gauge-chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0      Finland               1            7.632                     1.305   \n",
       "1       Norway               2            7.594                     1.456   \n",
       "2      Denmark               3            7.555                     1.351   \n",
       "3      Iceland               4            7.495                     1.343   \n",
       "4  Switzerland               5            7.487                     1.420   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  \n",
       "0                     0.874    0.681  \n",
       "1                     0.861    0.686  \n",
       "2                     0.868    0.683  \n",
       "3                     0.914    0.677  \n",
       "4                     0.927    0.660  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from scipy.stats import sem\n",
    "\n",
    "# Hide warning messages in notebook\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "dataset_2018 = \"dataset/2018.csv\"\n",
    "dataset_2018_df = pd.read_csv(dataset_2018)\n",
    "\n",
    "countries_list = list(dataset_2018_df[\"Country\"])\n",
    "dataset_2018_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open GUI for Country List dropdown menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "you select United States before hit the Quit \n",
      "you Quit, and this is your Country choise: United States\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # Python2\n",
    "    import Tkinter as tk\n",
    "except ImportError:\n",
    "    # Python3\n",
    "    import tkinter as tk\n",
    "    from tkinter import *\n",
    "\n",
    "    \n",
    "# def quit(root):\n",
    "#     root.destroy()    \n",
    "\n",
    " \n",
    "root = tk.Tk()\n",
    "root.title(\"World Happiness Visualization (Gauge Chart)\")\n",
    " \n",
    "# Add a grid\n",
    "mainframe = Frame(root)\n",
    "mainframe.grid(column=0,row=0, sticky=(N,W,E,S) )\n",
    "mainframe.columnconfigure(0, weight = 1)\n",
    "mainframe.rowconfigure(0, weight = 1)\n",
    "mainframe.pack(pady = 100, padx = 100)\n",
    "\n",
    "# Create a Tkinter variable\n",
    "tkvar = StringVar(root)\n",
    " \n",
    "# Dictionary with options\n",
    "choices = countries_list\n",
    "tkvar.set('United State') # set the default option\n",
    " \n",
    "popupMenu = OptionMenu(mainframe, tkvar, *choices)\n",
    "Label(mainframe, text=\"Choose a Country\").grid(row = 1, column = 1)\n",
    "popupMenu.grid(row = 2, column =1)\n",
    " \n",
    "# on change dropdown value\n",
    "def change_dropdown(*args):\n",
    "#     print( tkvar.get() )\n",
    "    country_selected = tkvar.get()\n",
    "    print(f\"you select {country_selected} before hit the Quit \")\n",
    " \n",
    "# link function to change dropdown\n",
    "tkvar.trace('w', change_dropdown)\n",
    "Button(root, text=\"Quit\", command=root.quit).pack() \n",
    "# tk.Button(root, text=\"Quit\", command=lambda root=root:quit(root)).pack()\n",
    "root.mainloop()\n",
    "country_selected = tkvar.get()\n",
    "print(f\"you Quit, and this is your Country choise: {country_selected}\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# start to creat Gauge Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('😆', '😄', '😐', '😞', '😟')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os, sys\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Circle, Wedge, Rectangle\n",
    "import emoji\n",
    "#print(emoji.emojize('Python is :thumbs_up:'))\n",
    "em_1 = emoji.emojize(':satisfied:', use_aliases=True)\n",
    "em_2 = emoji.emojize(':smile:', use_aliases=True)\n",
    "em_3 = emoji.emojize(':neutral_face:', use_aliases=True)\n",
    "em_4 = emoji.emojize(':disappointed:', use_aliases=True)\n",
    "em_5 = emoji.emojize(':worried:', use_aliases=True)\n",
    "\n",
    "em_1,em_2,em_3,em_4,em_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def degree_range(n): \n",
    "    start = np.linspace(0,180,n+1, endpoint=True)[0:-1]\n",
    "    end = np.linspace(0,180,n+1, endpoint=True)[1::]\n",
    "    mid_points = start + ((end-start)/2.)\n",
    "    return np.c_[start, end], mid_points\n",
    "def rot_text(ang): \n",
    "    rotation = np.degrees(np.radians(ang) * np.pi / np.pi - np.radians(90))\n",
    "    return rotation\n",
    "def gauge(labels=[ em_5, em_4, em_3, em_2, em_1],\n",
    "          colors=['red','orangered','orange','skyblue','blue'], \n",
    "          arrow=\"\", \n",
    "          title=\"\", \n",
    "          fname=False):     \n",
    "    \n",
    "    \"\"\"\n",
    "    some sanity checks first\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    N = len(labels)\n",
    "    \n",
    "    if arrow > N: \n",
    "        raise Exception(\"\\n\\nThe category ({}) is greated than \\\n",
    "        the length\\nof the labels ({})\".format(arrow, N)) \n",
    " \n",
    "\n",
    "    \"\"\"\n",
    "    begins the plotting\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(7, 5))\n",
    "    fig.subplots_adjust(0,0,2,1)\n",
    "\n",
    "    ang_range, mid_points = degree_range(N)\n",
    "\n",
    "    labels = labels[::-1]\n",
    "    \n",
    "    \"\"\"\n",
    "    plots the sectors and the arcs\n",
    "    \"\"\"\n",
    "    patches = []\n",
    "    for ang, c in zip(ang_range, colors): \n",
    "        # sectors\n",
    "        patches.append(Wedge((0.,0.), .4,*ang, facecolor='w', lw=2 ))\n",
    "        # arcs\n",
    "        patches.append(Wedge((0.,0.), .4,*ang, width=0.2, facecolor=c, lw=2, alpha=0.5,))\n",
    "    \n",
    "    [ax.add_patch(p) for p in patches]\n",
    "\n",
    "    \n",
    "    \"\"\"\n",
    "    set the labels\n",
    "    \"\"\"\n",
    "\n",
    "    for mid, lab in zip(mid_points, labels): \n",
    "\n",
    "        ax.text(0.42 * np.cos(np.radians(mid)), 0.42 * np.sin(np.radians(mid)), lab, \\\n",
    "            horizontalalignment='center', verticalalignment='center', fontsize=40, \\\n",
    "            fontweight='bold', rotation = rot_text(mid))\n",
    "\n",
    "    \"\"\"\n",
    "    set the bottom banner and the title\n",
    "    \"\"\"\n",
    "    \n",
    "    r = Rectangle((-0.4,-0.1),0.8,0.1, facecolor='w', lw=2)\n",
    "    ax.add_patch(r)\n",
    "    \n",
    "\n",
    "    \n",
    "    ax.text(0, -0.1, title, horizontalalignment='center', \\\n",
    "         verticalalignment='center', fontsize=18 )\n",
    "\n",
    "    \"\"\"\n",
    "    plots the arrow now\n",
    "    \"\"\"\n",
    "    \n",
    "    pos = mid_points[abs(arrow - N)]\n",
    "    \n",
    "    ax.arrow(0, 0, 0.225 * np.cos(np.radians(pos)), 0.225 * np.sin(np.radians(pos)), \\\n",
    "                 width=0.04, head_width=0.09, head_length=0.1, fc='k', ec='k')\n",
    "    \n",
    "    ax.add_patch(Circle((0, 0), radius=0.02, facecolor='k'))\n",
    "    ax.add_patch(Circle((0, 0), radius=0.01, facecolor='w', zorder=11))\n",
    "\n",
    "    \"\"\"\n",
    "    removes frame and ticks, and makes axis equal and tight\n",
    "    \"\"\"\n",
    "    \n",
    "    ax.set_frame_on(False)\n",
    "    ax.axes.set_xticks([])\n",
    "    ax.axes.set_yticks([])\n",
    "    ax.axis('equal')\n",
    "    \n",
    "    \n",
    "#     plt.tight_layout()\n",
    "    if fname:\n",
    "        fig.savefig(fname, dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United States</td>\n",
       "      <td>18</td>\n",
       "      <td>6.886</td>\n",
       "      <td>1.398</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Happiness Rank  Happiness Score  Economy (GDP per Capita)  \\\n",
       "17  United States              18            6.886                     1.398   \n",
       "\n",
       "    Health (Life Expectancy)  Freedom  \n",
       "17                     0.819    0.547  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_country_row = dataset_2018_df.loc[dataset_2018_df['Country'] == country_selected]\n",
    "selected_country_row_happy_score = selected_country_row[\"Happiness Score\"].values[0]\n",
    "selected_country_row = pd.DataFrame(selected_country_row)\n",
    "\n",
    "if (selected_country_row_happy_score < 4): \n",
    "    arrow = 1\n",
    "elif (selected_country_row_happy_score < 5): \n",
    "    arrow = 2\n",
    "elif (selected_country_row_happy_score < 6): \n",
    "    arrow = 3\n",
    "elif (selected_country_row_happy_score < 7): \n",
    "    arrow = 4\n",
    "else:\n",
    "    arrow = 5\n",
    "selected_country_row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gauge Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gauge(title= (f\"HAPPINESS SCORE \\n Country: {country_selected} \\n 2018 \"), arrow = arrow)"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}