Cleaning DataLesson 12Free preview
Cleaning Wrong Data
This lesson is part of Pandas · All levels
Summary
The wrong data can be values beyond the reasonable range, such as negative age or extreme values that are not possible. To deal with it, you need to find a suspicious line and decide to delete or fix it. NumPy modules help to mark a certain value so it can be easily filtered.
Key points
- Wrong data is a value that is unreasonable or out of range
- The value location can be marked and then filtered to check
- Action options: delete the line, change the value, or leave it with a note
- Keeping records of changes is essential for transparency
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