11 - Practice Problem 4 - Bar Graphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the DGP of the 10 most populous countries in 2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('data/countries.csv')\n",
    "data['gdp']=(data.gdpPerCapita*data.population/10**9) #Add column dpg in billions"
   ]
  },
  {
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExpectancy</th>\n",
       "      <th>population</th>\n",
       "      <th>gdpPerCapita</th>\n",
       "      <th>gdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "      <td>31889923</td>\n",
       "      <td>974.580338</td>\n",
       "      <td>31.079292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2007</td>\n",
       "      <td>76.423</td>\n",
       "      <td>3600523</td>\n",
       "      <td>5937.029526</td>\n",
       "      <td>21.376411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>72.301</td>\n",
       "      <td>33333216</td>\n",
       "      <td>6223.367465</td>\n",
       "      <td>207.444852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>42.731</td>\n",
       "      <td>12420476</td>\n",
       "      <td>4797.231267</td>\n",
       "      <td>59.583896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Americas</td>\n",
       "      <td>2007</td>\n",
       "      <td>75.320</td>\n",
       "      <td>40301927</td>\n",
       "      <td>12779.379640</td>\n",
       "      <td>515.033625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country continent  year  lifeExpectancy  population  gdpPerCapita  \\\n",
       "11  Afghanistan      Asia  2007          43.828    31889923    974.580338   \n",
       "23      Albania    Europe  2007          76.423     3600523   5937.029526   \n",
       "35      Algeria    Africa  2007          72.301    33333216   6223.367465   \n",
       "47       Angola    Africa  2007          42.731    12420476   4797.231267   \n",
       "59    Argentina  Americas  2007          75.320    40301927  12779.379640   \n",
       "\n",
       "           gdp  \n",
       "11   31.079292  \n",
       "23   21.376411  \n",
       "35  207.444852  \n",
       "47   59.583896  \n",
       "59  515.033625  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2007=data[data.year==2007]\n",
    "data_2007.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExpectancy</th>\n",
       "      <th>population</th>\n",
       "      <th>gdpPerCapita</th>\n",
       "      <th>gdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>72.961</td>\n",
       "      <td>1318683096</td>\n",
       "      <td>4959.114854</td>\n",
       "      <td>6539.500929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>64.698</td>\n",
       "      <td>1110396331</td>\n",
       "      <td>2452.210407</td>\n",
       "      <td>2722.925439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>United States</td>\n",
       "      <td>Americas</td>\n",
       "      <td>2007</td>\n",
       "      <td>78.242</td>\n",
       "      <td>301139947</td>\n",
       "      <td>42951.653090</td>\n",
       "      <td>12934.458535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>70.650</td>\n",
       "      <td>223547000</td>\n",
       "      <td>3540.651564</td>\n",
       "      <td>791.502035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Americas</td>\n",
       "      <td>2007</td>\n",
       "      <td>72.390</td>\n",
       "      <td>190010647</td>\n",
       "      <td>9065.800825</td>\n",
       "      <td>1722.598680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country continent  year  lifeExpectancy  population  gdpPerCapita  \\\n",
       "299           China      Asia  2007          72.961  1318683096   4959.114854   \n",
       "707           India      Asia  2007          64.698  1110396331   2452.210407   \n",
       "1619  United States  Americas  2007          78.242   301139947  42951.653090   \n",
       "719       Indonesia      Asia  2007          70.650   223547000   3540.651564   \n",
       "179          Brazil  Americas  2007          72.390   190010647   9065.800825   \n",
       "\n",
       "               gdp  \n",
       "299    6539.500929  \n",
       "707    2722.925439  \n",
       "1619  12934.458535  \n",
       "719     791.502035  \n",
       "179    1722.598680  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10=data_2007.sort_values('population',ascending=False).head(10)\n",
    "top10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=range(10)\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.bar(x,top10.population/10**6)\n",
    "plt.xticks([],[])\n",
    "plt.title('10 most populous countries')\n",
    "plt.legend(['Population in millons'])\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(x,top10.gdp)\n",
    "plt.xticks(x,top10.country,rotation='vertical')\n",
    "plt.legend(['GDP in billions'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np # We're going to import np for np.arange().\n",
    "# np.arange(10) is similar to range(10), and it allows us to shift\n",
    "# each value in it by the bar width as you can see below.\n",
    "x = np.arange(10)\n",
    "\n",
    "# We need to create subplots in order to overlay two bar plots\n",
    "# with proper axes on the left hand side and the right hand side.\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "width = 0.3 # This is the width of each bar in the bar plot.\n",
    "plt.xticks(x, top10.country, rotation='vertical')\n",
    "population = ax1.bar(x, top10.population / 10**6, width)\n",
    "plt.ylabel('Population')\n",
    "\n",
    "# ax1.twinx() gives us the same x-axis with the y-axis on the right.\n",
    "ax2 = ax1.twinx()\n",
    "gdp = ax2.bar(x + width, top10.gdp / 10**9,width, color='orange')\n",
    "plt.ylabel('GDP')\n",
    "plt.legend([population, gdp],['Population in Millions', 'GDP in Billions'])\n",
    "figure = plt.gcf() # get current figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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