Data Analysis in Python II Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis in Python II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1 - Visualising Data\n",
    "\n",
    "Let's start again by importing the modules we'll be using in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Graphs\n",
    "\n",
    "Plot the lines with the points given in the following two arrays, with `X` on the X axis and `Y` on the Y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nk2L/qdmzZxMXF8egQWf/ZH7//v3l2n9SUhJZWVnleqw/aS7vnC2XHNyPe//v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084480d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = [1, 2, 3, 4, 5]\n",
    "Y = [10, 20, -5, 12, 24]\n",
    "\n",
    "plt.plot(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following values are the goals scored by the top 5 Premier League teams between 2011 and 2016, plot these all on the same graph. \n",
    "\n",
    "Make sure you have the legend on the graph and label the X and Y axes.  \n",
    "Also have your graph start from 0 on the Y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rud3HBImGdh/jZWjR3csvv0xiYmJY3z8axAIgKSkJq9VKeXk59+7dIzc3d9p6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\nliQLIYQQ1ZJkIYQQolr/HwjzNTkWg2HgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f38510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [2011, 2012, 2013, 2014, 2015, 2016]\n",
    "arsenal = [72, 74, 72, 68, 71, 65]\n",
    "chelsea = [69, 65, 75, 71, 73, 59]\n",
    "liverpool = [59, 47, 71, 101, 52, 63]\n",
    "man_city = [60, 93, 66, 102, 83, 71]\n",
    "man_utd = [78, 89, 86, 64, 62, 49]\n",
    "\n",
    "plt.plot(years, arsenal, label='Arsenal')\n",
    "plt.plot(years, chelsea, label='Chelsea')\n",
    "plt.plot(years, liverpool, label='Liverpool')\n",
    "plt.plot(years, man_city, label='Man City')\n",
    "plt.plot(years, man_utd, label='Man Utd')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Goals Scored')\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the graph for:\n",
    "\n",
    "$ y = \\dfrac{1}{x} $\n",
    "\n",
    "For x between 0.5 and 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zL0z6DoHv/pCqitXYtW9RX7ma+t/9K/zuX6F3P0z+SEz+SBg4FOP1up0YaEfr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108579ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.arange(0.5, 4, 0.05)\n",
    "Y = 1 / X\n",
    "\n",
    "plt.plot(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the same graph, draw the lines for\n",
    "\n",
    "$ y = x^2 $  \n",
    "and  \n",
    "$ y = x^4 $\n",
    "\n",
    "for x between -3 and 3.\n",
    "\n",
    "Also plot markers for the individual points where X is an integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZJZdBgwbx73//u8nPzZkzJ+iBIlX13XdjX7GChD//mdK33waDHNMlxI+xvf8+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\nFFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108683690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.arange(-3, 3.05, 0.05)\n",
    "Y1 = X ** 2\n",
    "Y2 = X ** 4\n",
    "\n",
    "Xints = np.arange(-3, 4, 1)\n",
    "Y1_Xints = Xints ** 2\n",
    "Y2_Xints = Xints ** 4\n",
    "\n",
    "plt.plot(X, Y1, label='$y = x^2$', color='b')\n",
    "plt.plot(X, Y2, label='$y = x^4$', color='r')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.scatter(Xints, Y1_Xints, color='b')\n",
    "plt.scatter(Xints, Y2_Xints, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the same graph draw the lines for\n",
    "\n",
    "$ y = 3 + \\sqrt{6x - x^2 -8} $  \n",
    "and  \n",
    "$ y = 3 - \\sqrt{6x - x^2 -8} $  \n",
    "\n",
    "between 2 < x < 4\n",
    "\n",
    "Draw the first line as a solid green line ('-') and the second line as a dot-dashed red line ('-.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L/9U74uTGLSFETaZWO3+dLfz1verTOqC12lGEEOKmOgZ1xKAzuLz7hjpZ+JPT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108852e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.arange(2, 4.01, 0.05)\n",
    "Y1 = 3 + np.sqrt(6 * X - X ** 2 - 8)\n",
    "Y2 = 3 - np.sqrt(6 * X - X ** 2 - 8)\n",
    "\n",
    "plt.plot(X, Y1, color='g', linestyle='-')\n",
    "plt.plot(X, Y2, color='r', linestyle='-.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots\n",
    "\n",
    "#### Litter Data Set\n",
    "\n",
    "The file `data/litters.csv` has a number of litters of mice, with the pups' body and brain weights.  \n",
    "\n",
    "Plot a scatter plot of litter size against body weight in grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs</th>\n",
       "      <th>lsize</th>\n",
       "      <th>bodywt</th>\n",
       "      <th>brainwt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.447</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9.780</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.155</td>\n",
       "      <td>0.417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.613</td>\n",
       "      <td>0.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8.850</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs  lsize  bodywt  brainwt\n",
       "0    1      3   9.447    0.444\n",
       "1    2      3   9.780    0.436\n",
       "2    3      4   9.155    0.417\n",
       "3    4      4   9.613    0.429\n",
       "4    5      5   8.850    0.425"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "litters = pd.read_csv('data/litters.csv')\n",
    "\n",
    "litters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REBAAZ2dnveXuZVTSkSNHcPLkSUydOhUAEB8fj9OnT2PSpEkNfu7y5ct3vU1z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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108be7cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(litters['lsize'], litters['bodywt'])\n",
    "plt.xlabel('Litter Size')\n",
    "plt.ylabel('Body Weight (g)')\n",
    "plt.title('Body Weight of Mice compared to Litter Size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Boxplot\n",
    "\n",
    "Load the iris dataset into a DataFrame as shown in the slides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   species  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "iris_df = pd.DataFrame(iris['data'], columns=iris['feature_names'])\n",
    "iris_df['species'] = iris['target']\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now draw 3 boxplots for species 0 (Iris setosa), 1 (Iris versicolor), or 2 (Iris virginica) against `petal length (cm)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6M3p/f39cuHABt27dAtDQj83dOlSj0aCkpAR9+vRBREREs//B9e3bF99++61u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\nhj8RkQgx/ImIRIjhT0QkQgx/IiIR+n9hswNkTQUsqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bf2450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setosa = iris_df[iris_df['species'] == 0]['petal length (cm)']\n",
    "versicolor = iris_df[iris_df['species'] == 1]['petal length (cm)']\n",
    "virginica = iris_df[iris_df['species'] == 2]['petal length (cm)']\n",
    "\n",
    "plt.boxplot([setosa, versicolor, virginica], labels=['I. setosa', 'I. versicolor', 'I. virginica'])\n",
    "plt.ylabel('petal length (cm)')\n",
    "plt.xlabel('Iris Species')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### UK Driver Deaths Data Set\n",
    "\n",
    "The file UK Driver Deaths contains the number of drivers that died in the UK for each month between 1969 and 1984 inclusive.  \n",
    "\n",
    "Draw a histogram showing the distribution of deaths per month with bins between 1000 and 3000 of width 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan 1969</td>\n",
       "      <td>1687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb 1969</td>\n",
       "      <td>1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar 1969</td>\n",
       "      <td>1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr 1969</td>\n",
       "      <td>1385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May 1969</td>\n",
       "      <td>1632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  deaths\n",
       "0  Jan 1969    1687\n",
       "1  Feb 1969    1508\n",
       "2  Mar 1969    1507\n",
       "3  Apr 1969    1385\n",
       "4  May 1969    1632"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths = pd.read_csv('data/uk_driver_deaths.csv')\n",
    "\n",
    "deaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UFJ5//nl75yMiIgeSVBQCAwOxcuVKXLp0Sbz6KDg42N7ZiIjIwbo0S2pwcDCLATlEaVkZ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084f38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(deaths['deaths'], bins=20, range=(1000, 3000), edgecolor='black')\n",
    "plt.xlabel('Deaths in Month')\n",
    "plt.ylabel('Number of Months')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a bar chart of the different types of energy fuel against their output in Gigawatt-hours (GWh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zs2bB3NxcbZpMJoNMJqtzOaVSCaVSKQ3n5eVptc6msLe3b1X1tARDa5OhtUdbDO3/SN/t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\nkYShQEREEoYCERFJ/h/YMMHO/i2vFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108807290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_types = ['Nuclear', 'Hydro', 'Gas', 'Oil', 'Coal', 'Biofuel']\n",
    "energy = [5, 6, 15, 22, 24, 8]\n",
    "\n",
    "# Your code here\n",
    "xs = [i for i, _ in enumerate(energy)]\n",
    "\n",
    "plt.bar(xs, energy)\n",
    "plt.ylabel(\"Energy Output (GWh)\")\n",
    "plt.title(\"Energy Output by Fuel\")\n",
    "\n",
    "plt.xticks(xs, energy_types)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot each type of medals against their respective country for the medals they won at the 2012 Olympic Games, pick colours of bars to match the medal colour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bronze</th>\n",
       "      <th>gold</th>\n",
       "      <th>silver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>38</td>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bronze  gold  silver\n",
       "USA          38    46      37\n",
       "GB           17    27      23\n",
       "China        26    26      18\n",
       "Russia       19    19      18\n",
       "Germany      15    17      10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medals_2012 = pd.DataFrame(\n",
    "    {\n",
    "        'gold': [46, 27, 26, 19, 17],\n",
    "        'silver': [37, 23, 18, 18, 10],\n",
    "        'bronze': [38, 17, 26, 19, 15]\n",
    "    }, index = ['USA', 'GB', 'China', 'Russia', 'Germany']\n",
    ")\n",
    "\n",
    "medals_2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ff4lhw4YJPT09cejQIWne5/sUSgQEBAhdXV3h5uZWatrzncFCCOHq6irmzZunNK5FixZi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9d0590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "N = 5\n",
    "ind = np.arange(N)\n",
    "width = 0.25\n",
    "\n",
    "plt.bar(ind, medals_2012['gold'], width, label='Golds', color='gold')\n",
    "plt.bar(ind + width, medals_2012['silver'], width, label='Silvers', color='silver')\n",
    "plt.bar(ind + 2 * width, medals_2012['bronze'], width, label='Bronzes', color='#CD853F')\n",
    "plt.ylabel('Number of Medals')\n",
    "\n",
    "plt.title('Olympic Medals by Country and Medal Colour')\n",
    "\n",
    "plt.xticks(ind + width, medals_2012.index)\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 2 - Data Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Student's Sleep Data Set\n",
    "\n",
    "The data set below shows 10 students, and their response to two soporific (sleep-inducing) drugs, compared to a control period.  \n",
    "\n",
    "The increase in sleep is given by the variable 'extra'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sleep = pd.DataFrame({\n",
    "    'extra': [0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0, 2, 1.9, 0.8, 1.1, 0.1, -0.1, 4.4, 5.5, 1.6, 4.6, 3.4],\n",
    "    'group': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
    "    'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using groupby, find the means of `extra` for `group` 1 and 2, which of the 2 is the more effective drug on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "1    0.75\n",
       "2    2.33\n",
       "Name: extra, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleep.groupby('group').mean()['extra']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also using groupby, what was the total effect (sum of `extra` sleep) of the soporific drugs for each of the students (`ID`). Which ID was the most susceptible to both drugs in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "7     9.2\n",
       "6     7.8\n",
       "10    5.4\n",
       "9     4.6\n",
       "1     2.6\n",
       "8     2.4\n",
       "3     0.9\n",
       "5    -0.2\n",
       "2    -0.8\n",
       "4    -1.1\n",
       "Name: extra, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_by_susceptibility = sleep.groupby('ID').sum()['extra'].sort_values(ascending=False)\n",
    "ordered_by_susceptibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most susceptible is patient 7 with a total of 9.2 hours extra sleep\n"
     ]
    }
   ],
   "source": [
    "print(\"The most susceptible is patient {} with a total of {} hours extra sleep\".format(\n",
    "    ordered_by_susceptibility.index[0], ordered_by_susceptibility.iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Product Sales Data Set (Mocked)\n",
    "\n",
    "The data set below describes sales of products from an online retailer. Run the cell below to see some of the sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Product_Id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rep. Ireland</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Product_Id  Quantity  Price  Revenue\n",
       "0     Australia          17         1      9        9\n",
       "1            UK          15         4     15       60\n",
       "2           USA           5         3      6       18\n",
       "3   Netherlands          12         3     18       54\n",
       "4  Rep. Ireland          17         3      9       27"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(50)\n",
    "# 20 products, 200 transactions, 7 countries\n",
    "prices = {x: np.random.randint(5, 20) for x in np.arange(1, 20)}\n",
    "sales_df = pd.DataFrame({\n",
    "    'Quantity': np.random.randint(1, 5, 200),\n",
    "    'Product_Id': np.random.randint(1, 20, 200),\n",
    "    'Country': np.random.choice(['UK', 'USA', 'France', 'Australia', 'Norway', 'Rep. Ireland', 'Netherlands'], 200)\n",
    "})\n",
    "sales_df['Price'] = sales_df['Product_Id'].map(lambda x: prices[x])\n",
    "sales_df['Revenue'] = sales_df['Price'] * sales_df['Quantity']\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country brings in the most revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Netherlands     1096\n",
       "USA              960\n",
       "Rep. Ireland     840\n",
       "Australia        788\n",
       "Norway           774\n",
       "UK               755\n",
       "France           470\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.groupby('Country').sum()['Revenue'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country had the most individual transactions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Netherlands     34\n",
       "Australia       34\n",
       "USA             32\n",
       "Norway          30\n",
       "UK              29\n",
       "Rep. Ireland    25\n",
       "France          16\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.groupby('Country').count()['Revenue'].sort_values(ascending=False)\n",
    "# Or: sales_df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country bought the most items of stock?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Netherlands     93\n",
       "USA             77\n",
       "Australia       77\n",
       "UK              75\n",
       "Rep. Ireland    74\n",
       "Norway          73\n",
       "France          43\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.groupby('Country').sum()['Quantity'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country bought the highest quantity of `Product_Id` 5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia       10\n",
       "UK               8\n",
       "USA              6\n",
       "Norway           5\n",
       "Netherlands      3\n",
       "France           2\n",
       "Rep. Ireland     1\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df[sales_df['Product_Id'] == 5].groupby(['Country']).sum()['Quantity'].sort_values(ascending=False)\n",
    "# or:\n",
    "# sales_df.groupby(['Product_Id', 'Country']).sum().loc[(5,), 'Quantity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nobel Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Nobel Foundation would like some information on a data set they have provided you with, it is an Excel document with two sheets named `nobel_prizes` and `population` respectively. \n",
    "\n",
    "This file is in the data directory as `\"data/nobel_prizes.xlsx\"`\n",
    "\n",
    "Read in the two Excel sheets into 2 separate Data Frames\n",
    "\n",
    "_Hint:_ http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nobel = pd.read_excel('data/nobel_prizes.xlsx', sheetname='nobel_prizes')\n",
    "population = pd.read_excel('data/nobel_prizes.xlsx', sheetname='population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They would first like to know what proportion of winners were female and which category has the most female winners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.33% of winners are female'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_counts = nobel['Sex'].value_counts()\n",
    "female_pct = round(gender_counts['Female'] * 100.0 / len(nobel), 2)\n",
    "\"{}% of winners are female\".format(female_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Peace         14\n",
       "Literature    14\n",
       "Medicine      12\n",
       "Chemistry      4\n",
       "Physics        2\n",
       "Economics      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nobel[nobel['Sex'] == 'Female'].groupby('Category').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country do the most Literature Nobel Prize winners come from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Birth Country\n",
       "France                      11\n",
       "United States of America     9\n",
       "Germany                      8\n",
       "Sweden                       7\n",
       "United Kingdom               6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nobel[nobel['Category'] == 'Literature'].groupby('Birth Country').size().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many Nobel Prize Winners have the first name \"Robert\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were 25 winners named Robert\n"
     ]
    }
   ],
   "source": [
    "roberts = nobel[nobel['Full Name'].map(lambda x: x.startswith('Robert'))]\n",
    "print(\"There were {} winners named Robert\".format(len(roberts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to replace some of the values in the `\"Birth Country\"` column in our `nobel` Data Frame.\n",
    "\n",
    "One of the Nobel Prize winners is listed as coming from French overseas territory `\"Guadeloupe Island\"`. Replace this value with `\"France\"`.\n",
    "\n",
    "Trinidad and Tobago is listed as `\"Trinidad\"`, replace this value with `\"Trinidad and Tobago\"`.\n",
    "\n",
    "`\"Northern Ireland\"` is listed Separately to United Kingdom, replace this value with `\"United Kingdom\"`.\n",
    "\n",
    "There was also a winner from Taiwan, but there is no population entry for Taiwan, so we will have to (perhaps controversially) assign Taiwan to China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nobel['Birth Country'] = nobel['Birth Country'].replace(\n",
    "    ['Guadeloupe Island', 'Trinidad', 'Northern Ireland', 'Scotland', 'Taiwan'],\n",
    "    ['France', 'Trinidad and Tobago', 'United Kingdom', 'United Kingdom', 'China']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Nobel Foundation would like to know the 5 countries with the most prize winners per capita that were born in that country, use your 2 data frames to calculate this.\n",
    "\n",
    "_Hint: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Birth Country</th>\n",
       "      <th>winners</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Population (000s)</th>\n",
       "      <th>Population</th>\n",
       "      <th>winners_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Saint Lucia</td>\n",
       "      <td>2</td>\n",
       "      <td>Saint Lucia</td>\n",
       "      <td>185.0</td>\n",
       "      <td>185000.0</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>570.0</td>\n",
       "      <td>570000.0</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>331.0</td>\n",
       "      <td>331000.0</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>29</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>9799.0</td>\n",
       "      <td>9799000.0</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Norway</td>\n",
       "      <td>12</td>\n",
       "      <td>Norway</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>5190000.0</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Birth Country  winners      Economy  Population (000s)  Population  \\\n",
       "55   Saint Lucia        2  Saint Lucia              185.0    185000.0   \n",
       "41    Luxembourg        2   Luxembourg              570.0    570000.0   \n",
       "29       Iceland        1      Iceland              331.0    331000.0   \n",
       "61        Sweden       29       Sweden             9799.0   9799000.0   \n",
       "48        Norway       12       Norway             5190.0   5190000.0   \n",
       "\n",
       "    winners_per_capita  \n",
       "55            0.000011  \n",
       "41            0.000004  \n",
       "29            0.000003  \n",
       "61            0.000003  \n",
       "48            0.000002  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_country = nobel.groupby('Birth Country').size().reset_index()\n",
    "grouped_by_country = grouped_by_country.rename(columns={0: 'winners'})\n",
    "\n",
    "population = population.dropna()\n",
    "population['Population'] = population['Population (000s)'] * 1000\n",
    "\n",
    "merged = grouped_by_country.merge(\n",
    "    population, how='left', \n",
    "    left_on='Birth Country', right_on='Economy'\n",
    ")\n",
    "\n",
    "merged['winners_per_capita'] = merged['winners'] / merged['Population']\n",
    "\n",
    "\n",
    "merged.sort_values('winners_per_capita', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 3 - Time Series Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the strings in the following Data Frame to datetimes using 3 separate functions. \n",
    "\n",
    "When you've completed, you should see that all the columns in the dataframe are the same.\n",
    "\n",
    "_Hint: Remember you can use http://www.strftime.org for information on date parsing codes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-04 15:03:00</td>\n",
       "      <td>15:03:00, 4 January 2015</td>\n",
       "      <td>01/04/2015 15:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-30 18:18:12</td>\n",
       "      <td>18:18:12, 30 December 2016</td>\n",
       "      <td>12/30/2016 18:18:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-23 09:13:04</td>\n",
       "      <td>09:13:04, 23 February 2017</td>\n",
       "      <td>02/23/2017 09:13:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     a                           b                    c\n",
       "0  2015-01-04 15:03:00    15:03:00, 4 January 2015  01/04/2015 15:03:00\n",
       "1  2016-12-30 18:18:12  18:18:12, 30 December 2016  12/30/2016 18:18:12\n",
       "2  2017-02-23 09:13:04  09:13:04, 23 February 2017  02/23/2017 09:13:04"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run this cell\n",
    "import datetime\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'a': ['2015-01-04 15:03:00', '2016-12-30 18:18:12', '2017-02-23 09:13:04'],\n",
    "    'b': ['15:03:00, 4 January 2015', '18:18:12, 30 December 2016', '09:13:04, 23 February 2017'],\n",
    "    'c': ['01/04/2015 15:03:00', '12/30/2016 18:18:12', '02/23/2017 09:13:04']\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-04 15:03:00</td>\n",
       "      <td>2015-01-04 15:03:00</td>\n",
       "      <td>2015-01-04 15:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-30 18:18:12</td>\n",
       "      <td>2016-12-30 18:18:12</td>\n",
       "      <td>2016-12-30 18:18:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-23 09:13:04</td>\n",
       "      <td>2017-02-23 09:13:04</td>\n",
       "      <td>2017-02-23 09:13:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a                   b                   c\n",
       "0 2015-01-04 15:03:00 2015-01-04 15:03:00 2015-01-04 15:03:00\n",
       "1 2016-12-30 18:18:12 2016-12-30 18:18:12 2016-12-30 18:18:12\n",
       "2 2017-02-23 09:13:04 2017-02-23 09:13:04 2017-02-23 09:13:04"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_a(dt_str):\n",
    "    return datetime.datetime.strptime(dt_str, '%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "def convert_b(dt_str):\n",
    "    return datetime.datetime.strptime(dt_str, '%H:%M:%S, %d %B %Y')\n",
    "\n",
    "def convert_c(dt_str):\n",
    "    return datetime.datetime.strptime(dt_str, '%m/%d/%Y %H:%M:%S')\n",
    "\n",
    "df['a'] = df['a'].map(convert_a)\n",
    "df['b'] = df['b'].map(convert_b)\n",
    "df['c'] = df['c'].map(convert_c)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by reading in our csv `'distances.csv'`\n",
    "\n",
    "This is a csv that contains mocked data for the sum of distances covered by 100 people in each minute of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-05 00:00:00</td>\n",
       "      <td>0.172029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-05 00:01:00</td>\n",
       "      <td>0.379799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-05 00:02:00</td>\n",
       "      <td>0.881766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-05 00:03:00</td>\n",
       "      <td>0.070449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-05 00:04:00</td>\n",
       "      <td>0.227322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  distance\n",
       "0  2018-02-05 00:00:00  0.172029\n",
       "1  2018-02-05 00:01:00  0.379799\n",
       "2  2018-02-05 00:02:00  0.881766\n",
       "3  2018-02-05 00:03:00  0.070449\n",
       "4  2018-02-05 00:04:00  0.227322"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/distances.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the function we created in the last section to parse column `a` to parse our `datetime` column here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['datetime'] = df['datetime'].map(convert_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot a line graph of distance against time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ipMUkiMjhG18FezCJZzvsoFwhg3pYi5pu/Z5lQxxLlBX2s++DnfblpMUgiBKJKmp9fX1Y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\nmiAIgiAIQoTy/wHDWEE8mQcWkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad653d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "# Your code here\n",
    "plt.plot(df['datetime'], df['distance'])\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make out a trend but it's hardly pretty, this data is for every minute of the day.\n",
    "\n",
    "Make the datetime column your Data Frame's index, then resample to an hourly granularity, summing up each of the values within each hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.set_index('datetime')\n",
    "df = df.resample('H').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, again plot a line graph distance against time and see how this compares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IrWJLS7ErFmE6d8M4EX7HOSnTuTuUlsBXOZU6js2cBdu34lx5Iybi1L5vp+9g2LUDVi6p\nVBYR8Z8KQBGpXVavhH17qv7l3x+1OxOiYyp1GdjuLMS+Pw269MB0TD/1LGd1g7gE3KzMUz+GiFQJ\nKgBFpFaxOQugTh3okOZ3lHIxgQDmzMNdQU6Fnf53KC3BueJXlc/ScyAsy8bu3lWpY4mIv1QAikit\nYa31ln/pmI6JivI7Tvl16Q47C+HbdRV+q/12ndfFY/DFmIaVbzBv+g6Gg6XYhXMrfSwR8Y8KQBGp\nPTath4K86nP59xBz1qGuIMsr1hXEW/blBYiNx1x0ZXCyNDsNWrbFztdlYJHqTAWgiNQaNucLMI73\nYEU1YmLjva4gFWwLZxfNh7WrMMN/gYmpF7w8fQZ5C1RvWh+0Y4pIeKkAFJFaw+YsgHYdMHHxfkep\nMNO5+6GuIHnl2t8WH8C+9RI0a4U557zgZuk5AAIBbJbWBBSprlQAikitYLdvhc0bMV16+h3llJgu\nFesKYj+eAQXbca66KejL3Zh6cZguPbEL5mJLS4J6bBEJDxWAIlIr2GULADBp1bMApFEzqN+oXAWg\nLczHfvAWdO2NOb1TSOKYvoOhaBcsXxSS44tIaKkAFJFaweYshKYtMQ0a+x3llJR1Bflq2Um7gth3\npoJ7EGfk6NAF6pgOCcm4ugwsUi2pABSRGs8W7YLVX1bby78/Ml16HOoKsuy4+9j132C/+DfmvGGY\n+o1ClyUiAtP7XFixCLuzMGTjiEhoqAAUkRrPLl8E1q2+l39/1K6j1xXkOJeBrbW4b74I8YmYC68I\neRzTZzC4LnbBpyEfS0SCSwWgiNR4dtkCSEyBlm38jlIpJhDpdQVZnn3MriB2wVxY/w3msuswdWNC\nn6dxM2h9Onb+bKy1IR9PRIJHBaCIhITdt5c9776JLfH3KVFbfABWLsGk9cA4NeBXXudDXUE2HdkV\nxB7Yj337FWjRxjszFyam72D4YRNsXBu2MUWk8mrAb0MRqYrsv9+n6G9PYmfP8jfIV8uh+EC16/5x\nPKbToa4gy468DGw/fAd25ONceVNYC13TrR9E1tGagCLVjApAEQk6ay02a473+fvTsLt2+Jdl2QKI\njoHTz/ItQzCZ2Hhoc8YRbeFs/nbsR+9gup2DaX9mePPE1MOk98YunIstKQ7r2CJy6lQAikjwrf8G\ntn1PzGW/gJJi7Mx/+BLDugexOQswZ52NCUT6kiEUTOfusGk9tjAfAPv2y972kTf4k6fvYNi7x+u0\nIiLVggpAEQk6mzUH6tSh3sgbMAMvxH72Mfb7b8MfZP1q2L0TqvvTvz9juni9jO3ybOzaVdjszzBD\nhmNSGvgT6IzOkJyqy8Ai1YgKQBEJKlt8wCtIuvbBiamHueQqiI7Bnfa3sD8panO+gIgA5qyzwzpu\nyDVu7nUFyVmA+8aLkJiMGXq5b3GM42B6D4Ivc8rOSopI1aYCUESCyi5bCPv2lD2JaurFeUXgqqWw\ncnF4s+QshNM7YWLqhXXcUCvrCrJyMXy7FjPil5i60f5m6jMYrIv9fI6vOUSkfFQAikhQ2azZkJwK\nP+lBawZeCA2bemcBS0vDk2PLZtj2ffVf/Pk4TGfvMjCt2mN6DvA3DHgt9tqfic2aozUBRaoBFYAi\nEjR2Rz58mYPpNeiIpUhMIIAz8gbYuhk778PwZMn5whu7S4+wjBd27c/CDLwA5/rbqsz6hqbPYNj2\nPaz72u8oInISVeO3hojUCPaLT72Wa73PPfrFLj3gjM7Yd1/H7ikKfZacBdCyLSY5NeRj+cEEAji/\n+DWm2Wl+Ryljzu4LUXX1MIhINRAIxyCTJ09myZIlJCQkMHHiRAD+/ve/s3jxYgKBAA0bNmTcuHHU\nq1eP3Nxc7rrrLpo0aQJAu3btGDNmDADr16/n2Wefpbi4mPT0dEaPHo0xJhzfgoicRNnaf23OwDRq\netTrxhicUTfiPnQn9v03MaNuDF2WHQWwYTXm0mtCNoYczdSNxpzdF5v9GfbKmzFRUX5HEpHjCMsZ\nwIEDB/LAAw8csa1z585MnDiRxx57jMaNGzN9+vSy1xo1asSECROYMGFCWfEH8MILLzB27Fieeuop\ntm7dSk5OTjjii0h5bFwLW77D9Bl03F1M81aYc87Dznkfu+2HkEWxyxeCtTX2/r+qzPQZDPv3YZdm\n+R1FRE4gLAVgx44diY2NPWJbly5diIiIAKB9+/YUFBSc8BiFhYXs27eP9u3bY4yhf//+ZGdnn/A9\nIhI+9vPZEFkH0+2cE+5nhv0CApG4b70cuiw5C6F+I2jaMmRjyHG0P9Nboma+LgOLVGVhuQR8MnPm\nzKFPnz5lX+fm5nLvvfcSHR3NVVddRYcOHSgoKCAlJaVsn5SUlBMWjZmZmWRmZgIwfvx4UlNDex9Q\nIBAI+RjVgebBU9vmwZYUsz37M+r2GkBCi9PKth9zHlJT2TPyeopee574Ld9Sp1Nw1+hz9+1h+1fL\niLlgBHH16wf12JVRm34mijIuYc/rL5DklhDRoPERr9WmeTgRzYNH8+DxYx58LwDfeecdIiIi6Nev\nHwBJSUlMnjyZuLg41q9fz4QJE8ruG6yIjIwMMjIyyr7Oy8sLWuZjSU1NDfkY1YHmwVPb5sEuzsIW\n7aa4a98jvu/jzYPtkwEfvEPhC4/j/P5xjBMRxCzzobSE/Wd04UAV+jeoTT8TtktPeONF8t9/G+eS\nq454rTbNw4loHjyaB8/P5+HH5yBCydengD/99FMWL17MHXfcUfYwR2RkJHFxcQC0bt2ahg0bsmXL\nFpKTk8nPP7zCfH5+PsnJyb7kFpEjuVmzITEZOnYp1/6mThTm8l/Cdxuwn/87qFlszgKIjYM2HYJ6\nXCk/k9LAe+I7azbWdf2OIyLH4FsBmJOTw8yZM7nvvvuI+smTYrt27cI99Atj27ZtbNmyhYYNG5KU\nlER0dDSrV6/GWsu8efPo1q2bX/FF5BC7qxBWLsb0OrdCZ/JM937Q5gzs9L9j9+8LTpbSUuzybEyn\n7piI4J1VlIozfQZD3jZY86XfUUTkGMJyCfiJJ55g1apV7N69m1tuuYVRo0Yxffp0SktLeeihh4DD\ny72sWrWKadOmERERgeM43HzzzWUPkNx0001MnjyZ4uJi0tLSSE9PD0d8ETkB+8VccN0TPv17LMYY\nnCt+hTv+XuyHb2OGX1v5MGu+hL17MOm9Kn8sqRST3hsbHYOdPxvzk64wIlI1hKUAvPPOO4/aNmjQ\nsf9n0atXL3r1OvYv7zZt2pzS/YAiEhre2n+zvXZkjZtX+P2mzRmYHv2xH8/A9jsfk1K5hzbssoUQ\nWQc6plXqOFJ5JioK0+0c7IK52GvGYOrG+B1JRH5CnUBE5NR9tx6+/7bCZ/9+yoz4JQD2namVimKt\nxS79AjqmYaLqVupYEhymbwYUH8Aumu93FBH5GRWAInLKbNYcCAS8+/lOkUmpjzlvOHbhXOz6b049\nzHcboGC7Fn+uSlqfDo2aqjWcSBWkAlBEToktLcEumIvp0hNTL65SxzIXXA4JSbjTpmCtPbU8OV+A\nMZjO3SuVRYLHGOM9DLJmFTY3dJ1fRKTiVACKyKlZsRiKdmH6Dq70oUzdaO8hkHVfYxf955SOYXMW\nQJsOmPjESueR4DG9zgXjYOfP8TuKiPyECkAROSVu1hxISIKOwXka3/QZBM1bYd9+BVtSXKH32vxc\n+G6DLv9WQSYpBc5Mw34+B+se9DuOiByiAlBEKszu3gkrsjE9BwZtvT3jROCMuhHyc7GfzKxYnpwF\n3jFUAFZJpk8GFObB18v9jiIih6gAFJEKswvmwsGDlXr691jMGZ0hrSf2X295C0yXN0/OAmjcHNMw\n9O2TpOJMWg+IicXO18MgIlWFCkARqTD7+Rxo2RbTtGXQj+2MHA2lJdgZr5Uvy57dsHqlzv5VYSay\nDqZnf+zSL3D37PY7joigAlBEKshu3gCb1mN6B/fs349MwyaYcy/C/ifTG+tkeVYs8jqRqACs0kyf\nwVBSzK6nH8GuysEe1P2AIn5SASgiFWKz5kBEANOjf8jGMBdfCTH1cKf97aTLwticBZCQDKe1C1ke\nCYKWbTHnDaN4WTbupAdx77kB99XJ2G9W6OEQER+EpRWciNQMtrQU+8Wn0KU7Ji4+ZOOYerGYS67G\nvvF/sDwbuvQ4dp6SYli5xHsYxdHfs1WZMQYz6kZSbryTvLkfYxf9B/v5v7FzP4SEJMzZfTHdzoE2\nZ+jfUiQMVACKSPl9uRR278QJ0eXfnzIDhmI//RfuP1/CObMrJnCMX1dfL4cD+zHpuvxbXZioKEzX\n3piuvbEH9mOXL8Iu+gz72cfYOe9BUqpXDHY/x+sxbYzfkUVqJBWAIlJubtZsiEuAs84O+VgmEMC5\nYjTu0w9h536AGXzJUfvYnAVQNxpO7xzyPBJ8JqquV+h1Pwe7fy82Z6F3ZvDT97GZMyGlAabbOV6r\nwRatVQyKBJEKQBEpF1u0C5YvxAy88Nhn40KhUzfomIad9Tq218AjWs5Z18UuW4g5sysmMjI8eSRk\nTN0YTK+B0Gsgdm8RNmcBNvs/2MyZ2I/egQaNDxWD50DT01QMilSSCkARKReb/RmUlnpPc4aJMQbn\nil/h/u+d2HffwFx18+EXN6yGnYWQ3itseSQ8TEys93PWZzC2aBd26RfY7M+wH7yN/dc/oVEzTPdz\nvIKwSQu/44pUSyoARaRcbNYcaNYK07xVWMc1zU7D9DsP++m/sAMvwDRq5uXJWQAREZhOob8cLf4x\nsfGYfkOg3xDsrh3YJVnYRfOx772JffcNaNrSKwTPvQhTL9bvuCLVRoUetdq9ezfz5s1j5kyvTVNB\nQQH5+fkhCSYiVYf9fhNsXIPpG/qHP47FDLsGIuvgvvXy4Uw5C6D9WZgY/U+/tjDxiTgDLyTi7odx\n/vIS5qoxEB2Dnfka7stP+h1PpFopdwG4atUq7rzzTj777DPefvttALZu3coLL7wQsnAiUjXYz2d7\nZ9t6DPBlfBOfhLlwFCxbiP1qGXbrZti6WYs/12ImMRln8MVE3Pco5uKrIGeB94eKiJRLuQvAl19+\nmTvvvJPf/e53RBxq/t62bVvWrVsXsnAi4j978CD2i7lw1tmY+ETfcpiMSyClAe60Kdgln3vbuqgA\nFDCDL4Y6UdgP3/Y7iki1Ue4CcPv27XTq1OmIbYFAgINq5yNSs63KgZ0FOGF8+ONYTGQdzOU3wOaN\n2PenQYs2mJT6vmaSqsHExmP6n49dOBebn+t3HJFqodwFYLNmzcjJyTli24oVK2jRQk9gidRk9vM5\nEBsHnbv5HQXTrS+07QDFB3T5V45gzhsGxsF+NN3vKCLVQrkLwOuuu46nn36aZ555huLiYv7v//6P\nyZMnc+2114Yyn4j4yO4pwi79AtNjACbg/1p7xhicq8ZAw6aYnqHrRSzVj0muj+k1APufT7C7dvgd\nR6TKK3cB2L59eyZMmEDz5s0599xzadCgAY888ght27YNZT4R8ZFd9B8oLcH08efp32MxLdsQ8afn\nMA2a+B1Fqhhz/uVQWoKd/Z7fUUSqvHKvA1hSUkJ8fDzDhg0r21ZaWkpJSQmRWoVfpEayWbOhaUto\n0cbvKCInZRo3g/ReXiu5oSMw0TF+RxKpssp9BvBPf/oT69evP2Lb+vXrefjhh4MeSkT8Z7duhvXf\nYHoPUtstqTacoSNh7x7svA/9jiJSpZW7ANy0aRPt2rU7Ylvbtm359ttvgx5KRPxns+aA42B6+rP2\nn8ipMK3aQYcu2E9mYkuK/Y4jUmWVuwCMiYlh586dR2zbuXMnUVFRQQ8lIv6y7kHsF5/CmV0xicl+\nxxGpEGfo5bCz0HuCXUSOqdwFYM+ePXnyySfZtGkTBw4cYNOmTTzzzDP07t07lPlExA9fL4fCPJwq\n9PCHSLl16AIt22I/fAfraq1akWMp90MgV111FVOnTuWBBx6gpKSEOnXqMHDgQK6++upyvX/y5Mks\nWbKEhIQEJk6cCEBRURGTJk1i+/bt1K9fn7vuuovY2Fistbz00kssXbqUqKgoxo0bR+vWrQH49NNP\neeeddwAYMWIEAwcOrOC3LCInY+fPgZhY6NLD7ygiFWaMwblgJO5fx2MXZ2G69/M7kkiVU+4CsE6d\nOtx0001gvh8GAAAgAElEQVTceOON7N69m7i4uArdGD5w4ECGDh3Ks88+W7ZtxowZdOrUieHDhzNj\nxgxmzJjBtddey9KlS9m6dStPPfUUa9as4cUXX+SRRx6hqKiIt956i/HjxwNw//33061bN2Jj1Qxe\nJFjs3j3YnM8xfQZjIuv4HUfk1KT3gkZNsR+8he12jh5kEvmZcl8CBti7dy/r1q1j06ZNfPnll6xc\nuZKVK1eW670dO3Y8qlDLzs5mwADvBvMBAwaQnZ0NwKJFi+jfvz/GGNq3b8+ePXsoLCwkJyeHzp07\nExsbS2xsLJ07dz6qO4mIVI5dPB+KizE+t34TqQzjOJjzR8B3G+DLJX7HEalyyn0G8NNPP2XKlCnU\nrVuXOnUOnxUwxvDMM8+c0uA7d+4kKSkJgMTExLKHTAoKCkhNTS3bLyUlhYKCAgoKCkhJSSnbnpyc\nTEFBwSmNLSLHZrPmQKNmcFq7k+8sUoWZXgOxM/+B+8HbRJx1tt9xRKqUcheAr7/+Or/5zW9IT08P\nSRBjTFBP0WdmZpKZmQnA+PHjjygoQyEQCIR8jOpA8+CprvNQumUz+WtXEXvdr6lXv36lj1dd5yEU\nNBeecM/Dnst+QdFLTxGft4U6Z3QK27gno58Hj+bB48c8lLsAdF2XLl26BHXwhIQECgsLSUpKorCw\nkPj4eMA7s5eXl1e2X35+PsnJySQnJ7Nq1aqy7QUFBXTs2PGYx87IyCAjI6Ps658eLxRSU1NDPkZ1\noHnwVNd5cP/1NhiHvZ16sC8I+avrPISC5sIT7nmwXfvCtJcofGMKEbf9Pmzjnox+HjyaB8/P56FJ\nk9C3uiz3PYDDhg3j7bffxnXdoA3erVs35s6dC8DcuXPp3r172fZ58+ZhrWX16tXExMSQlJREWloa\ny5Yto6ioiKKiIpYtW0ZaWlrQ8ojUZtZ1vcu/HbtgklJO/gaRasDUjcYMugiWLcR+v8nvOCJVRrnP\nAL7//vvs2LGDWbNmHfUwx3PPPXfS9z/xxBOsWrWK3bt3c8sttzBq1CiGDx/OpEmTmDNnTtkyMADp\n6eksWbKEO+64gzp16jBu3DgAYmNjufzyy/ntb38LwMiRI/UEsEiwrF4JBdsxl//S7yQiQWUGXYz9\naDr2w7cxN97ldxyRKqHcBeDtt99eqYHuvPPOY25/8MEHj9pmjOGmm2465v6DBg1i0CAtTisSbDZr\nNkTXw6T19DuKSFCZ2HhM//Oxc97DDv8FJqWB35FEfFfuAvB499qJSPVn9+/DLvkc03MApo7aO0rN\nY84bjv33v7AfTcdcM9bvOCK+K3cBCLBx40a++uordu/ejbW2bPuVV14Z9GAiEj52cRYc2I/prbPr\nUjOZ5FRMrwHY/3yCvfhKTHyi35FEfFXuh0AyMzP5wx/+wMqVK5k5cyabNm3ivffeY+vWraHMJyJh\nYLNmQ4Mm0OYMv6OIhIw5/3IoLcHOfs/vKCK+K/cZwJkzZ/LAAw/QoUMHRo8ezT333MPSpUuZP39+\nKPOJSJBY14X9e2FPEezb433cuwe7swBWr8QMv1btsqRGM42bQXov7KfvY4eOwETH+B1JxDflLgB3\n7dpFhw4dAO8hDdd1SU9P56mnngpZOBE5knUPwt49h/4rgr1F2D2HP/eKukOF3aGPZdv37QV7nGWc\noupiep8b3m9GxAfO0JG4Sz7HzvvQaxUnUkuVuwBMTk4mNzeXBg0a0LhxYxYtWkRcXByBQIVuIxSR\ncrKlpfD9t9gNq2HDau/j1s3wk/tvjxIIQEys91+9WIhPxDRsCvXqHbHdlH1eD6JjIS5eD39IrWBa\ntYMOXbCfzMQOuhgTWefkbxKpgcpdvQ0bNozvv/+eBg0aMHLkSB5//HFKS0u54YYbQhhPpHaw1kLe\ntkPF3hrshm9g03ooKfZ2iEuAVu0x6b0hLh5iYjH1Yg8XdTGHCrw6dXQZV+QknAtG4j7+B+znczD9\nh/odR8QX5S4ABw4cWPZ5eno6L730EqWlpdStWzcUuURqNLtn96FCb3XZGT6KdnkvRtaBlm0wAy/w\nir5W7SGlgQo7kWA5ozO0bIv98B3sOedhnAi/E4mEXbkLwHvvvZe//OUvh98YCBAIBLj//vsZP358\nSMKJ1AS2pAS+W4/dsAY2fON9zP3Be9EYaNQM06U7tDrduzzVpCVGt1aIhIwxBufCkbjPjccuzsJ0\n7+d3JJGwK/f/ZY613Iu1lm3btgU1kEh1Z0uK2ffph7jLF3tn977bAAdLvRcTkr2zeudkYE5rB6e1\n05OIIn5I6wWNmmI/eAvb7RydYZda56QF4DPPPANAaWlp2ec/2r59O82bNw9NMpFqyr78FLsWzoOo\nul6Bl3EppnV7OK09JjnV73giAhjHwQy9HPvyU/DlEjjrbL8jiYTVSQvAhg0bHvNzYwynn346vXv3\nDk0ykWrIrliEXTiPeiN/yb7zhuveIpEqzPQcgJ35D9wP3iYijAWgtfaIbloifjhpAXjFFVcA0K5d\nO9LS0kIeSKS6svv34b76HDRuTr1Ro9m/c5ffkUTkBEwgEjNkGPbNKdh1X2PC0AnHLsvGffMFdp2Z\nDr/4dcjHEzmecreCCwQC5ObmArBjxw6eeeYZJk+ezI4dO0IWTqQ6sTNfg4LtONffqrXFRKoJc84Q\nqBeH+8FbIR3H5m/n4LOP4D7zEOzeyf5PP8B+tyGkY4qcSLkLwClTpuA43u6vvPIKBw8exBjD888/\nH7JwItWF3bAGO/s9zMALMG07+h1HRMrJ1I3GDLoYli3Efv9t0I9vS0txP5qO+8dbYdUSzIhf4jz8\nPCYmFve9N4I+nkh5lbsALCgoIDU1lYMHD7Js2TLGjh3LzTffzOrVq0OZT6TKs6WluFOfhoREzGXX\n+x1HRCrIDLoIoupiP3wnqMe1a7/C/dNd2LdegtM74fzPszgXXI6JTyTmklGw5HPspvVBHVOkvMpd\nAEZHR7Njxw5WrVpFs2bNyhaALi0tDVk4kerAfjIDNm/EueYWTEw9v+OISAWZ2HhMv/OxC+di8yq/\ntJkt2oU79RncR++DfXtwxj2Ac9vvMamHH6SMueRKiK6ns4Dim3KvAzh06FB++9vfHtH+7euvv6Zp\n06ahyiZS5dncH7DvvgFde2PSe/kdR0ROkTlvGPbf72M/noG5ZuwpHcNai82a453x21uEGXIZ5pKr\nMHWjj9rXqReHybgE++4b2E3rMS1aV/ZbEKmQcheAw4cPp0ePHjiOQ6NGjQBITk7mlltuCVk4karM\nWov798kQiMS5eozfcUSkEkxyKqbXQOx/PsFefCUmPrFC77ffb8J9bTKsWQVtzsC59teYZq1OPGbG\npdjMd3HffYOIWx+oTHyRCqtQv6kmTZqc8GuR2sRmzYavl2OuHYdJTPE7johUkhk6Aps123ug67Jr\ny/Uee+AA9v03sB/PgLoxmOtvw/TNwDgnv8PKxMR6ReC7r2M3rcO0aFPZb0Gk3E5YAN51111MmjQJ\ngF//+vjrFT333HPBTSVSxdldhdhpf4N2HTH9hvgdR0SCwDRqBum9vUvBQ0ectE2jXZaN+/rzkJ+L\n6TMYM/IGTFxCxcbMuAQ7e9ahs4C/q0x8kQo5YQE4duzh+yBuv/32kIcRqS7sGy9C8X6c624r11/6\nIlI9OBdcjrskCzvvQ8z5I465j83fjvvGC5DzBTRujnPPI5j2Z53SeN5ZwGHYWf/QWUAJqxMWgMuW\nLWPZsmUnPUjHjlr3TGoPu2IRNvszzKXXYBo38zuOiASROa0ddOiC/WQmdtDFRyzqbktLsbPfxb77\nOlgXM+KXmPMuxQQiKzfm4EuwmTNxZ71OxG2/r+R3IFI+JywA8/Pzyz4vLi5mwYIFtG3bltTUVPLy\n8li7di09e/YMeUiRquKn7d7MBZf7HUdEQsC5YCTu43/Afj4H038ocGhNv1cnw/ffQufuOFePOWJZ\nl8owMfW8p5Bn/gP77TpMS50FlNA7YQE4bty4ss+feOIJ/uu//otevQ4vdbFgwQI+//zz0KUTqWLs\njFehMA/n3vGV/qtfRKqoMzpDy7bYD9/BpvXCzngV+9nHkJyKM+4BSOuJMSaoQ5pBl2A/mYX7rs4C\nSniU++alpUuX0qNHjyO2devWjaVLlwY9lEhVZDesxs55DzPgAkzbDn7HEZEQMcbgXDgStm/F/e1N\n2PmZmCGX4fzPs5j0XkEv/uDwWUCWLcR+uzboxxf5uXIXgI0aNeLDDz88YtvHH39ctiagSE1mS0tx\nX3kaEpIxI9TuTaTGS+sFrU+Hlm1x/jAJ54rRx1zQOZjM4EsgJhZ31ushHUcEKrAO4C233MJjjz3G\nrFmzSE5OpqCggIiICP77v/87lPlEqgT78XT4/lucWx846dIQIlL9Gcch4rcTwjtmdAxmyHDvkvPG\nNZjT2oV1fKldyl0AtmrViieffJI1a9ZQWFhIYmIi7du3JxCo0FrSItWO3fZju7c+mDS1exOR0DGD\nLsZ+MtNbF/D2P/gdR2qwClVvgUCADh2Cd+/TDz/8ULbQNEBubi6jRo1iz549zJ49m/j4eACuvvpq\nunbtCsD06dOZM2cOjuMwevRo0tLSgpZH5Oe8dm/PQmQdtXsTkZAz0THeE8EzXsVuWINppbOAEhq+\nnr5r0qQJEyZ4p9hd12Xs2LH06NGDf//731x00UVceumlR+y/efNmsrKyePzxxyksLOShhx7iySef\nxNFCvBIidn4mfLMCc904TGKy33FEpBY4fBbwdSLueNDvOFJDVZnKacWKFTRq1Ij69esfd5/s7Gz6\n9OlDZGQkDRo0oFGjRqxdq6elJDTsrkLsP1/y2r2do3ZvIhIeP94LyIpF2A2r/Y4jNVSVuYFv/vz5\n9O3bt+zrjz76iHnz5tG6dWuuv/56YmNjKSgooF27w6fDf3wY5VgyMzPJzMwEYPz48aSmpoY0fyAQ\nCPkY1UFNmocdrzzJgeIDpNzxewINGlTovTVpHipD83CY5sKjefCcbB7cK64nL3MWgY/eJun3E8OY\nLLz08+DxYx6qRAFYWlrK4sWLueaaawAYMmQII0eOBODNN99k6tSpRyxKXR4ZGRlkZGSUfZ2Xlxe8\nwMfwY3eU2q6mzINdno37n9mYYdewo24sVPB7qinzUFmah8M0Fx7Ng6dc83DeMIrfmcr2hfMxrU8P\nT7Aw08+D5+fz0KRJk5CPWSUuAS9dupRWrVqRmJgIQGJiIo7j4DgOgwcPZt26dYB3xu+n7ekKCgpI\nTtZ9WRJcdv9e3NeegyYtMEPV7k1E/GHOvRBi43DffcPvKFIDVYkC8OeXfwsLC8s+X7hwIc2bNwe8\nziNZWVmUlJSQm5vLli1baNu2bdjzSs1mZ7wGhfk419+mdm8i4htTNwYzZASsXIxd97XfcaSG8f0S\n8P79+1m+fDljxhxeYuPVV19l48aNGGOoX79+2WvNmzend+/e/OY3v8FxHG688UY9ASxBVdbubeAF\nmDZn+B1HRGo5c+6F2I/fwX3vDSL+6//5HUdqEN8LwLp16/K3v/3tiG233377cfcfMWIEI0aMCHUs\nqYWOaPd2mdq9iYj/TN1ozJAR2Hdewa77Wn+YStDo9JnIIWXt3n5xi9q9iUiV4d0LGI/7rnoES/Co\nABThJ+3ezu6DSevpdxwRkTKmbjTm/Mvgy6W6F1CCRgWg1HpHtHu7Su3eRKTqMede5J0FnKWzgBIc\nKgCl1itr9zbyl2r3JiJVkomqixk6AlYtxa79yu84UgOoAJRaTe3eRKS6MAMvhLgE3QsoQaECUGo1\n+8aLULwf57rbMFpSSESqMBNVF3P+CFiVg127yu84Us3p/3hSa9mcBdjszzAXjcI0buZ3HBGRkzID\nL/DOAupeQKkkFYBSK9nNG3GnPA7NW6ndm4hUG2X3An61DLtGZwHl1KkAlFrH7ijAffp/oW40zm1/\nULs3EalWzADdCyiVpwJQahV7YD/uM3+CPUVe8Zec6nckEZEKMVFR3pULnQWUSlABKLWGdQ/ivjgR\nNq3HufkeTMs2fkcSETklZsAFEJ+os4ByylQASq1h33oZchZgrrwJ06W733FERE7ZEWcBV3/pdxyp\nhlQASq3gfvov7CczMYMuxhl8sd9xREQqzfQfqrOAcspUAEqNZ1csxv7j/6Bzd8yVN/odR0QkKExU\nFOaCy+Hr5djVK/2OI9WMCkCp0ex3G3Cf/ws0Pw3n5rsxToTfkUREgsb0HwoJSVoXUCpMBaDUWHZH\nPu7TD0F0jPfEb91ovyOJiASVqXPoXsBvVmC/0VlAKT8VgFIj2QP7cZ/+E+wtwrn9D5ikFL8jiYiE\nhOl/PiQk615AqRAVgFLjWPcg7guPwXcbcMbcg2nR2u9IIiIhY+ocuhfwmxXYb1b4HUeqCRWAUuPY\nf74EyxZirr4Z01nLvYhIzWf6DfHOAr76HLYw3+84Ug2oAJQaxf33+9jMWZjBl+Cce5HfcUREwsLU\nicK5+W7YkY/76H3Yrd/7HUmqOBWAUmPYFYuwr78AXXpgRv3K7zgiImFlTj8L5+5HoPgA7l/ux367\n1u9IUoWpAJQawW5aj/v8BGjeCuem/9ZyLyJSK5mWbXDuHQ91onAn/A771TK/I0kVpQJQqj1beGi5\nl5h6OLf/Xsu9iEitZho1xbnvUUipj/vU/2AXZ/kdSaogFYBSrdn9+3CfeQj27cW54w+YRC33IiJi\nklJw7v0ztGyL+/xfcOd95HckqWJUAEq1dXi5l404Y+/FNGvldyQRkSrD1IvDueshOKsr9u/P4r4/\nDWut37GkilABKNWWnfY3WJ6NuWYMptPZfscREalyTFQUzrgHML0GYme8ip02Beu6fseSKiDgdwCR\nU+HOfg87+11MxjCcgRf6HUdEpMoygQCMvhNi47GZs6BoF/zyDm+71Fr615dqxy7Lxr75IqT1xFxx\ng99xRESqPOM4MOpGrwic8Sp2TxHO2PswUVF+RxOfVIkC8NZbb6Vu3bo4jkNERATjx4+nqKiISZMm\nsX37durXr89dd91FbGws1lpeeuklli5dSlRUFOPGjaN1a7X6qi3spnW4L0yAFq213IuISAUYYzAX\njcKNS8C++hzupD94vdLrxfkdTXxQJQpAgD/+8Y/Ex8eXfT1jxgw6derE8OHDmTFjBjNmzODaa69l\n6dKlbN26laeeeoo1a9bw4osv8sgjj/iYXMLFFuR5y73Ui8W57feYqLp+RxIRqXac/udjY+NwX3gM\nd8IDOP/1/zBJWkGhtqmyD4FkZ2czYMAAAAYMGEB2djYAixYton///hhjaN++PXv27KGwsNDPqBIG\ndv9eb7mX/ftwbn8Qk5jsdyQRkWrLdO2Dc8cfIS/Xax237Qe/I0mYVZkzgA8//DAA5513HhkZGezc\nuZOkpCQAEhMT2blzJwAFBQWkpqaWvS8lJYWCgoKyfX+UmZlJZmYmAOPHjz/iPaEQCARCPkZVV7pp\nPXv/+R7RjgOBSExkJAQCmEAdiPQ+mkBk2ecEApjIOt6NyGX7R2IiD70W8N6Pe5Adf/4zxd9vIvH3\nE4hK6+b3t3pS+nnwaB4O01x4NA+eKjEP/QZT0qQphf/7G+xf7ifxwUlEtjk9rBGqxDxUAX7MQ5Uo\nAB966CGSk5PZuXMnf/rTn2jSpMkRrxtjMMZU6JgZGRlkZGSUfZ2XlxeUrMeTmpoa8jGqMru3CPfB\nO2BnQfAPHhEBBw9irh3H7uZt2V0N5rm2/zz8SPNwmObCo3nwVJl5SEjF3PNn3Cf+SMHvx+Hc+jvM\nGZ3DNnyVmQef/Xwefl4HhUKVKACTk73LeQkJCXTv3p21a9eSkJBAYWEhSUlJFBYWlt0fmJycfMQk\n5efnl71f/GPfngq7dpD86AvsiEuC0lIoLTny48ESKDn08Wev29JSOFgKJSXex5+/v2kLnF7n+v1t\niojUOD+2jnOf+CPuk/8P5+a7MV37+B1LQsz3AnD//v1Ya4mOjmb//v0sX76ckSNH0q1bN+bOncvw\n4cOZO3cu3bt3B6Bbt258+OGH9O3blzVr1hATE3PU5V8JL7t6JXbeh5jzhhHZ/kxMXh5E1qnQMSp2\nfldERILpx9Zx7tMP4f71L5hrf43T/3y/Y0kI+V4A7ty5k8ceewyAgwcPcs4555CWlkabNm2YNGkS\nc+bMKVsGBiA9PZ0lS5Zwxx13UKdOHcaNG+dn/FrPlhTjTn0WUhpghv3C7zgiInKKfmwd5/51vNc6\nrmgX5oKRFb4FS6oH3wvAhg0bMmHChKO2x8XF8eCDDx613RjDTTfdFI5oUg72vWmw7XucO/9Hy7KI\niFRzJioK59bfYV9+Ejv977B7F1wx2ltIWmoU3wtAqb7s5g3Yj97G9B6EOTPd7zgiIhIEJhCAX911\nqHXcTCjaqdZxNZD+NeWUWPcg7ivPQEwsZtSv/I4jIiJBZBwHrrwJ4hK81nHrvoaGTTBJqZCcCkn1\nMcmpkFwfklIwddRSrrpRASinxM55Dzauwdx8NyY2/uRvEBGRaqWsdVxqQ+yi+VCYh/12Hez21uW1\nP905Nv5QYZjqFYZJ9SE51SsYU+pDQrLOIFYx+teQCrN527DTX4VO3TDd+/kdR0REQsjpOQB6Dij7\n2pYUQ2G+VxDmb4fCPCjIwxbmQd427JovYe8eb98f32QMJCTBoTOI5lCBWNKrP8RpJQ8/qACUCrHW\n4v59MhgH5xe/1tNhIiK1jImsAw0aQ4PGx13Cy+7fCwU/KQwL8qBwO7YgDzZ/i12xCIqLKZjxKs59\n4zEt2oT1exAVgFJB9otPYdVSzNVjMCn1/Y4jIiJVkKkbA01aQJMWxywSrbWQnwuPPYA7+c84v3sc\nE6fbicJJz3VLudldO7BvvghtzsAMvMDvOCIiUk0ZYzCpDUm898+wswD3hQnYgwf9jlWrqACUcrNv\nToH9+3Cuuw3jRPgdR0REqrnI9h0xv/g1fLUMO32q33FqFRWAUi52xSLswrmYC0dimrbwO46IiNQQ\nzjnnYQZegP1oOm72Z37HqTVUAMpJ2f17cV+dDI2bYy64wu84IiJSw5grb4I2Z2Bffgq7eaPfcWoF\nFYByUnbGa1CYj3P9bZjISL/jiIhIDWMCkTi33A/R9XAnP4Lds9vvSDWeCkA5Ibvua+yc9zADL8S0\n7eB3HBERqaFMYjLOr++HgjzcFydiXT0UEkoqAOW4bGkJ7tRnIDEFM+I6v+OIiEgNZ9qcgbl6DKxc\ngp35D7/j1GgqAOW47Idvww+bvAWf68b4HUdERGoB0/98TL8h2H/9E7s4y+84NZYKQDkmu+U77PvT\nMN37Ybp09zuOiIjUEsYYzNVjoVV73JeexP6wye9INZIKQDmKdV3v0m9UNOaqm/2OIyIitYyJjMT5\n9W8hKgr32Uewh3oLS/CoAJSj2HkfwtqvMKN+hYlP9DuOiIjUQiYpBWfsfZC/DXfK41jX9TtSjaIC\nUI5gC/Kwb78CHbpgeg/yO46IiNRipv2Z3hqBy7Ox773hd5waRQWglLHW4v7jr+AexLnuVow5Vgtv\nERGR8DEDL8T0HoR99w1szgK/49QYKgCljF00H5YtxAz7BaZ+I7/jiIiIeA+FXPtraNnWuxS8ZbPf\nkWoEFYACgN2zG/v689CyLWbwpX7HERERKWPqRHkPhQQivU4h+/b6HanaUwEoANh//g327PbavUVE\n+B1HRETkCCalPs7YeyH3B9y/PaGHQipJBaBgV+Vg58/GnD8C06K133FERESOyZzRGXPFaMj5AvvB\nW37HqdZUANZy9sAB3FcnQ4MmmIuv9DuOiIjICZnBl2J6DsDOfA27YpHfcaotFYC1nH33H7B9q3fp\nt06U33FEREROyBiDue42aHYa7gsTsbk/+B2pWlIBWIvZb9dhP56J6TcEc/pZfscREREpFxMVhTPu\nAXAcr1PI/n1+R6p2VADWUra0FPeVpyA+ATPyBr/jiIiIVIhJbYgz5m7Yshn78lNYa/2OVK2oAKyl\nbOZM+G4DzjVjMTGxfscRERGpMNMxHXP59djF87EfveN3nGol4OfgeXl5PPvss+zYsQNjDBkZGVx4\n4YVMmzaN2bNnEx8fD8DVV19N165dAZg+fTpz5szBcRxGjx5NWlqan99CtWRzf8DOeh3Se2G69vE7\njoiIyCkzQy6DjWux7/wd27w15sx0vyNVC74WgBEREVx33XW0bt2affv2cf/999O5c2cALrroIi69\n9MgFiTdv3kxWVhaPP/44hYWFPPTQQzz55JM4jk5klpfdVYj73KMQCOBcM9bvOCIiIpVijIEb7sBu\n+Q73/ybg/P5xdbMqB18rp6SkJFq39tadi46OpmnTphQUFBx3/+zsbPr06UNkZCQNGjSgUaNGrF27\nNlxxqz2btw330fsh93ucMfdgElP8jiQiIlJpJqqu91AI1usUcmC/35GqvCpz6iw3N5cNGzbQtm1b\nAD766CPuvvtuJk+eTFFREQAFBQWkpBwuWpKTk09YMMph9vtNuI/eB0W7cO56CHPW2X5HEhERCRrT\noDHOzXfD99/ivvQEtrTE70hVmq+XgH+0f/9+Jk6cyA033EBMTAxDhgxh5MiRALz55ptMnTqVcePG\nVeiYmZmZZGZmAjB+/HhSU1ODnvunAoFAyMc4VcVfr2DHY7/FiYwi8ZG/EtmyTcjGqsrzEE6aB4/m\n4TDNhUfz4NE8eII+DwPPZ8/OfIpefoaI/ftIvPdhnPjE4B0/RPz4efC9ACwtLWXixIn069ePnj17\nApCYePgfa/DgwTz66KOAd8YvPz+/7LWCggKSk5OPedyMjAwyMjLKvs7LywtF/DKpqakhH+NU2JVL\ncJ/7MyQkYe76X3bWS4AQ5qyq8xBumgeP5uEwzYVH8+DRPHhCMg99h2Ai6lDyytNsv/tXOLf9HtOk\nRXDHCLKfz0OTJk1CPqavl4Cttfz1r3+ladOmXHzxxWXbCwsLyz5fuHAhzZs3B6Bbt25kZWVRUlJC\nbiF+FBMAAA/ESURBVG4uW7ZsKbtkLEdzF87DfeZP0KAJzn2P6qZYERGpFZxeA3HufhgO7Mf98z1q\nGXcMvp4B/Oabb5g3bx4tWrTgnnvuAbwlX+bPn8/GjRv/f3t3H1RVue8B/PusvUlBBQEREE0B84jO\nJd8SIe1IqDnZ3ByGrO41oRuRiqiZL5kvOOXbZBwZOaiMhOIu02jq2mnOjM5BzRRfITqD5IwgMooI\nIakIke69nvvHzm1e3zawN2vD+n5mHGC711q/5+uw5uez9rMWhBDw8/NDUlISAKBfv36IjIzEggUL\noCgK3nrrLa4Afgj14D8hv8gCBoZZ//fDe/0REZGOiNDBUJalQf37aqgZqyHiEiAmvmxdNUwQUie3\nzr582bnPCnSV6XwpJeR3eyC/3QU8Pdq62rcdn/HrKjlojTlYMYe7mIUVc7BiDlbtkYP8vRlqzkag\n6BjEsxMgps+CMLo59ZgtpbtLwORYUlUhd2+D/HYXRGQ0lJnvt2vzR0RE5GpEl65Q3lkCMWUa5NF/\nQf3bCsiG61qXpTk2gJ2ENJshczZCHvgOYsLLEAnzIIyar/EhIiLSnFAUKFOnQyS+B1Scg7rmPciq\nSq3L0hQbwE5A/v679caXJ76HmDodYtr/QPCzkURERPdQIv4KZdFawHwb6vrFkP8+pXVJmmGX0MHJ\nxptQ01cCJYUQ02dDmTKNH3AlIiJ6CBHyFygfpAG9+1gXiOz/BjpZDnEPNoAdmLxWD3XDUqDiHJSk\nRVD+OlnrkoiIiFye8OkFZfE6YEQkZN52yNxNkLf19eQQNoAdlKyttj7ara4GytwVEKPGal0SERFR\nhyG6dIWStBjipVchj+brbnEIG8AOSF6ssDZ/vzVBeW81xJDhWpdERETU4QhFgfLyf0O8vRCoLNPV\n4hA2gB2MPFcKdcMHgMEIZcl6iOBBWpdERETUoSmjn/tjcYgZ6rrFkD91/sUhbAA7EPnvU1A3rgQ8\ne1of7RbYT+uSiIiIOgURPAjKsjQgIAhq5mqo+77u1ItD2AB2EOqxg1Az1wB9nrTO/Pn6aV0SERFR\npyK8faEsWgcxIgryqx2QOzrv4hDeKbgDUP/1LeSebOAv/wEleRmEu4fWJREREXVKoksXIGkR8F0/\nyH/shqy9DGXWUgjPnlqX5lCcAXQA+etVWOpqIK/VQzZch2y8CdncBHnrd+sTOlo5hSylhPq/n1mb\nv+FjoMxLZfNHRETkZEJRoPznf0EkLQYqy6GuXQh56YLWZTkUZwAdQN30IeouVTz6TUIBDAqgGACD\nwfpVUe5+/6Cv5ttAVSXE2IkQ02dDGAztMyAiIiKC8sxYSD9/qJlroK5fAiVxAcSwCK3Lcgg2gA6g\nvPw6ukOi4fp1QLUAFssfX9V7f37ga+qf3m+BvPN3f/wREeMhJsfy6R5EREQaEAOegvJBmrUJ/PRv\nUNZtg+juqXVZbcYG0AHEsDFw79ULjXV1WpdCREREDnZncQguVXSK5g9gA0hERET0WKJLFyB0sNZl\nOAwXgRARERHpDBtAIiIiIp1hA0hERESkM2wAiYiIiHSGDSARERGRzrABJCIiItIZNoBEREREOsMG\nkIiIiEhn2AASERER6QwbQCIiIiKdEVJKqXURRERERNR+OAPoIO+//77WJbgE5mDFHKyYw13Mwoo5\nWDEHK+ZgpUUObACJiIiIdIYNIBEREZHOGFatWrVK6yI6i5CQEK1LcAnMwYo5WDGHu5iFFXOwYg5W\nzMGqvXPgIhAiIiIineElYCIiIiKdMWpdgBaKi4uxfft2qKqKmJgYTJ06FQCwadMmlJeXw2g0IjQ0\nFElJSTAa74+otrYW6enpaGhoQEhICFJSUmA0GnHo0CGYTCb4+PgAACZPnoyYmBi7j/+w/eothy1b\ntuD8+fOQUiIwMBDJycno2rWr7nKQUmL37t04fvw4FEXBxIkT8eKLL+ouh5KSEphMJpjNZgQHB2PW\nrFkwGAydNofNmzejqKgIXl5eSEtLs71uMplQWFgIo9EIf39/zJ49G926dXNSCq6bw5dffon8/Hx4\nenoCAF5//XWMGDHCGRHYuGoWFy5cwLZt23Dr1i0YDAYkJiZi4MCBTkpB2xzq6uqQmZmJa9euQQiB\nCRMm2M6Hx44dQ15eHqqqqrB27VqEhoY6LQNn5gAABQUFyMvLgxAC/fv3x7x58+w+fot7CKkzFotF\nzpkzR165ckXevn1bLly4UF68eFFKKWVhYaFUVVWqqio3btwo9+3b98B9pKWlySNHjkgppczKyrK9\n7+DBgzI7O7vVx3/Yfp3BlXNobGy0vW/Hjh3ym2++afN4W1OH1jkcOHBAZmRkSIvFIqWU8tq1aw4Z\nc0vr0DIHi8UiZ86cKauqqqSUUu7evVvm5+c7ath21yFl++QgpZRnzpyR5eXlcsGCBfe8XlxcLM1m\ns5RSSpPJJE0mU6vH+TiunMOePXvk3r172zK8FnHlLD766CNZVFRkqyU1NbW1w3wsrXOor6+X5eXl\nUkopm5qa5Ny5c23Hv3jxoqyqqpKpqamyrKzMIeN9GGfmcPnyZblo0SLZ0NAgpXzwOd+RPYTuLgGX\nlZUhICAA/v7+MBqNiIqKwqlTpwAAI0aMgBACQggMHDgQV69evW97KSXOnDmDMWPGAADGjx9v274t\nx2/rflvKVXMAAA8PD9sxbt261dahtroOrXPYv38/4uLioCjWX1MvL6+2DrdVdWiZw82bN2E0GtGn\nTx8AQHh4OE6cOOGAEbesDqB9cgCAIUOGoHv37ve9/vTTT9tmPgcNGoT6+vqWDs9urpxDe3PlLIQQ\n+O233wAATU1N8Pb2bunw7KZ1Dt7e3rZFEu7u7ggKCrL9DvTt29d2jnA2Z+aQn5+PF154wfZv/aBz\nviN7CN1dAq6vr4evr6/tZ19fX5w7d+6e95jNZvzwww9ISEi4b/uGhgZ4eHjYTsQ+Pj73nIhPnDiB\nn3/+GYGBgYiPj0evXr3sOv7j9utorprDHZs3b8aPP/6Ivn37YsaMGW0a66O4cg41NTUoKCjAyZMn\n4enpiTfffBOBgYFtHvODuGoOPXr0gMViQXl5OUJDQ3H8+HHU1dU5YsgPpHUO9jpw4ACioqJata09\nXD2Hffv24fDhwwgJCcGMGTOc2ii6chbx8fFYs2YNTCYTVFXF6tWrWzg6+7lSDrW1taioqHDq5e6H\ncWYOly9fBgCsWLECqqrilVdewbBhw+w6fmt6CN3NANojOzsbYWFhCAsLa9F2I0eORGZmJj755BOE\nh4cjMzPTSRW2Dy1zmD17NrKyshAUFISCgoIWb+9IWuVw+/ZtuLm5Yf369YiJicGWLVtatL2jaZGD\nEALz589Hbm4uli5dCnd3d9uMqFa0Pj98/fXXMBgMGDduXKu2dxStcpg0aRIyMjLw8ccfw9vbGzt3\n7mzR9s6gVRb79+9HfHw8tmzZgvj4eGzdurVF2ztae+TQ3NyMtLQ0JCQk2K4WuZrW5qCqKqqrq5Ga\nmop58+YhKysLjY2NTqpShw2gj4/PPdOyV69etX3wFADy8vJw48aNe2ad1qxZg0WLFmHr1q3o0aMH\nmpqaYLFYAFi78Tvb9+jRA25ubgCAmJgYnD9/3u7jP2q/zuCqOfyZoiiIiopy6iU/V87B19cXERER\nAIDRo0ejsrLSUcNuUR2AtjkMGjQIH374IdatW4ewsDCnzYI+rg7A+Tk8zqFDh1BYWIi5c+dCCNGq\nMdrDlXPo2bMnFEWBoiiIiYlBeXl5q8dpD1fO4vvvv7edIyIjI1FWVta6QdrBFXIwm81IS0vDuHHj\nbONub87MwcfHB6NGjYLRaETv3r0RGBiI6upqu47fmh5Cdw1gaGgoqqurUVtbC7PZjIKCAowaNQqA\n9fr7Tz/9hPnz598zy7Bs2TJs2LABM2fOhBACQ4cOxfHjxwFYT8h3tv/1119t25w+fRp9+/a1+/iP\n2q+ecpBS4sqVKwCsn5U4ffq0Uz/b4ao5AMAzzzyDkpISAEBpaaluc7h+/ToA64zo3r17MWnSJOeE\n8Jg62iOHRykuLsbevXuxZMkSdOnSpa1DfSRXzuHP2588eRL9+vVr9Tjt4cpZ+Pj4oLS0FIB1tXxA\nQECbxvooWucgpcTWrVsRFBSEl156yWnjfBxn5jB69GicOXMGAHDjxg1UV1fD39/fruO3pofQ5Y2g\ni4qKkJubC1VVER0djdjYWADAa6+9Bj8/P9stRyIiIhAXF3ff9jU1NUhPT8fNmzcRHByMlJQUuLm5\nYdeuXTh9+jQMBgO6d++OxMREBAUF2X38h+1XTzmoqorU1FQ0NTUBAPr374/ExESnTvW7Yg4A0NjY\niE2bNqGurg5du3bF22+/jQEDBuguB5PJhKKiIqiqikmTJmHKlClOy+BRdbRXDunp6SgtLUVDQwO8\nvLwwbdo0PP/880hJSYHZbLZ93u2pp55CUlKS7nLIyMjAhQsXIISAn58fkpKSnLr4wZWzOHv2rO12\nIG5ubkhMTHTq0yS0zOHs2bNYuXIlnnzySdvs951bAJ08eRI5OTm4ceMGunXrhgEDBmDZsmUdLgcp\nJXbu3Ini4mIoioLY2Fg8++yzdh+/pT2ELhtAIiIiIj3T3SVgIiIiIr1jA0hERESkM2wAiYiIiHSG\nDSARERGRzrABJCIiItIZNoBERP9PXV0d3njjDaiqqnUpREROwdvAEBEBSE5OxjvvvIPw8HCtSyEi\ncjrOABIRERHpDGcAiUj3MjIycOTIERiNRiiKgri4OHz++ef44osvYDAYsGrVKgwePBglJSWorKzE\n0KFDkZycjO3bt6OwsBB9+vTBu+++i969ewMAqqqqkJOTg/Pnz8PT0xOvvvoqoqKiNB4lEdFdnAEk\nIt1LSUlBr169sGTJEphMJkRGRt73nqNHj2LOnDnIyspCTU0Nli9fjvHjxyMnJwdBQUH46quvAADN\nzc1YvXo1xo4di+zsbMyfPx+ffvopLl261N7DIiJ6KDaARER2iI6ORkBAADw8PDB8+HD4+/sjPDwc\nBoMBY8aMQUVFBQDrczr9/PwQHR0Ng8GA4OBgRERE4NixYxqPgIjoLqPWBRARdQReXl6275944on7\nfm5ubgYA/PLLLzh37hwSEhJsf2+xWPDcc8+1W61ERI/DBpCIyIF8fX0xZMgQrFixQutSiIgeipeA\niYgA9OzZE7W1tW3ez8iRI1FdXY3Dhw/DbDbDbDajrKyMnwEkIpfCGUAiIgBTp05FTk4OPvvsM8TG\nxrZ6P+7u7li+fDlyc3ORm5sLKSX69++P+Ph4B1ZLRNQ2vA0MERERkc7wEjARERGRzrABJCIiItIZ\nNoBEREREOsMGkIiIiEhn2AASERER6QwbQCIiIiKdYQNIREREpDNsAImIiIh0hg0gERERkc78H6EM\noRTn50OeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab63e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(df.index, df['distance'])\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first graph there would have been no way of noticing the peaks at 09:00 - 10:00 or 18:00 - 19:00, but in our resampled graph we can see the same general trends but peaks and troughs are much easier to discern."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timezones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By convention, the Z in the following string denotes that this time is UTC.\n",
    "\n",
    "Take this string parse it to a datetime, then `localize()` it so that it has a UTC timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "utc_dt_str = '2017-05-03T14:15:00Z'\n",
    "\n",
    "import pytz\n",
    "\n",
    "# Your code here\n",
    "dt = datetime.datetime.strptime(utc_dt_str, '%Y-%m-%dT%H:%M:%SZ')\n",
    "utc_tz = pytz.timezone('UTC')\n",
    "utc_dt = utc_tz.localize(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Olson timezone `'America/Los_Angeles'`, `normalize()` this date so that it is now in Los Angeles time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 5, 3, 7, 15, tzinfo=<DstTzInfo 'America/Los_Angeles' PDT-1 day, 17:00:00 DST>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_tz = pytz.timezone('America/Los_Angeles')\n",
    "la_dt = la_tz.normalize(utc_dt)\n",
    "la_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write a function `convert_to_la_time()` to do the above and use the `map()` function to convert all the times in the Series below to Los Angeles time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-05-03 07:00:00-07:00\n",
       "1   2017-05-03 07:15:00-07:00\n",
       "2   2017-05-03 07:30:00-07:00\n",
       "3   2017-05-03 07:45:00-07:00\n",
       "dtype: datetime64[ns, America/Los_Angeles]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetimes = pd.Series(['2017-05-03T14:00:00Z', '2017-05-03T14:15:00Z', \n",
    "                       '2017-05-03T14:30:00Z', '2017-05-03T14:45:00Z'])\n",
    "\n",
    "# Your code here\n",
    "def convert_to_la_time(dt_str):\n",
    "    dt = datetime.datetime.strptime(dt_str, '%Y-%m-%dT%H:%M:%SZ')\n",
    "    utc_tz = pytz.timezone('UTC')\n",
    "    utc_dt = utc_tz.localize(dt)\n",
    "    la_tz = pytz.timezone('America/Los_Angeles')\n",
    "    la_dt = la_tz.normalize(utc_dt)\n",
    "    return la_dt\n",
    "\n",
    "local_datetimes = datetimes.map(convert_to_la_time)\n",
    "\n",
    "local_datetimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 4 - K-nearest Neighbors Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will be using a data set with lots of features about breast cancer to try and predict whether a tumour is benign or malignant.\n",
    "\n",
    "We will be using K-nearest Neighbors to classify unseen observations into benign (0) or malignant (1).\n",
    "\n",
    "Let's start by loading the data and taking a look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>malignancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension     ...      worst texture  worst perimeter  \\\n",
       "0                 0.07871     ...              17.33           184.60   \n",
       "1                 0.05667     ...              23.41           158.80   \n",
       "2                 0.05999     ...              25.53           152.50   \n",
       "3                 0.09744     ...              26.50            98.87   \n",
       "4                 0.05883     ...              16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  malignancy  \n",
       "0                0.2654          0.4601                  0.11890           1  \n",
       "1                0.1860          0.2750                  0.08902           1  \n",
       "2                0.2430          0.3613                  0.08758           1  \n",
       "3                0.2575          0.6638                  0.17300           1  \n",
       "4                0.1625          0.2364                  0.07678           1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run this cell\n",
    "\n",
    "breast_cancer_df = pd.read_csv('data/breast_cancer.csv')\n",
    "breast_cancer_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have 30 feature columns that describe our tumours, including \"mean radius\", \"mean texture\", \"mean perimeter\"...\n",
    "\n",
    "There is a single column (you need to scroll right in the output of the cell above) named `'malignancy'` and this tells us whether a tumour is benign (0) or malignant (1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by splitting our data out into test data and training data.\n",
    "\n",
    "Create a variable `X` that is a Data Frame with all the columns except `\"malignancy\"`.\n",
    "\n",
    "Create a variable `y` that is the series of the column `\"malignancy\"`.\n",
    "\n",
    "Then we split `X` and `y` into test and training data, give the output data the names: \n",
    "* `X_train` - This data will be the features you use to train the models.\n",
    "* `X_test` - This data will be the features that act as unseen data to make predictions against.\n",
    "* `y_train` - This data will be the labels for training the model.\n",
    "* `y_test` - This will be the unseen labels you score your predictions against."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create variables X and y here\n",
    "\n",
    "X = breast_cancer_df.drop('malignancy', axis=1)\n",
    "y = breast_cancer_df['malignancy']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split your data here into X_train, X_test, y_train and y_test\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the scale of some of our columns are over 1000 and some of our columns have a scale of less than 1. Therefore some features would skew the model disproportionately than others.\n",
    "\n",
    "Therefore we must scale our data.\n",
    "\n",
    "Here we will use the `MinMaxScaler()` to scale our data. For more details on this, see my [blog post](http://benalexkeen.com/feature-scaling-with-scikit-learn/) or the [official docs](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html).\n",
    "\n",
    "We first fit the scaler to our training data, then we use it to scale our training data. We will later also use it to scale our test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# Returns a 2D array\n",
    "scaled_X_train = scaler.transform(X_train)\n",
    "# Convert back to a DataFrame\n",
    "X_train = pd.DataFrame(scaled_X_train, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a K Neighbors Classifier model object that uses the 6 nearest neighbors.\n",
    "\n",
    "Fit this model to your training data (`X_train` and `y_train`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_model = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the scaler to transform your `X_test` data as we did for the `X_train` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use your model to predict the labels from the scaled `X_test` data and store these predictions in the variable `y_predict`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `accuracy_score` to compare how your prediction performed against the unseen `y_test` labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94405594405594406"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 5 - Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we wil be using some factors indicative of economical wellbeing to predict the price of a Big Mac.\n",
    "\n",
    "We'll start be reading in the data. This data has the price of a Big Mac in US dollars, the GDP per capita in dollars, the life expectancy in years and the unemployment rate as a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>big_mac_price</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3.47</td>\n",
       "      <td>20200</td>\n",
       "      <td>77.69</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>4.28</td>\n",
       "      <td>48800</td>\n",
       "      <td>82.15</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.12</td>\n",
       "      <td>15200</td>\n",
       "      <td>73.53</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Britain</td>\n",
       "      <td>3.73</td>\n",
       "      <td>42500</td>\n",
       "      <td>80.54</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4.51</td>\n",
       "      <td>46200</td>\n",
       "      <td>81.76</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  big_mac_price  gdp_per_capita  life_expectancy  unemployment\n",
       "0  Argentina           3.47           20200            77.69           9.3\n",
       "1  Australia           4.28           48800            82.15           5.8\n",
       "2     Brazil           5.12           15200            73.53          13.7\n",
       "3    Britain           3.73           42500            80.54           4.6\n",
       "4     Canada           4.51           46200            81.76           6.6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/bigmac.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scatter plot of gdp per capita against the price of a big mac to see if there is any correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gcV/bpCsmvfZK3xZBIABS59qU+tN6JV/6b6X+tJ7XPvp5tuKWOGWI4OK1j6F4uuLmdj4E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\nAFjm/wGKBCN58DphAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10adf4150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['gdp_per_capita'], df['big_mac_price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the variables `X` and `y`. \n",
    "\n",
    "`X` will be a Data Frame of the columns `'gdp_per_capita'`, `'life_expectancy'` and `'unemployment'`.  \n",
    "`y` will be the `big_mac_price` Series from our Data Frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Your code here\n",
    "\n",
    "X = df[['gdp_per_capita', 'life_expectancy', 'unemployment']]\n",
    "y = df['big_mac_price']\n",
    "\n",
    "# random_state defined for repeatability\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a linear regression model and fit it on our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `score()` method from our model and our `X_test` and `y_test` test data sets, calculate the $ R^2 $ value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47806780802246307"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This $R^2$ value may seem small but remember a 0.5 $ R^2 $ value corresponds to a correlation of over 70%. \n",
    "\n",
    "Also note that there are far more factors that might affect a Big Mac's price, including obesity rates, taxation on fast food, shipping costs etc. etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the coefficients from the model's `coef_` attribute and the intercept from the model's `intercept_` attribute. \n",
    "\n",
    "Use these to construct an equation in the form:\n",
    "\n",
    "$ Y = \\beta_0 + \\beta_1 X_1 + \\beta_2 X_2 + \\beta_3 X_3 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.49446069e-05   7.50138129e-02   2.97214719e-02]\n",
      "-2.9487852594\n"
     ]
    }
   ],
   "source": [
    "# Your code here\n",
    "print(regression_model.coef_)\n",
    "print(regression_model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Y = -2.95 + 0.0000149 * GDP_Per_Capita + 0.00750 * Life_Expectancy + 0.00297 * Unemployment_Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `predict()` method of our linear regression model to predict a new `y` value as follows:\n",
    "\n",
    "`regression_model.predict([[x1, x2, x3]])`\n",
    "\n",
    "It is a list of lists as you could provide multiple lists of `[x1, x2, x3]` to get multiple `y` predictions.\n",
    "\n",
    "McDonald's currently has no restaurants in Macedonia. \n",
    "\n",
    "It has a GDP per capita of \\$14,500, a life expectancy of 76.02 and an unemployment rate of 23.1%, what would we expect to pay for a Big Mac in Macedonia in \\$ if it was to be released?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We would expect to pay $3.66 for a Big Mac in Macedonia\n"
     ]
    }
   ],
   "source": [
    "price = regression_model.predict([[14500, 76.02, 23.1]])[0]\n",
    "print(\"We would expect to pay ${} for a Big Mac in Macedonia\".format(round(price, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 6 - Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A telephone company has decided to erect 7 more telephone masts in Cornwall, UK.\n",
    "\n",
    "It has obtained some GPS data from mobile of people that visited Cornwall on holiday.\n",
    "\n",
    "Start by reading in the data for these phones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.410785</td>\n",
       "      <td>-5.062822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.405181</td>\n",
       "      <td>-5.071455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.406556</td>\n",
       "      <td>-5.074560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.411259</td>\n",
       "      <td>-5.077382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.408687</td>\n",
       "      <td>-5.066740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    latitude  longitude\n",
       "0  50.410785  -5.062822\n",
       "1  50.405181  -5.071455\n",
       "2  50.406556  -5.074560\n",
       "3  50.411259  -5.077382\n",
       "4  50.408687  -5.066740"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell\n",
    "df = pd.read_csv('data/cornwall_phones.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a K means clustering model with 7 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=7, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Your code here\n",
    "kmeans_model = KMeans(n_clusters=7) \n",
    "kmeans_model.fit(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model now has 7 centroids (cluster centers), use the `cluster_centers_` method of the model to determine where these are.\n",
    "\n",
    "Store the centroids in a variable named `centroids`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 50.06522047,  -5.70080888],\n",
       "       [ 50.55409691,  -4.92748908],\n",
       "       [ 50.19281637,  -5.06118423],\n",
       "       [ 50.14290874,  -5.5332235 ],\n",
       "       [ 50.09092473,  -5.2999759 ],\n",
       "       [ 50.41266955,  -5.07091787],\n",
       "       [ 50.59126648,  -4.83287364]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = kmeans_model.cluster_centers_\n",
    "centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cells below to create a map with your centroids plotted on. \n",
    "\n",
    "This may take a minute or two to download and render.\n",
    "\n",
    "\n",
    "Explore the map and see where the best positions for new masts to deal with the influx of summer tourists are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ask your instructor for the access token\n",
    "mapbox_access_token = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~benalexkeen/115.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell\n",
    "\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import Data, Scattermapbox, Layout, Marker\n",
    "\n",
    "data = Data([\n",
    "    Scattermapbox(\n",
    "        lat=[x[0] for x in centroids],\n",
    "        lon=[x[1] for x in centroids],\n",
    "        mode='markers',\n",
    "        marker=Marker(\n",
    "            size=14\n",
    "        ),\n",
    "        text=['Centroid'],\n",
    "    )\n",
    "])\n",
    "\n",
    "layout = Layout(\n",
    "    autosize=True,\n",
    "    hovermode='closest',\n",
    "    mapbox=dict(\n",
    "        accesstoken=mapbox_access_token,\n",
    "        bearing=0,\n",
    "        center=dict(\n",
    "            lat=50.2,\n",
    "            lon=-5\n",
    "        ),\n",
    "        pitch=0,\n",
    "        zoom=7\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Centroid plot')"
   ]
  }
 ],
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