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What is Data Cleaning?

Last updated: Apr 5, 2025

Definition

Data cleaning (also called data cleansing or data scrubbing) is the process of identifying and correcting errors, inconsistencies, and quality issues in extracted data. Raw data from web scraping is rarely ready for direct use — it typically requires normalization, deduplication, type conversion, and validation before it can reliably feed into applications or analysis.

Common Data Quality Issues

Formatting Inconsistencies

The same data point may appear in different formats across sources: dates as "04/05/2025", "April 5, 2025", or "2025-04-05"; prices as "$79.99", "79.99 USD", or "7999 cents"; phone numbers with or without country codes.

Whitespace and Encoding

Extracted text often contains excess whitespace, non-breaking spaces, invisible Unicode characters, or HTML entities (&,  ). These must be normalized to produce clean output.

Missing Values

Not every page contains every field you are extracting. Handling missing data — through defaults, null values, or exclusion — must be consistent across your dataset.

Duplicates

Scraping overlapping pages or running incremental scrapes produces duplicate records that need identification and merging.

Type Mismatches

A price scraped as the string "79.99" needs conversion to a number. A date string needs parsing into a proper date object. Boolean values may appear as "Yes"/"No", "true"/"false", or "1"/"0".

Data Cleaning Techniques

  • Normalization — converting values to a consistent format
  • Deduplication — identifying and merging duplicate records
  • Validation — checking values against expected types, ranges, and patterns
  • Imputation — filling missing values with defaults or computed values
  • Trimming — removing excess whitespace and invisible characters

Data Cleaning in ScrapeGraphAI

ScrapeGraphAI reduces data cleaning overhead by producing structured, typed output from the extraction step itself. When you define a schema with specific field types, the AI extraction engine delivers data that already conforms to your expected format — prices as numbers, dates in consistent formats, and clean text without HTML artifacts.