.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/matplotlib/plot_main05_b_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_b_clustermap.py>`
        to download the full example code

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

.. _sphx_glr__examples_matplotlib_plot_main05_b_clustermap.py:


05.b ``sns.clustermap`` with ``network.csv``
--------------------------------------------

Plot a matrix dataset as a hierarchically-clustered heatmap.

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

.. GENERATED FROM PYTHON SOURCE LINES 10-48



.. image-sg:: /_examples/matplotlib/images/sphx_glr_plot_main05_b_clustermap_001.png
   :alt: plot main05 b clustermap
   :srcset: /_examples/matplotlib/images/sphx_glr_plot_main05_b_clustermap_001.png
   :class: sphx-glr-single-img





.. code-block:: default
   :lineno-start: 11


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

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

    # Create variables
    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).map(network_lut)

    # The side colors are drawn with a heatmap, which matplotlib thinks
    # of as quantitative data and thus there's not a straightforward way
    # to get a legend directly from it. Instead of that, we'll add an
    # invisible barplot with the right colors and labels, then add a
    # legend for that.
    g = sns.clustermap(networks.corr(),
        row_cluster=False, col_cluster=False,                 # turn-off clustering
        row_colors=network_colors, col_colors=network_colors, # add class labels
        linewidths=0, xticklabels=False, yticklabels=False)   # improve visual wih many rows

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

    # Move the colorbar to the empty space.
    g.ax_col_dendrogram.legend(loc="center", ncol=6)
    g.cax.set_position([.15, .2, .03, .45])

    # Show
    plt.show()

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

   **Total running time of the script:** ( 0 minutes  0.442 seconds)


.. _sphx_glr_download__examples_matplotlib_plot_main05_b_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_b_clustermap.py <plot_main05_b_clustermap.py>`



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

     :download:`Download Jupyter notebook: plot_main05_b_clustermap.ipynb <plot_main05_b_clustermap.ipynb>`


.. only:: html

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

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