What is data cleaning?

02 · See it in action

Follow the steps

1. Standardise obvious whitespace and spelling variants in category names.
2. Identify missing values separately from zero values.
3. Detect duplicates using a meaningful record identifier.
4. Define how to flag questionable records for review.

Worked example

Raw cities: [" Pune", "pune", "PUNE", "Mumbai"]
Normalised: ["pune", "pune", "pune", "mumbai"]
Record ID, not matching city alone, determines duplicates.

Read the example once, then explain each part aloud in your own words. If it includes Python, you can understand the idea first and run the code later in a local Python environment. Code is shown as text; this page does not execute it.

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