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.
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