How to Check for NaN Values in Python?[With Examples]

Checking for NaN values is crucial in data analysis and scientific computing as NaN represents missing or undefined data. By identifying and handling NaN values, you can ensure the accuracy of your calculations, prevent errors, and make informed decisions based on reliable data.

No, NaN values are specific to floating-point data types in Python. For other data types like integers or strings, NaN values are not applicable. However, Python provides other mechanisms to represent missing or undefined data for non-floating-point data types.

NaN values represent missing or undefined data, while zero (0) is a specific numeric value indicating nothing or the absence of quantity. Empty values, on the other hand, refer to variables or objects that have not been assigned any value. Understanding these distinctions is essential for accurate data analysis and proper handling of missing or undefined data in Python.