Python Resources, Packages, and Tools

Python resources

Python source code, binaries, and documentation are available at:

• Python’s official website.
• Python’s official documentation.

Program development in Python can be done in three ways:

• Using built-in IDLEs.
• Using third-party IDEs.
• Using online Python shells.

Third-party development tools and the links from where they can be downloaded are:

• The NetBeans IDE for Python.
• The PyCharm IDE for Python.
• The Visual Studio Code IDE.

If you don’t want to install any Python development tools on your machine, you can use any of the following online shells:

• The Python shell
• The Ideone shell
• The Replit shell

Third-party packages

Pythonistas in the Python community create packages (libraries) and make them available for other programmers. They use Python Package Index (PyPI) to distribute their packages. PyPI maintains the list of such third-party Python packages available.

There are third-party packages available for doing literally anything under the sun. Some packages that are popularly used for creating data science applications include:

• NumPy: An advanced mathematical operations library with support for large multidimensional arrays and matrices.
• SciPy: A scientific computing library for optimization, integration, interpolation, signal processing, image processing, etc.
• Pandas: A library for manipulating numerical tables and time series.
• MatPlotLib: A 2D and 3D Data visualization library.
• OpenCV: An open-source computer vision library.

You too can register at PyPI and upload your packages there. You should follow the guidelines to create the package, build it, and upload it to the Python Package Index.

To use a package available at PyPI, we need to download and install it first. The installation is done using a package manager utility called pip, which is installed when Python is installed.

More sophisticated tools

Many tools have been built to help Python programmers build and document their data science and artificial intelligence applications. These include:

• Jupyter Notebook: It is a very flexible browser-based tool that lets us interactively work with Python (and many other languages). It lets us put our Python code, the output of the code, and any kind of visualization or plot, etc., in the same document, called Notebook. It is a great tool for modular program development.
• Google Colab: This tool provides a free Jupyter Notebook environment to execute code on Google’s cloud servers. As a result, we can leverage the power of Google’s hardware.
• Spyder: This tool provides a scientific Python development environment with sophisticated testing and debugging features.

Both Jupyter and Spyder are part of a very popular software distribution called Anaconda. So once you download and install Anaconda, you get Jupyter and Spyder ready-made.