Missing values and NANs are commonplace occurrences in a dataset and need to be taken care of before data can be put to any use. We will look into various sources of missing values and the different types, as well as how to handle them in the upcoming sections.
Handling missing values
Sources of missing values
A missing value can enter a dataset because of or during the following processes.
Data extraction
This entails the data that's available but we missed during its extraction from a source. It deals with engineering tasks such as the following:
- Scraping from...
