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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plotting__ridge.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_plotting__ridge.py:


==================
6.10 ridge plot
==================

.. GENERATED FROM PYTHON SOURCE LINES 7-18

.. code-block:: default


    # sphinx_gallery_thumbnail_number = 2

    import numpy as np
    import pandas as pd
    from easy_mpl import ridge
    import matplotlib.pyplot as plt
    from easy_mpl.utils import version_info

    version_info()  # print version information of all the packages being used





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {'easy_mpl': '0.21.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'pandas': '1.5.3', 'scipy': '1.13.1'}



.. GENERATED FROM PYTHON SOURCE LINES 19-23

.. code-block:: default


    data_ = np.random.random(size=100)
    _ = ridge(data_)




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_001.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_001.png, /auto_examples/plotting/images/sphx_glr__ridge_001_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 24-28

.. code-block:: default


    data_ = np.random.random((100, 3))
    _ = ridge(data_)




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_002.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_002.png, /auto_examples/plotting/images/sphx_glr__ridge_002_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 29-30

specifying colormap

.. GENERATED FROM PYTHON SOURCE LINES 30-33

.. code-block:: default


    _ = ridge(data_, color="Blues")




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_003.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_003.png, /auto_examples/plotting/images/sphx_glr__ridge_003_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 34-35

The data can also be in the form of pandas DataFrame

.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: default


    _ = ridge(pd.DataFrame(data_))




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_004.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_004.png, /auto_examples/plotting/images/sphx_glr__ridge_004_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 39-40

if we don't want to fill the ridge, we can specify the color as white

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default


    _ = ridge(np.random.random(100), color=["white"])




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_005.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_005.png, /auto_examples/plotting/images/sphx_glr__ridge_005_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 44-45

we can draw all the ridges on same axes as below

.. GENERATED FROM PYTHON SOURCE LINES 45-49

.. code-block:: default


    df = pd.DataFrame(np.random.random((100, 3)), dtype='object')
    _ = ridge(df, share_axes=True, fill_kws={"alpha": 0.5})




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_006.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_006.png, /auto_examples/plotting/images/sphx_glr__ridge_006_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 50-51

we can also provide an existing axes to plot on

.. GENERATED FROM PYTHON SOURCE LINES 51-55

.. code-block:: default


    _, ax = plt.subplots()
    _ = ridge(df, ax=ax, fill_kws={"alpha": 0.5})




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_007.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_007.png, /auto_examples/plotting/images/sphx_glr__ridge_007_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 56-57

The data can also be in the form of list of arrays

.. GENERATED FROM PYTHON SOURCE LINES 57-62

.. code-block:: default


    x1 = np.random.random(100)
    x2 = np.random.random(100)
    _ = ridge([x1, x2], color=np.random.random((3, 2)))




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_008.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_008.png, /auto_examples/plotting/images/sphx_glr__ridge_008_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 63-64

The length of arrays need not to be constant/same. We can use arrays of different lengths

.. GENERATED FROM PYTHON SOURCE LINES 64-69

.. code-block:: default


    x1 = np.random.random(100)
    x2 = np.random.random(90)
    _ = ridge([x1, x2], color=np.random.random((3, 2)))




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_009.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_009.png, /auto_examples/plotting/images/sphx_glr__ridge_009_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: default

    f = "https://raw.githubusercontent.com/AtrCheema/AI4Water/master/ai4water/datasets/arg_busan.csv"
    df = pd.read_csv(f, index_col='index')
    print(df.shape)
    df.head()




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    (1446, 25)


.. 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>tide_cm</th>
          <th>wat_temp_c</th>
          <th>sal_psu</th>
          <th>air_temp_c</th>
          <th>pcp_mm</th>
          <th>...</th>
          <th>aac_coppml</th>
          <th>Total_otus</th>
          <th>otu_5575</th>
          <th>otu_273</th>
          <th>otu_94</th>
        </tr>
        <tr>
          <th>index</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>6/19/2018 0:00</th>
          <td>36.407149</td>
          <td>19.321232</td>
          <td>33.956058</td>
          <td>19.780000</td>
          <td>0.0</td>
          <td>...</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 0:30</th>
          <td>35.562515</td>
          <td>19.320124</td>
          <td>33.950508</td>
          <td>19.093333</td>
          <td>0.0</td>
          <td>...</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 1:00</th>
          <td>34.808016</td>
          <td>19.319666</td>
          <td>33.942532</td>
          <td>18.733333</td>
          <td>0.0</td>
          <td>...</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 1:30</th>
          <td>30.645216</td>
          <td>19.320406</td>
          <td>33.931263</td>
          <td>18.760000</td>
          <td>0.0</td>
          <td>...</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 2:00</th>
          <td>26.608980</td>
          <td>19.326729</td>
          <td>33.917961</td>
          <td>18.633333</td>
          <td>0.0</td>
          <td>...</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
      </tbody>
    </table>
    <p>5 rows × 25 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 75-88

.. code-block:: default


    cols = ['air_temp_c',
            'wat_temp_c',
            'sal_psu',
            'tide_cm',
            'rel_hum',
            'pcp12_mm',
            'wind_dir_deg',
            'wind_speed_mps'
            ]

    _ = ridge(df[cols])




.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_010.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_010.png, /auto_examples/plotting/images/sphx_glr__ridge_010_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 89-94

.. code-block:: default

    f = 'https://media.githubusercontent.com/media/HakaiInstitute/essd2021-hydromet-datapackage/main/2013-2019_Discharge1015_5min.csv'
    df = pd.read_csv(f)
    df.index = pd.to_datetime(df.pop('Datetime'))
    print(df.shape)
    df.head()




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    (543568, 12)


.. 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>Watershed</th>
          <th>Qlevel</th>
          <th>Qflag</th>
          <th>Qrate</th>
          <th>Qrate_min</th>
          <th>...</th>
          <th>Qvol_min</th>
          <th>Qvol_max</th>
          <th>Qmm</th>
          <th>Qmm_min</th>
          <th>Qmm_max</th>
        </tr>
        <tr>
          <th>Datetime</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2014-07-31 13:50:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>...</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 13:55:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>...</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:00:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>...</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:05:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>...</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:10:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>...</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
      </tbody>
    </table>
    <p>5 rows × 12 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 95-101

.. code-block:: default

    groupby_year = df.groupby(lambda x: x.year)

    _ = ridge(
        [grp['Qrate'].resample('D').interpolate(method='linear') for _, grp in groupby_year],
        labels=[name for name, _ in groupby_year],
        )



.. image-sg:: /auto_examples/plotting/images/sphx_glr__ridge_011.png
   :alt:  ridge
   :srcset: /auto_examples/plotting/images/sphx_glr__ridge_011.png, /auto_examples/plotting/images/sphx_glr__ridge_011_2_0x.png 2.0x
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  7.286 seconds)


.. _sphx_glr_download_auto_examples_plotting__ridge.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/AtrCheema/python-seekho/master?urlpath=lab/tree/notebooks/auto_examples/plotting/_ridge.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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