.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/matplotlib/plot_main05_c_clustermap.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_matplotlib_plot_main05_c_clustermap.py>`
        to download the full example code

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

.. _sphx_glr__examples_matplotlib_plot_main05_c_clustermap.py:


05.c ``sns.clustermap`` multiple categories
-------------------------------------------

Plot a matrix dataset as a hierarchically-clustered heatmap.

.. note:: The hierarchical clustering has been deactivated.

.. GENERATED FROM PYTHON SOURCE LINES 10-17

.. code-block:: default
   :lineno-start: 10

    # Libraries
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt

    from matplotlib.pyplot import gcf








.. GENERATED FROM PYTHON SOURCE LINES 18-19

Let's load the dataset

.. GENERATED FROM PYTHON SOURCE LINES 19-24

.. code-block:: default
   :lineno-start: 20


    # Load dataset
    networks = sns.load_dataset("brain_networks",
        index_col=0, header=[0, 1, 2])








.. GENERATED FROM PYTHON SOURCE LINES 25-26

Let's create the network colors

.. GENERATED FROM PYTHON SOURCE LINES 26-37

.. code-block:: default
   :lineno-start: 27


    # Create network colors
    network_labels = networks.columns.get_level_values("network")
    network_pal = sns.cubehelix_palette(network_labels.unique().size,
        light=.9, dark=.1, reverse=True, start=1, rot=-2)
    network_lut = dict(zip(map(str, network_labels.unique()), network_pal))

    network_colors = \
        pd.Series(network_labels, index=networks.columns) \
            .map(network_lut)








.. GENERATED FROM PYTHON SOURCE LINES 38-39

Let's create the node colors

.. GENERATED FROM PYTHON SOURCE LINES 39-49

.. code-block:: default
   :lineno-start: 40


    # Create node colors
    node_labels = networks.columns.get_level_values("node")
    node_pal = sns.cubehelix_palette(node_labels.unique().size)
    node_lut = dict(zip(map(str, node_labels.unique()), node_pal))

    node_colors = \
        pd.Series(node_labels, index=networks.columns) \
            .map(node_lut)








.. GENERATED FROM PYTHON SOURCE LINES 50-51

Let's combine them.

.. GENERATED FROM PYTHON SOURCE LINES 51-57

.. code-block:: default
   :lineno-start: 52


    # Combine
    network_node_colors = \
        pd.DataFrame(network_colors) \
            .join(pd.DataFrame(node_colors))








.. GENERATED FROM PYTHON SOURCE LINES 58-59

Let's display the clustermap

.. GENERATED FROM PYTHON SOURCE LINES 59-87

.. code-block:: default
   :lineno-start: 60


    # Display
    g = sns.clustermap(networks.corr(),
        row_cluster=False, col_cluster=False, # turn off clusters
        row_colors = network_node_colors, # add colored labels
        col_colors = network_node_colors, # add colored labels
        linewidths=0, xticklabels=False, yticklabels=False,
        center=0, cmap="vlag", figsize=(7, 7))


    # Add legend for networks
    for label in network_labels.unique():
        g.ax_col_dendrogram.bar(0, 0,
            color=network_lut[label], label=label, linewidth=0)

    l1 = g.ax_col_dendrogram.legend(title='Network',
        loc="center", ncol=5, bbox_to_anchor=(0.53, 0.9),
        bbox_transform=gcf().transFigure)

    # Add legend for nodes
    for label in node_labels.unique():
        g.ax_row_dendrogram.bar(0, 0,
            color=node_lut[label], label=label, linewidth=0)

    l2 = g.ax_row_dendrogram.legend(title='Node',
        loc="center", ncol=1, bbox_to_anchor=(0.86, 0.9),
        bbox_transform=gcf().transFigure)

    plt.show()


.. image-sg:: /_examples/matplotlib/images/sphx_glr_plot_main05_c_clustermap_001.png
   :alt: plot main05 c clustermap
   :srcset: /_examples/matplotlib/images/sphx_glr_plot_main05_c_clustermap_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.405 seconds)


.. _sphx_glr_download__examples_matplotlib_plot_main05_c_clustermap.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_main05_c_clustermap.py <plot_main05_c_clustermap.py>`



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

     :download:`Download Jupyter notebook: plot_main05_c_clustermap.ipynb <plot_main05_c_clustermap.ipynb>`


.. only:: html

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

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