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Version: 0.18.9

Connect to GX Cloud with Python

Learn how to use GX Cloud from a Python script or interpreter, such as a Jupyter Notebook. You'll install Great Expectations, configure your GX Cloud environment variables, connect to sample data, build your first Expectation, validate data, and review the validation results through Python code.

Get the most out of GX Cloud

To get the most out of GX Cloud, GX recommends deploying the GX Agent. If you do not deploy the GX Agent, some features and functionality might be unavailable. To deploy the GX Agent, see Deploy the GX Agent.

If you don't want to deploy the GX Agent, use the GX API to create a Data Source for your GX Cloud organization. See Connect to a Data Source.

Prerequisites​

  • You have internet access and download permissions.
  • You have a GX Cloud account.

Prepare your environment​

  1. Download and install Python. See Active Python Releases.

  2. Download and install pip. See the pip documentation.

Install GX​

  1. Run the following command in an empty base directory inside a Python virtual environment:

    Terminal input
    pip install great_expectations

    It can take several minutes for the installation to complete.

Get your user access token and organization ID​

You'll need your user access token and organization ID to set your environment variables. Don't commit your access tokens to your version control software.

  1. In GX Cloud, click Settings > Tokens.

  2. In the User access tokens pane, click Create user access token.

  3. In the Token name field, enter a name for the token that will help you quickly identify it.

  4. Click Create.

  5. Copy and then paste the user access token into a temporary file. The token can't be retrieved after you close the dialog.

  6. Click Close.

  7. Copy the value in the Organization ID field into the temporary file with your user access token and then save the file.

    GX recommends deleting the temporary file after you set the environment variables.

Set the GX Cloud Organization ID and user access token as environment variables​

Environment variables securely store your GX Cloud access credentials.

  1. Save your GX_CLOUD_ACCESS_TOKEN and GX_CLOUD_ORGANIZATION_ID as environment variables by entering export ENV_VAR_NAME=env_var_value in the terminal or adding the command to your ~/.bashrc or ~/.zshrc file. For example:

    Terminal input
    export GX_CLOUD_ACCESS_TOKEN=<user_access_token>
    export GX_CLOUD_ORGANIZATION_ID=<organization_id>
    note

    After you save your GX_CLOUD_ACCESS_TOKEN and GX_CLOUD_ORGANIZTION_ID, you can use Python scripts to access GX Cloud and complete other tasks. See the GX OSS guides.

  2. Optional. If you created a temporary file to record your user access token and Organization ID, delete it.

Create a Data Context​

  • Run the following Python code to create a Data Context object:

    Python
    context = gx.get_context()

    The Data Context will detect the previously set environment variables and connect to your GX Cloud account.

Connect to a Data Asset​

  • Run the following Python code to connect to existing .csv data stored in the great_expectations GitHub repository and create a Validator object:

    Python
    validator = context.sources.pandas_default.read_csv(
        "https://raw.githubusercontent.com/great-expectations/gx_tutorials/main/data/yellow_tripdata_sample_2019-01.csv"
    )

    The code example uses the default Data Source for Pandas to access the .csv data from the file at the specified URL path.

Create Expectations​

  • Run the following Python code to create two Expectations and save them to the Expectation Suite:

    Python
    validator.expect_column_values_to_not_be_null("pickup_datetime")
    validator.expect_column_values_to_be_between(
        "passenger_count", min_value=1, max_value=6
    )
    validator.save_expectation_suite(discard_failed_expectations=False)

    The first Expectation uses domain knowledge (the pickup_datetime shouldn't be null).

    The second Expectation uses explicit kwargs along with the passenger_count column.

Validate data​

  1. Run the following Python code to define a Checkpoint and examine the data to determine if it matches the defined Expectations:

    Python
    checkpoint = context.add_or_update_checkpoint(
        name="my_quickstart_checkpoint",
        validator=validator,
    )

  2. Use the following command to return the Validation Results:

    Python
    checkpoint_result = checkpoint.run()

  3. Run the following Python code to view an HTML representation of the Validation Results in the generated Data Docs:

    Python
    context.view_validation_result(checkpoint_result)