Useful tools for Python developers

Python courses for children – an opportunity to master the profession of a programmer from scratch.

Python has become one of the most popular programming languages in the last few years. According to StackOverflow’s 2022 Developer Survey, Python is the fourth most popular programming language. Its success is due to its ease of learning and large ecosystem.

In this article, we’ll look at some of the best tools available to Python developers that will make your workflow easier.

1. Helios – monitoring and troubleshooting platform

Helios is a developer platform that makes it easy to track the flow of your Python applications. It uses the OpenTelemetry context distribution framework to create end-to-end visibility into microservices, without backend functions, databases, or third-party APIs. You can try out the platform in sandbox mode or take advantage of the free version by registering here.

Helios features:

• Provides a single view of data flow throughout your application.
• Easily integrates with live logs, tests, error monitoring, etc.
• Provides distributed trace information in full context to assist in troubleshooting.
• Allows you to reproduce precise workflows such as lambda calls, HTTP requests, Kafka, and RabbitMQ messages.
• Supports many programming languages, including Python, JavaScript, Node.js, Java, Ruby, .NET, Go, C++, and Collector.

2. PyCharm – Integrated Development Environment (IDE)

PyCharm is a popular integrated development environment (IDE) for Python programming. Developed by JetBrains, it has advanced features such as smart code completion, syntax highlighting, and debugging tools. There is a free Community Edition and a paid Professional Edition, so you can choose according to your needs.

PyCharm features:

• Syntax highlighting, code completion, navigation, and refactoring.
• Built-in code review and analysis tools to identify errors and improve code quality.
• A customizer for quickly navigating code, setting breakpoints, and analyzing runtime behavior.
• Support for various testing frameworks such as unit test, pytest, and doctest.
• Integration with version control systems such as Git, Mercurial, and Subversion.

3. Pip Package – package management

Pip is a widely used package management system for Python that makes it easy to install, manage, and update packages and their dependencies. This system comes with an installation of Python version 3.4 and above, making it easily accessible to developers. With Pip, developers can search for packages using keywords, specify package versions and requirements, and manage virtual environments.
Python courses for children are convenient, interesting, and useful!

Features of Pip:

• Allows you to easily install Python packages from different sources.
• Automatically resolves and installs dependencies.
• Developers can specify specific package versions or version ranges.
• Supports the creation and management of isolated virtual environments.

4. Scikit-Learn, Keras, and Numpy – for machine learning

Scikit-Learn, Keras, and Numpy are three key libraries for data analysis and machine learning in Python.
Scikit-Learn: Provides a complete set of classification, regression, and clustering algorithms and tools.
Keras: Built on top of TensorFlow, Keras makes it easy to build and train deep learning models by providing easy-to-use API and pre-built neural network components.
NumPy: A fundamental computing library that offers efficient data structures and mathematical functions for working with large arrays and matrices.
Combined, these libraries create a robust ecosystem for data manipulation, machine learning, and deep learning in Python.

Features of Scikit-Learn:

• Wide range of machine learning algorithms and tools.
• Easy to use and efficient implementation of various classification, regression, clustering, and dimensionality reduction methods.
• Supports data preprocessing, feature selection, and model evaluation.

Features of Keras:

• User-friendly and intuitive interface for creating and training deep learning models.
• Support for ultra-precise and recurrent neural networks.
• Flexible backend running on top of TensorFlow, Theano, or CNTK.

NumPy Features:

• Efficient data structures for multidimensional arrays and matrices.
• Wide range of mathematical functions and array operations.
• Easy integration with other libraries and tools for data manipulation and analysis.

5. Selenium – automation and testing

Selenium is a popular open-source framework designed to automate web browsers. It provides a programming interface for interacting with web elements, simulating user actions, and performing tasks.
such as filling out forms, clicking buttons, and navigating web pages. Selenium supports different programming languages and browsers, making it ideal for online testing and scraping applications.

Selenium Features:

• Provides an API for interacting with site elements and performing actions such as clicks, text input, and form submissions.
• Has multiple ways to search for elements on a web page, including ID, class name, tag name, and CSS selector.
• Supports browser automation for testing and data extraction.
• Provides cross-browser compatibility to automate processes across different browsers.

Python courses for children are an introduction to the basic principles of writing, reading, and understanding code.

GoMother IT School offers courses in learning the Python programming language for children from 8 years old. We have an individual approach to each child, a convenient schedule, and the opportunity to study in the office and online.

We invite you to your first free lesson!