# Null Values

Null values represent missing or undefined data within a dataset, signaling that a specific observation or entry is absent. In Python, this is typically represented by `None`, while libraries like pandas utilize `NaN` (Not a Number) to denote missing numeric information. Handling these values is a fundamental step in data cleaning, as they must be identified and addressed to ensure that statistical calculations and machine learning models perform accurately.

Visit the following resources to learn more:

- [@article@Python Null (None): Guide to Missing Values and NoneType](https://roadmap.sh/python/null#the-most-common-uses-of-none)
- [@article@Null in Python: Understanding Python's NoneType Object](https://realpython.com/null-in-python/)