{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 5 - Combining dataframes and scraping Canadian weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "line_width has been deprecated, use display.width instead (currently both are\n",
      "identical)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "\n",
    "# Make the graphs a bit prettier, and bigger\n",
    "pd.set_option('display.mpl_style', 'default')\n",
    "figsize(15, 5)\n",
    "\n",
    "# Always display all the columns\n",
    "pd.set_option('display.line_width', 5000) \n",
    "pd.set_option('display.max_columns', 60) \n",
    "\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012_final = pd.read_csv('./data/weather_2012.csv', index_col='Date/Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>89</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.26</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.27</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>101.23</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)               Weather\n",
       "Date/Time                                                                                                                              \n",
       "2012-01-01 00:00:00      -1.8                -3.9           86                4              8.0           101.24                   Fog\n",
       "2012-01-01 01:00:00      -1.8                -3.7           87                4              8.0           101.24                   Fog\n",
       "2012-01-01 02:00:00      -1.8                -3.4           89                7              4.0           101.26  Freezing Drizzle,Fog\n",
       "2012-01-01 03:00:00      -1.5                -3.2           88                6              4.0           101.27  Freezing Drizzle,Fog\n",
       "2012-01-01 04:00:00      -1.5                -3.3           88                7              4.8           101.23                   Fog"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012_final[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b23bf90>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ITZaJ4a6YjhWqUWdimNxk3XkwxmeTpfNRr1lX2FwLo22H2vsA+zlo4c45NgJsXDf1Nk8F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\n959heNbp7YkxTpyLYOxVRDAKwkLGzTQm7nw8FoWSwAA+lm2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kBBHGIZre0ESGzBLMyU1o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\niIiIyBUDRiIiIiIiInL1/wGcFHaau++tVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b330a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_2012_final['Temp (C)'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Downloading one month of weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = url_template.format(month = 3, year = 2012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012_mar = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Data Quality</th>\n",
       "      <th>Temp (°C)</th>\n",
       "      <th>Temp Flag</th>\n",
       "      <th>Dew Point Temp (°C)</th>\n",
       "      <th>Dew Point Temp Flag</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Rel Hum Flag</th>\n",
       "      <th>Wind Dir (10s deg)</th>\n",
       "      <th>Wind Dir Flag</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Wind Spd Flag</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Visibility Flag</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Stn Press Flag</th>\n",
       "      <th>Hmdx</th>\n",
       "      <th>Hmdx Flag</th>\n",
       "      <th>Wind Chill</th>\n",
       "      <th>Wind Chill Flag</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>01:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>02:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>03:00</td>\n",
       "      <td></td>\n",
       "      <td>-4.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>04:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year  Month  Day   Time Data Quality  Temp (°C)  Temp Flag  Dew Point Temp (°C)  Dew Point Temp Flag  Rel Hum (%)  Rel Hum Flag  Wind Dir (10s deg)  Wind Dir Flag  Wind Spd (km/h) Wind Spd Flag  Visibility (km)  Visibility Flag  Stn Press (kPa)  Stn Press Flag  Hmdx  Hmdx Flag  Wind Chill Wind Chill Flag Weather\n",
       "Date/Time                                                                                                                                                                                                                                                                                                                                       \n",
       "2012-03-01 00:00:00  2012      3    1  00:00                     -5.5        NaN                  -9.7                  NaN           72           NaN                   5            NaN               24           NaN              4.0              NaN           100.97             NaN   NaN        NaN         -13             NaN    Snow\n",
       "2012-03-01 01:00:00  2012      3    1  01:00                     -5.7        NaN                  -8.7                  NaN           79           NaN                   6            NaN               26           NaN              2.4              NaN           100.87             NaN   NaN        NaN         -13             NaN    Snow\n",
       "2012-03-01 02:00:00  2012      3    1  02:00                     -5.4        NaN                  -8.3                  NaN           80           NaN                   5            NaN               28           NaN              4.8              NaN           100.80             NaN   NaN        NaN         -13             NaN    Snow\n",
       "2012-03-01 03:00:00  2012      3    1  03:00                     -4.7        NaN                  -7.7                  NaN           79           NaN                   5            NaN               28           NaN              4.0              NaN           100.69             NaN   NaN        NaN         -12             NaN    Snow\n",
       "2012-03-01 04:00:00  2012      3    1  04:00                     -5.4        NaN                  -7.8                  NaN           83           NaN                   5            NaN               35           NaN              1.6              NaN           100.62             NaN   NaN        NaN         -14             NaN    Snow"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012_mar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b4b2590>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D5jUnv/6wjU6QE2rqWQpJtEwxWCMiIlpqYpHE+9Um1DcBXsxFm14GWVvvLLPkVopgTarr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\nc2S5OCLX/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4b2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_2012_mar[u\"Temp (°C)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012_mar = weather_2012_mar.dropna(axis=1, how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Data Quality</th>\n",
       "      <th>Temp (°C)</th>\n",
       "      <th>Dew Point Temp (°C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.97</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>01:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>79</td>\n",
       "      <td>26</td>\n",
       "      <td>2.4</td>\n",
       "      <td>100.87</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>02:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>80</td>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.80</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>03:00</td>\n",
       "      <td></td>\n",
       "      <td>-4.7</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.69</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>04:00</td>\n",
       "      <td></td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>83</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>100.62</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year  Month  Day   Time Data Quality  Temp (°C)  Dew Point Temp (°C)  Rel Hum (%)  Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather\n",
       "Date/Time                                                                                                                                                          \n",
       "2012-03-01 00:00:00  2012      3    1  00:00                     -5.5                  -9.7           72               24              4.0           100.97    Snow\n",
       "2012-03-01 01:00:00  2012      3    1  01:00                     -5.7                  -8.7           79               26              2.4           100.87    Snow\n",
       "2012-03-01 02:00:00  2012      3    1  02:00                     -5.4                  -8.3           80               28              4.8           100.80    Snow\n",
       "2012-03-01 03:00:00  2012      3    1  03:00                     -4.7                  -7.7           79               28              4.0           100.69    Snow\n",
       "2012-03-01 04:00:00  2012      3    1  04:00                     -5.4                  -7.8           83               35              1.6           100.62    Snow"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012_mar[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012_mar = weather_2012_mar.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Temp (°C)</th>\n",
       "      <th>Dew Point Temp (°C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
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       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td>-5.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.97</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td>-5.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>79</td>\n",
       "      <td>26</td>\n",
       "      <td>2.4</td>\n",
       "      <td>100.87</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>80</td>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.80</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.69</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>83</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>100.62</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (°C)  Dew Point Temp (°C)  Rel Hum (%)  Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather\n",
       "Date/Time                                                                                                                    \n",
       "2012-03-01 00:00:00        -5.5                  -9.7           72               24              4.0           100.97    Snow\n",
       "2012-03-01 01:00:00        -5.7                  -8.7           79               26              2.4           100.87    Snow\n",
       "2012-03-01 02:00:00        -5.4                  -8.3           80               28              4.8           100.80    Snow\n",
       "2012-03-01 03:00:00        -4.7                  -7.7           79               28              4.0           100.69    Snow\n",
       "2012-03-01 04:00:00        -5.4                  -7.8           83               35              1.6           100.62    Snow"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012_mar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Plotting the temperature by hour of day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperatures = weather_2012_mar[['Temp (C)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangshuailong/Library/Python/2.7/lib/python/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "temperatures['Hour'] = temperatures.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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X/zKK9tRDUpVqv+zo2GQfWQVWkAD4RYnvCDk5Obr99ttVqVIlHXPMMapVq1Y8cwEAgFLy\n5x6XgkA2aGTUUdKO5VeRdeopdeopD7dISxbFNguZ/E/ppx9lg0bJeg6kOQMgSTIv5XDXV199pcmT\nJ+v888/f7vPWrl1bmssACScrK0uFhYVRxwDiirpOHUXz39OGu65T/nX3KqhaLeo4kUq0ut7yzRJt\nfPAuhT/9qNyjT1fmvh2jjoQklGh1jR3Lz8/f5tdK/fFMZmamMjIydvg8mjKkmvz8fOoaKYe6Tg2+\neqXCO69VcPxftT4jU0rz39OEq+uqu8vPHCPNe0fr/3mbVLOuglHHy2rVizoZkkjC1TV2qEyasttu\nu00rV65Ubm6uTjjhhJK+DQAAiCMPtyi8/1ZZtz6yFvtGHQfbYGZS244KWu4nf/k5hTdcKOt0kOyQ\nP8S24QeQVko9fXFnFRQUlMdlgHLDJ1RIRdR18gufe1z+2UcKzrlathMzWdJBMtS1r1kln/qw/KO3\nZYccITuwH79/2K5kqGv8Wp06dbb5NQ4rAQAgRfjnH8tnz1Bw4nn8QJ9krHJVBUefoeAvl8vfeVXh\nVX+Vf/ZR1LEAlBO2/AEAIAX4mlUKx9+i4NizZLtVjzoOSsgaNFZw3jXS+28onHCHVL+xgpHHyWrU\njjoagDLESBkAAEnOw1Dh/WNlnXvKWrWLOg5Kycxk7boouOouWcO9FF53nsInJsg3bYg6GoAyQlMG\nAECS8+eflDZvlA05MuooiCPLzFIwaJSCy26XVq9UeMlpCufOlIdh1NEAxBlNGQAAScwXfSqf+bSC\nk8/nIOIUZVWrKzj+rwpOv1g+d6bCa86VL/o06lgA4oimDACAJOUr/q1w/M0KjjlLVm2PqOOgjFmj\npgouvEHWb5jC+25WeO9N8p+WRx0LQBzQlAEAkIT80w8VXnuurPchsrYdoo6DcmJmCg7ooeCqu6Ra\ndRVe9VeFTz8i37wp6mgASoGmDACAJOJhqPC5xxX+8zYFJ5yjoN/wqCMhApado2DIHxWMuU36cZnC\nMacpfHMW682AJMXkcwAAkoSvW6Pw/lulTZsUXHwLW99DVn0P2UnnyRd/qvDx8fJZ0xUcfpKsUbOo\nowHYBTRlAAAkAf/yc4X33Chr3002/E9s6oFfsb1aKPjbzfI3Zim861rZPm1lhx5N4w4kCe7oAAAk\nMHeXz5omf26igqNOk+3fOepISFAWBLKuveXtOsunT1F4xVmyvkNj/2RlRx0PwHawpgwAgATlmzbK\n77tZ/upMBRfdQEOGnWI5eQoOPVrBxbfIl36h8NLT5e/OlbtHHQ3ANtCUAQCQgLxgqcJrzpWycxSM\nvlFWo07UkZBkbI9ayjh1tILj/qJw2iSFN42WL/0i6lgAfgfTFwEASDDhW6/IH79PNuJYBV37RB0H\nSc72bq1gzNjYwdPjrpC16SAbfpSs8m5RRwPwM0bKAABIEF5UpPCRf8ifeVTBOVfRkCFuLMhQ0L1/\n7Hyz3DyFl52h8Pkn5UVFUUcDIJoyAAASgv/7B4U3XChfs0rBxbfK6jeKOhJSkOVVUjDqBAUX3ihf\n+InCy8+Qf/gm682AiDF9EQCAiPm8dxQ+cLtswAhZnyEys6gjIcVZrbrKOHOM/JMPFE4cL/3fkwoG\njpRat6f+gAiYl9NHIwUFBeVxGaDc5Ofna+3atVHHAOKKui5fvmWL/JlH5W/MUnDyebK9WkQdKSVR\n19vn4Rb5e6/Lp0+WZLKBI2XtOsuCjKijYTuo6+RTp862N2xipAwAgAj4mpUK771ZCgIFl9wqq1w1\n6khIUxZkyDocKG/fTZr3rsLpk+TPPCLrP0J2QA8OKgfKAX/KAAAoZ77wE4X33Szr2ls25AhGJJAQ\nzExq20FBm/bSgnkKZ0yRP/OorP+hsq59OIAaKEM0ZQAAlBN3l78wVf78kwqO+6usdbuoIwG/YWZS\n87bKaN5W/sWCWHM2bVJsvWPPAbKcvKgjAimHpgwAgHLgG9Yp/Nft0qqfFFx8i6x6jagjATtkTfZR\nxhmXyL9dIp8+ReHok2W9BsoOGiyrVDnqeEDKYEt8AADKmC/9UuHV58h2q6bggutpyJB0rF4jBSef\nr+CiG6WVPym85FSFk/8lX7Ui6mhASmD3RaCE2PUIqYi6jr/w1RfkTz4oO/wkBQf0iDpOWqKu489X\nLJc//5T8zdmyjgfKDj5UtnvNqGOlFeo6+Wxv90WaMqCEuBkiFVHX8eObN8sfu1v+5UIFp14kq10/\n6khpi7ouO75mlfzFp+VzXpC1aS8bMFJWu17UsdICdZ18tteUMX0RAIA48x8KFF5/vlRUrOBvN9OQ\nIWVZ5aoKDj1GwTX3SDVqK7xptLbcfb186RdRRwOSCht9AAAQR/7+6wofuks29I+yHgNiO9kBKc4q\nVpINPlzeZ6h8zvMK77hKqtdIwaCRHIoO7IRSN2XFxcX6y1/+oiFDhujggw+ORyYAAJKOFxfLn5wg\nf/8NBWddJmvUNOpIQLmznFxZv2HyXoPkr7+k8P6xUrXdFQwcJbXYlw8pgG0odVP2wgsvqFGjRvHI\nAgBAUvKVPym890Ypt6KCMWNlFfOjjgREyjIzZT36y7v1lb8zR+HE8VJWtoJBo6S2HWUBK2iA/1aq\npqywsFDz5s1T586dtWnTpnhlAgAgafhnHym8/9bYuU39D+OHTeC/WEaGrFMvecce0odvKnxuojT1\nYdmAEbIOB8oyMqKOCCSEUu2+OHXqVDVs2FCrVq3S5s2btzt9kd1hkGqysrJUWFgYdQwgrqjrnedh\nqM1TH9HmmU8r7/S/KbPV/lFHwjZQ14nD3VU87x1teuoR+Yp/K3vI4crqcbAsMyvqaEmHuk4++fnb\nnkVR4pGyDRs2aMGCBRo2bJhmz56tHfV2NGVINWxFi1REXe8cX7cmtlZm00YFf7tZm6pW1yb+vyUs\n6jrBNG4unXu1tPATbZwxWRunTJD1GybrfrAsOyfqdEmDuk4+ZdKULViwQEVFRRo3bpx+/PFHhWGo\nVq1aqV49zqYAAKQuX7JQ4T03ytp3lQ37k6wCGxkDJWHNWiqjWUv514sVTp8inzFF1vsQWa+BsrxK\nUccDylVcDo9+5ZVXtGnTpu1OX+TwaKQaPqFCKqKut81X/iR/9QX57OkK/nS6bL9OUUfCTqKuk4MX\nLJXPeEI+/93YqFnfobL8KlHHSljUdfLZ3uHRcWnKdgZNGVINN0OkIur617y4SProHYWvvSh9sSA2\nOnbwobIataOOhl1AXScXX/69/Pkn5e/MlXXuJes3XFZt96hjJRzqOvnQlAFlgJshUhF1HePffiV/\n7UX5W69IdRrIuvaR7d9Flp0ddTSUAHWdnHzVT/KZT8vnvihr10XW/1BZjW3/UJtuqOvkQ1MGlAFu\nhkhF6VzXvmGd/O1X5XNnSmtWybocJOvSm1GxFJDOdZ0KfN0a+UvPymdPl7XYTzZwpKzunlHHihx1\nnXy215SxOhkAkLY8DKXP58dGxea9K2uxr4JhR0ot9pUFnJ8EJAKrVFk29Eh5v+Hy2TMU3jpGary3\ngoEjZY2aRR0PiAuaMgBA2vGflstff0n+2otSbp6sW18FfzhJll856mgAtsFy82QDDpMfNFj+2kyF\nd18v1ayrYNAoqVkrmVnUEYESoykDAKQFLyqUf/iWfO6L0tLFsg4HKjh1tNSgMT/MAUnEsrNlBw2W\ndz9Y/uZshQ/+XcqvrGDgSKl1e/48IymxpgwoIeZyIxWlYl370i/kc1+UvzNHqt/45007Ossys6KO\nhnKSinWNX3i4Rf7e6/LpkyVZbM1Zu84pPwWZuk4+bPQBlAFuhkhFqVLXvm6N/K058tdmShvWxzbs\n6HKQbPeaUUdDBFKlrrF97i7Ne0fhtEnSxvWy/iNkB/RI2QPeqevkw0YfAICU5+EW6bN5sU07Pn5f\n1rq9gpHHS3u3lgVB1PEAlDEzk9p2VNCmg7RgnsIZU+TPPBrbSr9rH1kWR1ogcdGUAQCSmi//PrZp\nx+svSflVZV37KDjyVFnFSlFHAxABM5Oat1VG87byLxbEmrNpk2R9hsh6DpDl5EUdEfgNpi8CJcS0\nAaSiZKlr37xZ/sHrsU07vvs6NkWpax9Z/UZRR0MCSpa6Rtnxb5fIp0+Rf/aRrNdA2UGDZZWSe7dV\n6jr5MH0RAJD03F36arF87kz5u3Nj5xT1Gii16SjLzIw6HoAEZvUayU4+X/5DgXzGFIUXnyLr1lfW\nd6isarWo4wGMlAElxSdUSEWJWNe+drX8zdmxM8UKN8dGxDofJKu2e9TRkCQSsa4RLf9pufyFp+Rv\nzpZ17C47eHjSbQREXScfdl8EygA3Q6SiRKprX7tG/ujd8k8+kO3bUda1r9S0BZt2YJclUl0jsfia\nVfIXn5bPeUHWpoNswAhZ7XpRx9op1HXyoSkDygA3Q6SiRKlr/6FA4e1XyPbrJBs4SpZXMepISGKJ\nUtdIXL5+nXzWc/KXp0nNWioYOFLWoEnUsbaLuk4+rCkDACQNX/iJwntukA07SsGB/aKOAyANWMVK\nssGHy/sMlc95XuEdV0n1GikYNFK2V4uo4yEN0JQBABJG+NYr8onjFZx4jqzFflHHAZBmLCdX1m+Y\nvNdA+esvK7x/rFRtDwUDRkj7tJZVYFMhlA2aMgBA5NxdPm2SfO5MBedeLau7Z9SRAKQxy8yS9egv\n79ZX/s4chU89JH3/jVSrnqx+Y6lBY1mDxlK9RrKc3KjjIgXQlAEAIuXFRfKH7pJ/97WCi25ke2oA\nCcMyMmSdekmdesk3b5a++0q+9Evpmy8Vvv6ytGyptNsesQat/s+NWoPGsvwqUUdHkqEpAwBExjes\nU/iP66XsHAXnXyvLzok6EgD8LsvOlhrvLWu899bHvLhY+v7bXxq16ZOlb5ZI2TlbR9OsQROpQWOp\n2h4yswi/AyQymjIAQCT83z8ovP1KWYt9ZaOOlwUZUUcCgF1iFSpI9RrK6jWUdJCknw+6//cP0tIv\n5Uu/VPjqC9LSL6Wiwl+mPf5nVK1WXe59kERTBgCIgC9ZqPDv18oGjFDQe3DUcQAgbsxM2qOWtEct\nWbsuWx/31St/btS+kH/4pvyZR6XVK6W6e/4y7bF+E6nenrLMrAi/A0SBpgwAUK78/TcUPnyXgmPO\nlLXtGHUcACgXVmU3qXU7Wet2Wx/zDeulb5fEpj8uXqBw1nTphwKpRu1fbyhSv5Esr1KE6VHWaMoA\nAOXC3eUzp8pnPqPgL5fL9kzsg1kBoKxZXkWpWStZs1ZbH/OiQqlgaaxRW/qFwnfnSt99LeVX+blJ\nayJr0Fi+Lx9qpRJzdy+PCxUUFJTHZYByk5+fr7Vr10YdA4irsqpr37JF/vi98kWfKjjzUln1PeJ+\nDWBbuF8j2Xm4Rfqh4OdGLTYF0pZ9Iw0+XNa9H+vSkkSdOnW2+TVGygAAZco3bVB4z01SGCq48AZZ\nbl7UkQAgqViQIdWuL6tdXzqghyQp99/fa92/xslnT1fwhxNlzdtGnBKlQVMGACgzvvKn2A6LjZrK\n/nhKbKcyAECpVWjUVMF510rvv65wwh1S/cYKRh4nq1E76mgogSDqAACA1OTfLFF4/fmyA7rL/nQ6\nDRkAxJmZydp1VXDVXbKGeym87jyFT0yQb9oQdTTsohL/Dfn444/r888/VxAE+vOf/6waNWrEMxcA\nIIn5/HcV/mucgj/+Wda+W9RxACClWWaWbNAoedfe8icfUnjJabJhR8q69JYFjMEkg1Jv9LFgwQLN\nmTNHJ5988nafx0YfSDUsHEcqikddh7Ony5+bqODU0bIm+8QpGVBy3K+RirZX175kocKJ46Xi4th6\ns6Ytyjkdfk+ZbvSxePFi1a1bt7RvAwBIch6G8icekM97R8EF17OuAQAiYo2aKbjwBvnbcxSOv1nW\npLnssGPZ+TaBlWqk7LLLLtOaNWt01VVXqVKl7R9oxydUSDVZWVkqLCyMOgYQVyWta9+8SRvuvFbh\nujWqeO6VCipVLoN0QMlwv0Yq2tm69k0btenZiSp8/ill9RuqnCFHyHJyyyEh/ld+fv42v1bq6YuL\nFy/W5MmTNXr06O0+j+mLSDVMh0EqKkld+5qVCu+8RlazruzoM2SZmWWUDigZ7tdIRbta1/7T8ths\nhsWfyQ720Vb8AAAgAElEQVQ9WnZAD5lZGSbE/yrT6YtVq1ZVGIalfRsAQBLygqUK77hK1rmX7JAj\n+AseABKUVd9DdvL58sWfKnx8vHzWNAWHnyRr1CzqaFApmrKxY8dq7dq1yszM1PHHHx/PTACAJOCf\nfaTwvptlI45T0OWgqOMAAHaC7dVCwd9ulr8xS+Fd18qat42NnFWtHnW0tFbq6Ys7i+mLSDVMh0Eq\n2tm6Dl97Sf7EAwr+fIFs79blkAwoOe7XSEXxqGvftEE+fYr81edlfYbK+g6VZWXHKSH+1/amL3Jw\nAQBgp7m7wqkPy6dNVHD+dTRkAJDELCdPwaFHK/jbLfKlXyi89HT5e6+pnMZs8F9KvaYMAJAevKhI\n/sDt8n9/r+CiG2WVq0YdCQAQB7ZHLWWcOlq+YF7sfLOXn4udb9agSdTR0gYjZQCAHfJ1axSOHSMv\nLlJw7tU0ZACQgmyfNgrGjJUd0EPhuCsUPninfM3KqGOlBZoyAMB2+Y8FCq+/UNZ479gaMtYbAEDK\nsiBDQff+Cq66S8rJVXjZGQqff0peXBR1tJRGUwYA2CZf/KnCGy6S9R2qYMRxsoC/NgAgHVheJQWj\nTlBw4Y3yhR8rvOwM+Ydvsd6sjLCmDADwu8J3XpU/dq+C4/8qa9Uu6jgAgAhYrbrKOHOM/JMPfr3e\nrO6eUUdLKXzkCQD4FXdXOG2SfMq/FJxzJQ0ZAEDWcj8Fl46TtT1A4S2XKHz0bvm6NVHHShk0ZQCA\nrby4WP7gnfL3X1cw+iZZvUZRRwIAJAirUEFB78EKrvy7JFN46ekKX3pWXlwcdbSkx+HRQAlxGClS\njW9Yr2D8zSqWKTj5fFlObtSRgLjgfo1UlAh17d8tVTjpfmnlvxWMPF5qtb/MLNJMiWx7h0fTlAEl\nlAg3QyBefO0ahbddpqy9W6r4sONkGRlRRwLihvs1UlGi1LW7S/PeVfjEA1JOroKBI6Q2HdkY6nds\nryljow8ASHO+eqXCW8fI2nZQ7tGna926dVFHAgAkCTOT2nZQ0Lqd9MGbCp+dKE19RDZghKxDN1nA\nh3w7g6YMANKYr1iu8JYxss69ZINGMe0EAFAiFgRSuy4K9u8sffK+wmmT5c88Kut/WOzvmAqZUUdM\naDRlAJCmfPn3Cm+5RHbQYAX9hkUdBwCQAsxMatVOGa3ayRd+onD6JPmzj8v6DZMdeLAsOzvqiAmJ\npgwA0pAv+1bh2EtlA0co6Dkw6jgAgBRkzVoqo9kV8q8WKZwxRT5jiqz3IbKeA2V5FaOOl1BYgQcA\naca/WRIbIRt2pGjIAABlzRo2VcapoxWce7W07FuFF5+s8KmH5Ws55+w/aMoAII34koUKx16q4PAT\nFXTpHXUcAEAasToNFJxwtoK/3SKtW6PwklMUThwvX/lT1NEiR1MGAGnCF36i8I6rFBxzpqx9t6jj\nAADSlO1RS8GfTlNw+R2SmcLLz1T40N/lPy6LOlpkaMoAIA34px8q/Md1Ck48R9a2Y9RxAACQ7VZd\nwagTFFx9t5RfReF15ykcf4v8u6VRRyt3bPQBACnOP3pH4YTbFZw6WtasZdRxAAD4FcuvLBt2lPzg\nQ+Wzpyu89RKp8T4KBo2UNWwadbxyQVMGACnM352r8NF7FJw5RtaoWdRxAADYJsvNkw0YIT/oEPnc\nmQr/cZ1Uq76CgSOlZi1T+ixNmjIASFHh6y/Ln5yg4OwrZfUbRR0HAICdYtnZst6D5T0Olr8xS+GD\nd0iVqyoYOEpqtX9KNmc0ZQCQgsLZM+TTJys492pZ7fpRxwEAYJdZhUzZgf3kXXvL331N4RMPSFMf\nio2c7ddJFmREHTFuaMoAIMWEL0yVv/ycgvOukdWoHXUcAABKxYIMWcfu8vbdpPnvKpw2SZr6iGzA\nYbKOPWQVkr+lSf7vAAAgSXJ3+bRJ8jdmKbjgOlm1PaKOBABA3FgQSG07KmjTQVowT+H0yfJnHpP1\nP1TWtY8sMyvqiCVGUwYAKcDd5U89KP/onVhDVmW3qCMBAFAmzExq3lYZzdvKv1gQa86emyTrO1TW\n42BZTl7UEXcZTRkAJDkPQ/mk++WLPlVw3rWy/MpRRwIAoFxYk32UceYY+TdL5DOmKBz9hKzXIFnv\nwbKK+VHH22kcHg0ASczDLfKH75J/tUjBuVfRkAEA0pLVb6Tg5PMVXHiDtGK5wotPUfjs4/LNm6OO\ntlNKPFJ27733atmyZXJ3nXbaaapRo0Y8cwEAdsC3bJH/8zb56hUK/nqFLCc36kgAAETKatWVHXuW\n/N8/yJ+YoPDS02SHHSPrcGBCb6Vv7u6leYOPP/5Yb7zxhk466aTtPq+goKA0lwESTn5+vtauXRt1\nDKQpLypSeN9NUlGRglMvkmVlx+V9qWukIuoaqYi63jm+8GOFE8dLWdkK/nCirGHTyLLUqVNnm18r\n9fTF3NxcZWZmlvZtAAA7yQs3K7zrGklScNrf4taQAQCQaqxZKwUX3yLr0lvhnVcrfGCcfPXKqGP9\nRqlHyu677z4NHDhQdevW3e7z6OSRarKyslRYWBh1DKQZ37RR62+6WFa1mvJOGy3LiO/BmdQ1UhF1\njVREXe8637Bem556WIWzpyt78B+UPWCELKv8ttHPz9/2xiOlasree+89ff/99xo0aNAOn8v0RaQa\npg2gvPmGdQpvv1JWp4HsqFNlQXwbMom6RmqirpGKqOuS8x8LFE7+l/Td1wpGHCft16lc1puVyfTF\nL7/8Up988slONWQAgNLxtWsU3jJGtudesqNOK5OGDACAdGA16ijj9IsV/Ol0hc88qvCWS+TfLok2\nU0lHys444wxVr15dQRCoQYMGOu6447b7fEbKkGr4hArlxVevVHjrGFnbDrLhR5fpp3nUNVIRdY1U\nRF3Hh2/ZIp/zvPzZx2T7dZYNO1KWX6VMrrW9kbJSrynbWTRlSDXcDFEefMVyhbdeKuvcS8GgUWV+\nPeoaqYi6RiqiruPL16+TP/uY/K1XZANGyA4aJKsQ380My3T3RQBA2fAflym8cbSsR/9yacgAAEhX\nVrGSgsNPUnDB9fLPPlJ4+Vnyee+onMavSn54NACg7PiybxWOvVQ2cKSCngOijgMAQFqw2vWU8ZfL\n5PPfVTjpfunl52Lnm9WuX6bXpSkDgATj3yxROO4K2aFHK+hyUNRxAABIO9a6vYLm+8pnT4vNWjmg\nh+yQw2UVt72tfWkwfREAEogvWahw7KUKDj+RhgwAgAhZhQoK+gxVcOVd0pZihWNOUzhrmnzLlrhf\ni6YMABKEL/xE4R1XKTjmTFn7blHHAQAAkiy/soIjT1VwzlXy999QeOVf5J9+ENdrMH0RABKAf/qh\nwvG3KDjxXFmLfaOOAwAA/ofVa6jgnKukD99S+PA/pDoNFIw8XlZz27sq7ixGygAgYv7RO7GG7NTR\nNGQAACQwM5Pt10nBFX+X7dVc4fXnK5z8L/mG9aV6X5oyAIiQvztX4YTbFZx5qaxpi6jjAACAnWCZ\nmQr6H6bg8jul9WsVXnqawldfkIclW2/G9EUAiIC7y+fOlD/9qIJzrpTVaxR1JAAAsIusym6yY8+S\nf71Y4ePj5bOmKTj8JFmzVrv0PjRlAFDOfOmXCifeJ61fp+Dcq2W160UdCQAAlILtuZeCC66Tv/ua\nwn/eJjXcS8GI42S719yp19OUAUA58TWr5FMfln/4lmzokbID+8qCjKhjAQCAODAzWYdu8rYd5C88\npfDqc2Q9+ssGjJDl5G73tawpA4Ay5sVFCp9/SuFlp0vZuQqu/oeCHv1pyAAASEGWla1g8OEKLh0n\nrVgeO9/s9Ze3+xpGygCgjLi79NHbCif/U6pZV8GFN8hqMVURAIB0YNV2l51wjvyLBQonjpdGHLXN\n59KUAUAZ8O++jt2AV61QcMSfZa32jzoSAACIgDXZR8FFN273OTRlABBHvm6N/JlH5e++Jhs0StZj\ngKwCt1oAANKZBdtfNcZPCgAQB15cLH9lhnzaJFn7rgqu/LusUuWoYwEAgCRAUwYApeQfv6dw0j+l\n3aorOPcaWd0GUUcCAABJhKYMAErIv/821oz98J2CkcdLbTvKzKKOBQAAkgxNGQDsIt+wTv7sRPmb\nL8v6j5CdOlqWmRl1LAAAkKRoygBgJ3m4RT7nBfmzj8nadlRwxZ2yyrtFHQsAACQ5mjIA2An+2Uex\nLe4rVlLwl8tkDZpEHQkAAKQImjIA2A5f/n3s8OelXyoYeZy0fxfWjQEAgLhKq6bMN22Uvl0i/3GZ\nbN8DZHmVoo4EIEH5pg3y6ZPlc16Q9R0qO+k8WWZW1LEAAEAKStmmzNeulpZ+KV/6pfTNz/9euVyq\n3UCqspv8iQmyQ46QHdhPlpERdVwACcLDUP7Gy/KnHpa1aKvg8ttlVatHHQsAAKSwpG/K3F1asfzn\nBuyLWPO19Etp80apfmNZg8ZSq3YKBo6UatWTVYh9y770S4UTx8tnT1fwhxNlzdtG/J0AiJov+jS2\nbqxCBQWn/03WqFnUkQAAQBowd/fyuFBBQUGp38PDLdL33/169Gvpl1Jm5tYGzBo0luo3lnavKQuC\n7b+fu/T+G7H1IvUbKxh5nKxG7VLnRHrIz8/X2rVro46BOPCflsufeEC++DPZYcfIOnZP23Vj1DVS\nEXWNVERdJ586deps82slHilbsGCBJkyYoJYtW+qoo44q6dtskxcVSt9+Lf/mi1+mIRYslSpXlRo0\nltVvrKDf8Nivq5RsS2ozk9p1UdCmvfyFqQqvO0/WrZ9s0EhZTl6cvyMAicY3b5L/35PyWdNkvQYp\nOOZMWXZO1LEAAECaKXFTVlRUpOHDh2vhwoWlDuEb1kvfLPl1A/bjMqlmHVn9xlKDxgo6dpfqNZLl\nVSz19f6XZWbJBo2Sd+0tf/IhhZecJht2pKxL7x2OtgFIPu4uf+sV+ZMPyvZqrmDMbbLqe0QdCwAA\npKkSN2WtW7fWp59+usuv81Urtk49/M80RK1ZJdXdMzb1cK8WCg4aHPvvct7pzKpWlx3/V/mSRQon\n3iefNV3B4SfJmrYo1xwAyo4vWajw8fukLVsUnHyebC/+fAMAgGiV20YfW8ZdHlv/tWXL1umHtn9n\n2bAjYyNiQeLsgGiNmiq48Ab523MU3nezbK/mssOO5ZN0IIn58u/lzz4m//Qj2fCjZJ0PYiQcAAAk\nhHJryir2H66Mhk1l1WskzwL6PoPl3Xpr07MTVXj12crsN1Q5Q46Q5eRGnQwJICsrS/n5+VHHwA5s\n+fYrbXr6URV/+Jay+gxVzsnnyXJZM7ot1DVSEXWNVERdp5ZSN2U7u3njpr1/3nJ+3brSXrL89T9M\n1qG7Cp+coM1nHy079GjZAT2Sp7lEmWDXo8TmXy9WOH2ytOhTWe9DZFffreK8SlpXvEXi922bqGuk\nIuoaqYi6Tj7ba6JL3JRNnTpVH374oVavXq2NGzfq5JNPLulbJQWrvofspPPkiz9V+Ph4+axpsfVm\nnGMEJBRf+InC6ZOk75bKDh4mO/5sdlQEAAAJLanOKUsUHobyN2bJpz4ka942NnJWtXrUsVDO+IQq\ncbi79MkHsWZs1QpZ/8Nia8YyM6OOlnSoa6Qi6hqpiLpOPmVyTlk6syCQde0tb9dZPn2KwivOkvUZ\nKus7VJaVHXU8IG14GEofvqlw+hSpqFA2cKSsfTdZRuJsHAQAALAjNGWlYDl5skOPlh/YT+GUf8kv\nPV3BiGOldl1ZbwaUId+yRf72HPmMKVJWtoJBo6S2HdlNEQAAJCWasjiwPWop49TR8s/nx84/mjVN\nwR9OlDVoEnU0IKV4UaH89Zfl//eEVG0PBYefKDXflw9BAABAUqMpiyPbu7WCMWPlc2cqHHeFrG1H\n2bAjZZV3izoakNR88yb5K/8nnzlVqt9YwQlnc+gzAABIGTRlcWZBhqx7f3n7bvLnJiq87AxZ/xGy\n3oNlFdh0ANgVvn6dfNZz8penSc1aKjhzDCPQAAAg5dCUlRHLqyQbdYK8e3+Fk/8pn/N/CkYeH1v3\nwlQrYLt8zUr5zGfkr74ga9NBwfnXyWrXizoWAABAmWBL/HLin3ygcOJ4qWq12HqzuntGk2PTRmnN\nSmnVSmnNSvnqldLP//h/Pa4tW2Ttusq69ZEaNqWR/B1sRRt/vmK5/Pmn5G/OlnXsLjt4uGz3mlHH\nSivUNVIRdY1URF0nn+1tiU9TVo68uDi2LmbaxFjDM/SPskqVS/++YSitW/NLc7X658Zq9Upp1YpY\ns7V6pbR6leRbpCrVpCq7SZV3k1XZLfbrKv/168q7SVu2yN+cJX/tRSkrW9a1j6xTT1l+lTj8n0gN\n3Azjx38okM+YIv/gTVm3vrHjJapWizpWWqKukYqoa6Qi6jr50JQlGF+3Rv7MY/J358oGjZL1GCCr\n8NuZpF64eWuj9ZtRrf96XGvXSLl5v26uKu8mVd1NqlItttHIfxqunNxdGvVyd2nRJ/K5M+Ufvi01\nb6Ogax+p5f5pfxYUN8PS82+XyKdPkX/2kazXQFnvQ2QV86OOldaoa6Qi6hqpiLpOPjRlCcq/W6pw\n0nhp5U+ylvtJa1b9qvFSUeHPI1dVY81V1Z+brf8d1apc9Xeburjn3bhB/s6rsdGzFctlnXvJuvSR\n1apb5tdORNwMS86/WKBwxhTpq0WxUbEe/WU5eVHHgqhrpCbqGqmIuk4+NGUJzN2lj9+TF3zz62ar\nSjUpr2LCruXygqXy116SvzlLqllH1rWvrF0XWU5u1NHKDTfDXePu0oJ5CqdPln5cJut/aGxabFZ2\n1NHwX6hrpCLqGqmIuk4+NGUoM15cLM1/V+FrL0qLPpHt30XWtY/UZJ+EbSjjhZvhznF3ad47sWZs\n/TrZgBGyA3qUy+gudh11jVREXSMVUdfJZ3tNGT8VoVSsQgVpv07K2K+TfNUK+ZuzFE64XZLJuvWR\ndeoVG/1D2vFwi/y91+XTJ0syBYNGSvt3lgXpvRYRAADgfzFShrhzd+mLBbHNQT54Q2raUkG3PlKr\n9ik1OsInVNvmn36gcOL9Um6egoEjpdbtU37kNFVQ10hF1DVSEXWdfJi+iMj4po3y916Tz31R+rEg\nNnLWrY+sdv2oo5UaN8Pf8h8KFE7+p1SwNHZY+r4H0IwlGeoaqYi6RiqirpMP0xcRGcvJja0x69pH\n/v138tdfVHjLGKn6HrFNHjocKMtl171k5xvWy6dNkr/+oqzfobI/XyjLzIw6FgAAQFJgpAzlzrds\nkT55X+HcmdKC+bJ9D5B16yM1bZlUoyp8QvXzurHXXpI//YisVTvZ8D+xhjDJUddIRdQ1UhF1nXwY\nKUNCsYwMqU0HZbTpIF+zSv7mbIUP/0PaUizr0jv2z27Vo46JHfCFHyt8/D4pO0fBmWNke+4VdSQA\nAICkRFOGSFnlqrJ+w+R9h0pfLZLPfVHh5WdKTfZR0LWP1LaDrALT4BKJ//sHhVP+JS1ZJBtxrKx9\nt6Qa4QQAAEg0TF9EwvHNm+Xvvy5/7UWpYGnsTKuufWT1GkYd7VfSbdqAb9oon/GE/JUZsj6HyPoN\n5+DnFJRudY30QF0jFVHXyYfpi0gqlp0t69xL6txL/uMy+esvKbz9Sqly1djOjR27y/IqRR0zbXgY\nyt+cLX/qIdnerRRcOk5WbfeoYwEAAKQMRsqQFDzcIn36oXzui/JPP5S1bh/bHGTv1rIgiCRTOnxC\n5V8sUDhxvCQp+MOJsib7RJwIZS0d6hrph7pGKqKukw8jZUh6FmRIrdrJWrWTr10jf/sVhZPulzZu\niE1t7NJbVn2PqGOmDF/xb/mTE+Sfz5cNP1rWqWdkzS8AAECqY6QMScvdpaVfyl+bKX/7VWnPJrEG\nbb9OssysMr9+Kn5C5Zs3y194Sv7Ss7IeA2QDDpPl5EYdC+UoFesaoK6Riqjr5LO9kTKaMqQELyqU\nf/CmfO5M6ZsvZR26x9afNWhSZtdMpZuhu8vfnSuf8oCsUTPZYcfI9qgVdSxEIJXqGvgP6hqpiLpO\nPkxfRMqzzCxZx+5Sx+7yf/8gf/1lhXddJ+VVlHXtKzugu6xS5ahjJiT/erHCx8dLmzcqOOFsWbNW\nUUcCAABIKzRlSDm2e03ZkCPkg/8gfT4/dvbZ04/IWu4n69ZXat4mtkYtzfnqlfKnHpR//L5s6JGy\nrr35/wIAABCBUjVl8+fP1+TJk2VmGjlypFq14hN2JA4LAql5W1nztvL16+Rvz1H41EPSg6tiG4N0\n6Z2WU/S8qEj+4jPyF56Udemj4Mq7ZHkVo44FAACQtkrclLm7Jk2apDFjxkiSrrnmGpoyJCyrWEnW\na6DUa6D8myXy115UeO15Ut09Y2vP9usiy07tg5DdXfrgTYVT/iXVaaDgoptkNbc9txkAAADlo8RN\n2bJly1S7dm1lZcV2uatZs6a+//571aqVfiMPSC5Wv5Hs8JPkhx0rzXtb4dwX5Y/dJ2vfLTa9seFe\nMrOoY8aVf7sktm5s7WoFR50qa7Ff1JEAAADwsxI3ZevWrVNeXp4mTJggd1deXp7Wrl1LU4akYZmZ\nUruuymjXNXYu1xsvK7zvJikrO7a1fqeesvwqUccsFV+7Wv70I/L335Adcrise39ZBuvGAAAAEkmJ\nm7JKlSppw4YNOvHEEyVJ9913n/Lz87f5/O19DYhcfr605wnyUcdpy4J52jx7hoouOUWZrfZXVs8B\nqtC242+amaysrIStay8u1uYXpmrzUw8rs2tv5Yx9UAG7T2InJHJdAyVFXSMVUdeppcRNWa1atbRs\n2TJJsbUqO5q6yDkKSBr1m0h/OkPBiONV/M6rKpoyQbr3Zlnng2IjaD+vw0rU80F8/rsKJ90vVa+h\n4PxrtaV2fa13SQmYFYknUesaKA3qGqmIuk4+22uiS3V49Lx587buvjhixAi1adNmm8/l8GgkMy9Y\nKn/tJfmbs6SadWRd+yq/Yzet27gx6mi/WLVC4VMPSst/UDDqeKl1+5RbG4eyx1/ySEXUNVIRdZ18\ntnd4dKmasl1BU4ZU4MXF0vx3Fc6dKftqkcrpj8/OycyU9Rkq6zVQViEz6jRIUvwlj1REXSMVUdfJ\nZ3tNGYdHA7vAKlSQ9uukjP06cTMEAABAXARRBwAAAACAdEZTBgAAAAARoikDAAAAgAjRlAEAAABA\nhGjKAAAAACBCNGUAAAAAECGaMgAAAACIEE0ZAAAAAESIpgwAAAAAIkRTBgAAAAARoikDAAAAgAjR\nlAEAAABAhGjKAAAAACBCNGUAAAAAECGaMgAAAACIEE0ZAAAAAESIpgwAAAAAIkRTBgAAAAARoikD\nAAAAgAjRlAEAAABAhGjKAAAAACBCNGUAAAAAECGaMgAAAACIEE0ZAAAAAESIpgwAAAAAIkRTBgAA\nAAARKnFTtmDBAo0ePVoPP/xwPPMAAAAAQFopcVNWVFSk4cOHxzMLAAAAAKSdEjdlrVu3VqVKleKZ\nBQAAAADSToUdPWHevHl6+umnZWZyd5mZjj76aDVo0KA88gEAAABAStthU9amTRu1adOm1BeqU6dO\nqd8DSDT5+flRRwDijrpGKqKukYqo69RR6t0X3T0eOQAAAAAgLe1wpGxbpk6dqg8//FCrV6/Wxo0b\ndfLJJ8czFwAAAACkBXOGugAAAAAgMhweDQAAAAARoikDAAAAgAjRlAEAAABAhEq80cfOmj9/viZP\nniwz08iRI9WqVauyviRQpu666y599913ysrKUs+ePdWjR4+oIwElsmDBAk2YMEEtW7bUUUcdJYl7\nNpLf79U1920ku3vvvVfLli2Tu+u0005TjRo1uF+nmDJtytxdkyZN0pgxYyRJ11xzDQWDlHD22Wdr\n9913jzoGUCpFRUUaPny4Fi5cKIl7NlLD/9b1f3DfRjL7zy7nH3/8sZ555hmdcMIJ3K9TTJlOX1y2\nbJlq166trKwsZWVlqWbN/2/v3lkaCcMojp9olExmRERkg0EFC0G8oIWliKWXSqwNXisLG0HQJuAH\nUNsUQSxVEEuL4BcQEsV0BkHQVIoXRKMkWywJrLgsrDu+zPD/dVO9TzEcOJlnJj+Uz+fdPBL4Fny0\nFH7Q09Mjx3Eq12Q2/ODjfV1GbsMPLMtSMBgkr33I1SdlT09PCofD2t7eVqlUUjgc1uPjoyKRiJvH\nAq4KhULa2tqS4ziKxWLcz/ANMht+RW7DL1KplEZHR8lrH3K1lDmOo+fnZ83NzUmSEomE6urq3DwS\ncN3MzIwk6fLyUjs7O1peXjY8EfB/kNnwK3IbfnBycqLm5mZFo1FdX1+T1z7j6vpiJBLRzc2NpF9r\nA/l8ngYP36ipqVF1dbXpMYAvK691kdnwk8/WFclteFUul9P5+bnGxsYkkdd+FCi5vGR9enpa+TLM\n5OSkent73TwOcN3Gxobu7u5kWZZmZ2fV1NRkeiTgnxwcHCidTuv+/l6dnZ1aWFhQJpPR3t4emQ3P\n+uy+JrfhdYuLi2psbFRVVZVaW1s1PT1NXvuM66UMAAAAAPBn/Hk0AAAAABhEKQMAAAAAgyhlAAAA\nAGAQpQwAAAAADKKUAQAAAIBBlDIAAAAAMIhSBgDwlHg8rlwuV7mempoyOA0AAF9HKQMAeFogEDA9\nAgAAXxI0PQAAAF9RKpUkSYVCQclkUldXVyoWixocHNTIyIgkKZvN6vDwUCsrK5Kk3d1dWZal8fFx\nSdLx8bGy2axeXl50e3urjo4OnsABAL4NpQwA4DmJREKhUEiS9Pb2Jkna39+XbdtaX19XoVBQPB5X\nS0uLuru7Jf39idrZ2ZnW1tYUjUbdHR4AgA8oZQAAz5mfn1d7e7skKRaLSZIymYyWlpYkSbW1tRoe\nHlY6na6Usr8ZGBigkAEAjOCdMgAAAAAwiFIGAPC08jtlfX19Ojo6kiS9vr4qlUqpv79fkmTbth4e\nHiRJ7+/vuri4MDMsAACfYH0RAOBp5XfFJiYmlEwmtbq6qmKxqKGhIXV1dUmS2tra1NDQoM3NTdXX\n1wCehoMAAABQSURBVMu2bZMjAwDwm0Cp/BMjAAAAAODbsb4IAAAAAAZRygAAAADAIEoZAAAAABhE\nKQMAAAAAgyhlAAAAAGAQpQwAAAAADKKUAQAAAIBBPwHqGKHEQhHg6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d32250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperatures_hour = temperatures.groupby('Hour').aggregate(np.median).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Getting the whole year of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_weather_data(year, month):\n",
    "    if month == 1:\n",
    "        year += 1\n",
    "    url = url_template.format(year = year, month = month)\n",
    "    data = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True)\n",
    "    data = data.dropna(axis = 1)\n",
    "    data = data.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_by_month = [get_weather_data(2012, i) for i in range(0, 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012 = pd.concat(data_by_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Temp (°C)</th>\n",
       "      <th>Dew Point Temp (°C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td>-5.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.97</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td>-5.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>79</td>\n",
       "      <td>26</td>\n",
       "      <td>2.4</td>\n",
       "      <td>100.87</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>80</td>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.80</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.69</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>83</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>100.62</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (°C)  Dew Point Temp (°C)  Rel Hum (%)  Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather\n",
       "Date/Time                                                                                                                  \n",
       "2012-03-01 00:00:00       -5.5                 -9.7           72               24              4.0           100.97    Snow\n",
       "2012-03-01 01:00:00       -5.7                 -8.7           79               26              2.4           100.87    Snow\n",
       "2012-03-01 02:00:00       -5.4                 -8.3           80               28              4.8           100.80    Snow\n",
       "2012-03-01 03:00:00       -4.7                 -7.7           79               28              4.0           100.69    Snow\n",
       "2012-03-01 04:00:00       -5.4                 -7.8           83               35              1.6           100.62    Snow"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Saving to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012.to_csv('./data/weather_2012.csv')"
   ]
  }
 ],
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