.. _dual-stack-pyramid-plots: Dual Stack Pyramid Plots ======================== `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 ` object. .. figure:: ../../_static/plot_gallery/fig_dual_stack_pyramid.png :width: 100% :alt: Dual stack pyramid plot :target: ../../_static/plot_gallery/fig_dual_stack_pyramid.html The plot above 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 used for this plot 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: .. code:: python import 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" }, height=430 ) fig.show() You should see the plot appearing as given above. .. note:: Any dataframe or CSV column names, or dictionary field labels, in the example above that are not specific to the dataset must be as given, otherwise the function may throw an exception or return an incorrect figure. Also, the ``height`` parameter, which is optional with a default of ``430``, can be used to customise the plot height. Refer to the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function docstring for more information.