---
id: chispa
version: "0.12.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# chispa — Pyspark test helper library

License: permissive · Maintenance: active · Downloads: 3.0M/mo

## What it is and what it does

Chispa is a testing library that simplifies writing unit tests by providing assertion methods like `assert_column_equality` and `assert_df_equality` that compare data structures while generating human-readable error messages that highlight mismatches. When a test fails, the output uses color-coding and formatted tables to show exactly which rows or values differ, making debugging much faster than reading raw error output.

The library supports flexible comparison modes: you can ignore row order, column order, specific columns, nullable schema properties, and NaN equality. It integrates with pytest and is designed as a development dependency. The package is actively maintained, supports Python 3.10, 3.11, and 3.12, and carries no known security vulnerabilities.

Use it for:

- Write unit tests with clear assertion failures and readable error output.
- Compare actual and expected data structures while ignoring row or column order.
- Debug data quality issues by viewing formatted side-by-side comparisons of mismatched rows.
- Test column-level transformations with descriptive failure messages.
- Validate schema changes while ignoring nullable property differences.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides assertion methods for testing PySpark code with descriptive error messages formatted for easy debugging.

Yes. Chispa is a lightweight, actively maintained testing utility with no security issues and low install friction. It directly addresses the pain point of making test failures readable and actionable. Install it as a development dependency if you write tests.

## Install

pip install chispa
uv add chispa
poetry add chispa

## Installing chispa

Before you install: Low friction install with a single runtime dependency (prettytable). Actively maintained with recent releases and no known vulnerabilities.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects.

Quickstart:

pip install chispa

from chispa.column_comparer import assert_column_equality

assert_column_equality(df, "actual_column", "expected_column")

Requires a working Spark environment; not declared as a runtime dependency but is the package's primary use case.

Verify before relying:

- Whether Spark is an implicit dependency or must be installed separately.
- Performance characteristics when comparing very large DataFrames.
- Full list of assertion methods beyond column and DataFrame equality.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pyspark dataframe testing, spark test assertions, pyspark unit testing, dataframe equality comparison, spark test helpers, pyspark debugging output, spark column comparison, testing, pytest

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