One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
- IsNull() and dropna() will help to find the columns/rows with missing data and drop them
- Fillna() will replace the wrong values with a placeholder value
Wekesa Mark
as a researcher you will definitely know if that is missing or corrupted. Any data collected has to undergo thorough analysis to identify if something is missing. If indeed its missing get back to the drawing and find it. in case data is corrupted, then omit the corrupt part. that is why data has to be cleaned.
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