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Setting up Your Python Environment

There are a few ways you can set up your Python environment for the notebooks that are going to be used for the courses here.

The notebooks can be downloaded from each course page by clicking the tab on the top right corner and selecting ‘.ipynb’.

Databricks Community Edition

Databricks offers a Community Edition of their Data Science ecosystem for running experiments in notebooks. You can sign up for a free account and start running the notebooks for the course. The advantages of using Databricks over other Data Science ecosystems:

  • Integration with MLflow for experiment management

  • Integration with Git for version control of your notebooks

  • Integration with your AWS, GCP and Azure accounts if you so desire

  • Easy to setup a cluster for scaling your Data Science workflows

  • Collaborate and comment on notebooks with your team

  • Easily publish your notebooks for public consumption

Binder

Once you are on a course page that has a Jupyter Notebook, click on the tab located at the top right corner of the page and select ‘Binder’. This deploys the notebook onto a cloud environment. Binder automatically pulls the Docker image and creates a working Python environment for you without any user intervention. This is the easiest way to get started using the notebooks. However, any changes made in the notebooks will not be persistent. If persistence is desired, it is recommended to use the Databricks Community Edition.

Anaconda

The preferred Python distribution is Anaconda. Anaconda is a cross-platform distribution that has a fairly comprehensive set of packages. Anaconda comes with a package management system called conda to organize your environment.

Installing Anaconda

To install Anaconda, download the binary and follow the instructions.

You can use the conda command to create and manage your environments. Please refer here for a comprehensive list of commands for conda. For a quick reference of the essentials please visit here.

Jupyter Notebooks

Jupyter notebooks offer an integrated and intuitive enviroment for rapid prototyping, teaching and presenting Data Science and Scientific Computing applications written in Python, R and Julia (although there is growing suport for additional languages such as C++).

It uses a browser-based interface with the following capabilities (not comprehensive)

  • The ability to write and execute Python commands.

  • Formatted output in the browser, including tables, figures, animation, etc.

  • The option to mix in formatted text and mathematical expressions.

Figure %s shows the execution of some code (borrowed from here) in a Jupyter notebook

../../_images/jp_demo.png

Fig. 1 A Jupyter notebook viewed in the browser

Starting the Jupyter Notebook

Once you have installed Anaconda, you can start the Jupyter notebook.

Either

  • search for Jupyter in your applications menu, or

  • open up a terminal and type jupyter notebook

    • Windows users should substitute “Anaconda command prompt” for “terminal” in the previous line.

If you use the second option, you will see something like this

../../_images/starting_nb.png

The output tells us the notebook is running at http://localhost:8888/

  • localhost is the name of the local machine

  • 8888 refers to port number 8888 on your computer

Thus, the Jupyter kernel is listening for Python commands on port 8888 of our local machine.

Hopefully, your default browser has also opened up with a web page that looks something like this

../../_images/nb.png