You've enabled Jupyter, now what?

Choose your preferred host.

You can choose to run the Jupyter app on either a personal server or Heroku.

Choose an app (or create a new app) with Heroku.

You can choose to work within a previously created version of Jupyter Notebook or create a new instance.

1. Choose Heroku from the host options in the Choose a host panel.
2. Choose a previously created app from your list, or click Create New to create a new iteration of Jupyter.

• If creating a new app, you will need to create a password and enter any required Python packages.

Configure & use a personal server.

Using a personal server is an easy way to run applications, allowing you full control over setup and configuration.

1. Choose Personal Server from the host options in the Choose a host panel.
2. [Locally]: Create your server
   • Install
     Use pip (Python 2.7 or 3.4+)

     pip install dwcontents
   • Configure
     Create or update ~/.jupyter/jupyter_notebook_config.py with settings:

     import dwcontents
import os
c = get_config()
c.NotebookApp.contents_manager_class = dwcontents.DwContents
c.DwContents.dw_auth_token = 'YOUR_API_TOKEN'


     You can get your API token in your data.world advanced settings.
   • Run
     Assuming jupyter and jupyterlab (optional) have already been installed, start as normal, for example:

     $ jupyter lab
3. [On data.world]: Click Continue, enter your server's URL, and open your notebook.
   
   If you'd like to use the same server each time you open Jupyter, remember to click the optional checkbox.

Use the Jupyter workspace.

Use Jupyter to create new notebooks or open & edit previous notebooks.

1. Create a new notebook or open any previous notebook (.ipynb files).

2. Start writing code!

What next?

Here are a few things you can do with Jupyter and data.world:

• Load data directly from data.world into your notebook with project ID using the import datadotworld function and var = dw.load_dataset('project-id')
  Example:

  import datadotworld as dw
lds = dw.load_dataset('garyhoov/us-food-imports-and-exports')
• Save tables locally as CSVs, using table_name.to_csv('csvname.csv', index=True), which will allow you to upload directly to data.world through Jupyter.
  Example:

  foodspivot.to_csv('foods.csv', index=True)
• Upload CSVs directly to your data.world project, all within your notebook file, using the data.world API. make sure you've imported data.world using import datadotworld
  Example:

  import datadotworld as dw
client = dw.api_client()
client.upload_files('sarakbarr/test-projects', 'foods.csv')
client.upload_files('sarakbarr/test-projects', 'foods_chart.png')
• Share documents containing live code, text, visualizations, and more.
• Manipulate data and use statistical modeling.