{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vehicle registration in Belgium (2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data downloaded from http://mobilit.belgium.be/nl/publicaties/open_data/,\n",
    "just some data-visualisation and screening of the dataset.\n",
    "\n",
    "*Stijn Van Hoey, 2015*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pad\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "registered = pd.read_excel(\"Registration transactions 2014_tcm466-262543.xlsx\", header=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Transaction Timest Year</th>\n",
       "      <th>Transaction Timest Month Code</th>\n",
       "      <th>New/Used Vehicle</th>\n",
       "      <th>Region Gewest</th>\n",
       "      <th>Province_label</th>\n",
       "      <th>Titular Type</th>\n",
       "      <th>Vehicle grouping by Design</th>\n",
       "      <th>Vehicle grouping by use</th>\n",
       "      <th>Exact Value Maxmass Cat</th>\n",
       "      <th>Fk M Vehicle Buildupcode Current Situation</th>\n",
       "      <th>Buildup Label current situation</th>\n",
       "      <th>Vehicle Euro Norm Code</th>\n",
       "      <th>CO2 Category</th>\n",
       "      <th>Vehicle Fuel Code</th>\n",
       "      <th>Vehicle Fuel Label</th>\n",
       "      <th># Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>New Vehicle</td>\n",
       "      <td>BRU</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>M</td>\n",
       "      <td>L</td>\n",
       "      <td>HEAVY 2-3-4 W</td>\n",
       "      <td>1-1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>01</td>\n",
       "      <td>BENZINE</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>New Vehicle</td>\n",
       "      <td>BRU</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>M</td>\n",
       "      <td>L</td>\n",
       "      <td>HEAVY 2-3-4 W</td>\n",
       "      <td>1-1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>01</td>\n",
       "      <td>BENZINE</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>New Vehicle</td>\n",
       "      <td>BRU</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>M</td>\n",
       "      <td>L</td>\n",
       "      <td>HEAVY 2-3-4 W</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>01</td>\n",
       "      <td>BENZINE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>New Vehicle</td>\n",
       "      <td>BRU</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>M</td>\n",
       "      <td>M1</td>\n",
       "      <td>CAR</td>\n",
       "      <td>1501-3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>01</td>\n",
       "      <td>BENZINE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>New Vehicle</td>\n",
       "      <td>BRU</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>M</td>\n",
       "      <td>M1</td>\n",
       "      <td>CAR</td>\n",
       "      <td>1501-3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5b</td>\n",
       "      <td>99</td>\n",
       "      <td>07</td>\n",
       "      <td>BENZINE + ELEKTR.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction Timest Year  Transaction Timest Month Code New/Used Vehicle  \\\n",
       "0                     2014                              1      New Vehicle   \n",
       "1                     2014                              1      New Vehicle   \n",
       "2                     2014                              1      New Vehicle   \n",
       "3                     2014                              1      New Vehicle   \n",
       "4                     2014                              1      New Vehicle   \n",
       "\n",
       "  Region Gewest Province_label Titular Type Vehicle grouping by Design  \\\n",
       "0           BRU        Brussel            M                          L   \n",
       "1           BRU        Brussel            M                          L   \n",
       "2           BRU        Brussel            M                          L   \n",
       "3           BRU        Brussel            M                         M1   \n",
       "4           BRU        Brussel            M                         M1   \n",
       "\n",
       "  Vehicle grouping by use Exact Value Maxmass Cat  \\\n",
       "0           HEAVY 2-3-4 W                  1-1000   \n",
       "1           HEAVY 2-3-4 W                  1-1000   \n",
       "2           HEAVY 2-3-4 W                 Unknown   \n",
       "3                     CAR               1501-3000   \n",
       "4                     CAR               1501-3000   \n",
       "\n",
       "  Fk M Vehicle Buildupcode Current Situation Buildup Label current situation  \\\n",
       "0                                        NaN                             NaN   \n",
       "1                                        NaN                             NaN   \n",
       "2                                        NaN                             NaN   \n",
       "3                                        NaN                             NaN   \n",
       "4                                        NaN                             NaN   \n",
       "\n",
       "  Vehicle Euro Norm Code  CO2 Category Vehicle Fuel Code Vehicle Fuel Label  \\\n",
       "0                    2              99                01            BENZINE   \n",
       "1                    3              99                01            BENZINE   \n",
       "2                    3              99                01            BENZINE   \n",
       "3                    5              99                01            BENZINE   \n",
       "4                    5b             99                07  BENZINE + ELEKTR.   \n",
       "\n",
       "   # Transactions  \n",
       "0               3  \n",
       "1              23  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rechtspersoon (M) of Natuurlijke persoon (P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7f46295d2350>,\n",
       " <matplotlib.text.Text at 0x7f46295db990>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kZk6XyiBJ0jApeaT9AmBdZu4H/AnwIeA9wLG91xYAhxTcviRJQ6VkaZ8GvHnGdn4H7JuZ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\nlfgvT6OL72QBLAAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f462989e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(100*registered.groupby('Titular Type')['Titular Type'].count()/(len(registered))).plot(kind='bar')\n",
    "plt.ylabel('% aantal wagens')\n",
    "plt.xlabel(' ')\n",
    "plt.gca().set_xticklabels(['Rechtsperson', 'Natuurlijk persoon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enkel voor Vlaanderen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7f462f42ded0>,\n",
       " <matplotlib.text.Text at 0x7f462f499610>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJEklGSmSJKkkI0WSJJVkpEiSpJKMFEmSVJKRIkmSSprU6wHGs/vvubvXI0iSesjngRXr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\n/wOV2ezZGiFamAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f462f4f5510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VL_registered = registered[registered['Region Gewest'] == 'VLA']\n",
    "(100*VL_registered.groupby('Titular Type')['Titular Type'].count()/(len(VL_registered))).plot(kind='barh', )\n",
    "plt.xlabel('% aantal wagens')\n",
    "plt.ylabel(' ')\n",
    "plt.gca().set_yticklabels(['Rechtsperson', 'Natuurlijk persoon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titular_gewest = registered.groupby('Region Gewest')['Titular Type'].value_counts(normalize=True).unstack().drop('N A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>M</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region Gewest</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BRU</th>\n",
       "      <td>0.582969</td>\n",
       "      <td>0.417031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VLA</th>\n",
       "      <td>0.519986</td>\n",
       "      <td>0.480014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAL</th>\n",
       "      <td>0.439105</td>\n",
       "      <td>0.560895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      M         P\n",
       "Region Gewest                    \n",
       "BRU            0.582969  0.417031\n",
       "VLA            0.519986  0.480014\n",
       "WAL            0.439105  0.560895"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titular_gewest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PXVLNWPbVEl4Y/rcfR0u+tv/vdKm6LF/8BY1bfl3r/5+7pJrlJ95JkpRShrwk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\nUur/AWk+JuTNaoO6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f462973bbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots()\n",
    "titular_gewest.plot(ax=ax, kind='barh', stacked=True, legend=False, \n",
    "                    fontsize=14)\n",
    "ax.set_yticks([0,1,2])\n",
    "ax.legend(['Rechtspersoon', 'Natuurlijk persoon'], loc='center', \n",
    "          bbox_to_anchor=(0.5, 0.95), fontsize=14, ncol=2)\n",
    "ax.set_xlabel('%', fontsize=14)\n",
    "ax.set_ylabel(' ')\n",
    "ax.set_axis_bgcolor('white')\n",
    "fig.suptitle('Inschrijvingen 2014 per gewest', size=14)\n",
    "plt.savefig('inschrijvingen_2014_titularis.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of registrations per province"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Transaction Timest Year', u'Transaction Timest Month Code', u'New/Used Vehicle', u'Region Gewest', u'Province_label', u'Titular Type', u'Vehicle grouping by Design', u'Vehicle grouping by use', u'Exact Value Maxmass Cat', u'Fk M Vehicle Buildupcode Current Situation', u'Buildup Label current situation', u'Vehicle Euro Norm Code', u'CO2 Category', u'Vehicle Fuel Code', u'Vehicle Fuel Label', u'# Transactions'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f464fa7e650>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PUMZnu567L+m8JL286oMaJelllHGh+Abpm1ZtDuLrqz85FdcEX3nHoOf9wCcy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f464fa7ebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "registered.groupby('Province_label')['Province_label'].count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting new and second hand vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_vehicle = registered[registered['New/Used Vehicle'] == 'New Vehicle']\n",
    "old_vehicle = registered[registered['New/Used Vehicle'] == 'Used Vehicle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGRI-VEHICLE           3494\n",
       "BUS                     739\n",
       "CAMION                 6752\n",
       "CAMPER                 2203\n",
       "CAR                    5588\n",
       "CARAVAN                 815\n",
       "HEAVY 2-3-4 W          2066\n",
       "LIGHT 2-3-4 W           986\n",
       "NOT CLASSIFIED YET      156\n",
       "ROAD TRACTOR           1242\n",
       "S-TRAILER              1896\n",
       "SPECIAL USE            1674\n",
       "TRAILER                4460\n",
       "VAN                   12085\n",
       "dtype: int64"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_vehicle['Vehicle grouping by use'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGRI-VEHICLE     2185\n",
       "BUS               417\n",
       "CAMION           2280\n",
       "CAMPER            635\n",
       "CAR                89\n",
       "CARAVAN           280\n",
       "HEAVY 2-3-4 W     638\n",
       "LIGHT 2-3-4 W     369\n",
       "ROAD TRACTOR      332\n",
       "S-TRAILER        1245\n",
       "SPECIAL USE       744\n",
       "TRAILER          3001\n",
       "VAN                46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vehicle['Vehicle grouping by use'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = pd.concat((old_vehicle['Vehicle grouping by use'].value_counts(), \n",
    "                  new_vehicle['Vehicle grouping by use'].value_counts()),\n",
    "                keys=['old', 'new'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f313b3d2610>"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Kx6b2qy6alORmtkVhOqFmR5bGCae+wXhLGyteJaEmY0hNNJYRKjevDcuH0L4Ma4G3Onu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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313a777810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot(kind='bar')\n",
    "plt.title('Number of registered vehicles grouped by use and splitted by used/new')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without being an expert on the car business, some interesting observations:\n",
    "\n",
    "* A lot, really a lot, of second-hand vans are registered?\n",
    "* Almost no new cars are registered (where are newly produced cars going to?)\n",
    "* Probably the HEAVY and LIGHT 2-3-4 W are the famous SUV-cars?\n",
    "* Unless the decreasing amount of farms in Belgium, more agricultural vehicles and tractors are registered than cars?\n",
    "\n",
    "Anyone can help/clarify on this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore some other aspects visually:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amount of new and old vehicles for each region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bgZuAb9Vu/tuB02oPwSTg5z3U81vgAmDv+u28q9v/OTJzCeUOvT9k5l9q8UeA/46Imygf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\nBgJJkoSBQJIkAf8fUYb0+9DU9k0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313c1e5810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(\"Region Gewest\", hue=\"New/Used Vehicle\",\n",
    "                    data=registered, kind=\"bar\",\n",
    "                    size=6, palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amount of new and old vehicle for each province:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ArgQOBk7J/16RT7kSuFTSaaSugunA3NyqsFzSjsBc4CBSmubVWrbsmb4OaamlS59a4+OX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\nBhEREdHjfxkW0QiNhuLCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313ba6bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(\"Province_label\", hue=\"New/Used Vehicle\",\n",
    "                    data=registered, kind=\"bar\",\n",
    "                    size=6, palette=\"muted\")\n",
    "xt = plt.xticks(rotation=90) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amount of new and old vehicle for each month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f313b5fc110>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZwOHALGAH4JqImJmZ/cBZwNGZOT8iLo+IOZl55RjaIkkaf/qBfweeEREHZOb1A1+IiL8A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\nkhoGA0mS1DAYSJKkhsFAkiQ1/h8u8aTdJwc6lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313b5fc250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(\"Transaction Timest Month Code\", hue=\"New/Used Vehicle\",\n",
    "                    data=registered, kind=\"bar\",\n",
    "                    size=6, palette=\"muted\")\n",
    "g.set_xticklabels(range(1, 13) + ['nan'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all new vehicles, check the usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zuxD4FrA4cct4d18W614LfM3dh8bzx4HL3H1Kor3TgU8DewJHuPvrsfwHwGh3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\niJYkTERERERL/gIk+Fj8uK0vRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313c378090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot('Region Gewest', hue='Vehicle grouping by use',\n",
    "                    data=new_vehicle, kind=\"bar\",\n",
    "                    size=6, palette=\"muted\")\n",
    "xt = plt.xticks(rotation=90) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CO$_2$ characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 99.,  nan])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered['CO2 Category'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dus alle registraties hebben een categorie 99.?!?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fuel characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle Fuel Code  Vehicle Fuel Label \n",
       "01                 BENZINE                 9092\n",
       "02                 GASOLIE                32096\n",
       "03                 LPG + BENZINE           2145\n",
       "04                 ELEKTRICITEIT              3\n",
       "05                 ANDEREN                   73\n",
       "06                 AARDGAS (CNG)             22\n",
       "07                 BENZINE + ELEKTR.         45\n",
       "08                 GASOLIE + ELEKTR.          2\n",
       "09                 WATERSTOF                  1\n",
       "10                 MENGSMERING                1\n",
       "11                 GASOLIE + LPG              4\n",
       "12                 ELEKTRICITEIT + LPG        2\n",
       "13                 BIO-ETHANOL                2\n",
       "15                 BENZINE + CNG              4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered.groupby(['Vehicle Fuel Code','Vehicle Fuel Label']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f313cf8e190>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RB2D7HknDkgaAYyhy/G1573HgvDUcPyIiIlZDq43vu66D8SdLml85P972pS3a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\nkZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313d28e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "registered.groupby('Vehicle Fuel Label')['Vehicle Fuel Label'].count().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's zoom in to see also the less-popular options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yf6bzJC1O9z6SfuOY9BsfAE6UtLrcM5o2G0PzpO1qDdHMzGzUqLTYfB8qrIWS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\nIiIiItqURCoiIiKiTUmkIiIiItr0fz2XYQJUNv8eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313d563a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "registered.groupby('Vehicle Fuel Label')['Vehicle Fuel Label'].count().plot(ax=ax, kind='barh')\n",
    "ax.set_xlim([0, 100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How popular are electric cars?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take together all the cars with elektricity to make the comparison easier. First, an overview of the fule characteristics for each type of vehicle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle grouping by use                     \n",
       "AGRI-VEHICLE             GASOLIE                5053\n",
       "                         BENZINE                 439\n",
       "                         ANDEREN                  52\n",
       "                         LPG + BENZINE             6\n",
       "                         BIO-ETHANOL               1\n",
       "BUS                      GASOLIE                1154\n",
       "                         BENZINE                   2\n",
       "CAMION                   GASOLIE                8911\n",
       "                         BENZINE                  85\n",
       "                         LPG + BENZINE            26\n",
       "                         AARDGAS (CNG)             6\n",
       "                         GASOLIE + ELEKTR.         2\n",
       "                         ANDEREN                   1\n",
       "                         BENZINE + CNG             1\n",
       "CAMPER                   GASOLIE                2476\n",
       "                         BENZINE                 181\n",
       "                         LPG + BENZINE           177\n",
       "                         AARDGAS (CNG)             1\n",
       "                         BENZINE + ELEKTR.         1\n",
       "                         BENZINE + CNG             1\n",
       "CAR                      BENZINE                2609\n",
       "                         GASOLIE                2433\n",
       "                         LPG + BENZINE           565\n",
       "                         BENZINE + ELEKTR.        43\n",
       "                         ANDEREN                  11\n",
       "                         AARDGAS (CNG)             4\n",
       "                         ELEKTRICITEIT             2\n",
       "                         MENGSMERING               1\n",
       "                         WATERSTOF                 1\n",
       "                         BENZINE + CNG             1\n",
       "                         BIO-ETHANOL               1\n",
       "HEAVY 2-3-4 W            BENZINE                2547\n",
       "                         GASOLIE                  40\n",
       "                         LPG + BENZINE             8\n",
       "                         ELEKTRICITEIT + LPG       2\n",
       "                         GASOLIE + LPG             2\n",
       "                         ELEKTRICITEIT             1\n",
       "                         AARDGAS (CNG)             1\n",
       "LIGHT 2-3-4 W            BENZINE                1226\n",
       "                         GASOLIE                 127\n",
       "                         AARDGAS (CNG)             2\n",
       "ROAD TRACTOR             GASOLIE                1554\n",
       "                         ANDEREN                   9\n",
       "                         BENZINE                   5\n",
       "                         AARDGAS (CNG)             4\n",
       "                         LPG + BENZINE             1\n",
       "                         GASOLIE + LPG             1\n",
       "S-TRAILER                GASOLIE                   3\n",
       "SPECIAL USE              GASOLIE                1338\n",
       "                         BENZINE                 202\n",
       "                         LPG + BENZINE            41\n",
       "VAN                      GASOLIE                9007\n",
       "                         BENZINE                1796\n",
       "                         LPG + BENZINE          1321\n",
       "                         AARDGAS (CNG)             4\n",
       "                         BENZINE + ELEKTR.         1\n",
       "                         GASOLIE + LPG             1\n",
       "                         BENZINE + CNG             1\n",
       "Length: 58, dtype: int64"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered.groupby(['Vehicle grouping by use'])['Vehicle Fuel Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'HEAVY 2-3-4 W', u'CAR', u'BUS', u'CAMION', u'ROAD TRACTOR',\n",
       "       u'SPECIAL USE', u'S-TRAILER', u'TRAILER', u'AGRI-VEHICLE',\n",
       "       u'CAMPER', u'CARAVAN', u'LIGHT 2-3-4 W', u'VAN',\n",
       "       u'NOT CLASSIFIED YET', nan], dtype=object)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered['Vehicle grouping by use'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a 'car_alike' category, that combines a few types of vehicles:\n",
    "\n",
    "* car\n",
    "* heavy 2-3-4 W\n",
    "* light 2-3-4 W\n",
    "* van"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car_alike = registered[registered['Vehicle grouping by use'].isin(['CAR','HEAVY 2-3-4 W','LIGHT 2-3-4 W','VAN'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fuel_label = car_alike.groupby('Vehicle Fuel Label')['Vehicle Fuel Label'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle Fuel Label\n",
       "AARDGAS (CNG)             11\n",
       "ANDEREN                   11\n",
       "BENZINE                 8178\n",
       "BENZINE + CNG              2\n",
       "BENZINE + ELEKTR.         44\n",
       "BIO-ETHANOL                1\n",
       "ELEKTRICITEIT              3\n",
       "ELEKTRICITEIT + LPG        2\n",
       "GASOLIE                11607\n",
       "GASOLIE + LPG              3\n",
       "LPG + BENZINE           1894\n",
       "MENGSMERING                1\n",
       "WATERSTOF                  1\n",
       "Name: Vehicle Fuel Label, dtype: int64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of registered vehicles with electicity is  49 on a total of 21758\n"
     ]
    }
   ],
   "source": [
    "electric = car_alike['Vehicle Fuel Label'].str.contains('ELEK').sum()\n",
    "print 'Total number of registered vehicles with electicity is ', electric, 'on a total of', fuel_label.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "electric_perc = 100*float(electric)/fuel_label.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That makes 0.225 %\n"
     ]
    }
   ],
   "source": [
    "print 'That makes %.3f %%' %electric_perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's only compare the new cars:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New electric cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "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>New/Used Vehicle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New/Used Vehicle</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New Vehicle</th>\n",
       "      <td>  1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Used Vehicle</th>\n",
       "      <td> 20725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  New/Used Vehicle\n",
       "New/Used Vehicle                  \n",
       "New Vehicle                   1142\n",
       "Used Vehicle                 20725"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(car_alike.groupby('New/Used Vehicle')['New/Used Vehicle'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_car_alike =  car_alike[car_alike['New/Used Vehicle'] == 'New Vehicle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values:\n",
    "sum(new_car_alike['Vehicle Fuel Label'].isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the mapping between code and label columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle Fuel Code  Vehicle Fuel Label\n",
       "01                 BENZINE               938\n",
       "02                 GASOLIE               191\n",
       "03                 LPG + BENZINE           3\n",
       "07                 BENZINE + ELEKTR.       8\n",
       "09                 WATERSTOF               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_car_alike.groupby(['Vehicle Fuel Code','Vehicle Fuel Label']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The codes and the labels do agree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if a code is given when the label is not:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting the new registered electric cars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of registered NEW cars with electicity is  8 on a total of 1141\n"
     ]
    }
   ],
   "source": [
    "electric_new = new_car_alike['Vehicle Fuel Label'].str.contains('ELEK').sum()\n",
    "print 'Total number of registered NEW cars with electicity is ', electric_new, 'on a total of', new_car_alike['Vehicle Fuel Label'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That makes 0.701 %!\n"
     ]
    }
   ],
   "source": [
    "electric_new_perc = 100*float(electric_new)/new_car_alike['Vehicle Fuel Label'].count()\n",
    "print 'That makes %.3f %%!' %electric_new_perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Euro norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f313c378d10>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAycz80ebdeuAQ8BW4Bhwc2a+1GzbB9wGXALuzMyHF6d0SZJ6y2xmyp8Adl227h7gkczc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313b81d5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "euro = new_car_alike['Vehicle Euro Norm Code'].value_counts(dropna=False).sort_index()\n",
    "euro.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New cars still have mostly a EURO-norm 2 (or 3), which relates to the years 1996 (2000 respectively)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euronorm all registered vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fRif9NLVlf5Bpc7Bbv4iImVQHXp1M9cKzFfhOy27TyexlIbA3M+8dMT4DeH6n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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313b159c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "euro = registered['Vehicle Euro Norm Code'].value_counts(dropna=False).sort_index()\n",
    "tt = euro.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some observations checking this dataset:\n",
    "\n",
    "* Missing values, so 1 registration has no value\n",
    "* Again, some 'missing values' by empty strings, i.e. cars without a given Norm Code\n",
    "* Values for the norm is given in both arabic and roman numbers\n",
    "* Values are also given in P1, P2,... Are these the same as 1, 2, 3,...?\n",
    "* EEV, after google: \"EEV, enhanced environmentally friendly vehicle\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's restructure this:\n",
    "\n",
    "* We put everything in 1, 2, 3...\n",
    "* For the ease of usage: 5a and 5b are combined as 5,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "registered['Vehicle Euro Norm Code'] = registered['Vehicle Euro Norm Code'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'2', u'3', u'5', u'5b', u'V', u'', u'EEV', u'VI', u'P3A', u'P3B',\n",
       "       u'6', u'5a', u'0', u'1', u'4', u'IV', u'P1', u'P2', u'6b', u'II',\n",
       "       u'III', u'6a', u'P4', u'I', nan], dtype=object)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registered['Vehicle Euro Norm Code'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map_dict = {'I': '1', 'II': '2', 'III': '3', 'IV': '4', 'V': '5', 'VI': '6',\n",
    "            '5a':'5', '5b': '5', 'P1':'1', 'P2':'2', 'P3A':'3', 'P3B':'3', 'P4':'4',\n",
    "            '6a' : '6', '6b': '6', '' : np.nan}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "registered['Vehicle Euro Norm Code'] = registered['Vehicle Euro Norm Code'].replace(map_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f313abc0390>"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f313dd9eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "registered['Vehicle Euro Norm Code'].value_counts(dropna=False).sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}