{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"country-gdp-2014.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify axis labels with pandas\n",
    "\n",
    "When you plot, you get back an `ax` element. It has a million and one methods, two of which are `set_xlabel` and `set_ylabel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Life expectancy at birth')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a graph with pandas and keep what's returned\n",
    "ax = df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy')\n",
    "\n",
    "# Set the x scale because otherwise it goes into weird negative numbers\n",
    "ax.set_xlim((0, 70000))\n",
    "\n",
    "# Set the x-axis label\n",
    "ax.set_xlabel(\"GDP (per capita)\")\n",
    "\n",
    "# Set the y-axis label\n",
    "ax.set_ylabel(\"Life expectancy at birth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify axis labels with matplotlib\n",
    "\n",
    "Just to mix it up a bit, this time we're going to use `plt.subplots()` to create a figure first. When we pull the GDP and life expectancy out of the dataframes they just look like lists to the `matplotlib` plotter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Life expectancy at birth')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a new figure\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Draw the graph\n",
    "ax.plot(df['GDP_per_capita'], df['life_expectancy'], linestyle='', marker='o')\n",
    "\n",
    "# Set the label for the x-axis\n",
    "ax.set_xlabel(\"GDP (per capita)\")\n",
    "\n",
    "# Set the label for the y-axis\n",
    "ax.set_ylabel(\"Life expectancy at birth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}