How to Load and Run Great Expectations Tests from a JSON File in Python

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What will you learn?

In this tutorial, you will master the process of loading a Great Expectations test suite stored in a JSON file and executing the tests it contains. This skill is essential for validating data quality within your data pipelines or ETL processes.

Introduction to the Problem and Solution

When working with data pipelines or ETL processes, ensuring data quality is paramount. Great Expectations is a powerful Python library designed to help define, manage, and validate these expectations efficiently. In this comprehensive guide, we will delve into loading a set of tests specified in a JSON file using Great Expectations. By doing so, we can effectively evaluate our data pipeline’s performance against these defined expectations.

Code

# Import necessary libraries
import great_expectations as ge

# Load the expectations suite from a JSON file
suite = ge.read_expectation_suite('path/to/your/expectation_suite.json')

# Get your batch of data for testing (e.g., Pandas DataFrame)
batch = ...

# Run the validation using your loaded expectation suite on your batch of data
results = ge.validate(batch, suite)

# Display the summary of validation results 
print(results.stats_map)

# Copyright PHD

To successfully execute this code, ensure you have installed the great_expectations library. If not already installed, run pip install great_expectations.

Credits: PythonHelpDesk.com

Explanation

Great Expectations empowers users to define their data quality expectations through an Expectation Suite, which can be saved as a JSON file. By leveraging read_expectation_suite(), we can seamlessly load this suite into our Python script and access all predefined expectations.

Once armed with both our dataset (data batch) and expectation suite, we utilize the validate() method from Great Expectations API to compare actual values in our batch against expected values outlined in our expectation suite. The resulting object furnishes detailed insights into each expectation scrutinized during validation.

    1. How do I create an Expectation Suite in Great Expectations? In Great Expectations, kickstart an empty expectation suite by generating an initial template using commands like great_expectaions init.

    2. Can I customize my expectations beyond what’s shown here? Absolutely! Tailor custom expectations based on specific requirements by crafting custom classes or functions within your project.

    3. Is visualization available for validation results? Yes! Visualize results effortlessly using Data Docs feature provided by Great Expectations.

    4. Can I schedule validations at regular intervals? Certainly! Automate validations by setting up schedules utilizing tools like Apache Airflow or Prefect.

    5. How does Great Expectaions handle missing values during validation? Great Expectations offer configurable behavior options for managing missing or unexpected values during the validation process.

    6. Can I integrate other testing frameworks with Great Expecatitions? While primarily standalone, integration is feasible through custom scripting or APIs provided by other frameworks.

    7. Does GE support different datasources apart from Pandas DataFrames? Yes! GE supports various datasources including SQL databases like PostgreSQL or MySQL among others.

    8. What should I do if my validation fails due to incorrect configurations? Review your expectations configurations and make necessary adjustments before rerunning validations.

    9. Is community support available for troubleshooting issues related to GE validations? Yes! Engage with an active community forum where users exchange experiences & solutions related to implementing GE features effectively.

Conclusion

Validating data integrity plays a pivotal role in constructing robust pipelines. Tools like Great Expectations streamline the process of ensuring that our data aligns with predefined criteria consistently without manual intervention. By following this guide and exploring further functionalities offered by this library on PythonHelpDesk.com, individuals can elevate their proficiency in managing data quality efficiently through automated testing mechanisms integrated into modern workflows.

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