piicleaner 0.2.1

Fast PII detection and cleaning for text data with Polars integration

PIICleaner

A fast, Rust-powered Python library for detecting and cleaning Personal Identifiable Information (PII) from text data, with seamless Polars integration.

Features

• Fast PII Detection: Rust-based regex engine for high-performance text processing
• Case-Insensitive Matching: Detects PII regardless of case by default, with optional case-sensitive mode
• Multiple PII Types: Detects emails, phone numbers, postcodes, National Insurance numbers, addresses, and more
• Flexible Cleaning: Replace or redact detected PII with customizable strategies
• Polars Integration: Native support for cleaning DataFrames and Series
• Easy to Use: Simple API for both single strings and batch processing

Installation

# Using uv
uv add piicleaner
# With Polars support
uv add 'piicleaner[polars]'

# Using  pip
pip install piicleaner
# With Polars support
pip install 'piicleaner[polars]'

Platform Support

PIICleaner provides pre-built wheels for:

• Windows: x86_64 (Intel/AMD 64-bit)
• macOS: x86_64 (Intel) and arm64 (Apple Silicon)
• Linux: x86_64 (Intel/AMD 64-bit)

Note: Linux ARM64 (aarch) wheels are not currently provides. Users on ARM64 Linux systems (e.g. Raspberry Pi, AWS Graviton) will need to build from source. See Building from Source below.

Building from Source

For platforms without pre-built wheels you'll need:

• Rust toolchain (1.70 or newer), install from rustup.rs
• Python development headers

# Using uv
uv add piicleaner --no-binary piicleaner

# Using pip
pip install piicleaner --no-binary piicleaner

Quick Start

Basic Usage

from piicleaner import Cleaner

# Instantiate a cleaner
cleaner = Cleaner()

# Clean a single string (case-insensitive by default)
text = "Contact John at [e-mail] or call [phone]"
cleaned = cleaner.clean_pii(text, "redact")
print(cleaned)  # "Contact John at ---------------- or call ----------------"

# Detect PII locations
matches = cleaner.detect_pii(text)
print(matches)  
# [{'start': 16, 'end': 32, 'text': '[e-mail]'}, 
#  {'start': 41, 'end': 58, 'text': '[phone]'}]

# Case-sensitive detection
matches_sensitive = cleaner.detect_pii("nino: ab123456c", ignore_case=False)
print(matches_sensitive)  # [] - no match because NINO pattern expects uppercase

matches_insensitive = cleaner.detect_pii("nino: ab123456c", ignore_case=True)
print(matches_insensitive)  # [{'start': 6, 'end': 15, 'text': 'ab123456c'}]

Polars Integration

import polars as pl
from piicleaner import Cleaner

# Create DataFrame with PII
df = pl.DataFrame({
    "text": [
        "Email: [e-mail]",
        "NINO: AB123456C", 
        "Phone: [phone]"
    ],
    "id": [1, 2, 3]
})

cleaner = Cleaner()

# Clean PII in DataFrame
cleaned_df = cleaner.clean_dataframe(df, "text", "redact", "cleaned_text")
print(cleaned_df)

# Detect PII in DataFrame  
pii_df = cleaner.detect_dataframe(df, "text")
print(pii_df)

Specific PII Types

# Use specific cleaners
email_cleaner = Cleaner(cleaners=["email"])
phone_cleaner = Cleaner(cleaners=["telephone", "postcode"])

# Case-insensitive cleaning with specific cleaners
text = "EMAIL: [e-mail]"
cleaned = email_cleaner.clean_pii(text, "redact", ignore_case=True)
print(cleaned)  # "EMAIL: ----------------"

# See available cleaners
print(Cleaner.get_available_cleaners())
# ['address', 'case-id', 'cash-amount', 'email', 'nino', 'postcode', 'tag', 'telephone']

Supported PII Types

Type	Description	Example
email	Email addresses	[e-mail]
telephone	UK phone numbers	[phone]
postcode	UK postcodes	SW1A 1AA
nino	National Insurance numbers	AB123456C
address	Street addresses	123 High Street
cash-amount	Currency amounts	£1,500, $2000
case-id	Case/reference IDs	UUIDs, reference numbers
tag	HTML/XML tags	<script>, <div>

Cleaning Methods

• "redact": Redact the PII, replacing it with dashes (---------)
• "replace": Replace the string entirely if any PII is detected

Case Sensitivity

By default, PIICleaner performs case-insensitive matching to catch PII regardless of how it's formatted:

• ignore_case=True (default): Detects ab123456c, AB123456C, and Ab123456C as valid NINOs
• ignore_case=False: Only detects patterns matching the exact case defined in regex patterns

This ensures maximum PII detection while allowing precise control when needed.

API Reference

Cleaner Class

class Cleaner(cleaners="all")

Parameters:

• cleaners (str | list[str]): PII types to detect. Use "all" for all types or specify a list like ["email", "telephone"]

Methods:

• detect_pii(text, ignore_case=True): Detect PII and return match locations
• clean_pii(text, cleaning, ignore_case=True): Clean PII from text
• clean_list(text_list, cleaning, ignore_case=True): Clean list of strings
• clean_dataframe(df, column, cleaning, output_column): Clean Polars DataFrame
• detect_dataframe(df, column): Detect PII in Polars DataFrame
• get_available_cleaners(): Get list of available PII types

Performance

PIICleaner is built with Rust for maximum performance:

• Compiled regex patterns for fast matching
• Efficient string processing
• Minimal Python overhead
• Scales well with large datasets

Requirements

• Python ≥ 3.9
• Polars ≥ 1.0.0 (optional, for DataFrame support)

License

MIT License - see LICENSE file for details.

Contributing

Contributions welcome! Please see the GitHub repository for development setup and guidelines.