7 Must-Have Python Tools for ML Devs and Data Scientists
From building complex machine learning applications, to creating intuitive graphical user interfaces, Python is a popular programming language that has become the preferred choice for software developers and data scientists alike. Python’s data science capabilities continue to be explored, especially for advanced data analysis and the creation of deep learning products. Python outperforms rival programming languages, such as C++, in this regard.
Python has a relatively easy learning curve and is regarded as very beginner-friendly. However, there are also a range of tools that need to be mastered if you are to use Python to its full potential.
In this article, we will focus on seven of the best Python tools that all programmers should consider learning in 2023, to help streamline the coding process and simplify time-consuming tasks. The tools featured in this article range from integrated development environments (IDEs) such as PyCharm, to the browser testing tool Selenium.
What Can You Do With Python?
When developing with Python, it is important to compile a suite of tools that can make the process more efficient, helping you to focus on the core product without having to worry about some of the more tedious aspects of coding. This is why we have compiled a list of some of the most common project types created by Python programmers.
- Software development — Web, game, graphical-user interface (GUI) or command-line interface (CLI). Python does not have built-in mobile development capabilities.
- Data science and mathematics — Science computing, data analysis, visualization and web scraping.
- Machine learning — Improving speech recognition, for example.
- Develop embedded systems and robots — Systems can include Raspberry Pi, BBC micro:bit and MicroPython.
- Automate and speed up workflows — DevOps, development environment, database systems, software packaging and deployment, and software testing.
The Best Python Development Tools of 2023
Our list of seven Python development tools can help programmers maximize the potential of Python, helping them to develop complex and efficient products in less time and using less code.
1. PyCharm IDE
Created by JetBrains, PyCharm is regarded as the best IDE available for Python, allowing developers to code and install packages extremely quickly.
With PyCharm, developers can create efficient code with an autocomplete feature suggesting usable code to save a lot of time. It also highlights code syntaxes so they can be easily located, as well as flagging up any errors such as typos or missing code elements.
PyCharm is the most popular IDE for Python due to its extensive range of tools and features, making the process as hassle-free as possible.
2. Jupyter Notebook
Much loved by data scientists and machine learning engineers, Jupyter Notebook is another IDE that makes creating and executing code much easier. This is because Jupyter allows programmers to test just one cell of the code instead of the whole program, speeding up the process significantly.
Jupyter Notebook works within the browser, and titles and notes can be added to help contributors understand your code better, making it one of the best collaboration IDEs out there. In addition, your organized code and notes can all be exported as a PDF, or .ipynp if you are planning to create a 3D visual.
3. Pip Package
Knowing how to program in Python is just part of the puzzle. To become a Python specialist in regards to data science, data analysis and machine learning, you will need to install a range of additional packages. Pip Package is a great tool that allows you to install them easily.
Pip is used by the majority of Python developers who generally consider it a must-have tool. It can quickly and simply install any Python package with an easy search function.
Note: Update Pip regularly to ensure you have access to the latest packages and to make sure the tool works to its optimal ability.
4. Keras
Keras works as an API built into the machine learning library TensorFlow as well as other libraries such as CNTK and Theano, making it a powerful artificial intelligence (AI) tool. Keras effectively creates a comprehensive neural network that can help to recreate brain processes, meaning it can be used to develop a range of machine learning or AI products.
It is entirely open source and is backed by a strong community of contributors who have introduced new features to make the creation of these neural networks even easier. In some cases, networks can be created with a simple command-line system and stacking layers.
5. Selenium
Selenium is a tool that allows developers to test web applications across multiple browsers. Using Python, the programmer can design simple scripts or conduct automation testing across any browser of their choosing, also working alongside other programming languages such as Java, Ruby and C#.
Selenium supports platforms such as Windows, Mac iOS and Linux, and of course supports all the top browsers, including Chrome, Firefox and Safari.
6. Python Anywhere
If you want to test Python without downloading it onto your computer, then Python Anywhere allows you to host and run your code online.
This is great for beginners or anyone who is just curious about what Python can do, allowing you to create code within your web browser. The basic version of Python Anywhere allows developers to test code elements and check the results. However, if this is not enough, a premium version offers more processing power, which costs $5 a month.
7. Sphinx
Code hosting websites like GitHub provide extensive libraries of code that can be applied to Python, plus many developers create their own open source software that is available to anyone. For people to apply this code or execute open source applications, documentation is required to instruct the user on how to use them.
This is where Sphinx comes in. Sphinx is a tool that generates documentation for code and programs written in Python, working on the terminal or cmd for Windows. It does this by asking questions about who has developed the program, its name, what it does, its release date and more.
Once Sphinx has gathered enough information, it will then generate documentation that is available in a range of file formats. This includes HTML so it can be directly published on a web page, or PDF so it can be packaged with the code.
Python Development Tools to Learn in 2023
Python is an extremely popular programming language that can be used to create projects that range anywhere from artificial intelligence to automating workflows. To improve Python further and speed up development times, check out the above tools to automate and simplify a range of tasks.
For example, Pip helps users effortlessly install new packages, while Sphinx can generate high-quality documentation to be packaged with any open source code or programs. Making use of such tools can help developers tackle much more ambitious and innovative projects in 2023.
