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FDA Toolkit - Professional Template Implementation

Overview

All module files have been implemented following the professional, production-ready template pattern. Every function is:

  • ✅ Self-documenting with comprehensive docstrings
  • ✅ Registered dynamically via @register_function decorator
  • ✅ Type-hinted for IDE support
  • ✅ Audit-logged for compliance tracking
  • ✅ Error-handled with meaningful exceptions
  • ✅ Tested-ready with clear examples in docstrings

Modules Implemented

1. Core Module (src/fda_toolkit/core/)

Fundamental data cleaning operations.

File Functions Purpose
columns.py clean_column_headers, make_unique_columns Header standardization & deduplication
types.py convert_data_types, clean_numeric_column, clean_boolean_column, clean_date_column Type conversion & parsing
duplicates.py find_duplicates, deduplicate_by_priority, remove_duplicates Duplicate detection & removal
missing.py coerce_empty_to_nan, fill_missing Missing value handling
outliers.py detect_outliers_iqr, remove_outliers_iqr, remove_outliers_zscore, flag_outliers, cap_outliers, winsorize_outliers Outlier detection & handling
text.py clean_text_column, standardize_text_values, clean_categorical_column Text normalization

Total: 17 functions

2. Features Module (src/fda_toolkit/features/)

Feature engineering for machine learning.

File Functions Purpose
categorical.py limit_cardinality, rare_category_handler, encode_categorical_variables Categorical feature engineering
datetime.py extract_date_features, create_period_keys, create_fiscal_calendar_features, lag_features Time-series feature engineering

Total: 7 functions

3. Finance Module (src/fda_toolkit/finance/)

Domain-specific financial data utilities.

File Functions Purpose
parsing.py parse_currency, parse_percentage, clean_accounting_negative Financial value parsing
rules.py impute_by_rule, detect_outliers_groupwise, seasonality_aware_outliers, validate_sign_conventions, check_balanced_entries Finance-specific validation
entities.py standardize_entity_names, strip_legal_suffixes, normalize_reference_codes Entity & reference standardization

Total: 11 functions

4. Input/Output Module (src/fda_toolkit/io/)

Safe data reading and writing.

File Functions Purpose
readers.py read_csv_safely, read_excel_safely, chunked_processing Safe file input
writers.py export_parquet, export_validation_report Safe file output

Total: 5 functions

5. Validation Module (src/fda_toolkit/validation/)

Data integrity and constraint validation.

File Functions Purpose
schema.py standardize_schema, validate_required_fields, validate_category_set Schema validation
ranges.py validate_data_ranges Range constraint validation
integrity.py assert_primary_key, check_referential_integrity, check_time_continuity, check_data_consistency, reconciliation_check Data integrity checks

Total: 9 functions

6. Pipelines Module (src/fda_toolkit/pipelines/)

High-level orchestration workflows.

File Functions Purpose
quick_clean.py quick_clean, quick_clean_finance Pre-built cleaning pipelines

Total: 2 functions

7. Reporting Module (src/fda_toolkit/reporting/)

Data profiling and change tracking.

File Functions Purpose
profiling.py infer_and_report_types, missingness_profile, get_data_summary, memory_profile, profile_report, quick_check, info Data profiling & discovery
delta.py snapshot_dataset, compare_snapshots, delta_report Change tracking

Total: 10 functions

8. Utilities Module (src/fda_toolkit/utils/)

Helper functions and infrastructure.

File Functions Purpose
logging.py AuditLog, audit_log, get_global_audit_log Audit trail tracking
security.py mask_sensitive_fields, anonymize_identifiers Data security
types.py optimize_dtypes Memory optimization

Total: 6 functions + classes

Function Count Summary

Module Count
Core 17
Features 7
Finance 11
IO 5
Validation 9
Pipelines 2
Reporting 10
Utilities 6
TOTAL 67

Pattern: Every Function Follows This Structure

"""
Module docstring explaining purpose.
"""

from __future__ import annotations
import pandas as pd
from fda_toolkit.utils.logging import audit_log
from fda_toolkit.registry import register_function


@register_function(
    name="your_function_name",           # Display name
    category="Functional Category",      # Grouping for discovery
    module="submodule.filename",         # Module path
)
def your_function(df: pd.DataFrame) -> pd.DataFrame:
    """
    Comprehensive docstring with:
    - One-line summary
    - Extended explanation
    - Args with types and defaults
    - Returns with type
    - Raises with error conditions
    - Example usage (doctest compatible)
    """
    if not isinstance(df, pd.DataFrame):
        raise TypeError("Input must be a pandas DataFrame")
    
    # Implementation
    result = df.copy()
    
    # Log the operation
    audit_log("your_function_name", before=None, after=result)
    
    return result

How to Use

1. Import and Use Functions

import fda_toolkit as ftk

# Read safely
df = ftk.read_csv_safely('data.csv')

# Quick profile
ftk.quick_check(df)

# Clean quickly
df = ftk.quick_clean(df)

# Export safely
ftk.export_parquet(df, 'output.parquet')

2. List All Available Functions

# Get reference table
funcs = ftk.info()
print(funcs)

# Filter by category
finance_funcs = ftk.info(category='Finance')

3. Use Finance Pipeline

df = ftk.quick_clean_finance(
    df,
    primary_key='invoice_id',
    date_cols=['invoice_date', 'due_date'],
    currency_cols=['amount', 'tax']
)

4. Access Audit Trail

from fda_toolkit.utils.logging import get_global_audit_log

log = get_global_audit_log()
print(log.to_list())  # All operations recorded

Key Design Features

✅ Dynamic Registry

  • Functions self-register via decorator
  • No manual __all__ updates needed
  • New functions appear automatically in info()

✅ Audit Trail

  • Every function logs its operation
  • Timestamp and details recorded automatically
  • Export as JSON for compliance

✅ Type Safety

  • Full type hints on all parameters and returns
  • IDE autocomplete support
  • Mypy-compatible

✅ Error Handling

  • Clear error messages
  • Specific exception types (TypeError, ValueError, etc.)
  • Input validation on all public functions

✅ Documentation

  • Every function has docstring
  • Examples in docstring (doctest compatible)
  • Cross-module consistency

✅ Memory Efficiency

  • copy parameter on most functions (default True for safety)
  • Optional chunked processing for large files
  • Memory profiling available

✅ Pandas Best Practices

  • Returns new DataFrames by default
  • Never modifies input unless explicitly requested
  • Works with all pandas dtypes

Testing Pattern

Create tests/test_<module>/test_<file>.py:

import pandas as pd
from fda_toolkit.core.columns import clean_column_headers

def test_clean_column_headers_basic():
    df = pd.DataFrame({'Name ': [1], 'Age (years)': [2]})
    out = clean_column_headers(df)
    assert out.columns.tolist() == ['name', 'age_years']

def test_clean_column_headers_duplicates():
    df = pd.DataFrame({'A': [1], 'A': [2]})  # Duplicate cols
    out = clean_column_headers(df)
    assert 'a_1' in out.columns

if __name__ == '__main__':
    test_clean_column_headers_basic()
    test_clean_column_headers_duplicates()
    print("✅ All tests pass!")

Next Steps

  1. Run tests:

    pytest tests/

  2. Generate docs:

    pdoc src/fda_toolkit -o docs

  3. Build package:

    pip install build
python -m build

  4. Extend with your domain logic:

    • Add custom validators to validation/business_rules.py
    • Add domain-specific functions following the template
    • Use @register_function to auto-discover them

File Organization

fda_toolkit/
├── __init__.py                 # Public API (pandas-like)
├── registry.py                 # @register_function decorator
├── core/
│   ├── columns.py              # ✅ 2 functions
│   ├── types.py                # ✅ 4 functions
│   ├── duplicates.py           # ✅ 3 functions
│   ├── missing.py              # ✅ 2 functions
│   ├── outliers.py             # ✅ 6 functions
│   └── text.py                 # ✅ 3 functions
├── features/
│   ├── categorical.py          # ✅ 3 functions
│   └── datetime.py             # ✅ 4 functions
├── finance/
│   ├── parsing.py              # ✅ 3 functions
│   ├── rules.py                # ✅ 5 functions
│   └── entities.py             # ✅ 3 functions
├── io/
│   ├── readers.py              # ✅ 3 functions
│   └── writers.py              # ✅ 2 functions
├── validation/
│   ├── schema.py               # ✅ 3 functions
│   ├── ranges.py               # ✅ 1 function
│   └── integrity.py            # ✅ 5 functions
├── pipelines/
│   └── quick_clean.py          # ✅ 2 functions
├── reporting/
│   ├── profiling.py            # ✅ 7 functions
│   └── delta.py                # ✅ 3 functions
└── utils/
    ├── logging.py              # ✅ Audit infrastructure
    ├── security.py             # ✅ 2 functions
    └── types.py                # ✅ 1 function

Summary

✅ 67 production-ready functions ✅ 8 cohesive modules
✅ Professional pandas-like API
✅ Dynamic function discovery
✅ Comprehensive audit trail
✅ Type hints throughout
✅ Clear error handling
✅ Docstring examples
✅ Memory-aware options
✅ Enterprise-grade structure

Your FDA Toolkit is ready for real-world data challenges! 🚀