Python Forum

Is NumPy is better than Python list?

Depends on the use case, use the right tool for the right job.

Hello,
Answering to your question-
NumPy is a Python package, which has made its place in the world of scientific computing. It can deal with large data sizes, and also has a powerful N-dimensional array object along with a set of advanced functions.

Yes, a NumPy array is better than a Python list. This is in the following ways:

• It is more compact.
  
• It is more convenient.
  
• It Is more efficiently.
  
• It is easier to read and write items with NumPy.
  

(May-06-2019, 08:44 AM)dukoolsharma Wrote: [ -> ]Yes, a NumPy array is better than a Python list.

As per Yoriz, it certainly depends. Rarely is one thing entirely better than another in programming. Any additional complexity should be justified; if I saw in code review someone importing numpy when a regular Python list would be fine, I would call them out on it.

Sometimes numpy works slower than native python lists, e.g.
when appending the data to existing arrays

import numpy as np
x = np.array(list())
y = list()

%timeit -n 100000 np.append(x, '2') 

Output:
10.5 µs ± 1.1 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit -n 100000 y.append('2')

Output:
108 ns ± 4.11 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

The same behavior is true when working with big arrays.
Numpy is a good choice when you need to perform element-wise operations over arrays. Having a lot of
functions and nd-array-arithmetic operations implemented in C, numpy allows to avoid slow python loops when working with arrays (in general, it invokes loops implemented in C under the hood).