Pandas and Graphical Representation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Data.xlsx')\n",
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Gender</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "      <td>M</td>\n",
       "      <td>72000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "      <td>M</td>\n",
       "      <td>61000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F</td>\n",
       "      <td>58000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>79000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "      <td>M</td>\n",
       "      <td>83000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>67000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased Gender  EstimatedSalary\n",
       "0   France  44.0  72000.0        No      M          72000.0\n",
       "1    Spain  27.0  48000.0       Yes      M          48000.0\n",
       "2  Germany  30.0  54000.0        No      F          54000.0\n",
       "3    Spain  38.0  61000.0        No      M          61000.0\n",
       "4  Germany  40.0      0.0       Yes      F              0.0\n",
       "5   France  35.0  58000.0       Yes      F          58000.0\n",
       "6    Spain   0.0  52000.0        No      F          52000.0\n",
       "7   France  48.0  79000.0       Yes      M          79000.0\n",
       "8  Germany  50.0  83000.0        No      M          83000.0\n",
       "9   France  37.0  67000.0       Yes      M          67000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.900000</td>\n",
       "      <td>57400.000000</td>\n",
       "      <td>57400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.247417</td>\n",
       "      <td>23248.416911</td>\n",
       "      <td>23248.416911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.250000</td>\n",
       "      <td>52500.000000</td>\n",
       "      <td>52500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>59500.000000</td>\n",
       "      <td>59500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>70750.000000</td>\n",
       "      <td>70750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>83000.000000</td>\n",
       "      <td>83000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age        Salary  EstimatedSalary\n",
       "count  10.000000     10.000000        10.000000\n",
       "mean   34.900000  57400.000000     57400.000000\n",
       "std    14.247417  23248.416911     23248.416911\n",
       "min     0.000000      0.000000         0.000000\n",
       "25%    31.250000  52500.000000     52500.000000\n",
       "50%    37.500000  59500.000000     59500.000000\n",
       "75%    43.000000  70750.000000     70750.000000\n",
       "max    50.000000  83000.000000     83000.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tools for Graphical Representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting Using Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age\n",
       "0   France  44.0\n",
       "1    Spain  27.0\n",
       "2  Germany  30.0\n",
       "3    Spain  38.0\n",
       "4  Germany  40.0\n",
       "5   France  35.0\n",
       "6    Spain   0.0\n",
       "7   France  48.0\n",
       "8  Germany  50.0\n",
       "9   France  37.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_by_Age = df[['Country', 'Age']]\n",
    "Country_by_Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"Country\", y=\"Age\", data=Country_by_Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    72000.0\n",
       "1    48000.0\n",
       "2    54000.0\n",
       "3    61000.0\n",
       "4        0.0\n",
       "5    58000.0\n",
       "6    52000.0\n",
       "7    79000.0\n",
       "8    83000.0\n",
       "9    67000.0\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean     57400.000000\n",
       "std      23248.416911\n",
       "min          0.000000\n",
       "25%      52500.000000\n",
       "50%      59500.000000\n",
       "75%      70750.000000\n",
       "max      83000.000000\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salary'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19681aa4208>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Salary'], bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19681b2e848>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(4,5)})\n",
    "sns.boxplot(x = \"Gender\", y = \"EstimatedSalary\", data = df, showfliers=False, showmeans=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Graphs with Plotly and Highcharts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conda install -c conda-forge cufflinks-py \n",
    "\n",
    "#### pip install plotly==3.10.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe style=\"border:0;outline:none;overflow:hidden\" srcdoc=\"&lt;!DOCTYPE html&gt; &lt;html lang=&quot;en&quot;&gt; &lt;head&gt; &lt;meta charset=&quot;utf-8&quot; /&gt; &lt;link href=&quot;https://www.highcharts.com/highslide/highslide.css&quot; rel=&quot;stylesheet&quot; /&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/highcharts.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/highcharts-more.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/modules/heatmap.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/modules/exporting.js&quot;&gt;&lt;/script&gt; &lt;/head&gt; &lt;body style=&quot;margin:0;padding:0&quot;&gt; &lt;div id=&quot;container&quot; style=&quot;width:850px;height:400px;&quot;&gt;Loading....&lt;/div&gt; &lt;script&gt; $(function(){ Highcharts.setOptions({&quot;global&quot;: {}, &quot;lang&quot;: {}}); var option = {&quot;chart&quot;: {&quot;renderTo&quot;: &quot;container&quot;, &quot;width&quot;: 850, &quot;height&quot;: 400, &quot;type&quot;: &quot;scatter&quot;, &quot;zoomType&quot;: &quot;xy&quot;}, &quot;colors&quot;: {}, &quot;credits&quot;: {&quot;enabled&quot;: false}, &quot;drilldown&quot;: {}, &quot;exporting&quot;: {}, &quot;labels&quot;: {}, &quot;legend&quot;: {&quot;layout&quot;: &quot;vertical&quot;, &quot;align&quot;: &quot;left&quot;, &quot;verticalAlign&quot;: &quot;top&quot;, &quot;x&quot;: 100, &quot;y&quot;: 70, &quot;floating&quot;: true, &quot;backgroundColor&quot;: (Highcharts.theme &amp;&amp; Highcharts.theme.legendBackgroundColor) || &#x27;#FFFFFF&#x27;, &quot;borderWidth&quot;: 1}, &quot;loading&quot;: {}, &quot;navigation&quot;: {}, &quot;pane&quot;: {}, &quot;plotOptions&quot;: {&quot;scatter&quot;: {&quot;marker&quot;: {&quot;radius&quot;: 5, &quot;states&quot;: {&quot;hover&quot;: {&quot;enabled&quot;: true, &quot;lineColor&quot;: &quot;rgb(100,100,100)&quot;}}}, &quot;states&quot;: {&quot;hover&quot;: {&quot;marker&quot;: {&quot;enabled&quot;: false}}}, &quot;tooltip&quot;: {&quot;headerFormat&quot;: &quot;&lt;b&gt;{series.name}&lt;/b&gt;&lt;br&gt;&quot;, &quot;pointFormat&quot;: &quot;{point.x} cm, {point.y} kg&quot;}}}, &quot;series&quot;: {}, &quot;subtitle&quot;: {&quot;text&quot;: &quot;Source: Heinz 2003&quot;}, &quot;title&quot;: {&quot;text&quot;: &quot;Height Versus Weight of 507 Individuals by Gender&quot;}, &quot;tooltip&quot;: {}, &quot;xAxis&quot;: {&quot;title&quot;: {&quot;enabled&quot;: true, &quot;text&quot;: &quot;Height (cm)&quot;}, &quot;startOnTick&quot;: true, &quot;endOnTick&quot;: true, &quot;showLastLabel&quot;: true}, &quot;yAxis&quot;: {&quot;title&quot;: {&quot;text&quot;: &quot;Weight (kg)&quot;}}}; var chart = new Highcharts.Chart(option); var data = [{&quot;data&quot;: [[161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6], [170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2], [172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0], [147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8], [159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8], [174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0], [154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8], [162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6], [168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3], [167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8], [167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3], [168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3], [156.0, 52.7], [160.0, 74.3], [163.0, 62.0], [165.7, 73.1], [161.0, 80.0], [162.0, 54.7], [166.0, 53.2], [174.0, 75.7], [172.7, 61.1], [167.6, 55.7], [151.1, 48.7], [164.5, 52.3], [163.5, 50.0], [152.0, 59.3], [169.0, 62.5], [164.0, 55.7], [161.2, 54.8], [155.0, 45.9], [170.0, 70.6], [176.2, 67.2], [170.0, 69.4], [162.5, 58.2], [170.3, 64.8], [164.1, 71.6], [169.5, 52.8], [163.2, 59.8], [154.5, 49.0], [159.8, 50.0], [173.2, 69.2], [170.0, 55.9], [161.4, 63.4], [169.0, 58.2], [166.2, 58.6], [159.4, 45.7], [162.5, 52.2], [159.0, 48.6], [162.8, 57.8], [159.0, 55.6], [179.8, 66.8], [162.9, 59.4], [161.0, 53.6], [151.1, 73.2], [168.2, 53.4], [168.9, 69.0], [173.2, 58.4], [171.8, 56.2], [178.0, 70.6], [164.3, 59.8], [163.0, 72.0], [168.5, 65.2], [166.8, 56.6], [172.7, 105.2], [163.5, 51.8], [169.4, 63.4], [167.8, 59.0], [159.5, 47.6], [167.6, 63.0], [161.2, 55.2], [160.0, 45.0], [163.2, 54.0], [162.2, 50.2], [161.3, 60.2], [149.5, 44.8], [157.5, 58.8], [163.2, 56.4], 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56.6], [162.6, 53.9], [164.5, 63.2], [176.5, 73.6], [168.9, 62.0], [175.3, 63.6], [159.4, 53.2], [160.0, 53.4], [170.2, 55.0], [162.6, 70.5], [167.6, 54.5], [162.6, 54.5], [160.7, 55.9], [160.0, 59.0], [157.5, 63.6], [162.6, 54.5], [152.4, 47.3], [170.2, 67.7], [165.1, 80.9], [172.7, 70.5], [165.1, 60.9], [170.2, 63.6], [170.2, 54.5], [170.2, 59.1], [161.3, 70.5], [167.6, 52.7], [167.6, 62.7], [165.1, 86.3], [162.6, 66.4], [152.4, 67.3], [168.9, 63.0], [170.2, 73.6], [175.2, 62.3], [175.2, 57.7], [160.0, 55.4], [165.1, 104.1], [174.0, 55.5], [170.2, 77.3], [160.0, 80.5], [167.6, 64.5], [167.6, 72.3], [167.6, 61.4], [154.9, 58.2], [162.6, 81.8], [175.3, 63.6], [171.4, 53.4], [157.5, 54.5], [165.1, 53.6], [160.0, 60.0], [174.0, 73.6], [162.6, 61.4], [174.0, 55.5], [162.6, 63.6], [161.3, 60.9], [156.2, 60.0], [149.9, 46.8], [169.5, 57.3], [160.0, 64.1], [175.3, 63.6], [169.5, 67.3], [160.0, 75.5], [172.7, 68.2], [162.6, 61.4], [157.5, 76.8], [176.5, 71.8], [164.4, 55.5], [160.7, 48.6], [174.0, 66.4], [163.8, 67.3]], &quot;type&quot;: &quot;scatter&quot;, &quot;color&quot;: &quot;rgba(223, 83, 83, .5)&quot;, &quot;name&quot;: &quot;Female&quot;}, {&quot;data&quot;: [[174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8], [181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6], [180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0], [184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9], [176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8], [178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9], [183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1], [170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1], [186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6], [182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1], [169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2], [163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2], [177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1], [182.0, 72.0], [167.0, 64.6], [177.8, 74.8], [164.5, 70.0], [192.0, 101.6], [175.5, 63.2], [171.2, 79.1], [181.6, 78.9], [167.4, 67.7], [181.1, 66.0], [177.0, 68.2], [174.5, 63.9], [177.5, 72.0], [170.5, 56.8], [182.4, 74.5], [197.1, 90.9], [180.1, 93.0], [175.5, 80.9], [180.6, 72.7], [184.4, 68.0], [175.5, 70.9], [180.6, 72.5], [177.0, 72.5], [177.1, 83.4], [181.6, 75.5], [176.5, 73.0], [175.0, 70.2], [174.0, 73.4], [165.1, 70.5], [177.0, 68.9], [192.0, 102.3], [176.5, 68.4], [169.4, 65.9], [182.1, 75.7], [179.8, 84.5], [175.3, 87.7], [184.9, 86.4], [177.3, 73.2], [167.4, 53.9], [178.1, 72.0], [168.9, 55.5], [157.2, 58.4], [180.3, 83.2], [170.2, 72.7], [177.8, 64.1], [172.7, 72.3], [165.1, 65.0], [186.7, 86.4], [165.1, 65.0], [174.0, 88.6], [175.3, 84.1], [185.4, 66.8], [177.8, 75.5], [180.3, 93.2], [180.3, 82.7], [177.8, 58.0], [177.8, 79.5], [177.8, 78.6], [177.8, 71.8], [177.8, 116.4], [163.8, 72.2], [188.0, 83.6], [198.1, 85.5], [175.3, 90.9], [166.4, 85.9], [190.5, 89.1], [166.4, 75.0], [177.8, 77.7], [179.7, 86.4], [172.7, 90.9], [190.5, 73.6], [185.4, 76.4], [168.9, 69.1], [167.6, 84.5], [175.3, 64.5], [170.2, 69.1], [190.5, 108.6], [177.8, 86.4], [190.5, 80.9], [177.8, 87.7], [184.2, 94.5], [176.5, 80.2], [177.8, 72.0], [180.3, 71.4], [171.4, 72.7], [172.7, 84.1], [172.7, 76.8], [177.8, 63.6], [177.8, 80.9], [182.9, 80.9], [170.2, 85.5], [167.6, 68.6], [175.3, 67.7], [165.1, 66.4], [185.4, 102.3], [181.6, 70.5], [172.7, 95.9], [190.5, 84.1], [179.1, 87.3], [175.3, 71.8], [170.2, 65.9], [193.0, 95.9], [171.4, 91.4], [177.8, 81.8], [177.8, 96.8], [167.6, 69.1], [167.6, 82.7], [180.3, 75.5], [182.9, 79.5], [176.5, 73.6], [186.7, 91.8], [188.0, 84.1], [188.0, 85.9], [177.8, 81.8], [174.0, 82.5], [177.8, 80.5], [171.4, 70.0], [185.4, 81.8], [185.4, 84.1], [188.0, 90.5], [188.0, 91.4], [182.9, 89.1], [176.5, 85.0], [175.3, 69.1], [175.3, 73.6], [188.0, 80.5], [188.0, 82.7], [175.3, 86.4], [170.5, 67.7], [179.1, 92.7], [177.8, 93.6], [175.3, 70.9], [182.9, 75.0], [170.8, 93.2], [188.0, 93.2], [180.3, 77.7], [177.8, 61.4], [185.4, 94.1], [168.9, 75.0], [185.4, 83.6], [180.3, 85.5], [174.0, 73.9], [167.6, 66.8], [182.9, 87.3], [160.0, 72.3], [180.3, 88.6], [167.6, 75.5], [186.7, 101.4], [175.3, 91.1], [175.3, 67.3], [175.9, 77.7], [175.3, 81.8], [179.1, 75.5], [181.6, 84.5], [177.8, 76.6], [182.9, 85.0], [177.8, 102.5], [184.2, 77.3], [179.1, 71.8], [176.5, 87.9], [188.0, 94.3], [174.0, 70.9], [167.6, 64.5], [170.2, 77.3], [167.6, 72.3], [188.0, 87.3], [174.0, 80.0], [176.5, 82.3], [180.3, 73.6], [167.6, 74.1], [188.0, 85.9], [180.3, 73.2], [167.6, 76.3], [183.0, 65.9], [183.0, 90.9], [179.1, 89.1], [170.2, 62.3], [177.8, 82.7], [179.1, 79.1], [190.5, 98.2], [177.8, 84.1], [180.3, 83.2], [180.3, 83.2]], &quot;type&quot;: &quot;scatter&quot;, &quot;color&quot;: &quot;rgba(119, 152, 191, .5)&quot;, &quot;name&quot;: &quot;Male&quot;}]; var dataLen = data.length; for (var ix = 0; ix &lt; dataLen; ix++) { chart.addSeries(data[ix]); } }); &lt;/script&gt; &lt;/body&gt; &lt;/html&gt;\" height=400 width=850></iframe>"
      ],
      "text/plain": [
       "<highcharts.highcharts.highcharts.Highchart at 0x19684ded0c8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from highcharts import Highchart\n",
    "H = Highchart(width=850, height=400)\n",
    "\n",
    "options = {\n",
    "\t'chart': {\n",
    "        'type': 'scatter',\n",
    "        'zoomType': 'xy'\n",
    "    },\n",
    "    'title': {\n",
    "        'text': 'Height Versus Weight of 507 Individuals by Gender'\n",
    "    },\n",
    "    'subtitle': {\n",
    "        'text': 'Source: Heinz  2003'\n",
    "    },\n",
    "    'xAxis': {\n",
    "        'title': {\n",
    "            'enabled': True,\n",
    "            'text': 'Height (cm)'\n",
    "        },\n",
    "        'startOnTick': True,\n",
    "        'endOnTick': True,\n",
    "        'showLastLabel': True\n",
    "    },\n",
    "    'yAxis': {\n",
    "        'title': {\n",
    "            'text': 'Weight (kg)'\n",
    "        }\n",
    "    },\n",
    "    'legend': {\n",
    "        'layout': 'vertical',\n",
    "        'align': 'left',\n",
    "        'verticalAlign': 'top',\n",
    "        'x': 100,\n",
    "        'y': 70,\n",
    "        'floating': True,\n",
    "        'backgroundColor': \"(Highcharts.theme && Highcharts.theme.legendBackgroundColor) || '#FFFFFF'\",\n",
    "        'borderWidth': 1\n",
    "    },\n",
    "    'plotOptions': {\n",
    "        'scatter': {\n",
    "            'marker': {\n",
    "                'radius': 5,\n",
    "                'states': {\n",
    "                    'hover': {\n",
    "                        'enabled': True,\n",
    "                        'lineColor': 'rgb(100,100,100)'\n",
    "                    }\n",
    "                }\n",
    "            },\n",
    "            'states': {\n",
    "                'hover': {\n",
    "                    'marker': {\n",
    "                        'enabled': False\n",
    "                    }\n",
    "                }\n",
    "            },\n",
    "            'tooltip': {\n",
    "                'headerFormat': '<b>{series.name}</b><br>',\n",
    "                'pointFormat': '{point.x} cm, {point.y} kg'\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "}\n",
    "\n",
    "\n",
    "data1 = [[161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6],\n",
    "        [170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2],\n",
    "        [172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0],\n",
    "        [147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8],\n",
    "        [159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8],\n",
    "        [174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0],\n",
    "        [154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8],\n",
    "        [162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6],\n",
    "        [168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3],\n",
    "        [167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8],\n",
    "        [167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3],\n",
    "        [168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3],\n",
    "        [156.0, 52.7], [160.0, 74.3], [163.0, 62.0], [165.7, 73.1], [161.0, 80.0],\n",
    "        [162.0, 54.7], [166.0, 53.2], [174.0, 75.7], [172.7, 61.1], [167.6, 55.7],\n",
    "        [151.1, 48.7], [164.5, 52.3], [163.5, 50.0], [152.0, 59.3], [169.0, 62.5],\n",
    "        [164.0, 55.7], [161.2, 54.8], [155.0, 45.9], [170.0, 70.6], [176.2, 67.2],\n",
    "        [170.0, 69.4], [162.5, 58.2], [170.3, 64.8], [164.1, 71.6], [169.5, 52.8],\n",
    "        [163.2, 59.8], [154.5, 49.0], [159.8, 50.0], [173.2, 69.2], [170.0, 55.9],\n",
    "        [161.4, 63.4], [169.0, 58.2], [166.2, 58.6], [159.4, 45.7], [162.5, 52.2],\n",
    "        [159.0, 48.6], [162.8, 57.8], [159.0, 55.6], [179.8, 66.8], [162.9, 59.4],\n",
    "        [161.0, 53.6], [151.1, 73.2], [168.2, 53.4], [168.9, 69.0], [173.2, 58.4],\n",
    "        [171.8, 56.2], [178.0, 70.6], [164.3, 59.8], [163.0, 72.0], [168.5, 65.2],\n",
    "        [166.8, 56.6], [172.7, 105.2], [163.5, 51.8], [169.4, 63.4], [167.8, 59.0],\n",
    "        [159.5, 47.6], [167.6, 63.0], [161.2, 55.2], [160.0, 45.0], [163.2, 54.0],\n",
    "        [162.2, 50.2], [161.3, 60.2], [149.5, 44.8], [157.5, 58.8], [163.2, 56.4],\n",
    "        [172.7, 62.0], [155.0, 49.2], [156.5, 67.2], [164.0, 53.8], [160.9, 54.4],\n",
    "        [162.8, 58.0], [167.0, 59.8], [160.0, 54.8], [160.0, 43.2], [168.9, 60.5],\n",
    "        [158.2, 46.4], [156.0, 64.4], [160.0, 48.8], [167.1, 62.2], [158.0, 55.5],\n",
    "        [167.6, 57.8], [156.0, 54.6], [162.1, 59.2], [173.4, 52.7], [159.8, 53.2],\n",
    "        [170.5, 64.5], [159.2, 51.8], [157.5, 56.0], [161.3, 63.6], [162.6, 63.2],\n",
    "        [160.0, 59.5], [168.9, 56.8], [165.1, 64.1], [162.6, 50.0], [165.1, 72.3],\n",
    "        [166.4, 55.0], [160.0, 55.9], [152.4, 60.4], [170.2, 69.1], [162.6, 84.5],\n",
    "        [170.2, 55.9], [158.8, 55.5], [172.7, 69.5], [167.6, 76.4], [162.6, 61.4],\n",
    "        [167.6, 65.9], [156.2, 58.6], [175.2, 66.8], [172.1, 56.6], [162.6, 58.6],\n",
    "        [160.0, 55.9], [165.1, 59.1], [182.9, 81.8], [166.4, 70.7], [165.1, 56.8],\n",
    "        [177.8, 60.0], [165.1, 58.2], [175.3, 72.7], [154.9, 54.1], [158.8, 49.1],\n",
    "        [172.7, 75.9], [168.9, 55.0], [161.3, 57.3], [167.6, 55.0], [165.1, 65.5],\n",
    "        [175.3, 65.5], [157.5, 48.6], [163.8, 58.6], [167.6, 63.6], [165.1, 55.2],\n",
    "        [165.1, 62.7], [168.9, 56.6], [162.6, 53.9], [164.5, 63.2], [176.5, 73.6],\n",
    "        [168.9, 62.0], [175.3, 63.6], [159.4, 53.2], [160.0, 53.4], [170.2, 55.0],\n",
    "        [162.6, 70.5], [167.6, 54.5], [162.6, 54.5], [160.7, 55.9], [160.0, 59.0],\n",
    "        [157.5, 63.6], [162.6, 54.5], [152.4, 47.3], [170.2, 67.7], [165.1, 80.9],\n",
    "        [172.7, 70.5], [165.1, 60.9], [170.2, 63.6], [170.2, 54.5], [170.2, 59.1],\n",
    "        [161.3, 70.5], [167.6, 52.7], [167.6, 62.7], [165.1, 86.3], [162.6, 66.4],\n",
    "        [152.4, 67.3], [168.9, 63.0], [170.2, 73.6], [175.2, 62.3], [175.2, 57.7],\n",
    "        [160.0, 55.4], [165.1, 104.1], [174.0, 55.5], [170.2, 77.3], [160.0, 80.5],\n",
    "        [167.6, 64.5], [167.6, 72.3], [167.6, 61.4], [154.9, 58.2], [162.6, 81.8],\n",
    "        [175.3, 63.6], [171.4, 53.4], [157.5, 54.5], [165.1, 53.6], [160.0, 60.0],\n",
    "        [174.0, 73.6], [162.6, 61.4], [174.0, 55.5], [162.6, 63.6], [161.3, 60.9],\n",
    "        [156.2, 60.0], [149.9, 46.8], [169.5, 57.3], [160.0, 64.1], [175.3, 63.6],\n",
    "        [169.5, 67.3], [160.0, 75.5], [172.7, 68.2], [162.6, 61.4], [157.5, 76.8],\n",
    "        [176.5, 71.8], [164.4, 55.5], [160.7, 48.6], [174.0, 66.4], [163.8, 67.3]]\n",
    "\n",
    "data2 = [[174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8],\n",
    "        [181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6],\n",
    "        [180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0],\n",
    "        [184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9],\n",
    "        [176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8],\n",
    "        [178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9],\n",
    "        [183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1],\n",
    "        [170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1],\n",
    "        [186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6],\n",
    "        [182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1],\n",
    "        [169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2],\n",
    "        [163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2],\n",
    "        [177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1], [182.0, 72.0],\n",
    "        [167.0, 64.6], [177.8, 74.8], [164.5, 70.0], [192.0, 101.6], [175.5, 63.2],\n",
    "        [171.2, 79.1], [181.6, 78.9], [167.4, 67.7], [181.1, 66.0], [177.0, 68.2],\n",
    "        [174.5, 63.9], [177.5, 72.0], [170.5, 56.8], [182.4, 74.5], [197.1, 90.9],\n",
    "        [180.1, 93.0], [175.5, 80.9], [180.6, 72.7], [184.4, 68.0], [175.5, 70.9],\n",
    "        [180.6, 72.5], [177.0, 72.5], [177.1, 83.4], [181.6, 75.5], [176.5, 73.0],\n",
    "        [175.0, 70.2], [174.0, 73.4], [165.1, 70.5], [177.0, 68.9], [192.0, 102.3],\n",
    "        [176.5, 68.4], [169.4, 65.9], [182.1, 75.7], [179.8, 84.5], [175.3, 87.7],\n",
    "        [184.9, 86.4], [177.3, 73.2], [167.4, 53.9], [178.1, 72.0], [168.9, 55.5],\n",
    "        [157.2, 58.4], [180.3, 83.2], [170.2, 72.7], [177.8, 64.1], [172.7, 72.3],\n",
    "        [165.1, 65.0], [186.7, 86.4], [165.1, 65.0], [174.0, 88.6], [175.3, 84.1],\n",
    "        [185.4, 66.8], [177.8, 75.5], [180.3, 93.2], [180.3, 82.7], [177.8, 58.0],\n",
    "        [177.8, 79.5], [177.8, 78.6], [177.8, 71.8], [177.8, 116.4], [163.8, 72.2],\n",
    "        [188.0, 83.6], [198.1, 85.5], [175.3, 90.9], [166.4, 85.9], [190.5, 89.1],\n",
    "        [166.4, 75.0], [177.8, 77.7], [179.7, 86.4], [172.7, 90.9], [190.5, 73.6],\n",
    "        [185.4, 76.4], [168.9, 69.1], [167.6, 84.5], [175.3, 64.5], [170.2, 69.1],\n",
    "        [190.5, 108.6], [177.8, 86.4], [190.5, 80.9], [177.8, 87.7], [184.2, 94.5],\n",
    "        [176.5, 80.2], [177.8, 72.0], [180.3, 71.4], [171.4, 72.7], [172.7, 84.1],\n",
    "        [172.7, 76.8], [177.8, 63.6], [177.8, 80.9], [182.9, 80.9], [170.2, 85.5],\n",
    "        [167.6, 68.6], [175.3, 67.7], [165.1, 66.4], [185.4, 102.3], [181.6, 70.5],\n",
    "        [172.7, 95.9], [190.5, 84.1], [179.1, 87.3], [175.3, 71.8], [170.2, 65.9],\n",
    "        [193.0, 95.9], [171.4, 91.4], [177.8, 81.8], [177.8, 96.8], [167.6, 69.1],\n",
    "        [167.6, 82.7], [180.3, 75.5], [182.9, 79.5], [176.5, 73.6], [186.7, 91.8],\n",
    "        [188.0, 84.1], [188.0, 85.9], [177.8, 81.8], [174.0, 82.5], [177.8, 80.5],\n",
    "        [171.4, 70.0], [185.4, 81.8], [185.4, 84.1], [188.0, 90.5], [188.0, 91.4],\n",
    "        [182.9, 89.1], [176.5, 85.0], [175.3, 69.1], [175.3, 73.6], [188.0, 80.5],\n",
    "        [188.0, 82.7], [175.3, 86.4], [170.5, 67.7], [179.1, 92.7], [177.8, 93.6],\n",
    "        [175.3, 70.9], [182.9, 75.0], [170.8, 93.2], [188.0, 93.2], [180.3, 77.7],\n",
    "        [177.8, 61.4], [185.4, 94.1], [168.9, 75.0], [185.4, 83.6], [180.3, 85.5],\n",
    "        [174.0, 73.9], [167.6, 66.8], [182.9, 87.3], [160.0, 72.3], [180.3, 88.6],\n",
    "        [167.6, 75.5], [186.7, 101.4], [175.3, 91.1], [175.3, 67.3], [175.9, 77.7],\n",
    "        [175.3, 81.8], [179.1, 75.5], [181.6, 84.5], [177.8, 76.6], [182.9, 85.0],\n",
    "        [177.8, 102.5], [184.2, 77.3], [179.1, 71.8], [176.5, 87.9], [188.0, 94.3],\n",
    "        [174.0, 70.9], [167.6, 64.5], [170.2, 77.3], [167.6, 72.3], [188.0, 87.3],\n",
    "        [174.0, 80.0], [176.5, 82.3], [180.3, 73.6], [167.6, 74.1], [188.0, 85.9],\n",
    "        [180.3, 73.2], [167.6, 76.3], [183.0, 65.9], [183.0, 90.9], [179.1, 89.1],\n",
    "        [170.2, 62.3], [177.8, 82.7], [179.1, 79.1], [190.5, 98.2], [177.8, 84.1],\n",
    "        [180.3, 83.2], [180.3, 83.2]]\n",
    "\n",
    "H.set_dict_options(options)\n",
    "H.add_data_set(data1, 'scatter', 'Female', color='rgba(223, 83, 83, .5)')\n",
    "H.add_data_set(data2, 'scatter', 'Male', color='rgba(119, 152, 191, .5)')\n",
    "\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<highcharts.highcharts.highcharts.Highchart at 0x19684e21288>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "from highcharts import Highchart\n",
    "\n",
    "H = Highchart()\n",
    "\n",
    "H.set_options('chart', {'zoomType': 'x'})\n",
    "H.set_options('xAxis', {'type': 'datetime',\n",
    "            'minRange': 14 * 24 * 3600000})\n",
    "\n",
    "H.set_options('yAxis',{ 'title': { 'text': 'Exchange rate'}})\n",
    "H.set_options('title', {'text': 'USD to EUR exchange rate from 2006 through 2008'})\n",
    "H.set_options('legend', {'enabled': False})\n",
    "\n",
    "\n",
    "\n",
    "data =  [ # copy dataset from http://www.highcharts.com/samples/data/jsonp.php?filename=usdeur.json&callback=?\n",
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    "    0.7035, 0.7045, 0.6956, 0.6988, 0.6915, 0.6914, 0.6859, 0.6778, 0.6815, 0.6815,\n",
    "    0.6843, 0.6846, 0.6846, 0.6923, 0.6997, 0.7098, 0.7188, 0.7232, 0.7262, 0.7266,\n",
    "    0.7359, 0.7368, 0.7337, 0.7317, 0.7387, 0.7467, 0.7461, 0.7366, 0.7319, 0.7361,\n",
    "    0.7437, 0.7432, 0.7461, 0.7461, 0.7454, 0.7549, 0.7742, 0.7801, 0.7903, 0.7876,\n",
    "    0.7928, 0.7991, 0.8007, 0.7823, 0.7661, 0.785, 0.7863, 0.7862, 0.7821, 0.7858,\n",
    "    0.7731, 0.7779, 0.7844, 0.7866, 0.7864, 0.7788, 0.7875, 0.7971, 0.8004, 0.7857,\n",
    "    0.7932, 0.7938, 0.7927, 0.7918, 0.7919, 0.7989, 0.7988, 0.7949, 0.7948, 0.7882,\n",
    "    0.7745, 0.771, 0.775, 0.7791, 0.7882, 0.7882, 0.7899, 0.7905, 0.7889, 0.7879,\n",
    "    0.7855, 0.7866, 0.7865, 0.7795, 0.7758, 0.7717, 0.761, 0.7497, 0.7471, 0.7473,\n",
    "    0.7407, 0.7288, 0.7074, 0.6927, 0.7083, 0.7191, 0.719, 0.7153, 0.7156, 0.7158,\n",
    "    0.714, 0.7119, 0.7129, 0.7129, 0.7049, 0.7095\n",
    "]\n",
    "\n",
    "H.add_data_set(data, 'area', 'USD to EUR', pointInterval=24 * 3600 * 1000, \n",
    "\tpointStart=datetime.datetime(2006,1,1))\n",
    "H.set_options('plotOptions', {\n",
    "            'area': {\n",
    "                'fillColor': {\n",
    "                    'linearGradient': { 'x1': 0, 'y1': 0, 'x2': 0, 'y2': 1},\n",
    "                    'stops': [\n",
    "                        [0, \"Highcharts.getOptions().colors[0]\"],\n",
    "                        [1, \"Highcharts.Color(Highcharts.getOptions().colors[0]).setOpacity(0).get('rgba')\"]\n",
    "                    ]},\n",
    "                'marker': {\n",
    "                    'radius': 2\n",
    "                },\n",
    "                'lineWidth': 1,\n",
    "                'states': {\n",
    "                    'hover': {\n",
    "                        'lineWidth': 1\n",
    "                    }\n",
    "                },\n",
    "                'threshold': None\n",
    "            }\n",
    "        })\n",
    "\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "import plotly.offline\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>218000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>54000.0</td>\n",
       "      <td>83000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>52000.0</td>\n",
       "      <td>109000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender         F         M\n",
       "Country                   \n",
       "France   58000.0  218000.0\n",
       "Germany  54000.0   83000.0\n",
       "Spain    52000.0  109000.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_Gender_Salary = df[['Country' ,'Gender', 'EstimatedSalary']]\n",
    "Country_Gender_Salary = Country_Gender_Salary.groupby(['Country', 'Gender'])['EstimatedSalary'].sum()\n",
    "Country_Gender_Salary = Country_Gender_Salary.reset_index()\n",
    "Country_Gender_Salary = Country_Gender_Salary.pivot_table('EstimatedSalary', ['Country'], 'Gender')\n",
    "Country_Gender_Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         "name": "F",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "uid": "4003894f-50a9-49aa-ae4f-3355b500503b",
         "x": [
          "France",
          "Germany",
          "Spain"
         ],
         "y": [
          58000,
          54000,
          52000
         ]
        },
        {
         "marker": {
          "color": "rgba(55, 128, 191, 0.6)",
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          "France",
          "Germany",
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         "y": [
          218000,
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          109000
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       "layout": {
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         "text": "Gender Wise Estimated Salraies Based On Countries"
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         "gridcolor": "#E1E5ED",
         "showgrid": true,
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         "tickfont": {
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         },
         "title": {
          "font": {
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         },
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "text/html": [
       "<div>\n",
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       "        \n",
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       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
       "                    \n",
       "                if (document.getElementById(\"6ac34937-96f9-42ef-b18b-779ecf1b695b\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        '6ac34937-96f9-42ef-b18b-779ecf1b695b',\n",
       "                        [{\"marker\": {\"color\": \"rgba(255, 153, 51, 0.6)\", \"line\": {\"color\": \"rgba(255, 153, 51, 1.0)\", \"width\": 1}}, \"name\": \"F\", \"orientation\": \"v\", \"text\": \"\", \"type\": \"bar\", \"uid\": \"4003894f-50a9-49aa-ae4f-3355b500503b\", \"x\": [\"France\", \"Germany\", \"Spain\"], \"y\": [58000.0, 54000.0, 52000.0]}, {\"marker\": {\"color\": \"rgba(55, 128, 191, 0.6)\", \"line\": {\"color\": \"rgba(55, 128, 191, 1.0)\", \"width\": 1}}, \"name\": \"M\", \"orientation\": \"v\", \"text\": \"\", \"type\": \"bar\", \"uid\": \"1917249a-fb16-4ae4-a23d-7656d7312f1c\", \"x\": [\"France\", \"Germany\", \"Spain\"], \"y\": [218000.0, 83000.0, 109000.0]}],\n",
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "Country_Gender_Salary.iplot(kind='bar',title = 'Gender Wise Estimated Salraies Based On Countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Purchased</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>72000.0</td>\n",
       "      <td>204000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>137000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>113000.0</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Purchased        No       Yes\n",
       "Country                      \n",
       "France      72000.0  204000.0\n",
       "Germany    137000.0       0.0\n",
       "Spain      113000.0   48000.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_Purchased_Salary = df[['Country' ,'Purchased', 'EstimatedSalary']]\n",
    "Country_Purchased_Salary = Country_Purchased_Salary.groupby(['Country', 'Purchased'])['EstimatedSalary'].sum()\n",
    "Country_Purchased_Salary = Country_Purchased_Salary.reset_index()\n",
    "Country_Purchased_Salary = Country_Purchased_Salary.pivot_table('EstimatedSalary', ['Country'], 'Purchased')\n",
    "Country_Purchased_Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Country_Purchased_Salary = Country_Purchased_Salary.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Purchased</th>\n",
       "      <th>Country</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>204000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>137000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Purchased  Country        No       Yes\n",
       "0           France   72000.0  204000.0\n",
       "1          Germany  137000.0       0.0\n",
       "2            Spain  113000.0   48000.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_Purchased_Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe style=\"border:0;outline:none;overflow:hidden\" srcdoc=\"&lt;!DOCTYPE html&gt; &lt;html lang=&quot;en&quot;&gt; &lt;head&gt; &lt;meta charset=&quot;utf-8&quot; /&gt; &lt;link href=&quot;https://www.highcharts.com/highslide/highslide.css&quot; rel=&quot;stylesheet&quot; /&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/highcharts.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/highcharts-more.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/modules/heatmap.js&quot;&gt;&lt;/script&gt; &lt;script type=&quot;text/javascript&quot; src=&quot;https://code.highcharts.com/6/modules/exporting.js&quot;&gt;&lt;/script&gt; &lt;/head&gt; &lt;body style=&quot;margin:0;padding:0&quot;&gt; &lt;div id=&quot;container&quot; style=&quot;width:750px;height:600px;&quot;&gt;Loading....&lt;/div&gt; &lt;script&gt; $(function(){ Highcharts.setOptions({&quot;global&quot;: {}, &quot;lang&quot;: {}}); var option = {&quot;chart&quot;: {&quot;renderTo&quot;: &quot;container&quot;, &quot;width&quot;: 750, &quot;height&quot;: 600}, &quot;colors&quot;: {}, &quot;credits&quot;: {&quot;enabled&quot;: false}, &quot;drilldown&quot;: {}, &quot;exporting&quot;: {}, &quot;labels&quot;: {}, &quot;legend&quot;: {&quot;layout&quot;: &quot;vertical&quot;, &quot;align&quot;: &quot;right&quot;, &quot;verticalAlign&quot;: &quot;top&quot;, &quot;x&quot;: -40, &quot;y&quot;: 80, &quot;floating&quot;: true, &quot;borderWidth&quot;: 1, &quot;backgroundColor&quot;: ((Highcharts.theme &amp;&amp; Highcharts.theme.legendBackgroundColor) || &#x27;#FFFFFF&#x27;), &quot;shadow&quot;: true}, &quot;loading&quot;: {}, &quot;navigation&quot;: {}, &quot;pane&quot;: {}, &quot;plotOptions&quot;: {&quot;bar&quot;: {&quot;dataLabels&quot;: {&quot;enabled&quot;: true}}}, &quot;series&quot;: {}, &quot;subtitle&quot;: {&quot;text&quot;: &quot;Source: &lt;a href=\\&quot;https://en.wikipedia.org/wiki/World_population\\&quot;&gt;Wikipedia.org&lt;/a&gt;&quot;}, &quot;title&quot;: {&quot;text&quot;: &quot;Country Wise Estimated Salary Based on Purchased&quot;}, &quot;tooltip&quot;: {&quot;valueSuffix&quot;: &quot; millions&quot;}, &quot;xAxis&quot;: {&quot;categories&quot;: [&quot;France&quot;, &quot;Germany&quot;, &quot;Spain&quot;], &quot;title&quot;: {&quot;text&quot;: null}}, &quot;yAxis&quot;: {&quot;min&quot;: 0, &quot;title&quot;: {&quot;text&quot;: &quot;Population (millions)&quot;, &quot;align&quot;: &quot;high&quot;}, &quot;labels&quot;: {&quot;overflow&quot;: &quot;justify&quot;}}}; var chart = new Highcharts.Chart(option); var data = [{&quot;data&quot;: [72000.0, 137000.0, 113000.0], &quot;type&quot;: &quot;bar&quot;, &quot;name&quot;: &quot;No&quot;}, {&quot;data&quot;: [204000.0, 0.0, 48000.0], &quot;type&quot;: &quot;bar&quot;, &quot;name&quot;: &quot;Yes&quot;}]; var dataLen = data.length; for (var ix = 0; ix &lt; dataLen; ix++) { chart.addSeries(data[ix]); } }); &lt;/script&gt; &lt;/body&gt; &lt;/html&gt;\" height=600 width=750></iframe>"
      ],
      "text/plain": [
       "<highcharts.highcharts.highcharts.Highchart at 0x19684ef6548>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from highcharts import Highchart\n",
    "H = Highchart(width=750, height=600)\n",
    "\n",
    "data1 = [72000.0, 137000.0, 113000.0]\n",
    "data2 = [204000.0, 0.0, 48000.0]\n",
    "\n",
    "options = {\n",
    "\t'title': {\n",
    "        'text': 'Country Wise Estimated Salary Based on Purchased'\n",
    "    },\n",
    "    'subtitle': {\n",
    "        'text': 'Source: <a href=\"https://en.wikipedia.org/wiki/World_population\">Wikipedia.org</a>'\n",
    "    },\n",
    "    'xAxis': {\n",
    "        'categories': ['France', 'Germany', 'Spain'],\n",
    "        'title': {\n",
    "            'text': None\n",
    "        }\n",
    "    },\n",
    "    'yAxis': {\n",
    "        'min': 0,\n",
    "        'title': {\n",
    "            'text': 'Population (millions)',\n",
    "            'align': 'high'\n",
    "        },\n",
    "        'labels': {\n",
    "            'overflow': 'justify'\n",
    "        }\n",
    "    },\n",
    "    'tooltip': {\n",
    "        'valueSuffix': ' millions'\n",
    "    },\n",
    "    'legend': {\n",
    "        'layout': 'vertical',\n",
    "        'align': 'right',\n",
    "        'verticalAlign': 'top',\n",
    "        'x': -40,\n",
    "        'y': 80,\n",
    "        'floating': True,\n",
    "        'borderWidth': 1,\n",
    "        'backgroundColor': \"((Highcharts.theme && Highcharts.theme.legendBackgroundColor) || '#FFFFFF')\",\n",
    "        'shadow': True\n",
    "    },\n",
    "    'credits': {\n",
    "        'enabled': False\n",
    "    },\n",
    "    'plotOptions': {\n",
    "        'bar': {\n",
    "            'dataLabels': {\n",
    "                'enabled': True\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "H.set_dict_options(options)\n",
    "\n",
    "H.add_data_set(data1, 'bar', 'No')\n",
    "H.add_data_set(data2, 'bar', 'Yes')\n",
    "\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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