fda-toolkit 0.2.3

Financial Data Analyst utility toolkit for data cleaning, validation, profiling, and pipelines.

📊 FDA Toolkit

Financial Data Analysis Made Simple — A production-grade Python toolkit for loading, cleaning, validating, and analyzing financial data with one-line pipelines.

Why FDA Toolkit?

Financial data analysis is messy. You spend 80% of your time cleaning, validating, and transforming data instead of analyzing it. FDA Toolkit eliminates that pain by providing:

• 67 production-ready functions grouped into 8 intelligent modules
• One-line pipelines for common workflows (e.g., ftk.quick_clean_finance())
• Finance-aware validation — understand sign conventions, entity names, currency formats
• Audit trail — every operation logged for compliance and debugging
• Type-safe — full type hints and IDE autocomplete throughout
• Memory efficient — optimize dtypes, handle large files with chunking
• Professional API — pandas-like, intuitive, well-documented

Module Overview

Module	Functions	Purpose
core	17	Column cleaning, types, duplicates, missing, outliers, text
features	7	Date & categorical feature engineering
finance	11	Currency parsing, entity standardization, financial validation
validation	9	Schema, ranges, integrity, reconciliation
reporting	10	Profiling, snapshots, delta reports, quick checks
io	5	Safe CSV/Excel reading, chunked processing, parquet export
pipelines	2	Pre-built quick_clean() and quick_clean_finance()
utils	6	Logging, security, memory optimization
TOTAL	67	Production-ready functions

Quick Start

Install

pip install -e .

Use in 3 Lines

import fda_toolkit as ftk

df = ftk.read_csv_safely("data/transactions.csv")
df_clean = ftk.quick_clean_finance(df, primary_key="transaction_id", 
                                   date_cols=["date"], currency_cols=["amount"])
ftk.quick_check(df_clean)  # Profile results

Discover All Functions

# See what's available
ftk.info()  # Browse by category

# Filter by domain
ftk.info(category="Finance")

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📚 What's Inside?

Core Data Cleaning (17 functions)

Handle the fundamentals with confidence:

from fda_toolkit.core import columns, duplicates, missing, outliers, text, types

df = columns.clean_column_headers(df)           # 'Name ' → 'name'
df = types.clean_numeric_column(df['amount'])   # '$1,234.56' → 1234.56
df = missing.fill_missing(df, strategy='mean')  # Handle NaN intelligently
df = duplicates.remove_duplicates(df, subset=['id'])
df = outliers.flag_outliers(df, 'amount')       # Flag statistical outliers

Finance-Specific (11 functions)

Domain expertise built-in:

from fda_toolkit.finance import parsing, entities, rules

df['amount'] = parsing.parse_currency(df['amount'])        # Handle $, €, £
df['vendor'] = entities.strip_legal_suffixes(df['vendor']) # ACME Ltd → ACME
rules.validate_sign_conventions(df, rules_config)          # Verify debit/credit

Feature Engineering (7 functions)

Prepare data for ML in seconds:

from fda_toolkit.features import datetime, categorical

df = datetime.extract_date_features(df, 'date')  # Add year, month, quarter
df['category'] = categorical.limit_cardinality(df['category'], top_n=10)

Validation Suite (9 functions)

Catch issues before they become problems:

from fda_toolkit.validation import schema, ranges, integrity

schema.validate_required_fields(df, ['id', 'date', 'amount'])
violations = ranges.validate_data_ranges(df, {'amount': (0, 1_000_000)})
integrity.reconciliation_check(original_df, clean_df, value_cols=['amount'])

Smart Pipelines (2 functions)

Pre-built, battle-tested workflows:

# Generic pipeline
df_clean = ftk.quick_clean(df)

# Finance pipeline (smart defaults for financial data)
df_clean = ftk.quick_clean_finance(
    df,
    primary_key="invoice_id",
    date_cols=["invoice_date", "due_date"],
    currency_cols=["amount", "tax"]
)

Reporting & Profiling (10 functions)

Understand your data instantly:

# Quick diagnosis
ftk.quick_check(df)

# Detailed profile
profile = ftk.profile_report(df)  # Types, missingness, memory, outliers

# Track changes
snapshot_v1 = ftk.snapshot_dataset(df_before, name="before_clean")
snapshot_v2 = ftk.snapshot_dataset(df_after, name="after_clean")
delta = ftk.compare_snapshots(snapshot_v1, snapshot_v2)

Secure I/O (5 functions)

Read and write without surprises:

# Safe reading with encoding detection
df = ftk.read_csv_safely("messy_file.csv")
df = ftk.read_excel_safely("workbook.xlsx", sheet_name="Data")

# Process huge files in chunks
for chunk in ftk.chunked_processing("huge_file.csv", chunksize=50_000):
    process(chunk)

# Export in optimized formats
ftk.export_parquet(df, "output.parquet")  # Fast, compressed

Architecture: Dynamic & Scalable

Every function self-registers via decorator — no manual __all__ lists:

from fda_toolkit.registry import register_function

@register_function(
    name="detect_fraud",
    category="Validation",
    module="custom.fraud"
)
def detect_fraud(df: pd.DataFrame) -> pd.DataFrame:
    """Your custom logic here."""
    result = df[df['amount'] > threshold]
    audit_log("detect_fraud", before=len(df), after=len(result))
    return result

# Automatically appears in ftk.info()!

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Audit Trail (Compliance Ready)

Every operation is logged automatically:

from fda_toolkit.utils.logging import get_global_audit_log

log = get_global_audit_log()

for event in log.events:
    print(f"✓ {event.name} at {event.timestamp_utc}")

# Export for compliance teams
audit_json = log.to_dict()  # JSON-ready

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💡 Real-World Example

import fda_toolkit as ftk

# 1. Load and diagnose
df = ftk.read_csv_safely("sales_transactions_2024.csv")
ftk.quick_check(df)
# → Reports: types, missing %, duplicates, outliers, memory usage

# 2. Clean for analysis
df_clean = ftk.quick_clean_finance(
    df,
    primary_key="transaction_id",
    date_cols=["date", "due_date"],
    currency_cols=["amount", "tax"]
)

# 3. Validate
from fda_toolkit.validation import integrity
integrity.reconciliation_check(
    original=df, 
    cleaned=df_clean,
    value_cols=["amount"],
    group_cols=["vendor_id"]
)

# 4. Engineer features for ML
df_ml = ftk.extract_date_features(df_clean, "date")
df_ml = ftk.limit_cardinality(df_ml, "vendor", top_n=20)

# 5. Export and log
ftk.export_parquet(df_ml, "ready_for_ml.parquet")
print("✅ Pipeline complete with full audit trail!")

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Testing

# Run all tests
pytest

# Run specific module
pytest tests/test_core/

# Verbose output
pytest -v

Example test:

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

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

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Installation & Development

From Source

# Clone or download
cd fda_toolkit_project

# Install in editable mode (dev)
pip install -e .

# With dev dependencies (if available)
pip install -e ".[dev]"

Requirements

• Python 3.9+
• pandas (data manipulation)
• numpy (numerical operations)

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Security & Compliance

• Audit logging — Every operation tracked with timestamps
• Data masking — mask_sensitive_fields() for PII protection
• Type safety — Full type hints prevent common errors
• Error handling — Clear, actionable error messages
• Memory optimization — Control data footprint

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📖 API Reference

Explore the full API:

ftk.info()                           # List all functions
ftk.info(category="Finance")         # Filter by domain
ftk.get_data_summary(df)            # Profile a dataset
ftk.profile_report(df)              # Detailed analysis

For detailed docs on each function:

from fda_toolkit.core.outliers import detect_outliers_iqr
help(detect_outliers_iqr)  # Full docstring with examples

See QUICK_REFERENCE.md for common patterns.

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🎯 Use Cases

✅ Financial Reporting — Prepare data for compliance audits
✅ ML Pipelines — Clean & engineer features for models
✅ Data Migration — Validate and transform during transfers
✅ Anomaly Detection — Flag outliers in transactions
✅ Time Series Analysis — Extract date features automatically
✅ Data Quality Monitoring — Profile and compare snapshots

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🚀 Next Steps

1. Explore functions: ftk.info()
2. Try examples: See examples/01_quick_check.py
3. Read docs: docs/function_reference.md
4. Run tests: pytest
5. Extend: Add your own functions using @register_function

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📝 License

MIT License — see LICENSE for details.

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🤝 Contributing

Found a bug? Have an idea? Open an issue or PR!

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Built for financial analysts who value time, accuracy, and peace of mind. 📊✨

FDA Toolkit: Where data cleaning stops being painful and starts being productive.