Initial commits

[ryanseq-gyg]

Initial Commits

This pull request sets up the initial structure for the dataframe-expectations repository. Key additions include:

• Sphinx Documentation Generator: Added to facilitate the creation of project documentation.
• Pre-commit Hooks: Implemented to ensure code quality and consistency before commits.
• GitHub Workflow: Configured to automate testing and other CI/CD processes.

These changes establish the foundational tools and processes for the repository, preparing it for further development and collaboration.

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[Copilot]

Pull Request Overview

This PR establishes the foundational infrastructure for the dataframe-expectations repository by adding comprehensive test coverage for the library's core functionality and setting up essential development tools.

Key changes:

• Comprehensive test suite covering expectations, result messages, and helper classes
• Sphinx documentation generator configuration
• Pre-commit hooks for code quality
• GitHub workflow for CI/CD automation

Reviewed Changes

Copilot reviewed 75 out of 84 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
tests/test_result_message.py	Tests for result message classes covering success/failure scenarios for pandas and PySpark
tests/test_expectations_suite.py	Tests for expectation suite functionality including success/failure cases and dataframe type handling
tests/expectations_implemented/template_test_expectation.py	Template test file defining standard test patterns for new expectations
tests/expectations_implemented/column_expectations/string_expectations/*.py	Comprehensive tests for string-based column expectations (starts_with, ends_with, contains, length validations)
tests/expectations_implemented/column_expectations/numerical_expectations/*.py	Tests for numerical column expectations (greater_than, less_than, between)
tests/expectations_implemented/column_expectations/any_value_expectations/*.py	Tests for generic value expectations (null checks, equality, set membership)
tests/expectations_implemented/aggregation_expectations/numerical_expectations/*.py	Tests for aggregated numerical expectations (mean, median, min, max, quantile)
tests/expectations_implemented/aggregation_expectations/any_value_expectations/*.py	Tests for row-level and percentage-based expectations (row counts, uniqueness, null percentages)
tests/expectations_helper_classes/*.py	Tests for utility functions and expectation registry mechanisms

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[alexbbo]

At this point look good enough to me (though I didn't really review the code -- there is too much)