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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_parallel_coordinates.py:


====================
Parallel Coordinates
====================

Parallel coordinate plots are very useful for Exploratory Data Analysis (EDA).

Typically the target variable will be colored, since it is the variable of most interest, though this is optional.

The interactive nature of plotly is a real asset for this particular plot. Records/samples can be highlighted by
clicking and dragging the mouse vertically at a given axis for a variable (feature or target).  Multiple selections
are possible.  Single clicking a selection will remove it.

.. GENERATED FROM PYTHON SOURCE LINES 15-22

.. code-block:: default


    import pandas as pd
    import plotly.io as pio
    from sklearn.datasets import load_diabetes, load_wine

    from elphick.sklearn_viz.features import plot_parallel_coordinates








.. GENERATED FROM PYTHON SOURCE LINES 23-25

Load Classification Data
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 25-31

.. code-block:: default


    wine = load_wine(as_frame=True)
    X, y = wine.data, wine.target.rename('target')
    df = pd.concat([X, y], axis=1)
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>alcohol</th>
          <th>malic_acid</th>
          <th>ash</th>
          <th>alcalinity_of_ash</th>
          <th>magnesium</th>
          <th>total_phenols</th>
          <th>flavanoids</th>
          <th>nonflavanoid_phenols</th>
          <th>proanthocyanins</th>
          <th>color_intensity</th>
          <th>hue</th>
          <th>od280/od315_of_diluted_wines</th>
          <th>proline</th>
          <th>target</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>14.23</td>
          <td>1.71</td>
          <td>2.43</td>
          <td>15.6</td>
          <td>127.0</td>
          <td>2.80</td>
          <td>3.06</td>
          <td>0.28</td>
          <td>2.29</td>
          <td>5.64</td>
          <td>1.04</td>
          <td>3.92</td>
          <td>1065.0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>13.20</td>
          <td>1.78</td>
          <td>2.14</td>
          <td>11.2</td>
          <td>100.0</td>
          <td>2.65</td>
          <td>2.76</td>
          <td>0.26</td>
          <td>1.28</td>
          <td>4.38</td>
          <td>1.05</td>
          <td>3.40</td>
          <td>1050.0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>13.16</td>
          <td>2.36</td>
          <td>2.67</td>
          <td>18.6</td>
          <td>101.0</td>
          <td>2.80</td>
          <td>3.24</td>
          <td>0.30</td>
          <td>2.81</td>
          <td>5.68</td>
          <td>1.03</td>
          <td>3.17</td>
          <td>1185.0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>14.37</td>
          <td>1.95</td>
          <td>2.50</td>
          <td>16.8</td>
          <td>113.0</td>
          <td>3.85</td>
          <td>3.49</td>
          <td>0.24</td>
          <td>2.18</td>
          <td>7.80</td>
          <td>0.86</td>
          <td>3.45</td>
          <td>1480.0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>13.24</td>
          <td>2.59</td>
          <td>2.87</td>
          <td>21.0</td>
          <td>118.0</td>
          <td>2.80</td>
          <td>2.69</td>
          <td>0.39</td>
          <td>1.82</td>
          <td>4.32</td>
          <td>1.04</td>
          <td>2.93</td>
          <td>735.0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>173</th>
          <td>13.71</td>
          <td>5.65</td>
          <td>2.45</td>
          <td>20.5</td>
          <td>95.0</td>
          <td>1.68</td>
          <td>0.61</td>
          <td>0.52</td>
          <td>1.06</td>
          <td>7.70</td>
          <td>0.64</td>
          <td>1.74</td>
          <td>740.0</td>
          <td>2</td>
        </tr>
        <tr>
          <th>174</th>
          <td>13.40</td>
          <td>3.91</td>
          <td>2.48</td>
          <td>23.0</td>
          <td>102.0</td>
          <td>1.80</td>
          <td>0.75</td>
          <td>0.43</td>
          <td>1.41</td>
          <td>7.30</td>
          <td>0.70</td>
          <td>1.56</td>
          <td>750.0</td>
          <td>2</td>
        </tr>
        <tr>
          <th>175</th>
          <td>13.27</td>
          <td>4.28</td>
          <td>2.26</td>
          <td>20.0</td>
          <td>120.0</td>
          <td>1.59</td>
          <td>0.69</td>
          <td>0.43</td>
          <td>1.35</td>
          <td>10.20</td>
          <td>0.59</td>
          <td>1.56</td>
          <td>835.0</td>
          <td>2</td>
        </tr>
        <tr>
          <th>176</th>
          <td>13.17</td>
          <td>2.59</td>
          <td>2.37</td>
          <td>20.0</td>
          <td>120.0</td>
          <td>1.65</td>
          <td>0.68</td>
          <td>0.53</td>
          <td>1.46</td>
          <td>9.30</td>
          <td>0.60</td>
          <td>1.62</td>
          <td>840.0</td>
          <td>2</td>
        </tr>
        <tr>
          <th>177</th>
          <td>14.13</td>
          <td>4.10</td>
          <td>2.74</td>
          <td>24.5</td>
          <td>96.0</td>
          <td>2.05</td>
          <td>0.76</td>
          <td>0.56</td>
          <td>1.35</td>
          <td>9.20</td>
          <td>0.61</td>
          <td>1.60</td>
          <td>560.0</td>
          <td>2</td>
        </tr>
      </tbody>
    </table>
    <p>178 rows × 14 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 32-34

Plot Classification Data
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 34-39

.. code-block:: default


    fig = plot_parallel_coordinates(df, color=y.name)
    # noinspection PyTypeChecker
    pio.show(fig)




.. raw:: html
    :file: images/sphx_glr_parallel_coordinates_001.html





.. GENERATED FROM PYTHON SOURCE LINES 40-41

The target is optional.  If the plot is too dense, then consider sampling as demonstrated.

.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: default


    fig = plot_parallel_coordinates(df.sample(frac=0.5))
    fig






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
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.. GENERATED FROM PYTHON SOURCE LINES 46-48

Load Regression Data
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 48-54

.. code-block:: default


    diabetes = load_diabetes(as_frame=True, scaled=False)
    X, y = diabetes.data, diabetes.target.rename('target')
    df = pd.concat([X, y], axis=1)
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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        }

        .dataframe tbody tr th {
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        .dataframe thead th {
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    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>age</th>
          <th>sex</th>
          <th>bmi</th>
          <th>bp</th>
          <th>s1</th>
          <th>s2</th>
          <th>s3</th>
          <th>s4</th>
          <th>s5</th>
          <th>s6</th>
          <th>target</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>59.0</td>
          <td>2.0</td>
          <td>32.1</td>
          <td>101.00</td>
          <td>157.0</td>
          <td>93.2</td>
          <td>38.0</td>
          <td>4.00</td>
          <td>4.8598</td>
          <td>87.0</td>
          <td>151.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>48.0</td>
          <td>1.0</td>
          <td>21.6</td>
          <td>87.00</td>
          <td>183.0</td>
          <td>103.2</td>
          <td>70.0</td>
          <td>3.00</td>
          <td>3.8918</td>
          <td>69.0</td>
          <td>75.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>72.0</td>
          <td>2.0</td>
          <td>30.5</td>
          <td>93.00</td>
          <td>156.0</td>
          <td>93.6</td>
          <td>41.0</td>
          <td>4.00</td>
          <td>4.6728</td>
          <td>85.0</td>
          <td>141.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>24.0</td>
          <td>1.0</td>
          <td>25.3</td>
          <td>84.00</td>
          <td>198.0</td>
          <td>131.4</td>
          <td>40.0</td>
          <td>5.00</td>
          <td>4.8903</td>
          <td>89.0</td>
          <td>206.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>50.0</td>
          <td>1.0</td>
          <td>23.0</td>
          <td>101.00</td>
          <td>192.0</td>
          <td>125.4</td>
          <td>52.0</td>
          <td>4.00</td>
          <td>4.2905</td>
          <td>80.0</td>
          <td>135.0</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>437</th>
          <td>60.0</td>
          <td>2.0</td>
          <td>28.2</td>
          <td>112.00</td>
          <td>185.0</td>
          <td>113.8</td>
          <td>42.0</td>
          <td>4.00</td>
          <td>4.9836</td>
          <td>93.0</td>
          <td>178.0</td>
        </tr>
        <tr>
          <th>438</th>
          <td>47.0</td>
          <td>2.0</td>
          <td>24.9</td>
          <td>75.00</td>
          <td>225.0</td>
          <td>166.0</td>
          <td>42.0</td>
          <td>5.00</td>
          <td>4.4427</td>
          <td>102.0</td>
          <td>104.0</td>
        </tr>
        <tr>
          <th>439</th>
          <td>60.0</td>
          <td>2.0</td>
          <td>24.9</td>
          <td>99.67</td>
          <td>162.0</td>
          <td>106.6</td>
          <td>43.0</td>
          <td>3.77</td>
          <td>4.1271</td>
          <td>95.0</td>
          <td>132.0</td>
        </tr>
        <tr>
          <th>440</th>
          <td>36.0</td>
          <td>1.0</td>
          <td>30.0</td>
          <td>95.00</td>
          <td>201.0</td>
          <td>125.2</td>
          <td>42.0</td>
          <td>4.79</td>
          <td>5.1299</td>
          <td>85.0</td>
          <td>220.0</td>
        </tr>
        <tr>
          <th>441</th>
          <td>36.0</td>
          <td>1.0</td>
          <td>19.6</td>
          <td>71.00</td>
          <td>250.0</td>
          <td>133.2</td>
          <td>97.0</td>
          <td>3.00</td>
          <td>4.5951</td>
          <td>92.0</td>
          <td>57.0</td>
        </tr>
      </tbody>
    </table>
    <p>442 rows × 11 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 55-57

Plot Regression Data
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 57-61

.. code-block:: default


    fig = plot_parallel_coordinates(df, color=y.name)
    fig






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 62-63

Categorical data is supported

.. GENERATED FROM PYTHON SOURCE LINES 63-67

.. code-block:: default


    df['sex'] = df['sex'].map({1: 'Male', 2: 'Female'}).astype('category')
    fig = plot_parallel_coordinates(df.sample(frac=0.5), color=y.name)
    fig





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

   **Total running time of the script:** ( 0 minutes  2.912 seconds)


.. _sphx_glr_download_auto_examples_parallel_coordinates.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: parallel_coordinates.py <parallel_coordinates.py>`

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

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


.. only:: html

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

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