Use this quickstart to install GX, 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 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.
You can use this quickstart with the open source Python version of GX or with Great Expectations Cloud.
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 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.
Data validation workflow
The following diagram illustrates the end-to-end GX data validation workflow that you'll implement with this quickstart. Click a workflow step to view the related content.
- An installation of Python, version 3.8 to 3.11. To download and install Python, see Python downloads.
- An internet browser
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.
Run the following Python code to import the
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:
context = gx.get_context()
Connect to data
Run the following command to connect to existing
.csvdata stored in the
great_expectationsGitHub repository and create a ValidatorUsed to run an Expectation Suite against data. object:
validator = context.sources.pandas_default.read_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
.csvdata from the file at the specified URL path.
"passenger_count", min_value=1, max_value=6
The first ExpectationA verifiable assertion about data. uses domain knowledge (the
pickup_datetimeshouldn't be null).
The second ExpectationA verifiable assertion about data. uses explicit kwargs along with the
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.:
checkpoint = context.add_or_update_checkpoint(
Run the following command to return the Validation ResultsGenerated when data is Validated against an Expectation or Expectation Suite.:
checkpoint_result = checkpoint.run()
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.:
If you're ready to continue your GX journey, the following topics can help you implement a solution for your specific environment and business requirements: