Question

I want to test the processing time between 2 identical lists, specifically for a normal list and a numpy list. My code is

import timeit
import numpy as np

t = timeit.Timer("range(1000)")
print t.timeit()

u = timeit.Timer("np.arange(1000)")
print u.timeit()

Calculation for t is fine, but for u NameError: global name 'np' is not defined is listed.

How should I code it to get the processing time?

Was it helpful?

Solution

The timeit.Timer class can be used in two different ways.

It can either take source code to be compiled an executed—in which case, the code is executed in a fresh environment where only the setup code has been run, or it can take a callable, in which case the callable is just called (in your current environment, like any other callable).

So, you have two options:

u = timeit.Timer("np.arange(1000)", setup='import numpy as np')

… or …

u = timeit.Timer(lambda: np.arange(1000))

In the first case, the fact that you happen to have done an import numpy as np is irrelevant; it has no effect on the environment in which np.arange(1000) is compiled and executed (and thus you must include it in the setup=... bit).

In the second case, the fact that you've done an import numpy as np obviously is relevant—it affects the environment in which your code, including the lambda: np.arange(1000), gets evaluated.

OTHER TIPS

Use setup parameter:

u = timeit.Timer("np.arange(1000)", setup='import numpy as np')

To use imported libraries with timeit you have to import them using the setup keyword argument (docs):

import timeit

# Doesn't require setup kwarg as range doesn't need to be imported.
t = timeit.Timer("range(1000)")
print t.timeit()

# Requires the import of numpy (as np) through the setup kwarg.
u = timeit.Timer("np.arange(1000)", setup = 'import numpy as np')
print(u.timeit())

The setup keyword argument allows you to setup your code, such as importing external libraries or importing functions from your code through the use of from __main__ import func.

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