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Version: 0.16.16

Great Expectations Quickstart

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

Great Expectations Cloud

This quickstart introduces you to the open source Python version of GX. A Cloud interface will soon be available to simplify collaboration between data teams and domain experts.

If you're interested in participating in the Great Expectations Cloud Beta program, or you want to receive progress updates, sign up for the Beta program.

Windows Support

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

Prerequisites​

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. Jupyter Notebook is included with Great Expectations, and it lets you edit code and view the results of code runs.

  2. Open Jupyter Notebook or Terminal and then run the following command to import the great_expectations module: 

    import great_expectations as gx

Create a DataContext​

  • Run the following command to import the existing DataContext object: 

    context = gx.get_context()

Connect to Data​

  • Run the following command to connect to existing .csv data stored in the great_expectations GitHub repository: 

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

    The example code uses the default Data Context Datasource for Pandas to access the .csv data in the file at the specified path.

Create Expectations​

  • Run the following command to create two Expectations. The first Expectation uses domain knowledge (the pickup_datetime shouldn't be null), and the second Expectation uses auto=True to detect a range of values in the passenger_count column. 

    validator.expect_column_values_to_not_be_null("pickup_datetime")
    validator.expect_column_values_to_be_between("passenger_count", auto=True)

Validate data​

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

    checkpoint = gx.checkpoint.SimpleCheckpoint(
        name="version-0.16.16 my_quickstart_checkpoint",
        data_context=context,
        validator=validator,
    )

  2. Run the following command to return the Validation results: 

    checkpoint_result = checkpoint.run()

  3. Run the following command to view an HTML representation of the Validation results: 

    validation_result_identifier = checkpoint_result.list_validation_result_identifiers()[0]
    context.open_data_docs(resource_identifier=validation_result_identifier)

If you're ready to continue your Great Expectations journey, the following topics can help you implement a tailored solution for your specific environment and business requirements: