PYTHON PROGRAMMING
NaN means Not-a-Quantity. You should use it in numerical libraries — but additionally within the Python commonplace library.
NaN stands for Not-a-Quantity. Thus, a NaN object represents what this very identify conveys — one thing that isn’t a quantity. It may be a lacking worth but additionally a non-numerical worth in a numerical variable. As we shouldn’t use a non-numerical worth in purely numerical containers, we point out such a worth as not-a-number, NaN. In different phrases, we are able to say NaN represents a lacking numerical worth.
On this article, we’ll talk about NaN objects out there within the Python commonplace library.
NaN values happen ceaselessly in numerical information. In case you’re considering particulars of this worth, you will see them, as an illustration, right here:
On this article, we won’t talk about all the small print of NaN values.¹ As a substitute, we’ll talk about a number of examples of how one can work with NaN values in Python.
Every programming language has its personal strategy to NaN values. In programming languages centered on computation, NaN values are basic. For instance, in R, you’ve gotten NULL (a counterpart of Python’s None), NA (for not out there), and NaN (for not-a-number):
In Python, you’ve gotten None and various objects representing NaN. It’s value to know that Pandas differentiates between NaN and NaT, a worth representing lacking time. This text will talk about NaN values in the usual library; NaN (and NaT, for that matter) within the mainstream numerical Python frameworks — resembling NumPy and Pandas — will likely be coated in a future article.
In case you haven’t labored with numerical information in Python, it’s possible you’ll not have encountered NaN in any respect. Nevertheless, NaN values are ubiquitous in Python programming, so it’s necessary to…
