{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Project 2:  Holiday weather\n",
    "\n",
    "by Jake Stokes, 2nd of July 2016.\n",
    "\n",
    "This is the project for Week 2 of The Open University's [_Learn to code for Data Analysis_](http://futurelearn.com/courses/learn-to-code) course.\n",
    "\n",
    "The purpose of the project is to examine historic weather data from the Weather Underground for London to try to predict the best dates this year to take a nice warm staycation. My aim will be to:\n",
    "- obtain weather data for the year of 2015\n",
    "- clean the obtained data\n",
    "- run some basic data analysis techniques on the data set to:\n",
    "- find two weeks with the highest mean temperature; and,\n",
    "- avoid precipitation where possible.\n",
    "\n",
    "The weather may of course may be very different this year to the weather of 2015, but it should give me some indication of when would be a good time to take a break.\n",
    "\n",
    "## Getting the data\n",
    "\n",
    "The weather data was obtained from the [Weather Underground](https://www.wunderground.com/history) website, using the dates 1st Jan 2015 til 31st Dec 2015, and saved as 'London_2015.csv'.\n",
    "\n",
    "To obtain the data you must first enter London, United Kingdom as the location, and hit submit. On the following page there are some tabs - select 'custom', and from here you can enter the dates. The option to see the data in a CSV format is at the very bottom of the page underneath the data. This can be right-click-saved, and renamed from a .html to a .csv ready for use.\n",
    "\n",
    "If you haven't the 'London_2014.csv' file, you can obtain the data as follows. Right-click on the following URL and choose 'Open Link in New Window' (or similar, depending on your browser):\n",
    "\n",
    "http://www.wunderground.com/history\n",
    "\n",
    "When the new page opens start typing 'London' in the 'Location' input box and when the pop up menu comes up with the option 'London, United Kingdom' select it and then click on 'Submit'. \n",
    "\n",
    "Once ready, as shown below, I have loaded the dataframe, ensuing that any extra spaces at the start of values are removed. I have also imported the whole pandas module for data analytics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore', FutureWarning)\n",
    "\n",
    "from pandas import *\n",
    "london = read_csv('London_2015.csv', skipinitialspace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Cleaning the data\n",
    "\n",
    "First I will display some of the data to see if there are any obvious issues.\n",
    "\n",
    "First we need to clean up the data. I'm not going to make use of `'WindDirDegrees'` in my analysis, but you might in yours so we'll rename `'WindDirDegrees< br />'` to `'WindDirDegrees'`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>GMT</th>\n",
       "      <th>Max TemperatureC</th>\n",
       "      <th>Mean TemperatureC</th>\n",
       "      <th>Min TemperatureC</th>\n",
       "      <th>Dew PointC</th>\n",
       "      <th>MeanDew PointC</th>\n",
       "      <th>Min DewpointC</th>\n",
       "      <th>Max Humidity</th>\n",
       "      <th>Mean Humidity</th>\n",
       "      <th>Min Humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>Max VisibilityKm</th>\n",
       "      <th>Mean VisibilityKm</th>\n",
       "      <th>Min VisibilitykM</th>\n",
       "      <th>Max Wind SpeedKm/h</th>\n",
       "      <th>Mean Wind SpeedKm/h</th>\n",
       "      <th>Max Gust SpeedKm/h</th>\n",
       "      <th>Precipitationmm</th>\n",
       "      <th>CloudCover</th>\n",
       "      <th>Events</th>\n",
       "      <th>WindDirDegrees&lt;br /&gt;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2015-1-1</td>\n",
       "      <td> 12</td>\n",
       "      <td> 8</td>\n",
       "      <td> 4</td>\n",
       "      <td> 11</td>\n",
       "      <td> 7</td>\n",
       "      <td> 3</td>\n",
       "      <td>  94</td>\n",
       "      <td> 88</td>\n",
       "      <td> 78</td>\n",
       "      <td>...</td>\n",
       "      <td> 18</td>\n",
       "      <td>  9</td>\n",
       "      <td> 5</td>\n",
       "      <td> 39</td>\n",
       "      <td> 21</td>\n",
       "      <td> 60</td>\n",
       "      <td> 0.51</td>\n",
       "      <td> 7</td>\n",
       "      <td> Rain</td>\n",
       "      <td> 209&lt;br /&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015-1-2</td>\n",
       "      <td> 11</td>\n",
       "      <td> 7</td>\n",
       "      <td> 4</td>\n",
       "      <td> 12</td>\n",
       "      <td> 4</td>\n",
       "      <td> 0</td>\n",
       "      <td>  94</td>\n",
       "      <td> 70</td>\n",
       "      <td> 41</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 16</td>\n",
       "      <td> 3</td>\n",
       "      <td> 35</td>\n",
       "      <td> 24</td>\n",
       "      <td> 50</td>\n",
       "      <td> 0.00</td>\n",
       "      <td> 2</td>\n",
       "      <td> Rain</td>\n",
       "      <td> 258&lt;br /&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015-1-3</td>\n",
       "      <td>  6</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2</td>\n",
       "      <td>  6</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 100</td>\n",
       "      <td> 91</td>\n",
       "      <td> 70</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 10</td>\n",
       "      <td> 2</td>\n",
       "      <td> 19</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 7.11</td>\n",
       "      <td> 5</td>\n",
       "      <td> Rain</td>\n",
       "      <td>  19&lt;br /&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015-1-4</td>\n",
       "      <td>  3</td>\n",
       "      <td> 1</td>\n",
       "      <td>-2</td>\n",
       "      <td>  3</td>\n",
       "      <td> 1</td>\n",
       "      <td>-2</td>\n",
       "      <td> 100</td>\n",
       "      <td> 97</td>\n",
       "      <td> 90</td>\n",
       "      <td>...</td>\n",
       "      <td> 13</td>\n",
       "      <td>  4</td>\n",
       "      <td> 0</td>\n",
       "      <td> 13</td>\n",
       "      <td>  6</td>\n",
       "      <td> 27</td>\n",
       "      <td> 0.00</td>\n",
       "      <td> 6</td>\n",
       "      <td>  Fog</td>\n",
       "      <td> 225&lt;br /&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015-1-5</td>\n",
       "      <td> 10</td>\n",
       "      <td> 6</td>\n",
       "      <td> 2</td>\n",
       "      <td>  8</td>\n",
       "      <td> 5</td>\n",
       "      <td> 2</td>\n",
       "      <td> 100</td>\n",
       "      <td> 86</td>\n",
       "      <td> 67</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 10</td>\n",
       "      <td> 3</td>\n",
       "      <td> 19</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.25</td>\n",
       "      <td> 6</td>\n",
       "      <td>  NaN</td>\n",
       "      <td> 199&lt;br /&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GMT  Max TemperatureC  Mean TemperatureC  Min TemperatureC  \\\n",
       "0  2015-1-1                12                  8                 4   \n",
       "1  2015-1-2                11                  7                 4   \n",
       "2  2015-1-3                 6                  4                 2   \n",
       "3  2015-1-4                 3                  1                -2   \n",
       "4  2015-1-5                10                  6                 2   \n",
       "\n",
       "   Dew PointC  MeanDew PointC  Min DewpointC  Max Humidity  Mean Humidity  \\\n",
       "0          11               7              3            94             88   \n",
       "1          12               4              0            94             70   \n",
       "2           6               3              1           100             91   \n",
       "3           3               1             -2           100             97   \n",
       "4           8               5              2           100             86   \n",
       "\n",
       "   Min Humidity          ...           Max VisibilityKm  Mean VisibilityKm  \\\n",
       "0            78          ...                         18                  9   \n",
       "1            41          ...                         31                 16   \n",
       "2            70          ...                         31                 10   \n",
       "3            90          ...                         13                  4   \n",
       "4            67          ...                         31                 10   \n",
       "\n",
       "   Min VisibilitykM  Max Wind SpeedKm/h  Mean Wind SpeedKm/h  \\\n",
       "0                 5                  39                   21   \n",
       "1                 3                  35                   24   \n",
       "2                 2                  19                   10   \n",
       "3                 0                  13                    6   \n",
       "4                 3                  19                   10   \n",
       "\n",
       "   Max Gust SpeedKm/h  Precipitationmm  CloudCover  Events  \\\n",
       "0                  60             0.51           7    Rain   \n",
       "1                  50             0.00           2    Rain   \n",
       "2                 NaN             7.11           5    Rain   \n",
       "3                  27             0.00           6     Fog   \n",
       "4                 NaN             0.25           6     NaN   \n",
       "\n",
       "   WindDirDegrees<br />  \n",
       "0             209<br />  \n",
       "1             258<br />  \n",
       "2              19<br />  \n",
       "3             225<br />  \n",
       "4             199<br />  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "There are some immediately obvious issues with the data:\n",
    "- the final column: 'WindDirDegrees`<br />`' and its contents have retained the html *line breaks* on the end of the data line\n",
    "- this means that the final column will be an object dtype as opposed to int64 as intended (as shown below)\n",
    "- the GMT column has the dtype 'object' as opposed to 'datetime' (as shown below)\n",
    "- there are various NaN values in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GMT                            object\n",
       "Max TemperatureC                int64\n",
       "Mean TemperatureC               int64\n",
       "Min TemperatureC                int64\n",
       "Dew PointC                      int64\n",
       "MeanDew PointC                  int64\n",
       "Min DewpointC                   int64\n",
       "Max Humidity                    int64\n",
       "Mean Humidity                   int64\n",
       "Min Humidity                    int64\n",
       "Max Sea Level PressurehPa       int64\n",
       "Mean Sea Level PressurehPa      int64\n",
       "Min Sea Level PressurehPa       int64\n",
       "Max VisibilityKm                int64\n",
       "Mean VisibilityKm               int64\n",
       "Min VisibilitykM                int64\n",
       "Max Wind SpeedKm/h              int64\n",
       "Mean Wind SpeedKm/h             int64\n",
       "Max Gust SpeedKm/h            float64\n",
       "Precipitationmm               float64\n",
       "CloudCover                    float64\n",
       "Events                         object\n",
       "WindDirDegrees<br />           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "First I will rename the column to remove the html *line breaks*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
   ],
   "source": [
    "london = london.rename(columns = {'WindDirDegrees<br />' : 'WindDirDegrees'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This next one is me just being anal about the title format for continuity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
   ],
   "source": [
    "london = london.rename(columns = {'Min VisibilitykM' : 'Min VisibilityKm'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now to remove the `<br />` html *line breaks* from the values in the `'WindDirDegrees'` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "london['WindDirDegrees'] = london['WindDirDegrees'].str.rstrip('<br />')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Here I change the values in the `'WindDirDegrees'` column to the `int64` dtype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "london['WindDirDegrees'] = london['WindDirDegrees'].astype('int64')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Finally, I change the values in the `'GMT'` column to the `datetime64` dtype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
   ],
   "source": [
    "london['GMT'] = to_datetime(london['GMT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "I also need to change the index from the default to the `datetime64` values in the `'GMT'` column so that it is easier to pull out rows between particular dates and display more meaningful graphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "london.index = london['GMT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now I need to address the `'NaN'` values in the data and then decide what to do with them. The intentions for this project are to use the 'Mean TemperatureC' and 'Precipitationmm' column values to establish the best dates for the staycation, so first I will check if here are any NaN values in these columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of NaN values in the mean temperature column and the precipitation column are 0 and 0 respectively.\n"
     ]
    }
   ],
   "source": [
    "meanTempNaN = len(london[london['Mean TemperatureC'].isnull()])\n",
    "precipitationmm = len(london[london['Precipitationmm'].isnull()])\n",
    "print (\"The number of NaN values in the mean temperature column and the precipitation column\\\n",
    " are %d and %d respectively.\" % (meanTempNaN, precipitationmm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Considering that there are no NaN values in the data I will actually be utilising for this project, I am able to ignore the NaN values in the dataframe for this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Finding a summer break\n",
    "\n",
    "According to meteorologists, summer extends for the whole months of June, July, and August in the northern hemisphere and the whole months of December, January, and February in the southern hemisphere. I'm in the northern hemisphere, so I'm going to create a dataframe that holds just those months, and starting from tomorrow's date (today is July 2nd 2016):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "remainingSummer = london.ix[datetime(2015,7,3) : datetime(2015,8,31)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "I now look for the days with warm temperatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>GMT</th>\n",
       "      <th>Max TemperatureC</th>\n",
       "      <th>Mean TemperatureC</th>\n",
       "      <th>Min TemperatureC</th>\n",
       "      <th>Dew PointC</th>\n",
       "      <th>MeanDew PointC</th>\n",
       "      <th>Min DewpointC</th>\n",
       "      <th>Max Humidity</th>\n",
       "      <th>Mean Humidity</th>\n",
       "      <th>Min Humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>Max VisibilityKm</th>\n",
       "      <th>Mean VisibilityKm</th>\n",
       "      <th>Min VisibilityKm</th>\n",
       "      <th>Max Wind SpeedKm/h</th>\n",
       "      <th>Mean Wind SpeedKm/h</th>\n",
       "      <th>Max Gust SpeedKm/h</th>\n",
       "      <th>Precipitationmm</th>\n",
       "      <th>CloudCover</th>\n",
       "      <th>Events</th>\n",
       "      <th>WindDirDegrees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMT</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>2015-07-03</th>\n",
       "      <td>2015-07-03</td>\n",
       "      <td> 27</td>\n",
       "      <td> 20</td>\n",
       "      <td> 13</td>\n",
       "      <td> 15</td>\n",
       "      <td> 11</td>\n",
       "      <td>  8</td>\n",
       "      <td>  83</td>\n",
       "      <td> 56</td>\n",
       "      <td> 23</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 21</td>\n",
       "      <td>  9</td>\n",
       "      <td> 27</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 8.89</td>\n",
       "      <td>  2</td>\n",
       "      <td>              Rain</td>\n",
       "      <td>  83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>2015-07-04</td>\n",
       "      <td> 27</td>\n",
       "      <td> 22</td>\n",
       "      <td> 17</td>\n",
       "      <td> 18</td>\n",
       "      <td> 14</td>\n",
       "      <td> 10</td>\n",
       "      <td> 100</td>\n",
       "      <td> 67</td>\n",
       "      <td> 33</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td>  8</td>\n",
       "      <td>  2</td>\n",
       "      <td> 27</td>\n",
       "      <td> 14</td>\n",
       "      <td> 42</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  2</td>\n",
       "      <td> Rain-Thunderstorm</td>\n",
       "      <td> 220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>2015-07-10</td>\n",
       "      <td> 27</td>\n",
       "      <td> 20</td>\n",
       "      <td> 13</td>\n",
       "      <td> 11</td>\n",
       "      <td>  7</td>\n",
       "      <td>  2</td>\n",
       "      <td>  82</td>\n",
       "      <td> 45</td>\n",
       "      <td> 11</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 23</td>\n",
       "      <td> 10</td>\n",
       "      <td> 23</td>\n",
       "      <td> 11</td>\n",
       "      <td> 39</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>               NaN</td>\n",
       "      <td> 182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>2015-07-11</td>\n",
       "      <td> 26</td>\n",
       "      <td> 20</td>\n",
       "      <td> 14</td>\n",
       "      <td> 14</td>\n",
       "      <td> 10</td>\n",
       "      <td>  8</td>\n",
       "      <td>  77</td>\n",
       "      <td> 49</td>\n",
       "      <td> 24</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 19</td>\n",
       "      <td> 10</td>\n",
       "      <td> 27</td>\n",
       "      <td> 13</td>\n",
       "      <td> 42</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  2</td>\n",
       "      <td>              Rain</td>\n",
       "      <td> 274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>2015-07-14</td>\n",
       "      <td> 23</td>\n",
       "      <td> 20</td>\n",
       "      <td> 17</td>\n",
       "      <td> 18</td>\n",
       "      <td> 15</td>\n",
       "      <td> 14</td>\n",
       "      <td> 100</td>\n",
       "      <td> 78</td>\n",
       "      <td> 51</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 13</td>\n",
       "      <td>  3</td>\n",
       "      <td> 24</td>\n",
       "      <td> 18</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 2.03</td>\n",
       "      <td>  6</td>\n",
       "      <td>              Rain</td>\n",
       "      <td> 252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>2015-07-16</td>\n",
       "      <td> 25</td>\n",
       "      <td> 20</td>\n",
       "      <td> 14</td>\n",
       "      <td> 15</td>\n",
       "      <td> 13</td>\n",
       "      <td>  9</td>\n",
       "      <td>  88</td>\n",
       "      <td> 65</td>\n",
       "      <td> 44</td>\n",
       "      <td>...</td>\n",
       "      <td> 26</td>\n",
       "      <td> 14</td>\n",
       "      <td>  6</td>\n",
       "      <td> 24</td>\n",
       "      <td> 13</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 7.11</td>\n",
       "      <td>  4</td>\n",
       "      <td> Rain-Thunderstorm</td>\n",
       "      <td>  90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>2015-07-17</td>\n",
       "      <td> 25</td>\n",
       "      <td> 20</td>\n",
       "      <td> 14</td>\n",
       "      <td> 17</td>\n",
       "      <td> 13</td>\n",
       "      <td>  9</td>\n",
       "      <td>  94</td>\n",
       "      <td> 67</td>\n",
       "      <td> 35</td>\n",
       "      <td>...</td>\n",
       "      <td> 23</td>\n",
       "      <td> 11</td>\n",
       "      <td>  6</td>\n",
       "      <td> 35</td>\n",
       "      <td> 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.25</td>\n",
       "      <td>  3</td>\n",
       "      <td>              Rain</td>\n",
       "      <td> 242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>2015-08-03</td>\n",
       "      <td> 25</td>\n",
       "      <td> 20</td>\n",
       "      <td> 16</td>\n",
       "      <td> 16</td>\n",
       "      <td> 13</td>\n",
       "      <td>  8</td>\n",
       "      <td>  83</td>\n",
       "      <td> 64</td>\n",
       "      <td> 40</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 14</td>\n",
       "      <td> 10</td>\n",
       "      <td> 34</td>\n",
       "      <td> 18</td>\n",
       "      <td> 47</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  3</td>\n",
       "      <td>              Rain</td>\n",
       "      <td> 200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-08</th>\n",
       "      <td>2015-08-08</td>\n",
       "      <td> 26</td>\n",
       "      <td> 20</td>\n",
       "      <td> 14</td>\n",
       "      <td> 15</td>\n",
       "      <td> 13</td>\n",
       "      <td> 10</td>\n",
       "      <td>  94</td>\n",
       "      <td> 62</td>\n",
       "      <td> 28</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 15</td>\n",
       "      <td> 10</td>\n",
       "      <td> 23</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  2</td>\n",
       "      <td>               NaN</td>\n",
       "      <td> 148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-21</th>\n",
       "      <td>2015-08-21</td>\n",
       "      <td> 26</td>\n",
       "      <td> 22</td>\n",
       "      <td> 17</td>\n",
       "      <td> 17</td>\n",
       "      <td> 16</td>\n",
       "      <td> 13</td>\n",
       "      <td>  88</td>\n",
       "      <td> 70</td>\n",
       "      <td> 39</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 13</td>\n",
       "      <td> 10</td>\n",
       "      <td> 26</td>\n",
       "      <td> 18</td>\n",
       "      <td> 37</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  4</td>\n",
       "      <td>               NaN</td>\n",
       "      <td> 199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-22</th>\n",
       "      <td>2015-08-22</td>\n",
       "      <td> 31</td>\n",
       "      <td> 23</td>\n",
       "      <td> 15</td>\n",
       "      <td> 17</td>\n",
       "      <td> 14</td>\n",
       "      <td> 12</td>\n",
       "      <td>  94</td>\n",
       "      <td> 63</td>\n",
       "      <td> 27</td>\n",
       "      <td>...</td>\n",
       "      <td> 31</td>\n",
       "      <td> 16</td>\n",
       "      <td>  7</td>\n",
       "      <td> 26</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.00</td>\n",
       "      <td>  4</td>\n",
       "      <td>               NaN</td>\n",
       "      <td> 114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GMT  Max TemperatureC  Mean TemperatureC  Min TemperatureC  \\\n",
       "GMT                                                                            \n",
       "2015-07-03 2015-07-03                27                 20                13   \n",
       "2015-07-04 2015-07-04                27                 22                17   \n",
       "2015-07-10 2015-07-10                27                 20                13   \n",
       "2015-07-11 2015-07-11                26                 20                14   \n",
       "2015-07-14 2015-07-14                23                 20                17   \n",
       "2015-07-16 2015-07-16                25                 20                14   \n",
       "2015-07-17 2015-07-17                25                 20                14   \n",
       "2015-08-03 2015-08-03                25                 20                16   \n",
       "2015-08-08 2015-08-08                26                 20                14   \n",
       "2015-08-21 2015-08-21                26                 22                17   \n",
       "2015-08-22 2015-08-22                31                 23                15   \n",
       "\n",
       "            Dew PointC  MeanDew PointC  Min DewpointC  Max Humidity  \\\n",
       "GMT                                                                   \n",
       "2015-07-03          15              11              8            83   \n",
       "2015-07-04          18              14             10           100   \n",
       "2015-07-10          11               7              2            82   \n",
       "2015-07-11          14              10              8            77   \n",
       "2015-07-14          18              15             14           100   \n",
       "2015-07-16          15              13              9            88   \n",
       "2015-07-17          17              13              9            94   \n",
       "2015-08-03          16              13              8            83   \n",
       "2015-08-08          15              13             10            94   \n",
       "2015-08-21          17              16             13            88   \n",
       "2015-08-22          17              14             12            94   \n",
       "\n",
       "            Mean Humidity  Min Humidity       ...        Max VisibilityKm  \\\n",
       "GMT                                           ...                           \n",
       "2015-07-03             56            23       ...                      31   \n",
       "2015-07-04             67            33       ...                      31   \n",
       "2015-07-10             45            11       ...                      31   \n",
       "2015-07-11             49            24       ...                      31   \n",
       "2015-07-14             78            51       ...                      31   \n",
       "2015-07-16             65            44       ...                      26   \n",
       "2015-07-17             67            35       ...                      23   \n",
       "2015-08-03             64            40       ...                      31   \n",
       "2015-08-08             62            28       ...                      31   \n",
       "2015-08-21             70            39       ...                      31   \n",
       "2015-08-22             63            27       ...                      31   \n",
       "\n",
       "            Mean VisibilityKm  Min VisibilityKm  Max Wind SpeedKm/h  \\\n",
       "GMT                                                                   \n",
       "2015-07-03                 21                 9                  27   \n",
       "2015-07-04                  8                 2                  27   \n",
       "2015-07-10                 23                10                  23   \n",
       "2015-07-11                 19                10                  27   \n",
       "2015-07-14                 13                 3                  24   \n",
       "2015-07-16                 14                 6                  24   \n",
       "2015-07-17                 11                 6                  35   \n",
       "2015-08-03                 14                10                  34   \n",
       "2015-08-08                 15                10                  23   \n",
       "2015-08-21                 13                10                  26   \n",
       "2015-08-22                 16                 7                  26   \n",
       "\n",
       "            Mean Wind SpeedKm/h  Max Gust SpeedKm/h  Precipitationmm  \\\n",
       "GMT                                                                    \n",
       "2015-07-03                   10                 NaN             8.89   \n",
       "2015-07-04                   14                  42             0.00   \n",
       "2015-07-10                   11                  39             0.00   \n",
       "2015-07-11                   13                  42             0.00   \n",
       "2015-07-14                   18                 NaN             2.03   \n",
       "2015-07-16                   13                 NaN             7.11   \n",
       "2015-07-17                   16                 NaN             0.25   \n",
       "2015-08-03                   18                  47             0.00   \n",
       "2015-08-08                   10                 NaN             0.00   \n",
       "2015-08-21                   18                  37             0.00   \n",
       "2015-08-22                   10                 NaN             0.00   \n",
       "\n",
       "            CloudCover             Events  WindDirDegrees  \n",
       "GMT                                                        \n",
       "2015-07-03           2               Rain              83  \n",
       "2015-07-04           2  Rain-Thunderstorm             220  \n",
       "2015-07-10         NaN                NaN             182  \n",
       "2015-07-11           2               Rain             274  \n",
       "2015-07-14           6               Rain             252  \n",
       "2015-07-16           4  Rain-Thunderstorm              90  \n",
       "2015-07-17           3               Rain             242  \n",
       "2015-08-03           3               Rain             200  \n",
       "2015-08-08           2                NaN             148  \n",
       "2015-08-21           4                NaN             199  \n",
       "2015-08-22           4                NaN             114  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remainingSummer[remainingSummer['Mean TemperatureC'] >= 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##### Summer 2015 had a toal of 11 days with a mean temperature of 20 Celsius or higher.\n",
    "\n",
    "From here it would be best to see a graph of the temperature for a better look at the trends in temperature, so next I tell Jupyter to display any graph created inside this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now to plot the `'Mean TemperatureC'` for the Summer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfb3c5390>"
      ]
     },
     "execution_count": 31,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cfb3c5d30>"
      ]
     },
     "execution_count": 31,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remainingSummer['Mean TemperatureC'].plot(grid=True, figsize=(18,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The graph shows that the mean temperature was generally in the 18ºC to 20ºC range, with the exceptions of the final weeks of both July and August.\n",
    "\n",
    "To get a better idea of when would be best for a staycation I will also put precipitation onto the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfb2f6dd8>"
      ]
     },
     "execution_count": 32,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cfb2c6780>"
      ]
     },
     "execution_count": 32,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remainingSummer[['Mean TemperatureC', 'Precipitationmm']].plot(grid=True, figsize=(18,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "It seems that there were days of high precipitation in the last week of both July and August.\n",
    "Both months seem very similar on face value, so for the best chance an enjoyable staycation, I will look at the mean of the 'mean temperatureC' for each month to see if there is statistically better option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean temperature for July : 18.1ºC\n",
      "Mean temperature for August: 17.8ºC\n"
     ]
    }
   ],
   "source": [
    "july = remainingSummer.ix[datetime(2015,7,1) : datetime(2015,7,31)]\n",
    "august = remainingSummer.ix[datetime(2015,8,1) : datetime(2015,8,31)]\n",
    "julyTempMean = float(july[['Mean TemperatureC']].values.mean())\n",
    "augTempMean = float(august[['Mean TemperatureC']].values.mean())\n",
    "MeanTemperatures = \"Mean temperature for July : %0.1fºC\\nMean temperature for August: %0.1fºC\" % (julyTempMean, augTempMean)\n",
    "print (MeanTemperatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "With the mean temperatures for each month varying by only 0.3ºC, not a noticable difference; I have decided to also examine the mean precipitation of both months to see if the result makes a particular month a clearer best choice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean precipitation for July : 2.1mm\n",
      "Mean precipitation for August: 3.2mm\n"
     ]
    }
   ],
   "source": [
    "julyPrecipMean = float(july[['Precipitationmm']].values.mean())\n",
    "augPrecipMean = float(august[['Precipitationmm']].values.mean())\n",
    "MeanPrecipitation = \"Mean precipitation for July : %0.1fmm\\nMean precipitation for August: %0.1fmm\" % (julyPrecipMean, augPrecipMean)\n",
    "print (MeanPrecipitation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "July had 0.9mm less precipitation on average per day than August."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Conclusions\n",
    "\n",
    "The graphs have shown both July and August both had very similar weather throughout the month. Ultimately, July had both a higher average mean temperature across the month, and a lower precipitation level, so I will take my 2 week staycation this month, starting immediately! (That way I can get more practice with Python)\n",
    "\n",
    "Of course these results are no guarantee that the weather pattern will repeat itself in future years. To make a sensible prediction I would need to analyse the summers for many more years. I am currently studying to expand my skills to be able to achieve this in future projects."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (system-wide)",
   "language": "python",
   "metadata": {
    "cocalc": {
     "description": "Python 3 programming language",
     "priority": 100,
     "url": "https://www.python.org/"
    }
   },
   "name": "python3",
   "resource_dir": "/ext/jupyter/kernels/python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}