Jupyter Flow

To use data from an upstream data source when only 1 upstream data source is connected, use the #ayx_input tag, followed by the assignment of the relevant variable on a new line.

#ayx_input
input = pd.DataFrame(data)

To use data from 2 or more upstream data sources, use the #ayx_input= tag—including the name of the data connection in the tag—followed by the assignment of the relevant variables on new lines. If you're not sure what the name of a data connection is, you can select the connection itself on the canvas to view its name.

#ayx_input=#1
input_1 = pd.DataFrame(data_1)

#ayx_input=#2
input_2 = pd.DataFrame(data_2)

To pass 1 output downstream through the 1st output anchor, use the #ayx_output tag, followed by the assignment of the relevant variable on a new line.

#ayx_output
output = df['column_1']

To pass 2 or more outputs downstream using any of the 5 output anchors, use the #ayx_output= tag—including the number of the output anchors you want to use as the names—followed by the assignment of the relevant variables on new lines.

#ayx_output=1
output_1 = df['column_1']

#ayx_output=2
output_2 = df['column_2']

This option allows you to choose the location where the data cache related to a notebook is. If you select Auto, the tool creates a data cache in the default location. To create a data cache in a different location, select Custom, and enter the path to that location.

To preserve the data cache from the most recent run of your notebook, select this option. This option allows you to reference the data cache so you can debug the notebook.

If you have a zip app (PYZ file) that you want to use in your workflow, select this option. Remember—if you don't want to make changes to that zip app, you need to make sure to toggle off the switch next to Packages.