Handling missing values
As the name suggests, missing values refer to the absence of data. Such absences are usually represented by tokens, which may or may not be implemented in a standard way.
Although using tokens is standard, the way those tokens are displayed may vary across different platforms. For example, relational databases represent missing data with NULL, core Python code will use None, and some Python libraries will represent missing numbers as (Not a Number (NaN).
Important note
For numerical fields, don't replace those standard missing tokens with zeros. By default, zero is not a missing value, but another number. I said "by default" because, in data science, we may face some data quality issues, which we will cover next.
However, in real business scenarios, you may or may not find those standard tokens. For example, a software engineering team might have designed the system to automatically fill missing data with specific tokens, such as ...
