extraction

Data Normalization

Data normalization standardises scraped values into a consistent format — stripping currency symbols, trimming whitespace, converting dates, and unifying units.

Raw scraped text is inconsistent: prices appear as `$1,299.00`, `1299`, `USD 1,299`, or `1.299,00` depending on locale. Dates appear as `June 25, 2026`, `2026-06-25`, `25/06/26`, or relative strings like `2 hours ago`. Normalization converts these varied representations into a canonical form — a float for the price, an ISO 8601 string for the date — so that records can be compared, sorted, and aggregated.

Normalization steps typically include: stripping whitespace and control characters, removing HTML entities, converting character encodings to UTF-8, parsing numbers by removing thousands separators and detecting locale-specific decimal notation, parsing dates with libraries like dateparser that handle multiple formats, and resolving relative URLs to absolute.

Normalizing data at the extract-transform boundary (rather than leaving it to downstream consumers) simplifies downstream logic and ensures that any anomaly — an unexpected format that the normaliser cannot parse — is surfaced immediately rather than silently producing wrong values.

Examples

import re
from dateparser import parse as parse_date

def normalize_price(raw: str) -> float:
    cleaned = re.sub(r"[^\d.,]", "", raw)
    # Handle European decimal comma
    if cleaned.count(",") == 1 and cleaned.count(".") == 0:
        cleaned = cleaned.replace(",", ".")
    return float(cleaned.replace(",", ""))

def normalize_date(raw: str) -> str:
    dt = parse_date(raw)
    return dt.date().isoformat() if dt else raw

Related Terms

Schema Validation
Schema validation checks that extracted data conforms to an expected structure and type constraints before it is written to a database or downstream system.
Data Pipeline
A data pipeline is an automated sequence of steps that ingests raw data from a source, transforms it, and delivers it to a destination such as a database, data warehouse, or analytics system.
ETL (Extract, Transform, Load)
ETL is a data integration pattern where raw data is Extracted from a source, Transformed into the desired format, and Loaded into a destination system.
Structured Data Extraction
Converting free-form HTML into typed JSON records using explicit schemas, producing clean structured output instead of raw markup.
Deduplication
Deduplication is the process of identifying and removing or merging duplicate records in a scraped dataset, ensuring each real-world entity appears exactly once.

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    Data Normalization — Web Scraping Glossary | AlterLab