{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Discrete distribution as horizontal bar chart\n\nStacked bar charts can be used to visualize discrete distributions.\n\nThis example visualizes the result of a survey in which people could rate\ntheir agreement to questions on a five-element scale.\n\nThe horizontal stacking is achieved by calling `~.Axes.barh()` for each\ncategory and passing the starting point as the cumulative sum of the\nalready drawn bars via the parameter ``left``.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\ncategory_names = ['Strongly disagree', 'Disagree',\n                  'Neither agree nor disagree', 'Agree', 'Strongly agree']\nresults = {\n    'Question 1': [10, 15, 17, 32, 26],\n    'Question 2': [26, 22, 29, 10, 13],\n    'Question 3': [35, 37, 7, 2, 19],\n    'Question 4': [32, 11, 9, 15, 33],\n    'Question 5': [21, 29, 5, 5, 40],\n    'Question 6': [8, 19, 5, 30, 38]\n}\n\n\ndef survey(results, category_names):\n    \"\"\"\n    Parameters\n    ----------\n    results : dict\n        A mapping from question labels to a list of answers per category.\n        It is assumed all lists contain the same number of entries and that\n        it matches the length of *category_names*.\n    category_names : list of str\n        The category labels.\n    \"\"\"\n    labels = list(results.keys())\n    data = np.array(list(results.values()))\n    data_cum = data.cumsum(axis=1)\n    category_colors = plt.colormaps['RdYlGn'](\n        np.linspace(0.15, 0.85, data.shape[1]))\n\n    fig, ax = plt.subplots(figsize=(9.2, 5))\n    ax.invert_yaxis()\n    ax.xaxis.set_visible(False)\n    ax.set_xlim(0, np.sum(data, axis=1).max())\n\n    for i, (colname, color) in enumerate(zip(category_names, category_colors)):\n        widths = data[:, i]\n        starts = data_cum[:, i] - widths\n        rects = ax.barh(labels, widths, left=starts, height=0.5,\n                        label=colname, color=color)\n\n        r, g, b, _ = color\n        text_color = 'white' if r * g * b < 0.5 else 'darkgrey'\n        ax.bar_label(rects, label_type='center', color=text_color)\n    ax.legend(ncols=len(category_names), bbox_to_anchor=(0, 1),\n              loc='lower left', fontsize='small')\n\n    return fig, ax\n\n\nsurvey(results, category_names)\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. admonition:: References\n\n   The use of the following functions, methods, classes and modules is shown\n   in this example:\n\n   - `matplotlib.axes.Axes.barh` / `matplotlib.pyplot.barh`\n   - `matplotlib.axes.Axes.bar_label` / `matplotlib.pyplot.bar_label`\n   - `matplotlib.axes.Axes.legend` / `matplotlib.pyplot.legend`\n\n.. tags::\n\n   domain: statistics\n   component: label\n   plot-type: bar\n   level: beginner\n\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.14.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}