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Version: 0.18.9

Quickstart

Use this quickstart to install GX OSS, connect to sample data, build your first Expectation, validate data, and review the validation results. This is a great place to start if you're new to GX OSS and aren't sure if it's the right solution for you or your organization. If you're using Databricks or SQL to store data, see Get Started with GX and Databricks or Get Started with GX and SQL.

Windows Support

Windows support for the open source Python version of GX OSS is currently unavailable. If you’re using GX OSS in a Windows environment, you might experience errors or performance issues.

Data validation workflow​

The following diagram illustrates the end-to-end GX OSS data validation workflow that you'll implement with this quickstart. Click a workflow step to view the related content.

Prerequisites​

  • An installation of Python, version 3.8 to 3.11. To download and install Python, see Python downloads.
  • pip
  • An internet browser

Install GX OSS​

  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.

  2. Run the following Python code to import the great_expectations module:

    Python
    import great_expectations as gx

Create a Data Context​

  • Run the following command to create a Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. object:

    Python
    context = gx.get_context()

Connect to data​

  • Run the following command to connect to existing .csv data stored in the great_expectations GitHub repository and create a ValidatorUsed to run an Expectation Suite against data. 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 ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. Data SourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. for Pandas to access the .csv data from the file at the specified URL path.

Create Expectations​

  • Run the following commands to create two ExpectationsA verifiable assertion about data. and save them to the Expectation SuiteA collection of verifiable assertions about data.:

    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 ExpectationA verifiable assertion about data. uses domain knowledge (the pickup_datetime shouldn't be null).

    The second ExpectationA verifiable assertion about data. uses explicit kwargs along with the passenger_count column.

    The basic workflow when creating an Expectation Suite is to populate it with Expectations that accurately describe the state of the associated data. Therefore, when an Expectation Suite is saved failed Expectations are not kept by default. However, the discard_failed_expectations parameter of save_expectation_suite(...) can be used to override this behavior if you have created Expectations that describe the ideal state of your data rather than its current state.

Validate data​

  1. Run the following command to define a CheckpointThe primary means for validating data in a production deployment of Great Expectations. and examine the data to determine if it matches the defined ExpectationsA verifiable assertion about data.:

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

  2. Run the following command to return the Validation ResultsGenerated when data is Validated against an Expectation or Expectation Suite.:

    Python
    checkpoint_result = checkpoint.run()

  3. Run the following command to view an HTML representation of the Validation ResultsGenerated when data is Validated against an Expectation or Expectation Suite. in the generated Data DocsHuman readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc.:

    Python
    context.view_validation_result(checkpoint_result)