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Run Python in the Astro Cloud IDE

A Python cell contains a Python function that you can run in isolation or as a dependency in your pipeline. Create Python cells to execute Python as part of your data pipeline.


Create a Python cell

  1. In the Cloud UI, select a Workspace and then select Cloud IDE.

  2. Select a project.

  3. On the Pipelines page, click a pipeline name to open the pipeline editor.

  4. Click Add Cell > Python.

  5. Click the cell name and enter a name for the cell.

  6. Add your Python code to the cell body.

Run a Python cell

Click Run to run a Python cell in the Cloud IDE and check that your Python code runs as expected.

After you run a cell, the Logs tab contains all logs generated by the cell run, including Airflow logs and Python errors. The Results tab appears if your cell runs successfully and contains the contents of your Python console.

When you run a cell, the Astro Cloud IDE sends a request to an isolated worker in the Astronomer-managed control plane. The worker executes your cell and returns the results to the Cloud IDE. Executing cells in the Cloud IDE is offered free of charge. For more information on execution, see Execution.

Create explicit dependencies for a Python cell

In a Python cell, click Dependencies and select a cell to make it an explicit upstream dependency of your Python cell. When you run your entire pipeline, the Python cell cannot begin running until the selected upstream cell finishes running.

To make a Python cell an upstream dependency for another cell, click Dependencies for the other cell and select the name of your Python cell.

Create data dependencies for a Python cell

You can use the output of other cells in your project within a Python function. You define these dependencies in Python, and the Cloud IDE automatically renders the dependencies in your project code and in the Pipeline view of your project.

Pass a value from one Python cell to another Python cell

Use the value of a Python cell's return statement in another Python cell by calling the name of the Python cell containing the return statement. Doing this automatically creates a dependency between the cells.

For example, consider two Python cells. One cell is named hello_world and includes the following code:

return "Hello, world!"

Another cell is named data_dependency and includes the following code:

my_string = hello_world
return my_string

The Pipeline view in the Cloud IDE shows the newly created dependency between these two cells.

New dependency graph

Pass a value from a SQL cell to a Python cell

Use the results of a SQL cell in your Python cell by calling the name of the SQL cell. The SQL cell must contain a SELECT statement.

The table created by the SELECT statement is automatically converted to pandas DataFrame and passed to the Python cell.

The following Python cell is dependent on a SQL cell named my_sql_cell.

df = my_sql_cell # my_sql_cell is a SQL cell which gets converted to a pandas DataFrame by default
df['col_a'] = df['col_a'] + 1
return df

View complete code for Python cells

To view your Python cell within the context of an Airflow DAG, click Code. The Airflow DAG includes your Python function as well as all of the code required to run it on Airflow.

All Python cells execute Python using aql.dataframe, which is a function available in the Astro SDK. See Astro SDK documentation.