perfplot extends Python's timeit by testing snippets with input parameters (e.g., the size of an array) and plotting the results. (By default, perfplot asserts the equality of the output of all snippets, too.)

For example, to compare different NumPy array concatenation methods, the script

import numpy
import perfplot

perfplot.show(
    setup=numpy.random.rand,
    kernels=[
        lambda a: numpy.c_[a, a],
        lambda a: numpy.stack([a, a]).T,
        lambda a: numpy.vstack([a, a]).T,
        lambda a: numpy.column_stack([a, a]),
        lambda a: numpy.concatenate([a[:, None], a[:, None]], axis=1)
        ],
    labels=['c_', 'stack', 'vstack', 'column_stack', 'concat'],
    n_range=[2**k for k in range(15)],
    xlabel='len(a)'
    )

produces

Clearly, stack and vstack are the best options for large arrays.

Benchmarking and plotting can be separated, too. This allows multiple plots of the same data, for example:

out = perfplot.bench(
    # same arguments as above
    )
out.show()
out.save('perf.png')

Other examples:

• Making a flat list out of list of lists in Python
• Most efficient way to map function over numpy array
• numpy: most efficient frequency counts for unique values in an array
• Most efficient way to reverse a numpy array
• How to add an extra column to an numpy array
• Initializing numpy matrix to something other than zero or one

perfplot is available from the Python Package Index, so simply do

pip install -U perfplot

to install or upgrade.

To run the perfplot unit tests, check out this repository and type

pytest

To create a new release

1. bump the __version__ number,

2. publish to PyPi and tag on GitHub:

   $ make publish
   

perfplot is published under the MIT license.