Create a discipline that uses pandas DataFrames

from __future__ import annotations

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


<RootLogger root (INFO)>

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 GEMSEO. Although GEMSEO 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 DisciplineData.

See also

DisciplineData has more information about how DataFrames are handled.

class DataFrameDiscipline(MDODiscipline):
    def __init__(self):
        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"]

Instantiate the discipline

discipline = DataFrameDiscipline()

Execute the discipline

Then, we can execute it easily, either considering default inputs:

{'df':      x    y
0  0.0  1.0}

or using new inputs:

discipline.execute({"df~x": [1.0]})
{'df':      x    y
0  1.0  0.8}

Total running time of the script: (0 minutes 0.018 seconds)

Gallery generated by Sphinx-Gallery