# Numpy – Find the Closest Value in the Array

The Numpy library in Python comes with a number of useful methods and features to help wrangle data when working with arrays. In this tutorial, we will look at how to get the closest value in a Numpy array to a given value with the help of some examples.

## How to get the closest value to a given value in a Numpy array?

You can use a combination of the Numpy’s `abs()` and the `argmin()` functions to find the closest value to a given value in a Numpy array.

Let’s say you want to find the closest value in the array `ar` to a given value `k`. You can use the following steps –

1. Find the absolute difference of each value in the array `ar` with the given value `k` using the `numpy.abs()` function.
2. The value in the array with the smallest absolute difference with `k` is the closest value you’re looking for. Use the `numpy.ndarray.argmin()` function to get the index of the value with the minimum absolute difference.
3. Use the index from step 2 to find the closest value in the array `ar`.

## Example

Let’s now look at a step-by-step example of using the above syntax.

First, we will create an input array `ar` and a value `k` that we will use throughout this tutorial.

```import numpy as np

# scaler value
k = 4.3
# create numpy array
ar = np.arange(1, 11)
# display the array
ar```

Output:

`array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])`

Here, we used the `numpy.arange()` function to create a 1d array of integers from 1 to 10. We also created a variable `k` that is set to 4.3 (an arbitrary value) for which we want to find the closest value in the array `ar`.

### Step 1 – Find the absolute difference with each value

First, we will compute the absolute difference of each value in the array with our given value `k`. For this, we will use the `numpy.abs()` function.

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```# get absolute diffence array
ar_diff = np.abs(ar - k)
# display the resulting array
ar_diff```

Output:

`array([3.3, 2.3, 1.3, 0.3, 0.7, 1.7, 2.7, 3.7, 4.7, 5.7])`

We get a Numpy array containing the absolute difference of each value in the above array with `k`.

### Step 2 – Get the index of the smallest absolute difference

Use the `numpy.ndarray.argmin()` function to get the index of the value with the smallest absolute difference. We’ll use this index in step 3.

```# find index of min value in ar_diff
index = ar_diff.argmin()
# display the resulting index
index```

Output:

`3`

We get the index of the smallest absolute difference.

### Step 3 – Find the closest value in the array using the index from step 2

The index of step 2 represents the index of the value having the smallest absolute difference with `k`. We can use this index to identify the value in the array `ar` that is closest to `k`.

```# closest value in ar to k
ar[index]```

Output:

`4`

We get the closest value to 4.3 in the array `ar` as 4.

## Summary

In this tutorial, we looked at the steps to find the closest value in a Numpy array to a given value. The steps (and code) mentioned in this tutorial can be summarized by the following block of code.

```# index of value closest to k in ar
index = np.abs(ar - k).argmin()
# closest value
closest_val = ar[index]
print(closest_val)```

Output:

`4`

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## Author

• Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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