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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_pandas.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_pandas.py:

=========================
Using moocore with Pandas
=========================

This example shows how to use ``moocore`` functions with Pandas (https://pandas.pydata.org/). This example requires pandas version >= 2.0.0

.. GENERATED FROM PYTHON SOURCE LINES 8-14

.. code-block:: Python


    import moocore
    import pandas as pd

    print(f"pandas version: {pd.__version__}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    pandas version: 2.2.3




.. GENERATED FROM PYTHON SOURCE LINES 15-16

First, we create a toy Pandas :class:`~pandas.DataFrame`.

.. GENERATED FROM PYTHON SOURCE LINES 16-27

.. code-block:: Python


    df = pd.DataFrame(
        dict(
            obj1=[1, 2, 3, 4, 5],
            obj2=[5, 4, 3, 2, 1],
            obj3=[100, 200, 200, 300, 100],
            algo=2 * ["foo"] + 2 * ["bar"] + ["foo"],
        )
    )
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>obj1</th>
          <th>obj2</th>
          <th>obj3</th>
          <th>algo</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>5</td>
          <td>100</td>
          <td>foo</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>4</td>
          <td>200</td>
          <td>foo</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>3</td>
          <td>200</td>
          <td>bar</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>2</td>
          <td>300</td>
          <td>bar</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>1</td>
          <td>100</td>
          <td>foo</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 28-29

Normalize it (only replace the objective columns!).

.. GENERATED FROM PYTHON SOURCE LINES 29-34

.. code-block:: Python


    obj_cols = ["obj1", "obj2", "obj3"]
    df[obj_cols] = moocore.normalise(df[obj_cols], to_range=[1, 2])
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>obj1</th>
          <th>obj2</th>
          <th>obj3</th>
          <th>algo</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1.00</td>
          <td>2.00</td>
          <td>1.0</td>
          <td>foo</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1.25</td>
          <td>1.75</td>
          <td>1.5</td>
          <td>foo</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1.50</td>
          <td>1.50</td>
          <td>1.5</td>
          <td>bar</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1.75</td>
          <td>1.25</td>
          <td>2.0</td>
          <td>bar</td>
        </tr>
        <tr>
          <th>4</th>
          <td>2.00</td>
          <td>1.00</td>
          <td>1.0</td>
          <td>foo</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 35-36

Calculate the hypervolume for each ``algo`` using :meth:`~pandas.DataFrame.groupby` and :meth:`~pandas.core.groupby.DataFrameGroupBy.apply`.

.. GENERATED FROM PYTHON SOURCE LINES 36-45

.. code-block:: Python


    ref = 2.1
    hv = (
        df.groupby("algo")[obj_cols]
        .apply(moocore.hypervolume, ref=ref)
        .reset_index(name="hv")
    )
    hv






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>algo</th>
          <th>hv</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>bar</td>
          <td>0.22475</td>
        </tr>
        <tr>
          <th>1</th>
          <td>foo</td>
          <td>0.34350</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-47

Or we can just use:

.. GENERATED FROM PYTHON SOURCE LINES 47-54

.. code-block:: Python


    hv = moocore.apply_within_sets(
        df[obj_cols], df["algo"], moocore.hypervolume, ref=ref
    )
    hv






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([0.3435 , 0.22475])



.. GENERATED FROM PYTHON SOURCE LINES 55-59

:func:`moocore.apply_within_sets()` processes each group in
order, even if the elements of the same group are not contiguous. That is, it
processes the groups like :meth:`pandas.Series.unique` and not like
:class:`set` or :func:`numpy.unique()`.

.. GENERATED FROM PYTHON SOURCE LINES 59-62

.. code-block:: Python


    df["algo"].unique()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array(['foo', 'bar'], dtype=object)



.. GENERATED FROM PYTHON SOURCE LINES 63-64

If we have multiple columns that we want to use to define the sets, such as ``algo`` and ``run``:

.. GENERATED FROM PYTHON SOURCE LINES 64-77

.. code-block:: Python


    df = pd.DataFrame(
        dict(
            obj1=[1, 2, 3, 4, 5, 6, 5, 4, 3, 1],
            obj2=[6, 5, 4, 3, 2, 1, 5, 4, 5, 6],
            obj3=[1, 2, 3, 4, 5, 6, 6, 7, 5, 2],
            algo=["a"] * 3 + ["b"] * 3 + ["a", "b"] * 2,
            run=[1, 1, 2, 1, 1, 2, 2, 2, 1, 1],
        )
    )
    obj_cols = ["obj1", "obj2", "obj3"]
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>obj1</th>
          <th>obj2</th>
          <th>obj3</th>
          <th>algo</th>
          <th>run</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>6</td>
          <td>1</td>
          <td>a</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>5</td>
          <td>2</td>
          <td>a</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>4</td>
          <td>3</td>
          <td>a</td>
          <td>2</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>3</td>
          <td>4</td>
          <td>b</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>2</td>
          <td>5</td>
          <td>b</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>6</td>
          <td>1</td>
          <td>6</td>
          <td>b</td>
          <td>2</td>
        </tr>
        <tr>
          <th>6</th>
          <td>5</td>
          <td>5</td>
          <td>6</td>
          <td>a</td>
          <td>2</td>
        </tr>
        <tr>
          <th>7</th>
          <td>4</td>
          <td>4</td>
          <td>7</td>
          <td>b</td>
          <td>2</td>
        </tr>
        <tr>
          <th>8</th>
          <td>3</td>
          <td>5</td>
          <td>5</td>
          <td>a</td>
          <td>1</td>
        </tr>
        <tr>
          <th>9</th>
          <td>1</td>
          <td>6</td>
          <td>2</td>
          <td>b</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 78-79

We can still use :meth:`~pandas.DataFrame.groupby` but we may need to reset and clean-up the index.

.. GENERATED FROM PYTHON SOURCE LINES 79-84

.. code-block:: Python


    df.groupby(["algo", "run"])[obj_cols].apply(
        moocore.filter_dominated
    ).reset_index(level=["algo", "run"])






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>algo</th>
          <th>run</th>
          <th>obj1</th>
          <th>obj2</th>
          <th>obj3</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>a</td>
          <td>1</td>
          <td>1</td>
          <td>6</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>a</td>
          <td>1</td>
          <td>2</td>
          <td>5</td>
          <td>2</td>
        </tr>
        <tr>
          <th>2</th>
          <td>a</td>
          <td>2</td>
          <td>3</td>
          <td>4</td>
          <td>3</td>
        </tr>
        <tr>
          <th>3</th>
          <td>b</td>
          <td>1</td>
          <td>4</td>
          <td>3</td>
          <td>4</td>
        </tr>
        <tr>
          <th>4</th>
          <td>b</td>
          <td>1</td>
          <td>5</td>
          <td>2</td>
          <td>5</td>
        </tr>
        <tr>
          <th>9</th>
          <td>b</td>
          <td>1</td>
          <td>1</td>
          <td>6</td>
          <td>2</td>
        </tr>
        <tr>
          <th>5</th>
          <td>b</td>
          <td>2</td>
          <td>6</td>
          <td>1</td>
          <td>6</td>
        </tr>
        <tr>
          <th>7</th>
          <td>b</td>
          <td>2</td>
          <td>4</td>
          <td>4</td>
          <td>7</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 85-87

Or we can combine the multiple columns as one to define the sets:


.. GENERATED FROM PYTHON SOURCE LINES 87-90

.. code-block:: Python

    sets = df["algo"].astype(str) + "-" + df["run"].astype(str)
    sets





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    0    a-1
    1    a-1
    2    a-2
    3    b-1
    4    b-1
    5    b-2
    6    a-2
    7    b-2
    8    a-1
    9    b-1
    dtype: object



.. GENERATED FROM PYTHON SOURCE LINES 91-93

then identify nondominated rows within each set:


.. GENERATED FROM PYTHON SOURCE LINES 93-96

.. code-block:: Python

    is_nondom = moocore.is_nondominated_within_sets(df[obj_cols], sets=sets)
    is_nondom





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([ True,  True,  True,  True,  True,  True, False,  True, False,
            True])



.. GENERATED FROM PYTHON SOURCE LINES 97-99

And use the boolean vector above to filter rows:


.. GENERATED FROM PYTHON SOURCE LINES 99-100

.. code-block:: Python

    df[is_nondom]





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>obj1</th>
          <th>obj2</th>
          <th>obj3</th>
          <th>algo</th>
          <th>run</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>6</td>
          <td>1</td>
          <td>a</td>
          <td>1</td>
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          <th>1</th>
          <td>2</td>
          <td>5</td>
          <td>2</td>
          <td>a</td>
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        <tr>
          <th>2</th>
          <td>3</td>
          <td>4</td>
          <td>3</td>
          <td>a</td>
          <td>2</td>
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        <tr>
          <th>3</th>
          <td>4</td>
          <td>3</td>
          <td>4</td>
          <td>b</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>2</td>
          <td>5</td>
          <td>b</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>6</td>
          <td>1</td>
          <td>6</td>
          <td>b</td>
          <td>2</td>
        </tr>
        <tr>
          <th>7</th>
          <td>4</td>
          <td>4</td>
          <td>7</td>
          <td>b</td>
          <td>2</td>
        </tr>
        <tr>
          <th>9</th>
          <td>1</td>
          <td>6</td>
          <td>2</td>
          <td>b</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.236 seconds)


.. _sphx_glr_download_auto_examples_plot_pandas.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_pandas.ipynb <plot_pandas.ipynb>`

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

      :download:`Download Python source code: plot_pandas.py <plot_pandas.py>`

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

      :download:`Download zipped: plot_pandas.zip <plot_pandas.zip>`


.. only:: html

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

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