.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_build/auto_examples/tutorial/01-First_benchmark.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download__build_auto_examples_tutorial_01-First_benchmark.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__build_auto_examples_tutorial_01-First_benchmark.py:


A first benchmark
=================

.. GENERATED FROM PYTHON SOURCE LINES 7-15

Let's run and plot a first simple benchmark in Python.

Suppose I want to compare the efficiency of a few different methods to pre-allocate memory for a list of strings in Python.

Let's define a separate python function for three different list pre-allocation strategies.
These functions all take an integer called ``n`` as an input, which stands for the length of the list to be pre-allocated.
The argument name ``n`` is the :data:`pyquickbench.default_ax_name` and can be changed in as described in :ref:`sphx_glr__build_auto_examples_tutorial_03-Preparing_inputs.py`.


.. GENERATED FROM PYTHON SOURCE LINES 15-34

.. code-block:: Python



    def comprehension(n):
        return ['' for _ in range(n)]

    def star_operator(n):
        return ['']*n

    def for_loop_append(n):
        l = []
        for _ in range(n):
            l.append('')
    
    all_funs = [
        comprehension   ,
        star_operator   ,
        for_loop_append ,
    ]
 







.. GENERATED FROM PYTHON SOURCE LINES 62-63

Let's define relevant sizes of lists to be timed.

.. GENERATED FROM PYTHON SOURCE LINES 63-67

.. code-block:: Python


    n_bench = 12
    all_sizes = [2**n for n in range(n_bench)]








.. GENERATED FROM PYTHON SOURCE LINES 68-69

Now, let's import pyquickbench, run and plot the benchmark

.. GENERATED FROM PYTHON SOURCE LINES 70-79

.. code-block:: Python


    import pyquickbench

    pyquickbench.run_benchmark(
        all_sizes   ,
        all_funs    ,
        show = True ,
    ) 




.. image-sg:: /_build/auto_examples/tutorial/images/sphx_glr_01-First_benchmark_001.png
   :alt: 01 First benchmark
   :srcset: /_build/auto_examples/tutorial/images/sphx_glr_01-First_benchmark_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 83-83

From this benchmark, it is easy to see that initializing a list to a given length filled with a single value is quickest using the star operator.


.. _sphx_glr_download__build_auto_examples_tutorial_01-First_benchmark.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 01-First_benchmark.ipynb <01-First_benchmark.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 01-First_benchmark.py <01-First_benchmark.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 01-First_benchmark.zip <01-First_benchmark.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_