{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> Afghanistan</td>\n",
       "      <td> 1952</td>\n",
       "      <td>  8425333</td>\n",
       "      <td> Asia</td>\n",
       "      <td> 28.801</td>\n",
       "      <td> 779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> Afghanistan</td>\n",
       "      <td> 1957</td>\n",
       "      <td>  9240934</td>\n",
       "      <td> Asia</td>\n",
       "      <td> 30.332</td>\n",
       "      <td> 820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> Afghanistan</td>\n",
       "      <td> 1962</td>\n",
       "      <td> 10267083</td>\n",
       "      <td> Asia</td>\n",
       "      <td> 31.997</td>\n",
       "      <td> 853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> Afghanistan</td>\n",
       "      <td> 1967</td>\n",
       "      <td> 11537966</td>\n",
       "      <td> Asia</td>\n",
       "      <td> 34.020</td>\n",
       "      <td> 836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> Afghanistan</td>\n",
       "      <td> 1972</td>\n",
       "      <td> 13079460</td>\n",
       "      <td> Asia</td>\n",
       "      <td> 36.088</td>\n",
       "      <td> 739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The datasets' url. Thanks Jennifer Bryan!\n",
    "url_csv = 'http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt'\n",
    "\n",
    "df = pd.read_csv(url_csv, sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot population as a function of the year for a few selected countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries = ['China', 'India', 'United States', 'Bangladesh', 'South Africa']\n",
    "fill_colors = ['#66c2a5', '#fc8d62', '#8da0cb', '#e78ac3', '#a6d854']\n",
    "gf = df.groupby('country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for country, fill_color in zip(countries[::-1], fill_colors):\n",
    "    group = gf.get_group(country)\n",
    "    years = group['year'].tolist()\n",
    "    length = len(years)\n",
    "    country_coords = [country] * length\n",
    "    pop = group['pop'].tolist()\n",
    "    zeros = [0] * length\n",
    "    \n",
    "    data.append(dict(\n",
    "        type='scatter3d',\n",
    "        mode='lines',\n",
    "        x=years + years[::-1] + [years[0]],  # year loop: in incr. order then in decr. order then years[0]\n",
    "        y=country_coords * 2 + [country_coords[0]],\n",
    "        z=pop + zeros + [pop[0]],\n",
    "        name='',\n",
    "        surfaceaxis=1, # add a surface axis ('1' refers to axes[1] i.e. the y-axis)\n",
    "        surfacecolor=fill_color,\n",
    "        line=dict(\n",
    "            color='black',\n",
    "            width=4\n",
    "        ),\n",
    "    ))\n",
    "\n",
    "layout = dict(\n",
    "    title='Population from 1957 to 2007 [Gapminder]',\n",
    "    showlegend=False,\n",
    "    scene=dict(\n",
    "        xaxis=dict(title=''),\n",
    "        yaxis=dict(title=''),\n",
    "        zaxis=dict(title=''),\n",
    "        camera=dict(\n",
    "            eye=dict(x=-1.7, y=-1.7, z=0.5)\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~etpinard/5568.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.iplot(fig, validate=False, filename='filled-3d-lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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