dataframe-expectations 0.1.1

Python library designed to validate Pandas and PySpark DataFrames using customizable, reusable expectations

🎯 DataFrameExpectations

DataFrameExpectations is a Python library designed to validate Pandas and PySpark DataFrames using customizable, reusable expectations. It simplifies testing in data pipelines and end-to-end workflows by providing a standardized framework for DataFrame validation.

Instead of using different validation approaches for DataFrames, this library provides a standardized solution for this use case. As a result, any contributions made here—such as adding new expectations—can be leveraged by all users of the library.

📚 View Documentation | 📋 List of Expectations

Installation:

pip install dataframe-expectations

Development setup

To set up the development environment:

# 1. Clone the repository
git clone https://github.com/getyourguide/dataframe-expectations.git
cd dataframe-expectations

# 2. Install UV package manager
pip install uv

# 3. Install development dependencies (this will automatically create a virtual environment)
uv sync --group dev

# 4. (Optional) To explicitly activate the virtual environment:
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 5. Run tests (this will run the tests in the virtual environment)
uv run pytest tests/ --cov=dataframe_expectations

Using the library

Pandas example:

from dataframe_expectations.expectations_suite import DataFameExpectationsSuite

suite = (
    DataFrameExpectationsSuite()
    .expect_value_greater_than("age", 18)
    .expect_value_less_than("age", 10)
)

# Create a Pandas DataFrame
import pandas as pd
test_pandas_df = pd.DataFrame({"age": [20, 15, 30], "name": ["Alice", "Bob", "Charlie"]})

suite.run(test_pandas_df)

PySpark example:

from dataframe_expectations.expectations_suite import DataFrameExpectationsSuite

suite = (
    DataFrameExpectationsSuite()
    .expect_value_greater_than("age", 18)
    .expect_value_less_than("age", 40)
)

# Create a PySpark DataFrame
test_spark_df = spark.createDataFrame(
    [
        {"name": "Alice", "age": 20},
        {"name": "Bob", "age": 15},
        {"name": "Charlie", "age": 30},
    ]
)

suite.run(test_spark_df)

Output:

========================== Running expectations suite ==========================
ExpectationValueGreaterThan ('age' greater than 18) ... FAIL
ExpectationValueLessThan ('age' less than 40) ... OK
============================ 1 success, 1 failures =============================

ExpectationSuiteFailure: (1/2) expectations failed.

================================================================================
List of violations:
--------------------------------------------------------------------------------
[Failed 1/1] ExpectationValueGreaterThan ('age' greater than 18): Found 1 row(s) where 'age' is not greater than 18.
Some examples of violations:
+-----+------+
| age | name |
+-----+------+
| 15  | Bob  |
+-----+------+
================================================================================

How to contribute?

Contributions are welcome! You can enhance the library by adding new expectations, refining existing ones, or improving the testing framework.

Versioning

This project follows Semantic Versioning (SemVer) and uses Release Please for automated version management.

Versions are automatically determined based on Conventional Commits:

• feat: - New feature → MINOR version bump (0.1.0 → 0.2.0)
• fix: - Bug fix → PATCH version bump (0.1.0 → 0.1.1)
• feat!: or BREAKING CHANGE: - Breaking change → MAJOR version bump (0.1.0 → 1.0.0)

Example commits:

git commit -m "feat: add new expectation for null values"
git commit -m "fix: correct validation logic in expect_value_greater_than"
git commit -m "feat!: remove deprecated API methods"

When changes are pushed to the main branch, Release Please automatically:

1. Creates or updates a Release PR with version bump and changelog
2. When merged, creates a GitHub Release and publishes to PyPI

No manual version updates needed - just use conventional commit messages!

Security

For security issues please contact [e-mail].

Legal

dataframe-expectations is licensed under the Apache License, Version 2.0. See LICENSE for the full text.