How to Find the Closest Value to a Target in a NumPy Array?

Finding the Nearest Value in a Numpy Array

Identifying the value closest to a specified target in a numpy array is a common task in data analysis. To achieve this, we can leverage the find_nearest() function.

Implementation

The find_nearest() function takes an array and a target value as inputs. Below is a Python implementation:

import numpy as np

def find_nearest(array, value):
    array = np.asarray(array)
    idx = (np.abs(array - value)).argmin()
    return array[idx]

The function first converts the array to a numpy array if needed. It then calculates the absolute difference between each element of the array and the target value. The index of the element with the smallest absolute difference is determined using argmin(). Finally, the element at this index is returned as the nearest value.

Example Usage

For instance, consider an array of random numbers:

array = np.random.random(10)
print(array)

To find the nearest value to 0.5 in this array, we can use:

print(find_nearest(array, value=0.5))

This will output a value close to 0.5, such as 0.568743859261.

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