Enhance data_expectations library with performance optimizations and developer experience improvements

[Copilot]

This PR implements comprehensive improvements to help the data_expectations library reach its full potential while maintaining complete backward compatibility with the existing interface.

Key Improvements

Performance & Memory Optimization

• Cached expectation lookups: Added @cache decorator to all_expectations() method to eliminate repeated dictionary creation and improve evaluation performance
• Optimized evaluation loops: Reduced redundant operations in record evaluation pipelines
• Enhanced streaming data support: Better memory management for processing large datasets

Enhanced Developer Experience

• Comprehensive type hints: Added proper type annotations throughout (Dict[str, Any], Optional[str], List[Expectation], etc.) for better IDE support and static analysis
• New utility methods:
  • list_available_expectations() - programmatically discover all available expectations
  • validate_configuration() - validate expectation sets before execution
  • __len__() and __iter__() - better object interaction capabilities

Improved Error Handling & Debugging

• Smart error messages: Enhanced ExpectationNotUnderstoodError with suggestions for similar expectation names when typos are detected
• Better error context: Improved ExpectationNotMetError with detailed context and truncated record display for readability
• Configuration validation: Early detection of invalid expectation configurations with specific error messages

Documentation & Code Quality

• Enhanced docstrings: Added comprehensive documentation with inline examples for key methods
• Consistent formatting: Applied Black code formatting across the entire codebase
• Parameter validation: Standardized input validation across all expectation methods

Examples of New Capabilities

Discover available expectations:

import data_expectations as de
expectations = de.Expectations.list_available_expectations()
print(f"Found {len(expectations)} available expectations")

Better error messages:

# Before: "Expectation not understood 'expect_column_to_be_awesome'"
# Now: "Expectation not understood: 'expect_column_to_be_awesome'
#       Did you mean one of: ['expect_column_to_exist', 'expect_column_values_to_be_of_type']"

Configuration validation:

expectations = de.Expectations(my_expectations)
errors = expectations.validate_configuration()
if errors:
    print(f"Configuration issues: {errors}")

Backward Compatibility

All existing functionality remains unchanged:

• ✅ All 43 existing tests pass without modification
• ✅ Same interface and method signatures
• ✅ Zero breaking changes to public API
• ✅ Maintains zero-dependency philosophy

The improvements enhance the library's robustness, performance, and developer experience while preserving its lightweight design and existing behavior.

Original prompt

I never felt like this library quite lived up to it's potential, can you review and suggest improvements - it is used so the interface can't change, but I think there's a lot of room for improvement for it to live up to it's potential

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