v0.5.0

Features

Tag-based Filtering

Add support for selective expectation execution using custom tags.

Key Features:

• New TagMatchMode enum with ANY (OR logic) and ALL (AND logic) options
• Tag expectations with "key:value" format (e.g., "priority:high", "env:prod")
• Filter expectations at build time

Example:

# Tag expectations
suite = (
    DataFrameExpectationsSuite()
    .expect_value_greater_than(column_name="age", value=18, tags=["priority:high", "env:prod"])
    .expect_value_not_null(column_name="name", tags=["priority:high"])
    .expect_min_rows(min_rows=1, tags=["priority:low", "env:test"])
)

# Run only high-priority checks (OR logic)
runner = suite.build(tags=["priority:high"], tag_match_mode=TagMatchMode.ANY)

# Run production-critical checks (AND logic)
runner = suite.build(tags=["priority:high", "env:prod"], tag_match_mode=TagMatchMode.ALL)

Programmatic Result Inspection

Enhanced SuiteExecutionResult for detailed validation analysis.

Key Features:

• Use raise_on_failure=False to inspect results without raising exceptions
• Access comprehensive metrics: total_expectations, total_passed, total_failed, pass_rate, total_duration_seconds
• Inspect individual expectation results with status, violation counts, descriptions, and timing
• View applied tag filters in execution results

Example:

# Get results without raising exceptions
result = runner.run(df, raise_on_failure=False)

# Inspect the results programmatically
print(f"Total expectations: {result.total_expectations}")
print(f"Passed: {result.total_passed}, Failed: {result.total_failed}")
print(f"Pass rate: {result.pass_rate:.2%}")
print(f"Applied filters: {result.applied_filters}")
print(f"Tag match mode: {result.tag_match_mode}")

# Access individual expectation results
for exp_result in result.results:
    if exp_result.status == "failed":
        print(f"Failed: {exp_result.description}")
        print(f"Violation count: {exp_result.violation_count}")

Documentation

• Added tag-based filtering examples to README.md and getting_started.rst
• Updated adding_expectations.rst with proper tag handling patterns for custom expectations
• Documented programmatic result inspection with comprehensive examples
• Reorganized documentation structure: user guide in getting_started.rst, developer notes in adding_expectations.rst

Full Changelog: v0.4.0...v0.5.0

v0.4.0

0.4.0 (2025-11-10)

⚠ BREAKING CHANGES

• ‼️ BREAKING CHANGE: Major codebase restructuring with new module organization. However, most changes are made to the internal modules.

What changed:

• All internal modules have been reorganized into a core/ package
• Expectation registry simplified from three-dictionary to two-dictionary structure with O(1) lookups
• Main imports updated from expectations_suite to suite

Migration guide:
Update your imports to use the new module structure:

# Before
from dataframe_expectations.expectations_suite import DataFrameExpectationsSuite

# After
from dataframe_expectations.suite import DataFrameExpectationsSuite

Features

• restructure codebase with core/ module and explicit imports (42a233a)
• restructure codebase, and registry refactoring (111bca1)
• simplified registry (c182858)

Bug Fixes

• consolidate imports (9a76467)
• deleted duplicate dataclass and enums from registry (82bec0c)
• deleted duplicate DataFrameExpectation codefrom expectations package (d47eb8b)
• import enums from types (fa84764)
• manually trigger CI for release-please PRs (49419e6)
• manually trigger CI for release-please PRs (9585cf5)
• return corrent version when package is built (82ff343)

Documentation

• remove unused imports (276589d)

Full Changelog: v0.3.0...v0.4.0

v0.3.0

🎯 DataFrame Expectations v0.3.0

⚠️ Breaking Changes

This release introduces a builder pattern for the DataFrameExpectationsSuite that changes how you create and run expectation suites.

Migration Guide:

# Before (v0.2.0)
suite = DataFrameExpectationsSuite()
suite.expect_min_rows(min_rows=3)
suite.run(df)

# After (v0.3.0)
suite = DataFrameExpectationsSuite()
suite.expect_min_rows(min_rows=3)
runner = suite.build()  # New: Build a runner
runner.run(df)          # Run on the runner

✨ New Features

🏗️ Builder Pattern & Immutable Runners

• Introduces DataFrameExpectationsSuiteRunner - an immutable runner created via .build()
• Allows reusing the same validation logic across multiple DataFrames
• Enables building multiple independent runners from the same suite at different stages

🎨 Decorator Pattern for Automatic Validation

Validate DataFrames returned by functions automatically using the @runner.validate decorator:

@runner.validate
def load_data():
    return pd.DataFrame({"col": [1, 2, 3]})

# Supports optional DataFrame returns
@runner.validate(allow_none=True)
def maybe_load_data():
    if condition:
        return pd.DataFrame(...)
    return None

🔍 Expectation Inspection

• Added expectation_count property to check the number of expectations
• Added list_expectations() method to view all expectations in a runner

📚 Documentation

• Added Spark session initialization to PySpark examples in README and documentation
• Improved example code to be immediately runnable

🔧 Maintenance

• Updated release configuration for simpler tag generation
• Dependency updates: pytest 9.0.0, ruff 0.14.4, pre-commit 4.4.0

📦 What's Changed

• fix: update release please config to generate simple tags by @ryanseq-gyg in #13
• feat!: implement builder pattern for expectation suite runner by @ryanseq-gyg in #18
• build(deps): bump pre-commit from 4.3.0 to 4.4.0 by @dependabot in #17
• build(deps): bump ruff from 0.14.3 to 0.14.4 by @dependabot in #16
• build(deps): bump pytest from 8.4.2 to 9.0.0 in the 01_major-updates group by @dependabot in #15

Full Changelog: v0.2.0...v0.3.0

v0.2.0

This release introduces a major refactoring of the expectation registration system, replacing 800+ lines of boilerplate with dynamic method generation from a central registry. The refactoring maintains full IDE type-ahead support through auto-generated stub files while significantly improving maintainability.

Features

• Dynamic Expectation Registration: Implement dynamic method generation with centralized registry system
  • Replaces manual method definitions in DataFrameExpectationsSuite
  • Maintains IDE type hints through auto-generated .pyi stub files
  • Reduces boilerplate and improves maintainability

Bug Fixes

• Handle pandas DataFrame.map() compatibility for older versions
• Convert expectation category to str while generating stubs

Documentation

• Update documentation for new registration system
• Remove API reference button on expectation cards
• Update README with additional badges

Chores

• Add publishing and release workflows
• Pin action commit hashes and update PR template
• Update sanity checks script for dynamic expectation calls
• Update release-please to approved version

What's Changed

• fix: updated release-please hash to approve version by @ryanseq-gyg in #6
• fix: added more badges to readme by @ryanseq-gyg in #8
• Pin uv to commit by @ryanseq-gyg in #9
• Bump ruff from 0.14.2 to 0.14.3 by @dependabot in #10
• Refactor expectation registration by @ryanseq-gyg in #12
• chore(main): release dataframe-expectations 0.2.0 by @github-actions in #7

Full Changelog: v0.1.1...dataframe-expectations-v0.2.0

v0.1.1

What's Changed

• Initial commits by @ryanseq-gyg in #1
• [dependabutler] update .github/dependabot.yml by @gygsecrobot in #2
• Bump ruff from 0.14.1 to 0.14.2 by @dependabot[bot] in #3
• Updated runners in CI by @ryanseq-gyg in #4
• fix: added publishing and release workflows by @ryanseq-gyg in #5

New Contributors

• @ryanseq-gyg made their first contribution in #1
• @gygsecrobot made their first contribution in #2
• @dependabot[bot] made their first contribution in #3

Full Changelog: https://github.com/getyourguide/dataframe-expectations/commits/v0.1.1