How to Check for NaN in Array

In this article, we will learn how to check for NaN values in Numpy arrays. NaN stands for “Not a Number” and is a special value that is used to represent missing or invalid data.

The `nan` and `isnan` Functions

There are two main functions that can be used to check for NaN values in an array:

* The `nan` function returns a NaN value.
* The `isnan` function returns a Boolean value that indicates whether the input value is NaN.

Example

The following code shows how to use the `nan` and `isnan` functions to check for NaN values in an array:

import numpy as np

my_array = np.array([1, 2, 4, np.nan])

is_nan = np.isnan(np.min(my_array))

print(f"Is there NaN element is my array? \n {is_nan}")

This code will print out a Boolean array, where each element indicates whether the corresponding element in the original array is NaN. In this case, the output array will be `[False, False, True, False]`.

The `np.where()` Function

The `np.where()` function can be used to find the indices of elements in an array that meet a certain condition. The condition in this case is that the element is NaN.

Example

The following code shows how to use the `np.isnan()` and `np.where()` functions to remove NaN values from an array:

import numpy as np

my_array = np.array([1, 2, np.nan, 4])

# Find the indices of NaN values in the array.
nan_indices = np.where(np.isnan(my_array))

# Remove the NaN values from the array.
new_array = my_array[~nan_indices]

print(new_array)

This code will print out the array `[1, 2, 4]`, which is the original array with the NaN values removed.

Conclusion

In this article, we learned how to check for NaN values in Numpy arrays. We learned about the `nan` and `isnan` functions and how to use them to check for NaN values in an array.

I hope this is what you are looking for. Let me know if you have any other questions.