{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib styles and data ink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "02-classwork.zip                     critiques.pdf\r\n",
      "Matplotlib styles and data ink.ipynb data-ink.gif\r\n",
      "NBA stats.xlsx                       scrabble-point-spread.csv\r\n",
      "country-gdp-2014.csv                 scrabble-tournament.csv\r\n",
      "country-gdp-timeseries.csv           timeuse.csv\r\n",
      "critiques.md                         tufte.pptx\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>GDP_per_capita</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>663</td>\n",
       "      <td>54.863</td>\n",
       "      <td>22856302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>4195</td>\n",
       "      <td>74.200</td>\n",
       "      <td>3071856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5098</td>\n",
       "      <td>68.963</td>\n",
       "      <td>30533827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2446</td>\n",
       "      <td>45.234</td>\n",
       "      <td>13926373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>N. America</td>\n",
       "      <td>12738</td>\n",
       "      <td>73.544</td>\n",
       "      <td>77656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country   Continent  GDP_per_capita  life_expectancy  \\\n",
       "0          Afghanistan        Asia             663           54.863   \n",
       "1              Albania      Europe            4195           74.200   \n",
       "2              Algeria      Africa            5098           68.963   \n",
       "3               Angola      Africa            2446           45.234   \n",
       "4  Antigua and Barbuda  N. America           12738           73.544   \n",
       "\n",
       "   Population  \n",
       "0    22856302  \n",
       "1     3071856  \n",
       "2    30533827  \n",
       "3    13926373  \n",
       "4       77656  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"country-gdp-2014.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108a9cf60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SkbqzaFHlJILr14dhsyeemGz7fDZWrKjsV/jf/77er5APlCAKyMaNYTqN224LN4MRKTTu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ac20f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Continent\")['life_expectancy'].median().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108bb10f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bcd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1091551d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f61ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax comes from the plotting\n",
    "ax = df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh')\n",
    "ax.set_ylabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108f617b8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bc6b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax)\n",
    "ax.set_ylabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e8a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax)\n",
    "ax.set_ylabel(\"\")\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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amZmVyI3VzMysRG6sZmZmJXJjNTMzK5Ebq5mZWYncWM3MzErkxmpmZlYiN1YzM7MSubGa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\nMyuRG6uZmVmJ3FjNzMxK9P9VQZDPUNM+0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a599b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='r', linestyle='-', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ny3uozHuswBrqZ5YAf7ST7X9O/VIukmYBbwaeSK99QNIbJO0FnAHcC/wU+LCkN6VtZkg6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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad128d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='MidnightBlue', linestyle='-', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pfaWkMen1fEmLJC2XdGrDEjAzs0Ehp7uCPwrcEhGrJT0laR/gUODmiPi6ilt6h6d9yx+N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\nwmpmZlZDLqxmZmY19P/y4HJhgtD2owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bfa278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='MidnightBlue', linestyle=':', linewidth=0.5)\n",
    "ax.set_axisbelow(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afdcbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='MidnightBlue', linestyle=':', linewidth=0.5)\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WiIiINsqn27SoeKgiIiK6RHqsERERbZTC2kMGHnKuWXLoDrXnUHv8kBxqlsIaERHRRnnc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b01b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='MidnightBlue', linestyle=':', linewidth=0.5)\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='x',     # changes apply to the x-axis\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    bottom='off', # ticks along the bottom edge are off\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DjYiIGEn5bSyF+v2MERERdTTaE6wZgUZERHQgBTQiIqIDYz6FK+l14AGqhVkDV9v+4lj3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\nRHQgBTQiIqID/x8BCYSz4nKGNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa09128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# When plotting the grid, you can send it options!\n",
    "ax.grid(color='MidnightBlue', linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a6bb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(color='MidnightBlue', linestyle=':', linewidth=0.5)\n",
    "ax.yaxis.grid(color='pink', linestyle=\"-.\", linewidth=5)\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QKgU0IiKiC+mBDssTERER7dIDjYiI6KUU0KIpa/PpgQ5OXHqggxOXHuhgxDUhh1YpoBER\nEV1ID3RYnoiIiGiXHmhEREQvpYAWTVmbTw90cOLSAx2cuPRAByOuCTm0SgGNiIjoQnqgw/JERERE\nu/RAIyIiemnbfk9gUGjE9xgREVFHQwusD6xcyeyFC8eM7zRuSM5AIyIiupAeaCGlBxoR0SQ9Km8j\nrk9O+hKupGeA71NNysDnbX90sucRERGxNfqxhLvR9nzbR5afHRdPSdtM5MQiIqJ5mvQ50C2eDku6\nX9Lu5f5Rkr5e7n9Q0uWSvgVcLmkHSZdKukPS9yQtLHGnS1ou6euS7pH0gZbHfp2k70paLeliKZcM\nRUTE1unHVbg7SlrN8BLuR2wvY/PPYbZuHwwca/spSe8CNtmeJ+lA4HpJB5S4FwCHAv8PWCXpWuDX\nwKnAH9l+RtKFwOuAf5qoBCMiYnB0emXteK7Ahf4U0F/bnr+F8dHOClfYfqrc/2Pg4wC275H0E2Bu\n+d0Nth8FkPTFEvsMcBRVQRUwHdiw1VlERMSUNkgfY3ma4flMb/vdxlH2ay28bhsf2v5MS9/1YNsf\n2rqpRkREXTS+BwrcT3WmCPAXo+z/TaolWCTNBZ4H3FN+93JJu0raEXg18G3gJuA1kn6/7LObpNlb\nl0JEREx1/VjCnd7WA73O9jnAh4BLJP0KWDnK/hcBF0u6A/gtcLrt35brgm4DrgKeC1xhezWApP9M\n1SudBjwFnAk8MBHJRUTEYJmoHmhjvkhB0unAUbbP6m7/fJFCRESTTPQXKQxSDzQiIqLnJqoH2pgv\nk7d9GXBZv+cRERFTQ2OWcHsgT0RERLTLEm5EREQvpYAWS5Zc11Fcr9fSex3XjzyakMNExPUyjybk\n0M+4TvJoQg4TcdxexjUhh1YpoBEREV1ID3RYnoiIiGiXHmhEREQvpYAWTVmbTw90cOLSAx2cuPRA\nByOuCTm0SgGNiIjoQnqgw/JEREREu/RAIyIieikFtGjK2nx6oIMTlx7o4MSlBzoYcU3IoVUKaERE\nRBfSAx2WJyIiItqlBxoREdFLjfnvzLaWRnyPERERvTba4ucDK1cye+HCMR+jX3FDcgYaERHRhfRA\nCyk90IiIyVKj0tOMHqikV0vaJGnuGHHXStplsuYVERFTT60KKPBa4Fpg8WhBtv/M9mOTM6WIiOil\nfA60xyTtBBwDnElVSJG0t6RvSFot6Q5Jx5bx+yXtXu5fLWmVpHWSzuhbAhER0Si16YFKOg14se23\nSvoGcDbwEmAH2x+RJGCG7Y2S7gOOtv1vkna1/aik6cCq8hiPbP746YFGREyWmpQeaEgPdDFwZbm/\nDDgNuA14s6QPAPNsbyy/b034bElrgVuBWcABkzTfiIhosFoUUEm7AScAl5Szy/cAJ9v+FnAc8HPg\nM5Je37bf8WW/Y2wfAawFpk/q5CMiYlzSA+2tk4HLbe9rez/bc4D7Jb0Y+IXtS4BPA/Pb9psJPGL7\nSUkHAQsmd9oREdFUteiBSroRON/29S1jb6fqg24EngYeB95g+4GhHijwBLAcmAPcA+wKnGv75s2P\nkR5oRMRkqUHpGTJiD7QWBXQypIBGREyeGpWeRlxEFBERU0B6oBEREQ2WJdxheSIiIqJdlnAjIiJ6\nKQW0WLLkuo7iBn1tvh95NCGHiYjrZR5NyKGfcZ3k0YQcJuK4vYxrQg6tUkAjIiK6kB7osDwRERHR\nLj3QiIiIXkoBLZqyNp8e6ODEpQc6OHHpgQ5GXBNyaJUCGhER0YX0QIfliYiIiHbpgUZERPRSCmjR\nlLX59EAHJy490MGJSw90MOKakEOrFNCIiIgupAc6LE9ERES0Sw80IiKil1JACym33HKb6rdOpAfa\nfVwTcmiVAhoREdGF9EALKT3QiKku/xzGFoy4NjFQZ6CSXi1pk6S5LWN/L2mdpPO3EP/nkt47ubOM\niIgYsAIKvBa4FljcMvYfgHm239caKGkb29fY/uhkTjAiprb0QLuPa0IOrQamgEraCTgGOJOqkCLp\nS8DOwPcknSxpqaSLJd0CnC/pdEmfKLF7SrpK0lpJayQtKONXS1pVzmLP6E92ERHRNAPTA5V0GvBi\n22+V9A3gbNtrJD1me5cSsxTYw/aisn06cJTtsyR9HviO7Y9LErCz7ccl7Wr7UUnTgVXlGI9sfvz0\nQCOmugH55zAGSy16oIuBK8v9ZQwv47ZPftkI+58AXAzgyuNl/GxJa4FbgVnAAT2bcURETFkDUUAl\n7UZVAC+RdB/wHuCUEcI3jjC+2XtHSceXxz3G9hHAWmD61s84Iqaq9EC7j2tCDq0GooACJwOX297X\n9n625wD3SzpuHI9xI/A2AEnTJO0CzAQesf2kpIOABT2feURETEkD0QOVdCNwvu3rW8b+GjgEeJ3t\nmWXsUuBa21eV7dYe6J7Ap4D9gKeB/wSsAZYDc4B7gF2Bc23fvPkc0gONmOoG4J/DGDwj9kAHooAO\nghTQiMg/h7EFtbiIKCJi4KUH2n1cE3JolQIaERHRhSzhDssTERER7bKEGxER0UspoEVT1ub7kUcT\ncpiIuF7m0YQc+hnXSR5NyGEijpse6MhSQCMiIrqQHuiwPBEREdEuPdCIiIheSgEtmrI2nx7o4MSl\nBzo4cemBDkZcE3JolSXciIiILuQMNCIiogspoBEREV1IAY2IiOhCCmhEREQXUkABSSdKulvSvZLe\n1+/5dErSJZI2SLqjZWw3SddLukfSVyXN7OccxyJplqSbJN0paZ2ks8p4bfKQtIOk70paU/L4cBmv\nTQ5Dyn9Gv1rSirJdxxx+Iun75fW4rYzVKg9JMyUtk/TD8mfqmBrmMLe8BqvLz19JOqtueYxmyhdQ\nSdOATwKvAA4FFks6qL+z6thSqnm3ej/wNdsHAjcBfzvpsxqfp4F32T4UeBFwZnn+a5OH7SeBl9g+\nEpgHnCDpWGqUQ4t3AHe1bNcxh03AQttH2n5hGatbHhcAX7Z9MHA4cDc1y8H2veU1mA8cBWwErqZm\neYzK9pS+AQuAr7Rsvx94X7/nNY75zwHuaNm+G9ir3N8buLvfcxxnPsuBl9U1D2AGcBtwSN1yAGYB\nNwALgRV1/fME3A/s0TZWmzyAXYAfb2G8NjlsYe5/Anyz7nm036b8GSjwXODBlu2flbG62tP2BgDb\n64E9+zyfjknaBzgCuJXqL1ht8ihLn2uA9cBK23dRsxyAjwHv4dlfa1m3HKCa/w2SVkk6o4zVKY99\ngYclLS3Ln5+SNIN65dDuVOCz5X6d83iWFNDmq8U3ZUjaGfgX4B22n2DzeQ90HrY3uVrCnQUcJ2kh\nNcpB0p8CG2yvZZTv/mSAc2hxrKtlw1dRtQSOo0avBbAtMB+4sOSxkWplrE45/I6k7YBFwLIyVMs8\ntiQFFH4OzG7ZnlXG6mqDpL0AJO0N/KLP8xmTpG2piucVtr9UhmuXB4Dtx4AvA0dTrxyOBRZJug/4\nHFUf9wpgfY1yAMD2Q+XnL6laAi+kXq/Fz4AHbd9etr9IVVDrlEOrVwLfs/1w2a5rHptJAYVVwP6S\n5kjaHngtsKLPcxoP8ewzhhXAG8v904Evte8wgC4F7rJ9QctYbfKQ9HtDVxJK2hF4ObCGGuVg+xzb\ns23vR/V34CbbbwCuoSY5AEiaUVYzkLQTVe9tHfV6LTYAD0qaW4ZeCtxJjXJos5jqTdmQuuaxmXwX\nLtXHWKiuepsGXGL7vD5PqSOSPkt1wccewAbgg1TvuJcBzwN+Cpxi+9F+zXEs5WrVm6n+kXO5nUN1\nIc6V1CAPSc8HLqN6IzON6kx6iaTdqUkOrSQdD7zb9qK65SBpX6orPU21FPrPts+rYR6HA58GtgPu\nA94EbEONcoDqDQ3VXPez/XgZq9VrMZoU0IiIiC5kCTciIqILKaARERFdSAGNiIjoQgpoREREF1JA\nIyIiupACGhER0YUU0IiIiC78fys1jSNsUVbKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b337cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NzKzHOn2KYq/60cB50Q9lSvxQJjOzkVOhqccPZcKPezYzs5KpUM1CYzhZMDMzs0K+DTHA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\njkU3CLiWLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a8fe6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='on',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QKgU0IiKiC+mBDssTERER7dIDjYiI6KUU0KIpa/PpgQ5OXHqggxOXHuhgxDUhh1YpoBER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\nIyIiupACGhER0YUU0IiIiC78fys1jSNsUVbKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a6c2908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VR1bV49pY3+o4FfhoVR0KPBq4jp7VUFWfb8/BUcDRwB3AmfSsjh2qqlV9AY4B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac0a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=1)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A+1NnBtqPujH00OUAlSRpBmagCzwQkqRpZqCSJM2TA7QZy9q8GWh/6sxA+1NnBtqPujH0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bba6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=0.5)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae6fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KORQ9jyrkUPQ8Ojo6aZ+X3a8CL+H28BNhZtZovh6omZlZa3MBTapynTpfD7Q4cb4eaHHi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\n4AJqZmZWBxdQMzOzOriAmpmZ1eH/AeWYGGGDIPQrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3b3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.04150000000001"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['life_expectancy'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 79)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PJ4LCf5UhRxkylCVHGTKUJUdZMjSNzLbNispTdS38QpiZ1VtTU/ZVHJ6qs6V4xMnMzMws\nJxdOSbU6vqu7sFKUIUcZMkA5cpQhA5QjhzOYNQYXTmZmZmY5eY1TC78QZmb15jVOVnAecTIzMzPL\nyYVTUpa59zLkKEMGKEeOMmSAcuRwBrPG4MLJzMzMLCevcWrhF8LMrN68xskKziNOZmZmZjm5cErK\nMvdehhxlyADlyFGGDFCOHM5g1hhcOJmZmZnl5DVOLfxCmJnVm9c4WcF5xMnMzMwsJxdOSVnm3suQ\nowwZoBw5ypABypHDGcwagwsnMzMzs5y8ximRvMbJzKzeRtLEKJpWybVW0o83r3GypfRY1ReUtAh4\nguwPYwDXRMTPVnU/zMzMzDprlRdOwLyIGPJhDpTUPSIWrewOmZmZmeXRFWuc2hz2lPSipI3S9i6S\n7k7bIyVdKekB4EpJvSRdLmmapMcl7Z/2O0nSTZLulvSspHNqzn28pEckTZZ0sSQPvZqZmVmndcWI\n01qSJtMyVfeTiBjHsvdRqn28PbBPRHwg6UxgcUQMkrQtcKek/mm/3YCBwPvAJEm3Ae8CxwF7R8Qi\nSRcCxwO/qVdAMzMzK6euKJzebWeqbnmjQLdExAdp+1+A8wEi4llJLwED0nN3RcSbAJKuT/suAnYh\nK6QErAnMXuEUZmZmttrpisKpPQtpmTpcs9Vz85ZzXG3BFa3amx//OiJ+sGLdMzMzs9Vdw6xxAl4k\nGxkC+OJyjr+fbKoNSQOALYBn03OHSNpQ0lrAkcCDwATgaEkbp2N6S9pyxSKYmZnZ6qgrRpzWbLXG\naXxEnA38P+AySW8B9yzn+IuAiyVNAxYAJ0XEgrTe+1HgBmBz4KqImAwg6Ydka6G6AR8ApwF/q0c4\nMzMzK69VXjhFRM922h8Atm2jfVSrx/OBr7Rz+lkRcVQb5xgHjOt8b83MzMxa+FeumJmZmeVUmsIp\nIq6IiNM/7PGjR48ngsJ/lSFHGTKUJUcZMpQlR1kyNI1cddczqwf/rroWfiHMzOqtqSn7Kg7fMNmW\nUpoRJzMzM7N6c+GUVKvju7oLK0UZcpQhA5QjRxkyQDlyOINZY3DhZGZmZpaT1zi18AthZlZvXuNk\nBecRJzMzM7OcXDglZZl7L0OOMmSAcuQoQwYoRw5nMGsMLpzMzMzMcvIapxZ+IczM6s1rnKzgPOJk\nZmZmlpMLp6Qsc+9lyFGGDFCOHGXIAOXI4QxmjcGFk5mZmVlOXuPUwi+EmVm9eY2TFZxHnMzMzMxy\ncuGUlGXuvQw5ypABypGjDBmgHDmcwawxuHAyMzMzy8lrnBLJa5zMzOptJE2MookC/ejxGidbSqFG\nnCQdKWmxpAEd7HebpPVXVb/MzMxs9VCowgkYBtwGDF/eThHxuYh4e9V0yczMzFYXhSmcJK0D7AGc\nRlZAIWkzSfdKmixpmqR9UvuLkjZK2zdKmiRpuqRTuyyAmZmZFV6Pru5AJxwB3BERMyW9Jmln4ABg\nfET8RJKAtdO+tbPnp0TEm5LWBCZJuj4i5qzivpuZmVkJFGbEiWx67rq0PQ74EvAo8BVJ5wCDImJe\ner52Md8ZkqYCE4E+QP9V1F8zMzMrmUKMOEnqDRwI7CApgO5ARMRZkvYFDgd+LennEfGbmuP2S8ft\nERHzJd0NrNkFEczMzKwEijLidAxwZUT0jYh+EbEV8KKkTwGvRcRlwKXAkFbHbQDMSUXTdsCeq7bb\nZmZmViaFGHECjgPOa9V2AzAWmCdpITAXOCE917zGaTzwNUkzgGeBh1dBX83MzKykClE4RcRBbbRd\nAFzQzv79ah4eVq9+mZmZ2eqlKFN1ZmZmZl3OhVMyevR4Iij8VxlylCFDWXKUIUNZcpQlQ9PIbNus\nqPy76lr4hTAzq7empuyrOPy76mwpHnEyMzMzy8mFU1Ktju/qLqwUZchRhgxQjhxlyADlyOEMZo3B\nhZOZmZlZTl7j1MIvhJlZvXmNkxWcR5zMzMzMcnLhlJRl7r0MOcqQAcqRowwZoBw5nMGsMbhwMjMz\nM8vJa5xa+IUwM6s3r3GygvOIk5mZmVlOLpySssy9lyFHGTJAOXKUIQOUI4czmDUGF05mZmZmOXmN\nUwu/EGZm9eY1TlZwHnEyMzMzy8mFU1KWufcy5ChDBihHjjJkgHLkcAazxuDCyczMulS1WzduP/HE\nJY8XL1rEhRtvzI1Dh3bqPNcecACzJ08G4IbPfY75b7+94n2TTqpK56ftf6tKX07b21alKVXp8arU\nd4UvVEdV6fsN0Id10+s1OX1/vSr9Ij23RlW6pio9X5UerkpbdnV/l8drnBLJa5zMVnf+53AVaGON\n05j11qN3//586eGH6dGrFy+OH8/9Z5/Nen368IVbbsl96msPOID9f/5zNh0yZKV1tyqdDOxSiTi9\nVft3ge6ViHNX2sXqpCrNrUSs19X9qFWVHgNGVCIerEpfB3asRHyjKh0HfKESMWwV9aNbJWJxZ47p\nUa/OmJmZ5dXvsMN44Q9/YMBRR/H01Vez3fDh/P3++wFY8O67/Plb3+J/Z8xg8YIF7DVyJNsMHcrC\n999n/Cmn8Pq0aWy07bYsfP/9Jee7pG9fTnj8cdbaaCNu+sIXmDtrFovef58hI0Yw6NRTgaxg22XE\nCP562230XHttjrz5ZtbeeON2+1iVRgLvAE8BZwALq9JBlYiDqtLxwOlAT+AR4BuVViMTVWkI8Atg\nHeAN4OT0/WGgUom4ryr9BFhYifhRVXoRuA74LPAu8KVKxAtV6aPA/we2SKf+diXioaq0DnABsCuw\nGBgF7A6sVZUmAzMqESdUpRuBPsCawJhKxKWpf3OBMcDn0vWOqES8XpU2SdfrR/ZBqq+nPv2zEjEm\nHfufwOxKxAUdvNVUpQHAxpWIB1PTEcDItP174FdtHDOqvetVpQpwLLAGcGMlYlTaZ3k5/xs4CDit\nKn0eGAosAO6sRPz78vrfUFN1ko6UtFjZi9rcNlrSdEnntbH/5yUtN6CZWRmUYX1Qexkkse2wYTxz\n9dUsnD+fN6ZN42N77LHk+Yk//jFbHXQQx0+cyLETJnDvWWex4L33mHrxxfRcZx1OmTGDvUeNYvZj\njy11zmaHjh3LCZMm8eVJk5g8Zgzvz5kDwIJ58/j43ntz0tSpbL7vvkz7n//JEyMqEX8kKyR+mYqm\n7YDjgL0rEUPIipbjl8ou9SArar5YidgNGAucW4lYRFZAXVyVDgI+DTTVHDqnEjEIuJCsqCF9/0Ul\nYg/gaODS1P4j4M1KxKBKxGBgQiXi+8C7lYghlYgT0n6npD7sBoyoSr1T+zrAQ+nY+4F/Te3nA/ek\n9iHADOBy4MSUTcAw4Dd5XsD0Wl1b83hzYCZAej3erEobtTqmzetVpUOA/pWI3YGdgV2r0r/kyPlw\nJWJn4BmyEa6BKd9/dtT5RhtxGgbcBgwnq5Qhe+N6R6vKXVL3iLgVuHXVdtHMzFa2jXfYgbdeeoln\nrr6afocfvtS86Ut33slfb72VSaNHA7Dogw+Y+7e/Meu++xgyYkR2/I47svFOOy05pvZHxuP/9V/8\n5aabAJg7axZznn+ej+2+Oz169aLfYYcBsOkuu/C3P/3pw3b/ILKCYlL6ob4mMLvVPtsCOwB3pX26\nAa8AVCKeqkq/Ifv5t0cqHppdk75fTTZaBXAwsH21pTpcN402HUxWlJDO+1Y7/T2jKh2ZtvsA/YFH\ngfmViNtT++PpfAAHAiekcwYwF5hbld6oSjsBmwGTKxFz2nuBWhkGfHk5zy9zC4hKxMttXa8qfRo4\nJI2oiawo6g88sJycC4EbUvtbwHtV6VLgD2TvwXI1TOGk7E3fA/gUcCcwStLNwLrA48qGLw8D3gcG\nAw9Kmg7sGhHfUhtDiRExUa2G6iIN1ZmZFUmlcmhXd2GFVSqHQtPEdp/fZuhQ7j3rLI675x7ee+ON\nliciOOL66+ndv//yL9DGIrWZ997LzAkTOP6RR+jRqxfXHnDAkim9bj17LtmvW/fuLF64sHOBWgi4\nohLxgw72ebISsU87z+8IzAE2bdUebWx3IyuwFtTuWJXaW6Wnmn32IyuE9qhEzK9Kd5P9fIRsqqrZ\nIlpqhPbOeylwClkhc3k7+yylKg0iWxs2paZ5Ftm04z+qUndg/UrEP3NeT8BPKhFLDRd2kPP95mnU\nSsSiqrQ7WfF7DPDNtN2uRpqqOwK4IyJmAq9J2jkijgDejYghETEu7bd5ROwVEZX0uPkNPR+4J5Ye\nSgQ4JWqG6tQyVGdmZg2geXRoh698hb1GjuSjAwcu9fzWn/kMk88/f8nj16ZOBaDPpz7F07/9LQCv\nP/kkr0+btsy557/1Fr1696ZHr1787zPP8MrElsJtJX446s/A0VVpY4Cq1LuNT4Y9C2xclfZM+/So\nSp9M20cBvckGDn5VldavOa55BGkY2VoogDuAEc07pFEYgLuA02raN0ybH6SCBGADsum/+WmKcc+a\na7V3s88/A99I5+xW07+bgEPJ1lTdUXPdp9s5D2QzSle3arsVOCltHwNMaOfYtq53B/CVNOJGVfp4\neh9y5UzHbViJGA+cCQxaTt+BxiqchpMtggMYlx7Dsm/kONp2IHAxQGTmpvYzJE0FJtIyVGdmVihl\nX+MEsN7mmzPkm99c5vm9fvQjFi9YwK8HDeLXO+7Ig+ecA8Dgr3+dBe+8w9iBA3moqYlNd911mXP2\nPfRQFi9YwNiBA3ng7LP52F57LbPPiqpEPA38ELizKj1BNmuyWat9FpCtRzqvmv1MmgLsVZU+ApwL\nfLUS8ReydVBjag7tnc75LeDbqW0E2VqeJ6rSk8C/pfYfAxtVpelVaQqwf2q/BJhela4C/gj0rEoz\n0nWbizFof2TpDOCAqjQNeAzYvibT3cB1zSM4Kc/yHMOyhdNlwEer0vPpWt9r68C2rleJuAv4HfBw\n6t84spmq8Tlzrgfcll7j+2h5jdvVELcjSKNAs4DXyAJ1J6t/tpY0N9LHKCWNBW6NiBvS45OAXSLi\ndEmzgT5RM3SpbKjuP4BDImK+sqG6kRFx37J98O0IzFZ3DfDPYbuq1fGFn66rVsdTeWeif+VKTulT\ndbu0M23V5apSN7K1UEdXIv6a2g4H+lYilvlkXD2u1xUaZcTpGODKiOgbEf0iYivgRUn7duIcS4YS\nJXVTNpS4ATAnFU2th+rMzAqj6EUTlCPDKtawpXxV2h54HrirtoipRPyhTkVTm9frCo2yOPw4oPXt\nBq4nm66rvTHV8v4QnQFcIumrZCvmv042VPc1ZUN1z7L0UJ2ZmVnDqkT06+o+tCdNT36irNdbnoaY\nqmsEnqozs0b+59BTdV2my6bqrDE1ylSdmZmZWcPziFMLvxBmZvXWxu+qa3AecbKleMTJzMzMLCcX\nTkkZ7pEC5chRhgxQjhxlyADlyOEMZo3BhZOZmZlZTl7j1MIvhJlZvXmNkxWcR5zMzMzMcnLhlJRl\n7r0MOcqQAcqRowwZoBw5nMGsMXiqzszMzCwnjziZmZmZ5eTCyczMzCwnF05mZmZmOblwMjMzM8vJ\nhRMg6VBJz0h6TtJ3u7o/eUm6TNJsSdNq2npLulPSs5LukLRBV/axI5L6SJogaYak6ZJOT+2FySGp\nl6RHJE1JOc5N7YXJ0ExSN0mTJd2SHhcxw0uSnkjvx6OprVA5JG0gaZykp9OfqT0KmGFAeg8mp+9v\nSTq9aDnMWlvtCydJ3YBfAZ8BBgLDJW3Xtb3KbSxZv2t9D/hTRGwLTAC+v8p71TkLgTMjYiCwF3Ba\nev0LkyMi5gMHRMTOwCDgQEn7UKAMNUYAT9U8LmKGxcD+EbFzROye2oqWYwxwe0RsD+wEPEPBMkTE\nc+k9GALsAswDbqRgOcxaW+0LJ2B34PmIeDkiFgDXAEd0cZ9yiYgHgDmtmo8ArkjbVwBHrtJOdVJE\nvBoRU9P2O8DTQB+Kl+PdtNmL7O/VHAqWQVIf4DDg0prmQmVIxLL/thUmh6T1gX0jYixARCyMiLco\nUIY2HAz8NSJmUuwcZi6cgM2BmTWPZ6W2otokImZDVpQAm3Rxf3KTtDUwGJgIbFqkHGmKawrwKnBP\nRDxFwTIAvwTOYulfP1S0DJD1/y5JkySdmtqKlKMv8IaksWma6xJJa1OsDK0dB/wubRc5h5kLp9VA\nIe5wKmlyHsu1AAACAklEQVRd4PfAiDTy1LrfDZ0jIhanqbo+wL6S9qdAGSQdDsxOo3/L+91cDZuh\nxj5peugwsqnffSnQewH0AIYAF6Yc88imt4qUYQlJPYGhwLjUVMgcZs1cOMHfgS1rHvdJbUU1W9Km\nAJI2A17r4v50SFIPsqLpqoi4OTUXLgdARLwN3A7sSrEy7AMMlfQCcDXZOq2rgFcLlAGAiHglfX8d\nuIlsOr5I78UsYGZEPJYeX09WSBUpQ63PAo9HxBvpcVFzmAEunAAmAdtI2krSGsAw4JYu7lNniKVH\nCG4BTk7bJwE3tz6gAV0OPBURY2raCpND0kebPxkkaS3gEGAKBcoQEWdHxJYR0Y/s78CEiDgBuJWC\nZACQtHYavUTSOsCngekU672YDcyUNCA1HQTMoEAZWhlOVow3K2oOM8C/qw7IbkdA9imWbsBlEfHT\nLu5SLpJ+B+wPfASYDYwk+x/2OGAL4GXg2Ih4s6v62JH06bP7yH64Rfo6G3gUuI4C5JC0I9ki1+ZF\nyVdFRFXSRhQkQy1J+wHfiYihRcsgqS/ZJ7eCbMrrtxHx0wLm2IlskX5P4AXgFKA7BcoAWSFL1td+\nETE3tRXqvTBrzYWTmZmZWU6eqjMzMzPLyYWTmZmZWU4unMzMzMxycuFkZmZmlpMLJzMzM7OcXDiZ\nmZmZ5eTCyczMzCyn/wN451GDobaX/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f92e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "# Get rid of this line, and the minor grid lines disappear\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "# Use ax.annotate to add text\n",
    "ax.annotate(s=\"Median life expectancy, 70 years\", xy=(71,0), color='darkred')\n",
    "\n",
    "ax.set_xlim((0,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 79)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e2o4HditErPMJprY438bWUUZqTgFuiIjdIqJXROwKzJE0eAOOsXr4TVInZcNv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae3d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "# Get rid of this line, and the minor grid lines disappear\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "# Use ax.annotate to add text\n",
    "ax.annotate(s=\"Median life expectancy, 70 years\", xy=(71,0), color='darkred')\n",
    "\n",
    "ax.set_xlim((30,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 79)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzArBNTVJc3OzA2FmDenw6dO5d/ToWnejak1NTZvydK6pqSP9NvUJJa0EHqP8QQng5xHx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e8d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', color='gray', ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "# Get rid of this line, and the minor grid lines disappear\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "# Use ax.annotate to add text\n",
    "ax.annotate(s=\"Median life expectancy, 70 years\", xy=(71,0), color='darkred')\n",
    "\n",
    "ax.set_xlim((30,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 79)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae67a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', color=['gray', 'darkred', 'gray', 'gray', 'gray', 'darkred'], ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "# Get rid of this line, and the minor grid lines disappear\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "# Use ax.annotate to add text\n",
    "ax.annotate(s=\"Median life expectancy, 70 years\", xy=(71,0), color='darkred')\n",
    "\n",
    "ax.set_xlim((30,79))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F0jxgh6go96lc3vof4MiIWKpyeWtsRNy35hw6KAua1YVG/Uh3ljV5CSqHLGui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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b315978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot first, take the ax\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "\n",
    "# Pass the ax to the .plot function\n",
    "df.groupby(\"Continent\")['life_expectancy'].median().plot(kind='barh', color=['darkred', 'gray', 'gray', 'gray', 'darkred', 'gray'], ax=ax, linewidth=0, width=0.4)\n",
    "ax.set_ylabel(\"\")\n",
    "\n",
    "# ax.xaxis.grid takes all the same options as ax.grid\n",
    "# but only applies to one of the axes\n",
    "ax.xaxis.grid(which=\"major\", color='MidnightBlue', linestyle=':', linewidth=1)\n",
    "# Turn on a minor grid, a smaller grid, a more frequent grid\n",
    "ax.xaxis.grid(which=\"minor\", color='darkred', linestyle=\":\", linewidth=0.5)\n",
    "# Get rid of this line, and the minor grid lines disappear\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labeltop='on', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "plt.tick_params(\n",
    "    which='minor', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='off')  # bottom label is on\n",
    "\n",
    "median = df['life_expectancy'].median()\n",
    "ax.plot([median, median], [-1, 10], c='red', linestyle=\"-\", linewidth=0.5)\n",
    "\n",
    "# Use ax.annotate to add text\n",
    "ax.annotate(s=\"Median life expectancy, 70 years\", xy=(71,0), color='darkred')\n",
    "\n",
    "ax.set_xlim((30,79))\n",
    "plt.savefig(\"life.pdf\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FiveThirtyEight scatterplot of countries' GDP and life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ac20208>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+3Efo0/gi8BKwEiBfmzGVU01j3bp1HHHEsYQxAp8nDBT7PNA9sa9UJ/OJyMhS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\n6Vs9jI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a92ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# I already made a chart, I'm going to send it to you\n",
    "df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10af09860>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RsUNSK7AY+ISkIyPiqfRlvwH8d7H9q/FHm5lZeaqeNIBXAaskiaQ57KsR8T1J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\nMzPLzEnDzMwy+/9FSZdwqdMa/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a63c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Continent'] == 'Africa'].plot(kind='scatter', x='GDP_per_capita', y='life_expectancy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b0e44a8>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b589978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u9H+8wsUJHAjcBvSwLWkUMc4ngTcPKytUnMBk4PEa5YWKc1hs84H/bHWcRVqw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b020898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b00d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Continent'] == 'Africa'].plot(kind='scatter', x='GDP_per_capita', y='life_expectancy')\n",
    "df[df['Continent'] == 'Asia'].plot(kind='scatter', x='GDP_per_capita', y='life_expectancy')\n",
    "df[df['Continent'] == 'Oceania'].plot(kind='scatter', x='GDP_per_capita', y='life_expectancy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a6bb240>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gSHefbGZDgQHuvr3w4ZWHRCLBCYMG8XwqxVeB4wn6/dr37GHi4MEcH4vtlxhK\nUWxQpUpE5FAddCDczNa4+6mHUibEzBLANmAv0Obu7zezkcBioAZIAFPdfVs350Z+IDyZTFI7ejS+\ndy+/Y1+X05nAZVddxWc/97mS36i1NaxI/1KyMiJm1gi8DzgKeDHzV4C7+7huT+x8jZeA09x9a8ax\nW4C/uvutZjYbGOnu13RzblkkjeOPPJIT9+xhKXRs93omsCkW46VXXy1pwtBsLZH+p1BJ46DdU+4+\n3cxGA48B5/fyfYz9Z2pdAEwMHy8CmoD9kkY5SCQSnDBkCC9t385JBN1TLwO7gDGxGIlEoqQ3547Z\nWq2tQOfZWkoaIpKLrAbC3f11gqUHveXAUjPbQ7DOYwFwhLtvSl/fzA4/hOuXVG1tLevb2jCCzJf+\nNv9B4NX29pLPitJsLRHJl2wGwh9w96lm9gydixJm3T0FnOXuG82sGlhiZs+xf4HDHvugbrzxxo7H\n9fX11NfXZ/GWxVNdXc3V11/PPV//eqe1F4cDl1x/PQDNzc0lG9fQbC2Rvq+pqYmmpqaCv082YxpH\nhjf8mu5+7+4tOb2h2Q3ADuBSoN7dN4XdXyvc/eRuXh/5MQ3oedxgzty5XDNrViQGoDV7SqT/iPx+\nGj2+gdkQgim6O8LpukuAm4BzgC3ufku5D4SnzfzSl7jr9ts7Fvh97pJLeKixMasBaN3QRSSfSjl7\najvddx2lu6eqDnL+8cBPw2tUAPe6+xwzGwU8ABwLtBBMuX2jm/PLImmkWxoPtbYyFNgJXDBoEGNi\nMVZt37ekpa6qivnLljFhwoSOY5oOKyL5VrYtjUNVLkmjubmZy885h8e2b++Ycnv2sGG82tbG47t3\n99jS0HRYESmEKBQslANYs2oVz4ZTbmcAJwGJ3bu5dd48JsXj1FVVMSke328AWsULRaScqKWRB8lk\nkncedxx7d+3if9k3rfVvKit5YcMGgB7HKw7U0jjQeSIiB6KWRoQlEgmOGDiQ42C/PcLTC+gmTJjQ\n7Y2/Yzpsl9bI8mXLtEmTiESOWhp5kEwmqT3ySHzPnk61p84Alv7mN5x11llZXSPdqgA0ziEih0Qt\njYgzMw4HJgF14Z+HAeedc05WrYTM1ojGOUQkqpQ08iCRSHBCPM524CFgfvhnK3DL7t3MuOQSkslk\n1tfLLPsBKvshItGhpJEHtbW1bGhvZzbwSYKl7ucR7N53F5DatYu53/lO1tfraZxDXVMiUmoa08iD\nxY2NXHoR/DGWAAANc0lEQVTRRexta+Nwgv3CK4Hf0nl8Y96dd3LZ5ZdnfV2tEheR3tLivojKnDL7\ne+DLwHAgDryQ8br3AolBg3hh/XolABEpOA2ER1R60PpIgs1AfgOsAbZApzGJV4HjNJgtImVOSeMQ\npQetlxKUDhkHVAPfI9i57xSCmVSzgdf27GHr1q05DYqLiESJksYhSg9aXzl4MOvY17o4GRhQUUEi\nFuOoYcO4ORYj1d7OtVOnarGeiJQtjWnkSTKZ5K7587nt5ps7bXR09uTJrF69mmkXXMDju3ZpsZ6I\nFIUGwstEdzOempubmTFlCk9u29bxuu5KpIuI5EuhkkZWe4RL9qqrq/drPWiPbhHpKzSmkWfJZJLm\n5uZOg91arCcifYW6p/IovQPfsQMGsH7v3v124NNiPREpFo1pRFwymeQdxxzDr1Opji6oD8ViPP/q\nq0oQIlJ0WtwXcatXr6Y6TBgQjF28PZVi9erVpQxLRCSvlDTy6DU6rwLfWMJYREQKQd1TeZJMJjnh\n6KOpbGujFkgAbZWVvLRhg7qnRKTo1D0VcdXV1SxYtAgfPJidQ4figwezYNEiJQwR6VPU0sgzzZAS\nkSjQ4r4ykU4U6Wq2Shwi0peoeyrPFjc2MramhhlTpqgwoYj0OeqeyqPMDZlUmFBESkkD4WUgvSFT\n5lqNGm28JCJ9iJJGHmUWJgQVJhSRvqdoScPMBpjZKjN7JHw+0syWmNlzZvaYmY0oViyFosKEItLX\nFW1Mw8xmAacBVe5+vpndAvzV3W81s9nASHe/ppvzymZMI03TbkWk1Mq6YKGZHQPcA3wL+OcwaawD\nJrr7JjMbDTS5+9huzi27pCEiUmrlPhA+F7gayLz7H+HumwDc/XXg8CLFIiIivVTwpGFmHwM2ufsa\n4EBZT80JEZGIK8aK8LOA883sPCAODDezHwGvm9kRGd1Tm3u6wI033tjxuL6+nvr6+sJGLCJSZpqa\nmmhqair4+xR1cZ+ZTQS+HI5p3EowEH5LXxsIFxEptXIf0+jOHGCKmT0HnBM+FxGRCFMZERGRPqgv\ntjT6nGQySXNzM8lkstShiIgUhJJGnqi6rYj0B+qeygNVtxWRqFH3VISpuq2I9BdKGnmg6rYi0l8o\naeSBqtuKSH+hMY08UnVbEYmKsq5yeyjKKWmIiESFBsJFRKTklDRERCRrShoiIpI1JQ0REcmakoaI\niGRNSUNERLKmpCEiIllT0hARkawpaYiISNaUNEREJGtKGiIikjUlDRERyZqShoiIZE1JQ0REsqak\nISIiWVPSEBGRrClpiIhI1pQ0REQka0oaIiKSNSUNERHJWsGThpkNMrPfm9lqM/uTmd0cHh9pZkvM\n7Dkze8zMRhQ6FhEROTQFTxruvhuY5O7jgXHA2WZ2FnANsMzdTwKWA9cWOpZCampqKnUIWSmHOMsh\nRlCc+aY4y0NRuqfc/a3w4aDwPbcCFwCLwuOLgAuLEUuhlMt/SOUQZznECIoz3xRneShK0jCzAWa2\nGngdaHL3Z4Ej3H0TgLu/DhxejFhERKT3KorxJu6+FxhvZlXAY2ZWD3jXlxUjFhER6T1zL+692sy+\nDrQCDUC9u28ys9HACnc/uZvXK5mIiPSCu1u+r1nwloaZvR1oc/dtZhYHpgA3AY8AFwO3ABcBP+/u\n/EL8pUVEpHcK3tIws1MIBrqNYAzlR+7+72Y2CngAOBZoAaa6+xsFDUZERA5J0bunRESkfBV9RbiZ\nfcrM/mhme8ysrsvvrjWz581srZmdm3G8zsyeNrM/m9l/ZByPmdn94Tn/Z2bHZfzuovD1z5nZ5wv4\n9/mIma0L32t2od6ny3suNLNNZvZ0xrEeF0vm83PNIcZjzGx5uKDzGTO7KqJx5rz4tBRxZlxrgJmt\nMrNHohqnmSXM7KnwM10Z4ThHmNmD4fv+ycxOj1qcZvbO8HNcFf65zcyuKmmc7l7UH+Ak4B0EC/rq\nMo6fDKwmGGepBV5gX0vo98CE8PGjwIfDx/8E3BE+ngbcHz4eCbwIjAAOSz8uwN9lQBhnDVAJrAHG\nFuEz/CBwKvB0xrFbgK+Gj2cDc8LH78rX55pjjKOBU8PHw4DngLFRizM8d0j450Dgd8BZUYwzPH8W\n8GPgkSj+u4fnvgSM7HIsinH+APhC+LiC4H4RuTgz4h0AvEbQpV+yOAt6czvIB7CCzknjGmB2xvNf\nAqcT3HyezTj+98D3wse/Ak4PHw8ENnd9Tfj8e8C0AvwdzgB+2dPfocCfXw2dk8Y6grUvhJ/Zujx+\nrsk8xPszYHKU4wSGACvD//EiFydwDLAUqGdf0ohinC8Db+tyLFJxAlXAi90cj1ScXWI7F/jfUscZ\npYKFRwPrM55vCI8dDbyacfzV8Finc9x9D7DNggH2nq5V6JgzYyu2w737xZL5+FzfCD/XXjGzWoKW\n0e/oeVFnyeK03BaflvLznAtcTec1TVGM04GlZtZsZpdGNM7jgb+Y2T1h18/3zWxIBOPMNA24L3xc\nsjgLMuXWzJYCR2QeIvgP6Xp3/0Uh3jPjfSTgB39J1nr9uZrZMOAnwEx332H7r7speZxe/MWnOcdp\nZh8DNrn7mjC+npT88wTOcveNZlYNLDGz57qJq9RxVgB1wJXu/gczm0vwLT1qcQYnmlUC5xN0RUEJ\n4yxIS8Pdp7j7uIyfU8I/D5QwNhD01aUdEx7r6Xinc8xsIFDl7lvC48f1cE4+Fet9srHJzI4AsGCx\n5ObweD4/15yYWQVBwviRu6fX4UQuzjR3f5Ogr/d9EYzzLOB8M3sJaCQo/Pkj4PWIxYm7bwz/TBJ0\nS76f6H2erwLr3f0P4fOHCJJI1OJM+yjwpLv/JXxesjhL3T2VmdEeAf4+HMk/HjgRWBk2vbaZ2fvN\nzIDPs28h4CMECwMBPk0wuA7wGDAlnB0xkmBB4WMFiL8ZONHMaswsRtBP+EgB3qc7xv6f38Xh44vo\n/Bnl63PN1d0E/ajzohqnmb09PfPE9i0+XR21ON39Onc/zt1PIPjvbLm7/wPwiyjFaWZDwtYlZjaU\noB/+GaL3eW4C1pvZO8ND5wB/ilqcGaYTfFlIK12chzIw08vBnAsJ+s9agY10Hki+lmC0fy1wbsbx\n0wj+w3semJdxfBDBAsHnCfrLazN+d3F4/M/A5wv49/kIwcyg54FrivQZ3kcwi2I38ArwBYIZY8vC\nWJYAhxXic80hxrOAPQQzylYDq8LPalTE4jwljG018BTwlfB4pOLsEvNE9g2ERypOgrGC9L/5M+n/\nJ6IWZ3id9xJ88VsDPEwweyqKcQ4BksDwjGMli1OL+0REJGul7p4SEZEyoqQhIiJZU9IQEZGsKWmI\niEjWlDRERCRrShoiIpI1JQ0REcmakoZEnpkdbmb3mtkLYRG8J8zsAjObaGZvmNmTFuxp0hTWaEqf\nd4OZvRoWpHvazP62lH+P3jKzI83sgfDxe83so6WOSfovJQ0pBz8jqD57ortPICijcUz4u1+7+2nu\nPhaYCdxuZpMyzv2Ou9cBUwnKmuRdWK+nYNx9o7tPDZ+eCpxXyPcTORAlDYk0Mzsb2O3ud6WPuft6\nd/9u19e6+1PAN4AvdvO7dUC7mb29h/e5x8y+F7Zk1qVbLBaUTb/Vgt391pjZZeHxiWb2azP7OUHN\nop7i/7zt28VuUXjs42b2u7CFtCSsBptuGf3QzH5rwY5sl4bHayzY/bAi/PtNDVtPnzazCeHrnzSz\n35jZO7L8aEV6pSCl0UXy6N0EtaGytQr4SteDZnY6sMf3VQntTo27TzCzE4EVZjaGoJDbG+5+eliU\n8gkzWxK+fjzwbnd/pbuLmdm7gOuAM919q5kdFv7qf939jPA1DcBXCfbJgKAW1unAcGC1mf2/8Li7\ne7uZ/Qtwmrunt88dBnzQ3fea2TnAt4FPHeQzEuk1JQ0pK2Z2O8F2tyn23Wg7vaTL8382s88B2wm6\nqA7kAQB3f8HMXiTYnvZc4BQz+3T4miqC7YrbCKqHdpswQmcDD7r71vC6b4THjw3HKI4k2Cb45Yxz\nfu7uKeCvZracoKz4Uwd4j8OAH4YtDEf/T0uBqXtKou5PBNU5AXD3LxKUsa6m+41n6giqe6Z9x93r\n3H2iu//2IO+Veb30xmEGfMndx4c/Y9x9WfianTn+XdL+C/hPdx8HzAAGHySGA/kmQZn0U4C/7XIt\nkbxT0pBIc/flwCAzuzzj8FD23Uw7WhZmNg74GnB7L9/u0xYYQ1Di+zmCfViuCMcTMLN3WLAtaDaW\nh9ccFZ47MjxeRVDaHvbtY5B2QbgXwtsISqA3d/n99vD8tCr2babzhSzjEuk1JQ0pBxcC9Wb2opn9\nDriHYNtLAz6YnnJL8A3+i+7e1Mv3eQVYCfw3cHnYTbQAeBZYZWbPAHcCWc2W8mCv8W8Bj1uwB/lt\n4a9uAn5iZs0E+yRkehpoAn4LfMODzXMyrQDelR4IB24F5pjZk+j/ZykC7achQjB7CviFuz9cwhhu\nALa7+3dKFYPIweibiUhA355EsqCWhvQrZnYdwT7I6UFuJ5jh9O1DuOYo4H/oPM7iwDnpmVMifYWS\nhoiIZE3dUyIikjUlDRERyZqShoiIZE1JQ0REsqakISIiWfv/bCxIgI1Fcl4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad84e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df[df['Continent'] == 'Africa'].plot(color=\"red\", kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax)\n",
    "df[df['Continent'] == 'Asia'].plot(color=\"blue\", kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax)\n",
    "df[df['Continent'] == 'Oceania'].plot(color=\"green\", kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MXvD+xER2513NnP37uXbrVs+5i1IUbIuyszsWen3glsPwg4PDvyQHShq5nmau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\nAPyKKKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa05dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for continent, selection in df.groupby(\"Continent\"):\n",
    "    #selection.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', ax=ax, label=continent)\n",
    "    ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], label=continent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      73.822\n",
       "19     62.994\n",
       "22     70.261\n",
       "33     77.010\n",
       "35     71.026\n",
       "48     73.353\n",
       "69     63.290\n",
       "130    70.073\n",
       "131    70.506\n",
       "158    67.851\n",
       "179    74.777\n",
       "182    72.432\n",
       "Name: life_expectancy, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection['life_expectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ioqXFGeg9gZYWI8/Z6nSyF8gbN86vfW/Gww/D1Vcz+MCBkKd8IrESsLvqoGh+NE6UssJo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\neGjCugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d409860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for continent, selection in df.groupby(\"Continent\"):\n",
    "    ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], label=continent, marker='o', linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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31/H11p+Kda7y+zRd9w1RwacG7Ov/NCxJuxRCiMHxWNDfX7+fA1+VmF8fiz1m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\nK3jfvoDjZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae8f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_facecolor('lightgray')\n",
    "ax.set_axis_bgcolor('lightgray')\n",
    "\n",
    "for continent, selection in df.groupby(\"Continent\"):\n",
    "    ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], label=continent, marker='o', linestyle='', markeredgewidth=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 85)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X/78SkWAd2pFce0EQBOdw20y/RsqmCsZOho7xJovpRCrkrdTavD68MJziXcUY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ebc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_facecolor('lightgray')\n",
    "ax.set_axis_bgcolor('lightgray')\n",
    "\n",
    "ax.set_prop_cycle('color', ['pink', 'purple', 'red', 'deeppink', 'lightcoral', 'magenta' ])\n",
    "\n",
    "ax.grid(linestyle='-')\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "plt.tick_params(\n",
    "    which='major', # both major and minor ticks are affected\n",
    "    top='off',   # ticks along the top edge are off\n",
    "    left='off', # ticks along the right edge are off\n",
    "    right='off', # ticks along the right edge are off\n",
    "    bottom='off', # ticks along the bottom edge are on\n",
    "    labelright='off',\n",
    "    labelleft='on',\n",
    "    labeltop='off', # top label is on\n",
    "    labelbottom='on')  # bottom label is on\n",
    "\n",
    "for continent, selection in df.groupby(\"Continent\"):\n",
    "    ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], label=continent, marker='o', linestyle='', markeredgewidth=0)\n",
    "    \n",
    "ax.legend(loc='lower right')\n",
    "ax.set_ylim((30, 85))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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