Are numpy arrays faster than lists? [closed]-CodePudding

I am new to using python and numpy arrays.While i was going through the notes online,saw a mention about how ndarray's are faster than lists..

"NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently."

So i tried using timeit on a map which sqr roots each element in the list

from timeit import timeit
timeit(stmt='import math;a_list = list(range(1,100000));list(map(math.sqrt,a_list))',number=1000)

8.955352897006378

and timeit on a map which sqr roots each element in a numpy

timeit(stmt='import numpy as np;import math;np_array=np.array(range(1,100000));list(map(math.sqrt,np_array))',number=1000)

24.691162549999717

doesn't make sense,lists seem faster

CodePudding user response：

introducing numpy in your code means introduce another kind of thinking at problems.

numpy in general are more expensive in the creation phase but blazing fast for vectorial computation.

I think this is more or less what you are searching for

timeit(stmt='list(map(math.sqrt,a_list))', setup='import math; a_list = list(range(1,100000))',number=1000)
#8.64

vs:

timeit(stmt='numpy.sqrt(a_list)', setup='import numpy; a_list = numpy.arange(100000)',number=1000)
#0.4

These are the results of timeit, only applied on the computation part. I mean, computing the sqrt and ignoring time to import the libraries.