Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to round a numpy array** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to round a numpy array?

**How to round a numpy array?**the problem is not really a missing feature of NumPy, but rather that this sort of rounding is not a standard thing to do. You can make your own rounding function which achieves this like so:

**round a numpy array**the problem is not really a missing feature of NumPy, but rather that this sort of rounding is not a standard thing to do. You can make your own rounding function which achieves this like so:

## Method 1

Numpy provides two identical methods to do this. Either use

np.round(data, 2)

or

np.around(data, 2)

as they are equivalent.

Examples:

>>> import numpy as np >>> a = np.array([0.015, 0.235, 0.112]) >>> np.round(a, 2) array([0.02, 0.24, 0.11]) >>> np.around(a, 2) array([0.02, 0.24, 0.11]) >>> np.round(a, 1) array([0. , 0.2, 0.1])

## Method 2

If you want the output to be

array([1.6e-01, 9.9e-01, 3.6e-04])

the problem is not really a missing feature of NumPy, but rather that this sort of rounding is not a standard thing to do. You can make your own rounding function which achieves this like so:

def my_round(value, N): exponent = np.ceil(np.log10(value)) return 10**exponent*np.round(value*10**(-exponent), N)

For a general solution handling `0`

and negative values as well, you can do something like this:

def my_round(value, N): value = np.asarray(value).copy() zero_mask = (value == 0) value[zero_mask] = 1.0 sign_mask = (value < 0) value[sign_mask] *= -1 exponent = np.ceil(np.log10(value)) result = 10**exponent*np.round(value*10**(-exponent), N) result[sign_mask] *= -1 result[zero_mask] = 0.0 return result

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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