.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\tableone\plot_main02.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_tableone_plot_main02.py>`
        to download the full example code

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

.. _sphx_glr__examples_tableone_plot_main02.py:


02. Summary statistic for Dengue data
=========================================================

This script demonstrates a fundamental application of the tableone
Python library for creating a clinical summary table. Using a dengue
patient dataset, it showcases the process of summarizing patient
characteristics. The script loads the data, selects key demographic
and clinical variables (like age, gender, and platelet count), and
defines a grouping variable to compare these characteristics across
different patient subgroups. Finally, it generates and displays the
formatted summary table.

.. GENERATED FROM PYTHON SOURCE LINES 14-47

.. code-block:: default
   :lineno-start: 14

    # Libraries
    import pandas as pd

    # Specific
    from pathlib import Path
    from tableone import TableOne


    # ------------------------
    # Load data
    # ------------------------
    # Load data
    path = Path('../../datasets/dengue-htd-dataset')
    data = pd.read_csv(path / 'dengue.csv')

    print(data)
    print(data.columns)
    # ------------------------
    # Create tableone
    # ------------------------
    # Columns
    columns = ['age', 'gender', 'haematocrit_percent', 'plt']

    # Categorical
    categorical = ['gender']

    # Groupby
    groupby = 'cvs_hos_split'

    #
    mytable = TableOne(data, columns=columns,
        categorical=categorical, groupby=groupby)





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

 Out:

 .. code-block:: none

         Unnamed: 0  index   age  gender  haematocrit_percent    plt  outcome  missing  ... split_2 split_3 split_4 split_5 split_6 split_7 split_8 split_9
    0             0      0  84.0       1                 42.0   20.0        0        0  ...   train   train   train   train   train   train   train   train
    1             1      1  84.0       1                 36.0  107.0        0        0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
    2             2      2  39.0       1                 32.0  145.0        0        0  ...   train   train   train   train   train   train   train   train
    3             3      3  39.0       1                 35.5  120.0        0        0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
    4             4      4  39.0       1                 29.0   93.0        0        0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
    ..          ...    ...   ...     ...                  ...    ...      ...      ...  ...     ...     ...     ...     ...     ...     ...     ...     ...
    520         520    520  30.0       1                 35.7   61.0        0        0  ...   train   train   train   train   train   train   train    test
    521         521    521  30.0       1                 38.5   93.0        0        0  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
    522         522    522  30.0       1                 42.1   61.0        0        0  ...   train   train   train   train   train   train   train    test
    523         523    523  30.0       1                 39.3   63.0        0        0  ...   train   train   train   train   train   train   train    test
    524         524    524  30.0       1                 37.8   78.0        0        0  ...   train   train   train   train   train   train   train    test

    [525 rows x 19 columns]
    Index(['Unnamed: 0', 'index', 'age', 'gender', 'haematocrit_percent', 'plt',
           'outcome', 'missing', 'cvs_hos_split', 'split_0', 'split_1', 'split_2',
           'split_3', 'split_4', 'split_5', 'split_6', 'split_7', 'split_8',
           'split_9'],
          dtype='object')




.. GENERATED FROM PYTHON SOURCE LINES 48-50

Lets see the table


.. GENERATED FROM PYTHON SOURCE LINES 50-53

.. code-block:: default
   :lineno-start: 51


    mytable.tableone






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th></th>
          <th colspan="4" halign="left">Grouped by cvs_hos_split</th>
        </tr>
        <tr>
          <th></th>
          <th></th>
          <th>Missing</th>
          <th>Overall</th>
          <th>cvs</th>
          <th>hos</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>n</th>
          <th></th>
          <td></td>
          <td>525</td>
          <td>393</td>
          <td>132</td>
        </tr>
        <tr>
          <th>age, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>37.3 (16.3)</td>
          <td>37.4 (16.3)</td>
          <td>36.9 (16.5)</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">gender, n (%)</th>
          <th>0</th>
          <td></td>
          <td>249 (47.4)</td>
          <td>190 (48.3)</td>
          <td>59 (44.7)</td>
        </tr>
        <tr>
          <th>1</th>
          <td></td>
          <td>276 (52.6)</td>
          <td>203 (51.7)</td>
          <td>73 (55.3)</td>
        </tr>
        <tr>
          <th>haematocrit_percent, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>38.5 (8.6)</td>
          <td>38.2 (8.6)</td>
          <td>39.3 (8.7)</td>
        </tr>
        <tr>
          <th>plt, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>106.5 (132.5)</td>
          <td>110.8 (146.6)</td>
          <td>93.8 (75.9)</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 54-57

Lets show the raw HTML

Html

.. GENERATED FROM PYTHON SOURCE LINES 57-61

.. code-block:: default
   :lineno-start: 57

    html = mytable.to_html()

    # show
    print(html)




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

 Out:

 .. code-block:: none

    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th></th>
          <th colspan="4" halign="left">Grouped by cvs_hos_split</th>
        </tr>
        <tr>
          <th></th>
          <th></th>
          <th>Missing</th>
          <th>Overall</th>
          <th>cvs</th>
          <th>hos</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>n</th>
          <th></th>
          <td></td>
          <td>525</td>
          <td>393</td>
          <td>132</td>
        </tr>
        <tr>
          <th>age, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>37.3 (16.3)</td>
          <td>37.4 (16.3)</td>
          <td>36.9 (16.5)</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">gender, n (%)</th>
          <th>0</th>
          <td></td>
          <td>249 (47.4)</td>
          <td>190 (48.3)</td>
          <td>59 (44.7)</td>
        </tr>
        <tr>
          <th>1</th>
          <td></td>
          <td>276 (52.6)</td>
          <td>203 (51.7)</td>
          <td>73 (55.3)</td>
        </tr>
        <tr>
          <th>haematocrit_percent, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>38.5 (8.6)</td>
          <td>38.2 (8.6)</td>
          <td>39.3 (8.7)</td>
        </tr>
        <tr>
          <th>plt, mean (SD)</th>
          <th></th>
          <td>0</td>
          <td>106.5 (132.5)</td>
          <td>110.8 (146.6)</td>
          <td>93.8 (75.9)</td>
        </tr>
      </tbody>
    </table>





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

   **Total running time of the script:** ( 0 minutes  0.135 seconds)


.. _sphx_glr_download__examples_tableone_plot_main02.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_main02.py <plot_main02.py>`



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

     :download:`Download Jupyter notebook: plot_main02.ipynb <plot_main02.ipynb>`


.. only:: html

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

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