Python Machine Learning By Example - Second Edition

Missing values

Quite often we miss values for certain features. This could happen for various reasons. It can be inconvenient, expensive, or even impossible to always have a value. Maybe we weren't able to measure a certain quantity in the past because we didn't have the right equipment or just didn't know that the feature was relevant. However, we're stuck with missing values from the past.

Sometimes, it's easy to figure out we're missing values and we can discover this just by scanning the data or counting the number of values we have for a feature and comparing to the number of values we expect based on the number of rows. Certain systems encode missing values with, for example, values such as 999,999 or -1. This makes sense if the valid ...