.. 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

        Click :ref:`here <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
===============================================

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.. code-block:: default

    from gemseo.api import configure_logger
    from gemseo.core.discipline import MDODiscipline
    from numpy import ndarray
    from pandas import DataFrame








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Import
------

.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. code-block:: default


    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 35-63

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.

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.. code-block:: default



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

        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"]









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

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.. code-block:: default

    discipline = DataFrameDiscipline()








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

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.. code-block:: default

    print(discipline.execute())





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

 Out:

 .. code-block:: none

    {'df':      x    y
    0  0.0  1.0}




.. GENERATED FROM PYTHON SOURCE LINES 93-94

or using new inputs:

.. GENERATED FROM PYTHON SOURCE LINES 94-95

.. code-block:: default

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




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

 Out:

 .. code-block:: none

    {'df':      x    y
    0  1.0  0.8}





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

   **Total running time of the script:** ( 0 minutes  0.015 seconds)


.. _sphx_glr_download_examples_discipline_dataframe.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: 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>`_