{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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": 5,
   "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": 5,
     "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10891db00>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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amj7ID394GePHj9cFV0RGtLJNPmhmN7v7JwDc/RfZbcAnSh1MMaFrbLb77O3A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\nhoiIxPL/AYaYIR84e0AgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10892d390>"
      ]
     },
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108170a90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O4MHe9ov2rQLWZTx+P/DfwDOEWgaEiRTP5NnfM9XWfsVDY1Tqtt7hcoeO6HFH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ffc320>"
      ]
     },
     "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}