10 Useful Tools for Python Developers
10 Useful Tools for Python Developers: Python is a widely used programming language in the software development industry due to its variety, ease of use, and accessibility. The language’s genuine worth is seen in the domains of mathematical computation, data science, machine learning and artificial intelligence, and IoT (Internet of Things), to name a few.
To get the most out of the language, developers should become acquainted with Python tools that will help them improve their SDLC (Software Development Life Cycle) skills.
This collection of tools will assist you in developing a more rounded Python coder in 2022 and beyond.
1. PyCharm IDE
PyCharm as an IDE provides simple auto-completion, hints, PEP8 checks, and other tools that improve code quality. Among other capabilities, you may rely on it for clever automatic code refactoring, testing support, and code inspections.
PyCharm includes support for several well-known frameworks, including Django, Flask, and web2py. The IDE has a few built-in database and developer tools for debugging and profiling, all of which interface easily with Visual Studio Code.
Its connections with the NumPy and Matplotlib libraries enhance its scientific computation capabilities. PyCharm’s broad feature set is completed with remote development, cross-technology interoperability, and integrated testing.
2. Jupyter Notebook
Jupyter Notebook includes all of the tools necessary for interactive development, documentation, and code execution. The Notebook has a console-based document authoring environment.
To begin, it integrates mathematics, rich media, computations, and written explanations into a browser-based web application component. Second, it can display the inputs and outputs of calculations, as well as rich-media augmented objects and other associated content.
This tool enables you to edit code by inspecting it in-browser and committing it with automatic syntax highlighting. Jupyter additionally allows you to customize indentation and tab completion. This tool’s code commentary functionality makes use of the Markdown markup language, which is not confined to plain-text.
With Jupyter’s LaTeX integration, you can insert mathematical notations directly into markdown cells and MathJax will render them natively.
Keras is a high-level neural network API package that serves as the foundation for Machine Learning and Deep Learning projects in Python. Keras enables you to extend the Python environment for artificial intelligence neural network programming with backends such as TensorFlow, Microsoft Cognitive Toolkit, PlaidML, and Theano, among others.
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Keras generates detailed and actionable error messages that let you to thoroughly troubleshoot your code. Given the framework’s ability to integrate with a variety of infrastructure configurations—whether a GPU cluster or an entire TPU pod—you can adapt it to any number of workflow use cases.
Integrating Keras can assist deep learning specialists in reducing their cognitive load associated with machine learning. The open-source library is extensively used due to its ease of use, versatility, and modular approach to programming.
4. Pip Package
Python’s Pip Package is the language’s default package management and is included with every installation. Pip enables you to use packages from other local or remote repositories, as long as they comply with Python Enhancement Proposal 503.
Pip maintains entire package lists along with their associated version numbers. It keeps track of your package management activities in a requirements file, which you can use to catalog the same products in a different desktop or virtual environment.
Pip continues to be a dependable, easily accessible package manager that is ideal for both novice and advanced package library administration. Nonetheless, many developers and teams rely on third-party package management solutions such as PyPL, despite the fact that Pip is included with Python 3 and later versions.
5. Python Anywhere
Python Anywhere has acquired a reputation for its dependability in managing end-to-end SDLC requirements. This PaaS (Platform as a Service) enables the development, execution, and hosting of Python applications on the web.
You can commit changes and deliver automated updates directly from your browser using the built-in Python NumPy, SciPy, Mechanize, BeautifulSoup, and PyCrypto libraries.
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Python Anywhere enables rapid deployment of your codebase on AWS EC2-based servers. Decentralized hosting facilitates remote study, learning, and development using Python.
Scikit-open-source Learn’s package is ready to help you achieve your Python-scripted machine learning goals. Scikit’s predictive analytic tools aid with object classification, continuous-valued attribute prediction and regression, support-vector machine clustering, gradient boosting, random forests, and automatic grouping of like items.
Scikit includes advanced machine learning capabilities such as dimensionality reduction and model selection for performing advanced machine learning analyses. It is based on the Matplotlib, NumPy, and SciPy libraries, all of which are well-suited for use in any custom machine learning scenario.
Sphinx is another Python documentation package that serves as a suitable replacement for Jupyter Notebook. It generates highly readable Python documentation in HTML, LaTeX, and ePub formats, among others.
Additionally, Sphinx can assist you in establishing automatic, language-specific indices and testing for code samples. Sphinx includes a number of third-party extensions in addition to its built-in docstrings libraries.
Selenium is the de facto standard for Python agile testing. The program enables you to test Python-based web applications manually, automatically, and cross-browser.
You can even convert automated testing to manual function tests using Selenium, depending on your requirements. You can create custom test scripts or cases for any piece of software.
Its extensive customization capabilities make it simple to integrate Selenium testing functionality into customized software packages.
The full Selenium testing suite is available as free source. Selenium can be used to test your Python application across several platforms if it runs on more than one. Selenium complies with the standards for Maven, Jenkins, and Docker testing.
9. Sublime Text
Sublime Text has been a feature-rich, lightweight integrated development environment (IDE) that has taken the development community by storm since its beginnings. Its extremely context-aware auto-completion and syntax definition engines assist you in swiftly honing your Python skills.
Sublime Text’s rich Python API documentation alleviates many novice concerns. The adaptive UI provides a highly beautiful programming interface that seamlessly adapts to the native color scheme of every platform.
While the latest version of Sublime Text supports the revised Python API, it retains backward compatibility with previous Python packages.
Sublime Text provides cross-platform Python compatibility for Raspberry Pi, Apple Silicon, and Linux Arm64. Sublime Text’s multi-tab selection and navigational properties make managing projects a breeze.
BeautifulSoup is a web scraping framework that is useful for obtaining, maintaining, and modifying data from Python web applications. The tool works in conjunction with any parser, allowing for the use of a broad variety of Python idioms to manipulate the parse tree.
Integrate your APIs with BeautifulSoup to scrape app and website data effortlessly. Additionally, utilize BeautifulSoup to extract data from Python websites in the form of CSV spreadsheets, HTML, XML, and JS files.
The Top Python Tools Every Programmer Should Know
If you’re a beginner or intermediate Python developer, you should be familiar with the numerous Python-compatible integrated development environments (IDEs) and which ones you can use to run your scripts quickly.
IDEs are not only text editors; they include time-saving tools, essential packages, and everything else you would need to sit down and code in a single graphical user interface.