In [2]:

#crosscompute

In [3]:

items = [1, 2, 3, 4, 5]

squared = []
for x in items:
    squared.append(x ** 2)

% timeit squared

29.4 ns ± 1.55 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [5]:

def sqr(x): return x ** 2
%timeit list(map(sqr, items))

2.91 µs ± 222 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [6]:

%timeit list(map((lambda x: x **2), items))

2.66 µs ± 54.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [7]:

def square():
    items = [1, 2, 3, 4, 5]
    squared = []
    for x in items:
        squared.append(x ** 2)

%timeit square()

2.33 µs ± 46 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
None

In [8]:

def sqr(x):
    return x ** 2

def square_map():
    return list(map(sqr, items))

%timeit square_map()

2.71 µs ± 122 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [9]:

def square_lambda():
    return list(map((lambda x: x **2), items))

%timeit square_lambda()

3.02 µs ± 149 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [18]:

import timeit

In [19]:

timeit.timeit(square) # for loop

Out[19]:

2.377551643992774

In [20]:

timeit.timeit(square_map) # map

Out[20]:

2.5558717439998873

In [21]:

timeit.timeit(square_lambda) # map with lambda

Out[21]:

2.781608500983566

In [ ]:

Map vs For Loop

Map vs For Loop¶

Pay Notebook Creator: Elaine Chan	0
Set Container: Numerical CPU with TINY Memory for 10 Minutes	0
Total	0