Install Great Expectations for Data Validation

Great Expectations is a Python library for data validation. It helps ensure data quality. This guide covers installation and basic usage.

What Is Great Expectations?

Great Expectations validates data pipelines. It checks for missing values, outliers, and schema changes. It's useful for ETL and ML workflows.

Like Prefect for workflows, it ensures reliability. But it focuses on data quality.

Prerequisites

Before installing, ensure you have:

• Python 3.7+
• pip installed
• A virtual environment (recommended)

Install Great Expectations

Use pip to install the package:

pip install great-expectations

This installs the core library. For database support, add extras:

pip install great-expectations[sqlalchemy]

Verify Installation

Check if installation worked:

import great_expectations as ge
print(ge.__version__)

0.15.50

Basic Usage Example

Here's a simple validation:

import pandas as pd
import great_expectations as ge

# Sample data
df = pd.DataFrame({
    'age': [25, 30, None, 40],
    'income': [50000, 60000, 70000, 80000]
})

# Create expectation suite
expectation_suite = ge.dataset.PandasDataset(df)

# Add expectations
expectation_suite.expect_column_values_to_not_be_null('age')
expectation_suite.expect_column_values_to_be_between('income', 40000, 100000)

# Validate
results = expectation_suite.validate()
print(results["success"])

False

The validation fails due to the null age value.

Advanced Configuration

Great Expectations supports:

• Data documentation
• Custom expectations
• Integration with databases

Initialize a project:

great_expectations init

This creates configuration files. They store validation rules.

Integration With Other Tools

Great Expectations works with:

• Pandas DataFrames
• SQL databases
• Spark DataFrames

For ML projects, combine it with PyCaret or Keras.

Common Issues

If you encounter errors:

• Check Python version
• Verify virtual environment
• Reinstall dependencies

Conclusion

Great Expectations ensures data quality in Python. It's easy to install and use. Follow this guide to validate your data pipelines.

For workflow automation, see Prefect installation.