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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_sources_visualisation_dual_stack_pyramid.py>`
        to download the full example code.

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

.. _sphx_glr_sources_visualisation_dual_stack_pyramid.py:


Dual Stack Pyramid Plots
========================

.. GENERATED FROM PYTHON SOURCE LINES 7-508

`Population pyramid <https://en.wikipedia.org/wiki/Population_pyramid>`_ plots can be generated using the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function, which returns a :py:class:`Plotly Go Figure <plotly.graph_objs._figure.Figure>` object.


The plot below was generated using a synthetic dataset of patients with subgroups indicating outcome (Death, Discharged, Censored). **Click** the image to view the full interactive and fully annotated Plotly Go figure.

The synthetic dataset is given below as a table (which can easily be converted to a CSV).

.. list-table:: Synthetic dataset of a patient population distribution by age group, sex and outcome
   :header-rows: 1
   :widths: auto

   * - Age Group
     - Sex
     - Outcome
     - Number
   * - 0-5
     - Male
     - death
     - 1
   * - 0-5
     - Male
     - censored
     - 19
   * - 0-5
     - Female
     - discharged
     - 29
   * - 0-5
     - Female
     - death
     - 3
   * - 0-5
     - Female
     - censored
     - 21
   * - 5-10
     - Male
     - discharged
     - 27
   * - 5-10
     - Male
     - death
     - 4
   * - 5-10
     - Male
     - censored
     - 19
   * - 5-10
     - Female
     - discharged
     - 29
   * - 5-10
     - Female
     - death
     - 1
   * - 5-10
     - Female
     - censored
     - 19
   * - 10-15
     - Male
     - discharged
     - 33
   * - 10-15
     - Male
     - death
     - 1
   * - 10-15
     - Male
     - censored
     - 18
   * - 10-15
     - Female
     - discharged
     - 30
   * - 10-15
     - Female
     - death
     - 1
   * - 10-15
     - Female
     - censored
     - 20
   * - 15-20
     - Male
     - discharged
     - 32
   * - 15-20
     - Male
     - death
     - 1
   * - 15-20
     - Male
     - censored
     - 17
   * - 15-20
     - Female
     - discharged
     - 27
   * - 15-20
     - Female
     - death
     - 9
   * - 15-20
     - Female
     - censored
     - 18
   * - 20-25
     - Male
     - discharged
     - 36
   * - 20-25
     - Male
     - death
     - 1
   * - 20-25
     - Male
     - censored
     - 23
   * - 20-25
     - Female
     - discharged
     - 30
   * - 20-25
     - Female
     - death
     - 7
   * - 20-25
     - Female
     - censored
     - 17
   * - 25-30
     - Male
     - discharged
     - 37
   * - 25-30
     - Male
     - death
     - 4
   * - 25-30
     - Male
     - censored
     - 19
   * - 25-30
     - Female
     - discharged
     - 31
   * - 25-30
     - Female
     - death
     - 8
   * - 25-30
     - Female
     - censored
     - 19
   * - 30-35
     - Male
     - discharged
     - 39
   * - 30-35
     - Male
     - death
     - 9
   * - 30-35
     - Male
     - censored
     - 17
   * - 30-35
     - Female
     - discharged
     - 34
   * - 30-35
     - Female
     - death
     - 11
   * - 30-35
     - Female
     - censored
     - 17
   * - 35-40
     - Male
     - discharged
     - 41
   * - 35-40
     - Male
     - death
     - 12
   * - 35-40
     - Male
     - censored
     - 25
   * - 35-40
     - Female
     - discharged
     - 36
   * - 35-40
     - Female
     - death
     - 5
   * - 35-40
     - Female
     - censored
     - 19
   * - 40-45
     - Male
     - discharged
     - 39
   * - 40-45
     - Male
     - death
     - 13
   * - 40-45
     - Male
     - censored
     - 19
   * - 40-45
     - Female
     - discharged
     - 33
   * - 40-45
     - Female
     - death
     - 9
   * - 40-45
     - Female
     - censored
     - 18
   * - 45-50
     - Male
     - discharged
     - 42
   * - 45-50
     - Male
     - death
     - 10
   * - 45-50
     - Male
     - censored
     - 19
   * - 45-50
     - Female
     - discharged
     - 40
   * - 45-50
     - Female
     - death
     - 12
   * - 45-50
     - Female
     - censored
     - 27
   * - 50-55
     - Male
     - discharged
     - 39
   * - 50-55
     - Male
     - death
     - 13
   * - 50-55
     - Male
     - censored
     - 20
   * - 50-55
     - Female
     - discharged
     - 45
   * - 50-55
     - Female
     - death
     - 9
   * - 50-55
     - Female
     - censored
     - 28
   * - 55-60
     - Male
     - discharged
     - 41
   * - 55-60
     - Male
     - death
     - 17
   * - 55-60
     - Male
     - censored
     - 26
   * - 55-60
     - Female
     - discharged
     - 46
   * - 55-60
     - Female
     - death
     - 17
   * - 55-60
     - Female
     - censored
     - 24
   * - 60-65
     - Male
     - discharged
     - 41
   * - 60-65
     - Male
     - death
     - 16
   * - 60-65
     - Male
     - censored
     - 28
   * - 60-65
     - Female
     - discharged
     - 49
   * - 60-65
     - Female
     - death
     - 20
   * - 60-65
     - Female
     - censored
     - 29
   * - 65-70
     - Male
     - discharged
     - 41
   * - 65-70
     - Male
     - death
     - 22
   * - 65-70
     - Male
     - censored
     - 28
   * - 65-70
     - Female
     - discharged
     - 49
   * - 65-70
     - Female
     - death
     - 21
   * - 65-70
     - Female
     - censored
     - 22
   * - 70-75
     - Male
     - discharged
     - 50
   * - 70-75
     - Male
     - death
     - 23
   * - 70-75
     - Male
     - censored
     - 25
   * - 70-75
     - Female
     - discharged
     - 43
   * - 70-75
     - Female
     - death
     - 23
   * - 70-75
     - Female
     - censored
     - 25
   * - 75-80
     - Male
     - discharged
     - 47
   * - 75-80
     - Male
     - death
     - 18
   * - 75-80
     - Male
     - censored
     - 28
   * - 75-80
     - Female
     - discharged
     - 54
   * - 75-80
     - Female
     - death
     - 24
   * - 75-80
     - Female
     - censored
     - 33
   * - 80-85
     - Male
     - discharged
     - 55
   * - 80-85
     - Male
     - death
     - 26
   * - 80-85
     - Male
     - censored
     - 33
   * - 80-85
     - Female
     - discharged
     - 54
   * - 80-85
     - Female
     - death
     - 21
   * - 80-85
     - Female
     - censored
     - 25
   * - 85-90
     - Male
     - discharged
     - 54
   * - 85-90
     - Male
     - death
     - 28
   * - 85-90
     - Male
     - censored
     - 33
   * - 85-90
     - Female
     - discharged
     - 52
   * - 85-90
     - Female
     - death
     - 24
   * - 85-90
     - Female
     - censored
     - 33
   * - 90-95
     - Male
     - discharged
     - 55
   * - 90-95
     - Male
     - death
     - 26
   * - 90-95
     - Male
     - censored
     - 27
   * - 90-95
     - Female
     - discharged
     - 52
   * - 90-95
     - Female
     - death
     - 28
   * - 90-95
     - Female
     - censored
     - 29
   * - 96-100
     - Male
     - discharged
     - 52
   * - 96-100
     - Male
     - death
     - 31
   * - 96-100
     - Male
     - censored
     - 37
   * - 96-100
     - Female
     - discharged
     - 58
   * - 96-100
     - Female
     - death
     - 33
   * - 96-100
     - Female
     - censored
     - 36

The :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function expects a dataframe with the following columns (in no particular order):

* ``"y_axis"`` - the age group label
* ``"side"`` - the sex
* ``"stack_group"`` - the patient outcome
* ``"value"`` - the number of patients in the category (combination of age group, sex, outcome)
* ``"left_side"`` - a boolean to indicate where the value should appear, with ``1`` indicating left and ``0`` indicating right

The data source can be in any appropriate form, such as, typically, a CSV. Here are the Python steps required to generate the plot, where males are on the left and females are on the right, using the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function:

.. GENERATED FROM PYTHON SOURCE LINES 508-650

.. code-block:: Python

    import io, pandas as pd
    from isaricanalytics.visualisation import fig_dual_stack_pyramid
    # Load the CSV data from a string buffer
    data = pd.read_csv(io.StringIO(
       """
       y_axis,side,stack_group,value,left_side
       0-5,Male,discharged,31,1
       0-5,Male,death,1,1
       0-5,Male,censored,19,1
       0-5,Female,discharged,29,0
       0-5,Female,death,3,0
       0-5,Female,censored,21,0
       5-10,Male,discharged,27,1
       5-10,Male,death,4,1
       5-10,Male,censored,19,1
       5-10,Female,discharged,29,0
       5-10,Female,death,1,0
       5-10,Female,censored,19,0
       10-15,Male,discharged,33,1
       10-15,Male,death,1,1
       10-15,Male,censored,18,1
       10-15,Female,discharged,30,0
       10-15,Female,death,1,0
       10-15,Female,censored,20,0
       15-20,Male,discharged,32,1
       15-20,Male,death,1,1
       15-20,Male,censored,17,1
       15-20,Female,discharged,27,0
       15-20,Female,death,9,0
       15-20,Female,censored,18,0
       20-25,Male,discharged,36,1
       20-25,Male,death,1,1
       20-25,Male,censored,23,1
       20-25,Female,discharged,30,0
       20-25,Female,death,7,0
       20-25,Female,censored,17,0
       25-30,Male,discharged,37,1
       25-30,Male,death,4,1
       25-30,Male,censored,19,1
       25-30,Female,discharged,31,0
       25-30,Female,death,8,0
       25-30,Female,censored,19,0
       30-35,Male,discharged,39,1
       30-35,Male,death,9,1
       30-35,Male,censored,17,1
       30-35,Female,discharged,34,0
       30-35,Female,death,11,0
       30-35,Female,censored,17,0
       35-40,Male,discharged,41,1
       35-40,Male,death,12,1
       35-40,Male,censored,25,1
       35-40,Female,discharged,36,0
       35-40,Female,death,5,0
       35-40,Female,censored,19,0
       40-45,Male,discharged,39,1
       40-45,Male,death,13,1
       40-45,Male,censored,19,1
       40-45,Female,discharged,33,0
       40-45,Female,death,9,0
       40-45,Female,censored,18,0
       45-50,Male,discharged,42,1
       45-50,Male,death,10,1
       45-50,Male,censored,19,1
       45-50,Female,discharged,40,0
       45-50,Female,death,12,0
       45-50,Female,censored,27,0
       50-55,Male,discharged,39,1
       50-55,Male,death,13,1
       50-55,Male,censored,20,1
       50-55,Female,discharged,45,0
       50-55,Female,death,9,0
       50-55,Female,censored,28,0
       55-60,Male,discharged,41,1
       55-60,Male,death,17,1
       55-60,Male,censored,26,1
       55-60,Female,discharged,46,0
       55-60,Female,death,17,0
       55-60,Female,censored,24,0
       60-65,Male,discharged,41,1
       60-65,Male,death,16,1
       60-65,Male,censored,28,1
       60-65,Female,discharged,49,0
       60-65,Female,death,20,0
       60-65,Female,censored,29,0
       65-70,Male,discharged,41,1
       65-70,Male,death,22,1
       65-70,Male,censored,28,1
       65-70,Female,discharged,49,0
       65-70,Female,death,21,0
       65-70,Female,censored,22,0
       70-75,Male,discharged,50,1
       70-75,Male,death,23,1
       70-75,Male,censored,25,1
       70-75,Female,discharged,43,0
       70-75,Female,death,23,0
       70-75,Female,censored,25,0
       75-80,Male,discharged,47,1
       75-80,Male,death,18,1
       75-80,Male,censored,28,1
       75-80,Female,discharged,54,0
       75-80,Female,death,24,0
       75-80,Female,censored,33,0
       80-85,Male,discharged,55,1
       80-85,Male,death,26,1
       80-85,Male,censored,33,1
       80-85,Female,discharged,54,0
       80-85,Female,death,21,0
       80-85,Female,censored,25,0
       85-90,Male,discharged,54,1
       85-90,Male,death,28,1
       85-90,Male,censored,33,1
       85-90,Female,discharged,52,0
       85-90,Female,death,24,0
       85-90,Female,censored,33,0
       90-95,Male,discharged,55,1
       90-95,Male,death,26,1
       90-95,Male,censored,27,1
       90-95,Female,discharged,52,0
       90-95,Female,death,28,0
       90-95,Female,censored,29,0
       96-100,Male,discharged,52,1
       96-100,Male,death,31,1
       96-100,Male,censored,37,1
       96-100,Female,discharged,58,0
       96-100,Female,death,33,0
       96-100,Female,censored,36,0
       """
    ), skipinitialspace=True)
    fig = fig_dual_stack_pyramid(
      data=data,
      title="Population Pyramid Plot of Synthetic Patient Dataset",
      xlabel="Count",
      ylabel="Age Group",
      base_color_map={
          "discharged": "#00c26f",
          "death": "#df0069",
          "censored": "#fff500"
      },
    )
    fig.update_layout(autosize=True)
    fig




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.. _sphx_glr_download_sources_visualisation_dual_stack_pyramid.py:

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