Anscombe_DataViz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anscombe's Quartet\n",
    "Using data visualization to understand your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://en.wikipedia.org/wiki/Anscombe%27s_quartet\">Anscombe's Quartet</a> is a great example to show the importance of fully understanding the variability in a data set. The goal of this exercise is to teach you never to trust summary statistics alone, however to always visualize your data.\n",
    "\n",
    "\n",
    "Imagine you had four datasets of few points each, where each point consists of two values X and Y, and you want to characterise your data using some statistical tool. Let's start with some imports (which you are familiar with by now)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will load the data as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "anscombe = data.anscombe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the data. We can see that there are 4 data sets (I - IV) each with different values of X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series</th>\n",
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       "      <td>5.68</td>\n",
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       "      <th>13</th>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>9</td>\n",
       "      <td>8.77</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>II</td>\n",
       "      <td>11</td>\n",
       "      <td>9.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>II</td>\n",
       "      <td>14</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>6</td>\n",
       "      <td>6.13</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>II</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>II</td>\n",
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       "      <td>9.13</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>II</td>\n",
       "      <td>7</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>5</td>\n",
       "      <td>4.74</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>10</td>\n",
       "      <td>7.46</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>III</td>\n",
       "      <td>8</td>\n",
       "      <td>6.77</td>\n",
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       "    <tr>\n",
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       "      <td>III</td>\n",
       "      <td>13</td>\n",
       "      <td>12.74</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>III</td>\n",
       "      <td>9</td>\n",
       "      <td>7.11</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
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       "      <td>7.81</td>\n",
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       "    <tr>\n",
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       "      <td>8.84</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>5.39</td>\n",
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       "    <tr>\n",
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       "      <td>III</td>\n",
       "      <td>12</td>\n",
       "      <td>8.15</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>III</td>\n",
       "      <td>7</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>III</td>\n",
       "      <td>5</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IV</td>\n",
       "      <td>19</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>5.56</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IV</td>\n",
       "      <td>8</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Series   X      Y\n",
       "0       I  10   8.04\n",
       "1       I   8   6.95\n",
       "2       I  13   7.58\n",
       "3       I   9   8.81\n",
       "4       I  11   8.33\n",
       "5       I  14   9.96\n",
       "6       I   6   7.24\n",
       "7       I   4   4.26\n",
       "8       I  12  10.84\n",
       "9       I   7   4.81\n",
       "10      I   5   5.68\n",
       "11     II  10   9.14\n",
       "12     II   8   8.14\n",
       "13     II  13   8.74\n",
       "14     II   9   8.77\n",
       "15     II  11   9.26\n",
       "16     II  14   8.10\n",
       "17     II   6   6.13\n",
       "18     II   4   3.10\n",
       "19     II  12   9.13\n",
       "20     II   7   7.26\n",
       "21     II   5   4.74\n",
       "22    III  10   7.46\n",
       "23    III   8   6.77\n",
       "24    III  13  12.74\n",
       "25    III   9   7.11\n",
       "26    III  11   7.81\n",
       "27    III  14   8.84\n",
       "28    III   6   6.08\n",
       "29    III   4   5.39\n",
       "30    III  12   8.15\n",
       "31    III   7   6.42\n",
       "32    III   5   5.73\n",
       "33     IV   8   6.58\n",
       "34     IV   8   5.76\n",
       "35     IV   8   7.71\n",
       "36     IV   8   8.84\n",
       "37     IV   8   8.47\n",
       "38     IV   8   7.04\n",
       "39     IV   8   5.25\n",
       "40     IV  19  12.50\n",
       "41     IV   8   5.56\n",
       "42     IV   8   7.91\n",
       "43     IV   8   6.89"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about taking a quick summary statistics of the data. Keep an eye to know if you will notice anything in the summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>50%</th>\n",
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       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>2.032890</td>\n",
       "      <td>4.26</td>\n",
       "      <td>6.315</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.57</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.500909</td>\n",
       "      <td>2.031657</td>\n",
       "      <td>3.10</td>\n",
       "      <td>6.695</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.95</td>\n",
       "      <td>9.26</td>\n",
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       "    <tr>\n",
       "      <th>III</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>2.030424</td>\n",
       "      <td>5.39</td>\n",
       "      <td>6.250</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.98</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IV</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.500909</td>\n",
       "      <td>2.030579</td>\n",
       "      <td>5.25</td>\n",
       "      <td>6.170</td>\n",
       "      <td>7.04</td>\n",
       "      <td>8.19</td>\n",
       "      <td>12.50</td>\n",
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      ],
      "text/plain": [
       "           X                                               Y            \\\n",
       "       count mean       std  min  25%  50%   75%   max count      mean   \n",
       "Series                                                                   \n",
       "I       11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500000   \n",
       "II      11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500909   \n",
       "III     11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500000   \n",
       "IV      11.0  9.0  3.316625  8.0  8.0  8.0   8.0  19.0  11.0  7.500909   \n",
       "\n",
       "                                                  \n",
       "             std   min    25%   50%   75%    max  \n",
       "Series                                            \n",
       "I       2.032890  4.26  6.315  7.58  8.57  10.84  \n",
       "II      2.031657  3.10  6.695  8.14  8.95   9.26  \n",
       "III     2.030424  5.39  6.250  7.11  7.98  12.74  \n",
       "IV      2.030579  5.25  6.170  7.04  8.19  12.50  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe.groupby('Series').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the summary statistics are, if not the same very similar (Mean: X=9 and Y=7.5). This means they exhibit the statistical properties, therefore should the same when visualized right? However that's not the case... \n",
    "\n",
    "\n",
    "Plotting the data in two dimensions will show otherwise... Let's visualize the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_anscombe(data):\n",
    "    anscombe_chart = alt.Chart(data).mark_circle().encode(\n",
    "        alt.X('X'),\n",
    "        alt.Y('Y'),\n",
    "        alt.Facet('Series'),\n",
    "        #tooltip= ['X', 'Y']\n",
    "    ).properties(\n",
    "        width=150,\n",
    "        height=150\n",
    "    )\n",
    "    \n",
    "    return anscombe_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-3d23a3381c61efd5a9c7a8abc82c6d65\"}, \"mark\": \"circle\", \"encoding\": {\"facet\": {\"type\": \"nominal\", \"field\": \"Series\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"X\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Y\"}}, \"height\": 150, \"width\": 150, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-3d23a3381c61efd5a9c7a8abc82c6d65\": [{\"Series\": \"I\", \"X\": 10, \"Y\": 8.04}, {\"Series\": \"I\", \"X\": 8, \"Y\": 6.95}, {\"Series\": \"I\", \"X\": 13, \"Y\": 7.58}, {\"Series\": \"I\", \"X\": 9, \"Y\": 8.81}, {\"Series\": \"I\", \"X\": 11, \"Y\": 8.33}, {\"Series\": \"I\", \"X\": 14, \"Y\": 9.96}, {\"Series\": \"I\", \"X\": 6, \"Y\": 7.24}, {\"Series\": \"I\", \"X\": 4, \"Y\": 4.26}, {\"Series\": \"I\", \"X\": 12, \"Y\": 10.84}, {\"Series\": \"I\", \"X\": 7, \"Y\": 4.8100000000000005}, {\"Series\": \"I\", \"X\": 5, \"Y\": 5.68}, {\"Series\": \"II\", \"X\": 10, \"Y\": 9.14}, {\"Series\": \"II\", \"X\": 8, \"Y\": 8.14}, {\"Series\": \"II\", \"X\": 13, \"Y\": 8.74}, {\"Series\": \"II\", \"X\": 9, \"Y\": 8.77}, {\"Series\": \"II\", \"X\": 11, \"Y\": 9.26}, {\"Series\": \"II\", \"X\": 14, \"Y\": 8.1}, {\"Series\": \"II\", \"X\": 6, \"Y\": 6.13}, {\"Series\": \"II\", \"X\": 4, \"Y\": 3.1}, {\"Series\": \"II\", \"X\": 12, \"Y\": 9.13}, {\"Series\": \"II\", \"X\": 7, \"Y\": 7.26}, {\"Series\": \"II\", \"X\": 5, \"Y\": 4.74}, {\"Series\": \"III\", \"X\": 10, \"Y\": 7.46}, {\"Series\": \"III\", \"X\": 8, \"Y\": 6.77}, {\"Series\": \"III\", \"X\": 13, \"Y\": 12.74}, {\"Series\": \"III\", \"X\": 9, \"Y\": 7.11}, {\"Series\": \"III\", \"X\": 11, \"Y\": 7.8100000000000005}, {\"Series\": \"III\", \"X\": 14, \"Y\": 8.84}, {\"Series\": \"III\", \"X\": 6, \"Y\": 6.08}, {\"Series\": \"III\", \"X\": 4, \"Y\": 5.39}, {\"Series\": \"III\", \"X\": 12, \"Y\": 8.15}, {\"Series\": \"III\", \"X\": 7, \"Y\": 6.42}, {\"Series\": \"III\", \"X\": 5, \"Y\": 5.73}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 6.58}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.76}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.71}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 8.84}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 8.47}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.04}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.25}, {\"Series\": \"IV\", \"X\": 19, \"Y\": 12.5}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.5600000000000005}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.91}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 6.89}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
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      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_anscombe(anscombe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viola! Plotting the data reveals that the data set are drastically different in appearance.\n",
    "\n",
    "\n",
    "Anscombe's quartet is often used as an example to justify that a statistical summary of a data set will naturally lose information and so should be accompanied by further study and understanding such as visualizing the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "1. Try axis ordering by scaling the X and Y axis to eliminate zero\n",
    "2. Try adding datatypes for the two axis\n",
    "3. Also make the chart interactive and add a tooltip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Visualization methods helps us to understand better patterns within the data. We have seen that when data are represented as effectively designed visualization, the human visual system can easily extract patterns from the data. However, some visual tasks (eg: finding a particular plot in a scatterplot) requires some operations.\n",
    "\n",
    "\n",
    "Here we are going to look at two visual tasks for finding patterns in a dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtration Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "anscombe_ID = list(anscombe['Series'].unique()) #list the series column so you understand what to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'II', 'III', 'IV']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#provides an aggregation of the data\n",
    "anscombe_pivot = pd.pivot_table(anscombe, values=['X', 'Y'], index=['Series']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>9</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II</th>\n",
       "      <td>9</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>III</th>\n",
       "      <td>9</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IV</th>\n",
       "      <td>9</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X         Y\n",
       "Series             \n",
       "I       9  7.500000\n",
       "II      9  7.500909\n",
       "III     9  7.500000\n",
       "IV      9  7.500909"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe_pivot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the data as a scatterplot\n",
    "def anscombe_filter(data):\n",
    "    anscombe_chart = alt.Chart(data).mark_circle().encode(\n",
    "        alt.X('X'),\n",
    "        alt.Y('Y'),\n",
    "        color = 'Series',\n",
    "        #tooltip= ['X', 'Y']\n",
    "    ).interactive()\n",
    "    \n",
    "    return anscombe_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "alt.Chart(...)"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe_filter(anscombe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "Perform filtering task\n",
    "\n",
    "1. Make the chart interactive (Implement panning, zooming and tooltip)\n",
    "2. Implement filtering for the chart so when you hover over a Series (for eg: IV) only the pattern shows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anscombe_select(data):\n",
    "    \n",
    "    #create the dropdown menu\n",
    "    input_dropDown = alt.binding_select(options = anscombe_ID)\n",
    "    \n",
    "    selectDropdown = alt.selection_single(\n",
    "        name= 'Select',\n",
    "        fields= ['Series'],\n",
    "        bind=input_dropDown\n",
    "    )\n",
    "    \n",
    "    \n",
    "    #conditions for selection\n",
    "    opacity = alt.condition(selectDropdown, alt.value(1.00), alt.value(0.20))\n",
    "    \n",
    "    anscombe_chart = alt.Chart(data).mark_circle().encode(\n",
    "        x='X',\n",
    "        y='Y',\n",
    "        tooltip= ['X', 'Y'],\n",
    "        opacity= opacity\n",
    "    ).add_selection(\n",
    "        selectDropdown\n",
    "    )\n",
    "    \n",
    "    return anscombe_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-3d23a3381c61efd5a9c7a8abc82c6d65\"}, \"mark\": \"circle\", \"encoding\": {\"opacity\": {\"condition\": {\"value\": 1.0, \"selection\": \"Select\"}, \"value\": 0.2}, \"tooltip\": [{\"type\": \"quantitative\", \"field\": \"X\"}, {\"type\": \"quantitative\", \"field\": \"Y\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"X\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Y\"}}, \"selection\": {\"Select\": {\"type\": \"single\", \"fields\": [\"Series\"], \"bind\": {\"input\": \"select\", \"options\": [\"I\", \"II\", \"III\", \"IV\"]}}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-3d23a3381c61efd5a9c7a8abc82c6d65\": [{\"Series\": \"I\", \"X\": 10, \"Y\": 8.04}, {\"Series\": \"I\", \"X\": 8, \"Y\": 6.95}, {\"Series\": \"I\", \"X\": 13, \"Y\": 7.58}, {\"Series\": \"I\", \"X\": 9, \"Y\": 8.81}, {\"Series\": \"I\", \"X\": 11, \"Y\": 8.33}, {\"Series\": \"I\", \"X\": 14, \"Y\": 9.96}, {\"Series\": \"I\", \"X\": 6, \"Y\": 7.24}, {\"Series\": \"I\", \"X\": 4, \"Y\": 4.26}, {\"Series\": \"I\", \"X\": 12, \"Y\": 10.84}, {\"Series\": \"I\", \"X\": 7, \"Y\": 4.8100000000000005}, {\"Series\": \"I\", \"X\": 5, \"Y\": 5.68}, {\"Series\": \"II\", \"X\": 10, \"Y\": 9.14}, {\"Series\": \"II\", \"X\": 8, \"Y\": 8.14}, {\"Series\": \"II\", \"X\": 13, \"Y\": 8.74}, {\"Series\": \"II\", \"X\": 9, \"Y\": 8.77}, {\"Series\": \"II\", \"X\": 11, \"Y\": 9.26}, {\"Series\": \"II\", \"X\": 14, \"Y\": 8.1}, {\"Series\": \"II\", \"X\": 6, \"Y\": 6.13}, {\"Series\": \"II\", \"X\": 4, \"Y\": 3.1}, {\"Series\": \"II\", \"X\": 12, \"Y\": 9.13}, {\"Series\": \"II\", \"X\": 7, \"Y\": 7.26}, {\"Series\": \"II\", \"X\": 5, \"Y\": 4.74}, {\"Series\": \"III\", \"X\": 10, \"Y\": 7.46}, {\"Series\": \"III\", \"X\": 8, \"Y\": 6.77}, {\"Series\": \"III\", \"X\": 13, \"Y\": 12.74}, {\"Series\": \"III\", \"X\": 9, \"Y\": 7.11}, {\"Series\": \"III\", \"X\": 11, \"Y\": 7.8100000000000005}, {\"Series\": \"III\", \"X\": 14, \"Y\": 8.84}, {\"Series\": \"III\", \"X\": 6, \"Y\": 6.08}, {\"Series\": \"III\", \"X\": 4, \"Y\": 5.39}, {\"Series\": \"III\", \"X\": 12, \"Y\": 8.15}, {\"Series\": \"III\", \"X\": 7, \"Y\": 6.42}, {\"Series\": \"III\", \"X\": 5, \"Y\": 5.73}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 6.58}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.76}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.71}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 8.84}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 8.47}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.04}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.25}, {\"Series\": \"IV\", \"X\": 19, \"Y\": 12.5}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 5.5600000000000005}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 7.91}, {\"Series\": \"IV\", \"X\": 8, \"Y\": 6.89}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe_select(anscombe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "\n",
    "1. Change the encodings, instead of the opacity, use color and encode the non-selected in a grayscale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework\n",
    "\n",
    "Use everything you learned in this notebook to explore and analyze DatasaurusDozen.tsv data set. Use pandas' read_csv function, with adequate parameters, to import the data.\n",
    "\n",
    "# Viel Spaß!\n"
   ]
  }
 ],
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