Access the Jupyter Notebook

When the container is running, execute this statement: docker logs jupyter

This will show something like:

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=f89b02dd78479d52470b3c3a797408b20cc5a11e067e94b8

The token is the value behind /?token=. You need that for logging in.

Next, you can open the Jupyter Notebook at https://[[HOST_SUBDOMAIN]]-8888-[[KATACODA_HOST]].environments.katacoda.com/

Note: you need the value of the Jupyter Token to login to the environment.

The Notebook opens in a new browser window. You can create a new notebook or open a local one. Check out the local folder work for several sample notebooks. For example: open and run pythonForDataAnalysis.ipynb in the work folder. Or open and run Example_word_clouds.ipynb. Or open folder learn-pandas/lessons and start with Python_101.ipynb or Cookbook - Select.ipynb.

The folder work/Data-Analysis contains many notebooks created by Will Koehrsen, who writes many great articles about Data Science and uses Jupyter Notebooks frequently (see his GitHub Repo: https://github.com/WillKoehrsen/Data-Analysis ).

Interactive Widgets

A nice advanced feature in Jupyter Notebooks are the interactive widgets. To have a quick tour of what these widgets can add to a notebook, open work\widgets\Widgets-Overview.ipynb. The code cell under the Data heading contains an erroneous file reference - or it did when I last checked. Change the contents of the cell to:

df = pd.read_parquet('https://github.com/WillKoehrsen/Data-Analysis/blob/master/medium/data/medium_data_2019_01_26?raw=true')
df.head()

Now the cell will correctly retrieve and process the data.

This article on Medium introduced the interactive widgets demonstrated in this notebook: Interactive Controls for Jupyter Notebooks.

Adding interesting Jupyter Notebooks to the container

You can easily add more notebooks to the container, by cloning them from GitHub straight into the container and subsequently opening them in Jupyter Notebook.

For example - to grab the world's most trivial notebook:

docker exec -it jupyter bash -c 'cd ~/work && git clone https://github.com/Noura/hello-jupyter'

After executing this command, this notebook can be opened in the Jupyter Notebook browser window from the folder work/hello-jupyter.