# Most efficient way to map function over numpy array

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What is the most efficient way to map a function over a numpy array? The way I"ve been doing it in my current project is as follows:

``````import numpy as np

x = np.array([1, 2, 3, 4, 5])

# Obtain array of square of each element in x
squarer = lambda t: t ** 2
squares = np.array([squarer(xi) for xi in x])
``````

However, this seems like it is probably very inefficient, since I am using a list comprehension to construct the new array as a Python list before converting it back to a numpy array.

Can we do better?

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We hope this article has helped you to resolve the problem. Apart from Most efficient way to map function over numpy array, check other array Python module-related topics.

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Dmitry Krasiko

Singapore | 2023-01-31

Python functions is always a bit confusing 😭 Most efficient way to map function over numpy array is not the only problem I encountered. Will get back tomorrow with feedback

Cornwall Danburry

Texas | 2023-01-31

Thanks for explaining! I was stuck with Most efficient way to map function over numpy array for some hours, finally got it done 🤗. I am just not quite sure it is the best method

Schneider Gonzalez

New York | 2023-01-31

Thanks for explaining! I was stuck with Most efficient way to map function over numpy array for some hours, finally got it done 🤗. I am just not quite sure it is the best method

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