A quick agreement plot modified to work with a pandas series

agree plot pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Stacked Bar Graph\n",
    "\n",
    "\n",
    "This is an example of creating a stacked bar plot without error bars\n",
    "using `~matplotlib.pyplot.bar`.  Note: the parameter *left* is used to stack the bars to the right.\n",
    "\n",
    "Original example came from https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import pandas as pd\n",
    "import textwrap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "agreement_type = CategoricalDtype(categories=['Strongly disagree',\n",
    "                                              'Moderately disagree',\n",
    "                                              'Neither agree nor disagree',\n",
    "                                              'Moderately agree',\n",
    "                                              'Strongly agree'],\n",
    "                                  ordered=True)\n",
    "blah = pd.Series([0.0, 0.06, 0.15, 0.65, 0.15], index=agreement_type.categories, name=\"argmt_group_completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agree_plot_pandas(series, title = \"Proportions of responses\"):\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    width = 0.2\n",
    "    means = [x for x in series.values]\n",
    "    bottoms = [sum(means[:i]) for i,x in enumerate(means)]\n",
    "    blue = .3\n",
    "    \n",
    "#     red_to_yellow = lambda n:  [(1,x,blue,1.0) for x in np.linspace(0,1,n, endpoint=False)]\n",
    "#     yellow_to_green = lambda n:  [(x,1,blue,1.0) for x in np.linspace(1,0,n+1)]\n",
    "#     red_to_green = lambda n: [*red_to_yellow(n//2), *yellow_to_green(n//2)]\n",
    "#     colors = red_to_green(len(series))\n",
    "    \n",
    "    colors = [(1,0,blue,1.0), (1,.75,blue,1), (1,1,blue,1), (.75,1,blue,1), (0,1,blue,1)]\n",
    "\n",
    "    ps = [plt.barh(0, [x], width, left=bottoms[i], color=colors[i]) for i,x in enumerate(means)]\n",
    "    \n",
    "    #set title\n",
    "    ax.set_title(textwrap.fill(title, width=44))\n",
    "    \n",
    "    # hide axis and spines\n",
    "    ax.axes.get_yaxis().set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    \n",
    "    # set shape\n",
    "    ax.set_aspect(aspect=.5)\n",
    "    ax.margins(0)\n",
    "    ax.set_ylim(-.1,.15)\n",
    "    ax.set_xticks(np.linspace(0.0, 1.0, 11))\n",
    "    \n",
    "    # create legend\n",
    "    ax.legend(ps[::-1], series.index[::-1], \n",
    "              bbox_to_anchor=(1.05, 1.4), \n",
    "              loc='upper left', \n",
    "              borderaxespad=0.2)\n",
    "    return ax;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hiyo = agree_plot_pandas(blah, title=\"n\"*200)\n",
    "plt.show()\n",
    "\n",
    "hiyo = agree_plot_pandas(blah, title=\"n\"*200)\n",
    "hiyo.get_legend().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}