Handling Missing Data in Python

Parking garages like the one pictured above aim to have every space filled; empty spots means unused capacity, which translates to less output in the form of revenue and profit. Similarly, data scientists want their data to have every observation completely filled; missing elements in a dataset translates to less information extracted from the raw data which negatively impacts analytical potential.

Unfortunately, perfect data is rare, but there are several tools and techniques …

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