.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/plotly/plot_main07_parallel_v2.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_plotly_plot_main07_parallel_v2.py>`
        to download the full example code

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

.. _sphx_glr__examples_plotly_plot_main07_parallel_v2.py:


07. Plot Parallel with dengue
===================================

This example plots a parallel graph.

.. GENERATED FROM PYTHON SOURCE LINES 8-117



.. raw:: html
    :file: images/sphx_glr_plot_main07_parallel_v2_001.html





.. code-block:: default
   :lineno-start: 9


    import plotly.graph_objects as go

    import numpy as np
    import pandas as pd
    from pandas.api.types import is_string_dtype
    from pandas.api.types import is_numeric_dtype

    from plotly.io import show

    try:
        __file__
        TERMINAL = True
    except:
        TERMINAL = False


    def load_gridsearch_sklearn_iris():
        """This method..."""
        # Define datapath
        FILEPATH = './data/sklearn-gridsearch/ls2d-iris.csv'
        # Load data
        df = pd.read_csv(FILEPATH)
        # Columns
        columns = [
            ('mean_train_spearman', 'Spearman'),
            ('mean_train_pearson', 'Pearson'),
            ('param_sae__max_epochs', 'Max Epochs'),
            ('param_sae__lr', 'Learning Rate'),
            ('mean_train_procrustes', 'Procrustes'),
            ('mean_train_calinski_target', 'Calinski'),
            ('mean_train_davies_b_target', 'Davies'),
            #('param_sae__module__layers', 'Layers')
        ]
        # Line
        line = dict(color=df.mean_train_calinski_target,
           colorscale='Electric',
           showscale=True,
           cmin=df.mean_train_calinski_target.min(),
           cmax=df.mean_train_calinski_target.max())
        # Return
        return df, line, columns



    def load_raw_dengue():
        """This method..."""
        # Define datapath
        FILEPATH = '../../datasets/dengue-htd-dataset/combined.csv'
        # Load data
        df = pd.read_csv(FILEPATH)
        # Columns
        columns = [
            ('age', 'Age'),
            ('body_temperature', 'Body Temperature'),
            ('weight', 'Weight'),
            ('plt', 'Platelets'),
            ('haematocrit_percent', 'Haematocrit')
        ]
        # Line
        line = dict(color=df.haematocrit_percent,
            colorscale='Electric',
            showscale=True,
            cmin=df.haematocrit_percent.min(),
            cmax=df.haematocrit_percent.max())
        # Return
        return df, line, columns

    def create_dimension(s):
        """This method creates the dimesions.

        Dimension: numeric
        dict(range = [32000,227900],
             constraintrange = [100000,150000],
             label = "Block Height",
             values = df['blockHeight'])

        Dimension: enumerated
        dict(tickvals = [0,0.5,1,2,3],
             ticktext = ['A','AB','B','Y','Z'],
             label = 'Cyclinder Material',
             values = df['cycMaterial'],
             visible = True)
        """
        if is_numeric_dtype(s):
            return dict(
                range=[s.min(), s.max()],
                constraintrange=[s.min(), s.max()],
                label=s.name, values=s)
        if is_string_dtype(s):
            s = s.apply(str)
            return dict(tickvals=np.arange(s.nunique()),
                ticktext=sorted(s.unique()),
                label=s.name, values=s)


    # Load data
    df, line, columns = load_raw_dengue()
    #df, columns = load_gridsearch_sklearn_iris()

    # Show
    fig = go.Figure(data=
        go.Parcoords(line=line,
            dimensions=[create_dimension(df[name])
                for name, label in columns]
        ))


    # Show
    show(fig)

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

   **Total running time of the script:** ( 0 minutes  3.885 seconds)


.. _sphx_glr_download__examples_plotly_plot_main07_parallel_v2.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: plot_main07_parallel_v2.py <plot_main07_parallel_v2.py>`



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

     :download:`Download Jupyter notebook: plot_main07_parallel_v2.ipynb <plot_main07_parallel_v2.ipynb>`


.. only:: html

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

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