its built-in NumPy, SciPy, Mechanize, BeautifulSoup, and PyCrypto Python libraries, you can commit changes and deploy automated updates right from your browser.
Python Anywhere helps you quickly deploy your codebase to its AWS EC2-based servers. Decentralized hosting enables remote Python-based research, learning, and development.
To download: Python Anywhere
Scikit-Learn’s open source library is ready and waiting to help you achieve your scripted ML goals in Python. Scikit’s predictive analytics tools accelerate object classification, facilitate continuous-valued attribute prediction and regression, support vector machine clustering, gradient boosting, random forests, and automatic object clustering. similar objects.
Scikit has advanced ML tools like dimensionality reduction and model selection for advanced ML analysis. It is built on Matplotlib, NumPy and SciPy libraries, each of which is ideal for any bespoke ML context.
To download: Scikit-Learn
Sphinx is another Python documentation that can be considered as a suitable alternative to Jupyter Notebook. It generates highly articulated Python documentation in HTML, LaTeX, ePub and other formats.
Sphinx can additionally help you set up language-specific auto indexes and test code snippets. Sphinx comes equipped with several third-party extensions in addition to its built-in docstring libraries.
To download: Sphinx
Selenium is the go-to tool for agile testing in Python. The tool helps you perform manual, automated, and cross-browser testing for Python-based web applications.
With Selenium, you can even convert automated tests to manual functional tests, as needed. You can write custom, goal-oriented scripts or test cases for any software.
Its wide range of customization makes it easy to implement Selenium test functions into bespoke software builds.
The entire complete Selenium test suite is open-source. If your Python program runs on multiple platforms, you can use Selenium for cross-platform testing. Selenium meets Maven, Jenkins and Docker testing requirements.
To download: Selenium
9. Sublime Text
Since its inception, Sublime Text has been a lightweight, feature-rich IDE that has taken the developer community by storm. Its highly context-aware auto-completion feature and syntax definition engine help you hone your Python skills quickly.
Sublime Text makes many beginner’s problems trivial with its comprehensive Python API documentation. The adaptive user interface offers a very aesthetically pleasing programming interface that intuitively switches to the native color scheme of any platform.
The latest version of Sublime Text lets you take advantage of the updated Python API, but you can still take advantage of backwards compatibility with packages from older versions of Python.
If you are using Raspberry Pi, Apple Silicon, or Linux Arm64, you can use Sublime Text for cross-platform Python support. Managing projects in Sublime Text is effortless, with its multi-tab selection and navigation properties.
To download: sublime text
10. Beautiful Soup
BeautifulSoup is a web scraping framework that comes in handy for accessing, managing, or manipulating Python web application data. The tool works synergistically with any parser, supporting a wide range of Python idioms to operate on the parse tree.
Integrate your APIs with BeautifulSoup to effortlessly retrieve data from apps and websites. Additionally, use BeautifulSoup to get data in CSV spreadsheets, HTML, XML, and JS data from Python sites.
To download: BeautifulSoup
If you are a beginner or intermediate Python developer, you should know about the different Python compatible IDEs and which ones you can use to run your scripts efficiently.
IDEs are not just text editors; they’re equipped with time-saving tools, necessary packages, and everything else you might need to sit down and code under a single GUI.
Ditch the default Python editor and don one of these brilliant IDEs. Your code will thank you.
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