Python is a popular programming language that has become the language of choice for both software developers and data scientists, from building complex machine learning applications to creating intuitive graphical user interfaces.
Python is considered a very beginner-friendly language. However, there are also a number of tools that need to be mastered if you want to use Python to its full potential. Today, we have chosen to introduce some of the most important Python tools for ML developers and data scientists and here’s who they are according to The New Stack.
Top 5 Python Tools for Developers and Data Scientists
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.
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.
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.
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.
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#.