semantica/cookbook/introduction/04_Data_Normalization.ipynb

Data Normalization

Overview

This notebook demonstrates how to normalize and clean data using Semantica's normalization modules. You'll learn to normalize text, entities, dates, numbers, and handle encoding issues.

Documentation: API Reference

Learning Objectives

• Use TextNormalizer for text cleaning and normalization
• Use EntityNormalizer for entity name standardization
• Use DateNormalizer for date format normalization
• Use NumberNormalizer for number and quantity normalization
• Use DataCleaner for general data cleaning
• Use LanguageDetector and EncodingHandler for data quality

Installation

Install Semantica from PyPI:

pip install semantica
# Or with all optional dependencies:
pip install semantica[all]

---

Step 1: Text Normalization

Normalize text content for consistency.

In [ ]:

!pip install semantica

In [ ]:

from semantica.normalize import TextNormalizer

text_normalizer = TextNormalizer()

sample_text = "Hello   World!!!  This is a test."

normalized = text_normalizer.normalize_text(sample_text, case="lower")
cleaned = text_normalizer.clean_text(sample_text, remove_special_chars=False)

sample_text, normalized, cleaned

Step 2: Entity Normalization

Normalize entity names to canonical forms.

In [ ]:

from semantica.normalize import EntityNormalizer

entity_normalizer = EntityNormalizer()

entity_variants = ["Apple Inc.", "Apple Inc", "Apple", "Apple Incorporated"]

normalized_entities = []
for entity in entity_variants:
    normalized = entity_normalizer.normalize_entity(entity, entity_type="Organization")
    normalized_entities.append(normalized)
    print(f"{entity} -> {normalized}")

Step 3: Date Normalization

Normalize dates to standard formats.

In [ ]:

from semantica.normalize import DateNormalizer

date_normalizer = DateNormalizer()

date_formats = ["2023-12-25", "12/25/2023", "December 25, 2023", "25 Dec 2023"]

for date_str in date_formats:
    normalized = date_normalizer.normalize_date(date_str)
    print(f"{date_str} -> {normalized}")

Step 4: Number Normalization

Normalize numbers and quantities.

In [ ]:

import importlib
import semantica.normalize.number_normalizer
importlib.reload(semantica.normalize.number_normalizer)

from semantica.normalize import NumberNormalizer

number_normalizer = NumberNormalizer()

numbers = ["1,000", "1.5M", "$100", "50%", "3.14e2"]

for num_str in numbers:
    normalized = number_normalizer.normalize_number(num_str)
    print(f"{num_str} -> {normalized}")

Step 5: Data Cleaning

Clean data using DataCleaner.

In [ ]:

from semantica.normalize import DataCleaner

data_cleaner = DataCleaner()

data = [
    {"name": "Apple Inc.", "value": 100},
    {"name": "Apple Inc", "value": 100},
    {"name": "Microsoft", "value": 200}
]

cleaned_data = data_cleaner.clean_data(data, remove_duplicates=True)

print(f"Original records: {len(data)}")
print(f"Cleaned records: {len(cleaned_data)}")

Step 6: Language Detection and Encoding

Detect language and handle encoding.

In [ ]:

from semantica.normalize import LanguageDetector, EncodingHandler

language_detector = LanguageDetector()
encoding_handler = EncodingHandler()

text_samples = [
    "Hello, this is English text.",
    "Bonjour, ceci est du texte français.",
    "Hola, este es texto en español."
]

for text in text_samples:
    detected_lang = language_detector.detect(text)
    print(f"Text: {text[:30]}... -> Language: {detected_lang}")

sample_bytes = "Hello World".encode('utf-8')
detected_encoding = encoding_handler.detect(sample_bytes)
print(f"\nDetected encoding: {detected_encoding}")

Summary

You've learned how to normalize and clean data:

• TextNormalizer: Text cleaning and normalization
• EntityNormalizer: Entity name standardization
• DateNormalizer: Date format normalization
• NumberNormalizer: Number and quantity normalization
• DataCleaner: General data cleaning
• LanguageDetector: Language detection
• EncodingHandler: Encoding detection and conversion

Next: Learn how to extract entities in the Entity_Extraction notebook.