7.6 List vs. array Performance: Introducing %timeit

  • Most array operations execute significantly faster than corresponding list operations
  • IPython %timeit magic command times the average duration of operations

Timing the Creation of a List Containing Results of 6,000,000 Die Rolls

In [1]:
import random
In [2]:
%timeit rolls_list = \
   [random.randrange(1, 7) for i in range(0, 6_000_000)]

6.88 s ± 276 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
  • By default, %timeit executes a statement in a loop, and it runs the loop seven times
  • If you do not indicate the number of loops, %timeit chooses an appropriate value
  • After executing the statement, %timeit displays the statement’s average execution time, as well as the standard deviation of all the executions

Timing the Creation of an array Containing Results of 6,000,000 Die Rolls

In [3]:
import numpy as np
In [4]:
%timeit rolls_array = np.random.randint(1, 7, 6_000_000)

75.2 ms ± 2.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

60,000,000 and 600,000,000 Die Rolls

In [5]:
%timeit rolls_array = np.random.randint(1, 7, 60_000_000)

916 ms ± 26.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]:
%timeit rolls_array = np.random.randint(1, 7, 600_000_000)

10.3 s ± 180 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Customizing the %timeit Iterations

In [7]:
%timeit -n3 -r2 rolls_array = np.random.randint(1, 7, 6_000_000)

74.5 ms ± 7.58 ms per loop (mean ± std. dev. of 2 runs, 3 loops each)

Other IPython Magics

IPython provides dozens of magics for a variety of tasks—for a complete list, see the IPython magics documentation. Here are a few helpful ones:

  • %load to read code into IPython from a local file or URL.
  • %save to save snippets to a file.
  • %run to execute a .py file from IPython.
  • %precision to change the default floating-point precision for IPython outputs.
  • %cd to change directories without having to exit IPython first.
  • %edit to launch an external editor—handy if you need to modify more complex snippets.
  • %history to view a list of all snippets and commands you’ve executed in the current IPython session.

©1992–2020 by Pearson Education, Inc. All Rights Reserved. This content is based on Chapter 5 of the book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.

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