{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.set_option('display.mpl_style', 'default')\n",
      "figsize(15,3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Summary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By the end of this chapter, we're going to have downloaded all of Canada's weather data for 2012, and saved it to a CSV. \n",
      "\n",
      "We'll do this by downloading it one month at a time, and then combining all the months together.\n",
      "\n",
      "Here's the temperature every hour for 2012!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012_final = pd.read_csv('../data/weather_2012.csv', index_col='Date/Time')\n",
      "weather_2012_final['Temp (C)'].plot(figsize=(15, 6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x345b5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jRVVp5KgKSTUEZyblKsEoRNzjWel+ycM5Wg8j2YKqz/oJF1AJRsr/S7AfeU57cz6ePdA1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704OJjHfR9Yj76xohqTccczGLlfVgMob8zZlnw2ErgphiKMcep4w3kHn/vlbnzxbTIqNpRzY9Xq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\nQRAEQRDyWdPZguUhUUFfuHABbt84r6LjzJ2WwGfeOddb/uQ75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slHb+sxEZBg5AiKslSO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vsmvXLqZOnep6vaZpsm3bNl5//XWCweCY5/bv38/999/Pgw8+6DpXREQkWXSFUUREZJSVK1fy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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x345b210>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 5.1 Downloading one month of weather data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When playing with the cycling data, I wanted temperature and precipitation data to find out of people like biking when it's raining. So I went to the site for [Canadian historical weather data](http://climate.weather.gc.ca/index_e.html#access), and figured out how to get it automatically.\n",
      "\n",
      "Here we're going to get the data for March 2012, and clean it up\n",
      "\n",
      "Here's an URL template you can use to get data in Montreal. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get the data for March 2013, we need to format it with `month=3, year=2012`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = url_template.format(month=3, year=2012)\n",
      "weather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is super great! We can just use the same `read_csv` function as before, and just give it a URL as a filename. Awesome.\n",
      "\n",
      "There are 16 rows of metadata at the top of this CSV, but pandas knows CSVs are weird, so there's a `skiprows` options. We parse the dates again, and set 'Date/Time' to be the index column. Here's the resulting dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 744 entries, 2012-03-01 00:00:00 to 2012-03-31 23:00:00\n",
        "Data columns (total 24 columns):\n",
        "Year                   744  non-null values\n",
        "Month                  744  non-null values\n",
        "Day                    744  non-null values\n",
        "Time                   744  non-null values\n",
        "Data Quality           744  non-null values\n",
        "Temp (\u00b0C)              744  non-null values\n",
        "Temp Flag              0  non-null values\n",
        "Dew Point Temp (\u00b0C)    744  non-null values\n",
        "Dew Point Temp Flag    0  non-null values\n",
        "Rel Hum (%)            744  non-null values\n",
        "Rel Hum Flag           0  non-null values\n",
        "Wind Dir (10s deg)     715  non-null values\n",
        "Wind Dir Flag          0  non-null values\n",
        "Wind Spd (km/h)        744  non-null values\n",
        "Wind Spd Flag          3  non-null values\n",
        "Visibility (km)        744  non-null values\n",
        "Visibility Flag        0  non-null values\n",
        "Stn Press (kPa)        744  non-null values\n",
        "Stn Press Flag         0  non-null values\n",
        "Hmdx                   12  non-null values\n",
        "Hmdx Flag              0  non-null values\n",
        "Wind Chill             242  non-null values\n",
        "Wind Chill Flag        1  non-null values\n",
        "Weather                744  non-null values\n",
        "dtypes: float64(14), int64(5), object(5)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 744 entries, 2012-03-01 00:00:00 to 2012-03-31 23:00:00\n",
        "Data columns (total 24 columns):\n",
        "Year                   744  non-null values\n",
        "Month                  744  non-null values\n",
        "Day                    744  non-null values\n",
        "Time                   744  non-null values\n",
        "Data Quality           744  non-null values\n",
        "Temp (\u00b0C)              744  non-null values\n",
        "Temp Flag              0  non-null values\n",
        "Dew Point Temp (\u00b0C)    744  non-null values\n",
        "Dew Point Temp Flag    0  non-null values\n",
        "Rel Hum (%)            744  non-null values\n",
        "Rel Hum Flag           0  non-null values\n",
        "Wind Dir (10s deg)     715  non-null values\n",
        "Wind Dir Flag          0  non-null values\n",
        "Wind Spd (km/h)        744  non-null values\n",
        "Wind Spd Flag          3  non-null values\n",
        "Visibility (km)        744  non-null values\n",
        "Visibility Flag        0  non-null values\n",
        "Stn Press (kPa)        744  non-null values\n",
        "Stn Press Flag         0  non-null values\n",
        "Hmdx                   12  non-null values\n",
        "Hmdx Flag              0  non-null values\n",
        "Wind Chill             242  non-null values\n",
        "Wind Chill Flag        1  non-null values\n",
        "Weather                744  non-null values\n",
        "dtypes: float64(14), int64(5), object(5)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's plot it!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012[u\"Temp (\\xb0C)\"].plot(figsize=(15, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x34e8990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Nqj1JQgghhMgXiqdg0y8eqKlRWzWXLyZgKlJ4MKGmaDINi37xR5dGozr1XKIowl+k62Ox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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aa1c50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice how it goes up to 25\u00b0 C in the middle there? That was a big deal. It was March, and people were wearing shorts outside. \n",
      "\n",
      "And I was out of town and I missed it. Still sad, humans.\n",
      "\n",
      "I had to write `'\\xb0'` for that degree character \u00b0. Let's get rid of that, to make it easier to type."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012.columns = [s.replace(u'\\xb0', '') for s in weather_mar2012.columns]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You'll notice in the summary above that there are a few columns which are are either entirely empty or only have a few values in them. Let's get rid of all of those with `dropna`.\n",
      "\n",
      "The argument `axis=1` to `dropna` means \"drop columns\", not rows\", and `how='any'` means \"drop the column if any value is null\". \n",
      "\n",
      "This is much better now -- we only have columns with real data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')\n",
      "weather_mar2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                     Year  Month  Day   Time Data Quality  Temp (C)  \\\n",
        "Date/Time                                                             \n",
        "2012-03-01 00:00:00  2012      3    1  00:00                   -5.5   \n",
        "2012-03-01 01:00:00  2012      3    1  01:00                   -5.7   \n",
        "2012-03-01 02:00:00  2012      3    1  02:00                   -5.4   \n",
        "2012-03-01 03:00:00  2012      3    1  03:00                   -4.7   \n",
        "2012-03-01 04:00:00  2012      3    1  04:00                   -5.4   \n",
        "\n",
        "                     Dew Point Temp (C)  Rel Hum (%)  Wind Spd (km/h)  \\\n",
        "Date/Time                                                               \n",
        "2012-03-01 00:00:00                -9.7           72               24   \n",
        "2012-03-01 01:00:00                -8.7           79               26   \n",
        "2012-03-01 02:00:00                -8.3           80               28   \n",
        "2012-03-01 03:00:00                -7.7           79               28   \n",
        "2012-03-01 04:00:00                -7.8           83               35   \n",
        "\n",
        "                     Visibility (km)  Stn Press (kPa) Weather  \n",
        "Date/Time                                                      \n",
        "2012-03-01 00:00:00              4.0           100.97    Snow  \n",
        "2012-03-01 01:00:00              2.4           100.87    Snow  \n",
        "2012-03-01 02:00:00              4.8           100.80    Snow  \n",
        "2012-03-01 03:00:00              4.0           100.69    Snow  \n",
        "2012-03-01 04:00:00              1.6           100.62    Snow  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Year/Month/Day/Time columns are redundant, though, and the Data Quality column doesn't look too useful. Let's get rid of those.\n",
      "\n",
      "The `axis=1` argument means \"Drop columns\", like before. The default for operations like `dropna` and `drop` is always to operate on rows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_mar2012 = weather_mar2012.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)\n",
      "weather_mar2012[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
        "Date/Time                                                        \n",
        "2012-03-01 00:00:00      -5.5                -9.7           72   \n",
        "2012-03-01 01:00:00      -5.7                -8.7           79   \n",
        "2012-03-01 02:00:00      -5.4                -8.3           80   \n",
        "2012-03-01 03:00:00      -4.7                -7.7           79   \n",
        "2012-03-01 04:00:00      -5.4                -7.8           83   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather  \n",
        "Date/Time                                                                       \n",
        "2012-03-01 00:00:00               24              4.0           100.97    Snow  \n",
        "2012-03-01 01:00:00               26              2.4           100.87    Snow  \n",
        "2012-03-01 02:00:00               28              4.8           100.80    Snow  \n",
        "2012-03-01 03:00:00               28              4.0           100.69    Snow  \n",
        "2012-03-01 04:00:00               35              1.6           100.62    Snow  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Awesome! We now only have the relevant columns, and it's much more manageable."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 2.3 Plotting the temperature by hour of day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This one's just for fun -- we've already done this before, using groupby and aggregate! We will learn whether or not it gets colder at night. Well, obviously. But let's do it anyway."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temperatures = weather_mar2012[[u'Temp (C)']]\n",
      "temperatures['Hour'] = weather_mar2012.index.hour\n",
      "temperatures.groupby('Hour').aggregate(np.median).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x34ec610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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APokVM7hLYIBJl/eO1OW9I/UfVzi070SNthTY9MSmAtU32TUxJUqTUqI0PDFCQexZA9BJ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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x369cfd0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So it looks like the time with the highest median temperature is 2pm. Neat."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 5.3 Getting the whole year of data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Okay, so what if we want the data for the whole year? Ideally the API would just let us download that, but I couldn't figure out a way to do that.\n",
      "\n",
      "First, let's put our work from above into a function that gets the weather for a given month. \n",
      "\n",
      "I noticed that there's an irritating bug where when I ask for January, it gives me data for the previous year, so we'll fix that too. [no, really. You can check =)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def download_weather_month(year, month):\n",
      "    if month == 1:\n",
      "        year += 1\n",
      "    url = url_template.format(year=year, month=month)\n",
      "    weather_data = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True)\n",
      "    weather_data = weather_data.dropna(axis=1)\n",
      "    weather_data.columns = [col.replace('\\xb0', '') for col in weather_data.columns]\n",
      "    weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)\n",
      "    return weather_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can test that this function does the right thing:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "download_weather_month(2012, 1)[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
        "Date/Time                                                        \n",
        "2012-01-01 00:00:00      -1.8                -3.9           86   \n",
        "2012-01-01 01:00:00      -1.8                -3.7           87   \n",
        "2012-01-01 02:00:00      -1.8                -3.4           89   \n",
        "2012-01-01 03:00:00      -1.5                -3.2           88   \n",
        "2012-01-01 04:00:00      -1.5                -3.3           88   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
        "Date/Time                                                                \n",
        "2012-01-01 00:00:00                4              8.0           101.24   \n",
        "2012-01-01 01:00:00                4              8.0           101.24   \n",
        "2012-01-01 02:00:00                7              4.0           101.26   \n",
        "2012-01-01 03:00:00                6              4.0           101.27   \n",
        "2012-01-01 04:00:00                7              4.8           101.23   \n",
        "\n",
        "                                  Weather  \n",
        "Date/Time                                  \n",
        "2012-01-01 00:00:00                   Fog  \n",
        "2012-01-01 01:00:00                   Fog  \n",
        "2012-01-01 02:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 03:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 04:00:00                   Fog  "
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can get all the months at once. This will take a little while to run."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we have this, it's easy to concatenate all the dataframes together into one big dataframe using `pd.concat`. And now we have the whole year's data!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012 = pd.concat(data_by_month)\n",
      "weather_2012"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 8784 entries, 2012-01-01 00:00:00 to 2012-12-31 23:00:00\n",
        "Data columns (total 7 columns):\n",
        "Temp (C)              8784  non-null values\n",
        "Dew Point Temp (C)    8784  non-null values\n",
        "Rel Hum (%)           8784  non-null values\n",
        "Wind Spd (km/h)       8784  non-null values\n",
        "Visibility (km)       8784  non-null values\n",
        "Stn Press (kPa)       8784  non-null values\n",
        "Weather               8784  non-null values\n",
        "dtypes: float64(4), int64(2), object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 8784 entries, 2012-01-01 00:00:00 to 2012-12-31 23:00:00\n",
        "Data columns (total 7 columns):\n",
        "Temp (C)              8784  non-null values\n",
        "Dew Point Temp (C)    8784  non-null values\n",
        "Rel Hum (%)           8784  non-null values\n",
        "Wind Spd (km/h)       8784  non-null values\n",
        "Visibility (km)       8784  non-null values\n",
        "Stn Press (kPa)       8784  non-null values\n",
        "Weather               8784  non-null values\n",
        "dtypes: float64(4), int64(2), object(1)"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 5.4 Saving to a CSV"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It's slow and unnecessary to download the data every time, so let's save our dataframe:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012.to_csv('../data/weather_2012.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we're done!"
     ]
    },
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