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

.. only:: html

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

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

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

.. _sphx_glr_examples_discipline_dataframe.py:


Create a discipline that uses pandas DataFrames
===============================================

.. GENERATED FROM PYTHON SOURCE LINES 22-28

.. code-block:: default

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo.core.discipline import MDODiscipline
    from pandas import DataFrame


.. GENERATED FROM PYTHON SOURCE LINES 29-31

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 31-35

.. code-block:: default


    configure_logger()



.. GENERATED FROM PYTHON SOURCE LINES 36-64

Create a discipline that uses a DataFrame
-----------------------------------------

We will create a class for a simple discipline that computes an output
variable ``y = 1 - 0.2 * x`` where ``x`` is an input variable.
For whatever reason, the business logic of this discipline uses a pandas DataFrame
to store the input and output values outside |g|.
Although |g| disciplines only handle input and output variables that are NumPy arrays,
their local data and default input values can use DataFrame objects.

The input and output grammars of the discipline shall use a naming convention
to access the names of the columns of a DataFrame.
The naming convention is built with the name of the input or output,
the character ``~`` (this can be changed) and
the name of the DataFrame column.

The code executed by the discipline is in the ``_run`` method,
where ``self.local_data``, i.e. the local data, has automatically been initialized
with the default inputs and updated with the inputs passed to the discipline.
A DataFrame can be retrieved by querying the corresponding key, e.g. ``df``,
in the local data and then changes can be made to this DataFrame, e.g.
``discipline.local_data["df"]["x"] = value``.

The default inputs and local data are instances of :class:`.DisciplineData`.

.. seealso::

 :class:`.DisciplineData` has more information about how DataFrames are handled.

.. GENERATED FROM PYTHON SOURCE LINES 64-82

.. code-block:: default



    class DataFrameDiscipline(MDODiscipline):
        def __init__(self):
            super().__init__(grammar_type=MDODiscipline.GrammarType.SIMPLE)
            self.input_grammar.update_from_names(["df~x"])
            self.output_grammar.update_from_names(["df~y"])
            self.default_inputs = {"df": DataFrame(data={"x": [0.0]})}

        def _run(self):
            df = self.local_data["df"]
            df["y"] = 1.0 - 0.2 * df["x"]

            # The code above could also have been written as
            # self.local_data["df~y"] = 1.0 - 0.2 * self.local_data["df~x"]
            # self.local_data["df"]["y"] = 1.0 - 0.2 * self.local_data["df"]["x"]



.. GENERATED FROM PYTHON SOURCE LINES 83-85

Instantiate the discipline
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 85-87

.. code-block:: default

    discipline = DataFrameDiscipline()


.. GENERATED FROM PYTHON SOURCE LINES 88-91

Execute the discipline
----------------------
Then, we can execute it easily, either considering default inputs:

.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. code-block:: default

    print(discipline.execute())


.. GENERATED FROM PYTHON SOURCE LINES 94-95

or using new inputs:

.. GENERATED FROM PYTHON SOURCE LINES 95-96

.. code-block:: default

    print(discipline.execute({"df~x": [1.0]}))


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_discipline_dataframe.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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