How do I deal with scraped data that has inconsistent formatting?

[Lana Sneferu]

Regex patterns can identify and correct common inconsistencies, such as different date formats or address styles, in the scraped data.

[Suhaila Kiyoshi]

Pandas is incredibly useful for normalizing scraped data by filling in missing values and aligning data types.

[Placidus Virgee]

I sometimes find it helpful to group similar data fields, such as phone numbers or names, for bulk formatting and error-checking.

[Goutam Victor]

Using custom validation functions to flag outliers ensures consistency, especially with data prone to user input variations.

[Rhouth Vilma]

If I expect certain formats, like currency or dates, I parse those fields specifically to convert them into standardized formats.

[Maksims Emmy]

Standardizing units (like kg vs. lbs) during the scraping process ensures that data can be analyzed consistently.

[Ratan Carol]

I add error logging to flag particularly messy fields for manual review, which saves time during data cleaning.