{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6de02024",
   "metadata": {},
   "source": [
    "# Analysis of Scotch Whisky Production and Flavor Profiles\n",
    "---\n",
    "\n",
    "Scotch Whisky is renowned for its intricate flavors and diverse sensory experiences. This uniqueness is further emphasized by the various regions of Scotland in which these spirits are crafted, each region contributing its distinct flavor profiles.\n",
    "\n",
    "The aim of this case study is to classify Scotch whiskies based on their intricate flavor characterisitcs. The central focus lies in the meticulous categorization of these iconic beverages to gain insights into their distinctive taste attributes.\n",
    "\n",
    "The dataset consists of taste ratings of a single malt Scotch whisky derived from nearly every active whisky distillery in Scotland. It is a repository of evaluations for 86 malt whiskies, each being appraised on a scale from 0 to 4 across 12 different taste categories. These taste categories provide comprehensive insights into the character of the whiskies, encompassing aspects such as sweetness, smokiness, medicinal attributes, spiciness and more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00b9f7f",
   "metadata": {},
   "source": [
    "## Task Description\n",
    "---\n",
    "\n",
    "**Task 1 - Finding Correlations**\n",
    "- Description:\n",
    "    * Finding correlations among 86 Scotch Whiskies, aiming to determine patterns and connections between different flavor characteristics\n",
    "\n",
    "**Task 2 - Clustering**\n",
    "- Description:\n",
    "    * Finding correlation matrix using `Pearson Correlation` among different flavor attributes\n",
    "    * Employing `Spectral Coclustering` to find approximate solution to clustering\n",
    "    * Identifying six distinct cluster blocks\n",
    "\n",
    "**Task 3 - Bokeh Plot**\n",
    "- Description:\n",
    "    * Using Bokeh to create an interactive plot of geographical map of distilleries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cb68e373",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718d5f49",
   "metadata": {},
   "source": [
    "## Loading Whisky Data\n",
    "---\n",
    "\n",
    "The first task involves loading and examining the data. This requires the utilization of two data files: `whiskies.txt` and `regions.txt`.\n",
    "\n",
    "The `regions.txt` file contains the regions from which each whisky originates. The `whiskies.txt` file contains all details about the whiskies.\n",
    "\n",
    "- Extracting whisky-related information from `whiskies.txt` and storing it in a dataframe\n",
    "\n",
    "- Reading in the `regions.txt` file and adding it as a column to the whisky dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b9b7bc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_path = \"./\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "df61b156",
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky_file = os.path.join(relative_path,'whiskies.txt')\n",
    "region_file = os.path.join(relative_path, 'regions.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3dc1c47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky_df = pd.read_csv(whisky_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "96f14fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky_df[\"Region\"] = pd.read_csv(region_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c7166f72",
   "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>RowID</th>\n",
       "      <th>Distillery</th>\n",
       "      <th>Body</th>\n",
       "      <th>Sweetness</th>\n",
       "      <th>Smoky</th>\n",
       "      <th>Medicinal</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Honey</th>\n",
       "      <th>Spicy</th>\n",
       "      <th>Winey</th>\n",
       "      <th>Nutty</th>\n",
       "      <th>Malty</th>\n",
       "      <th>Fruity</th>\n",
       "      <th>Floral</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Aberfeldy</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tPH15 2EB</td>\n",
       "      <td>286580</td>\n",
       "      <td>749680</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Aberlour</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tAB38 9PJ</td>\n",
       "      <td>326340</td>\n",
       "      <td>842570</td>\n",
       "      <td>Speyside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>AnCnoc</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tAB5 5LI</td>\n",
       "      <td>352960</td>\n",
       "      <td>839320</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ardbeg</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>\\tPA42 7EB</td>\n",
       "      <td>141560</td>\n",
       "      <td>646220</td>\n",
       "      <td>Islay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ardmore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>\\tAB54 4NH</td>\n",
       "      <td>355350</td>\n",
       "      <td>829140</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowID Distillery  Body  Sweetness  Smoky  Medicinal  Tobacco  Honey  Spicy  \\\n",
       "0      1  Aberfeldy     2          2      2          0        0      2      1   \n",
       "1      2   Aberlour     3          3      1          0        0      4      3   \n",
       "2      3     AnCnoc     1          3      2          0        0      2      0   \n",
       "3      4     Ardbeg     4          1      4          4        0      0      2   \n",
       "4      5    Ardmore     2          2      2          0        0      1      1   \n",
       "\n",
       "   Winey  Nutty  Malty  Fruity  Floral     Postcode   Latitude   Longitude  \\\n",
       "0      2      2      2       2       2   \\tPH15 2EB     286580      749680   \n",
       "1      2      2      3       3       2   \\tAB38 9PJ     326340      842570   \n",
       "2      0      2      2       3       2    \\tAB5 5LI     352960      839320   \n",
       "3      0      1      2       1       0   \\tPA42 7EB     141560      646220   \n",
       "4      1      2      3       1       1   \\tAB54 4NH     355350      829140   \n",
       "\n",
       "      Region  \n",
       "0  Highlands  \n",
       "1   Speyside  \n",
       "2  Highlands  \n",
       "3      Islay  \n",
       "4  Highlands  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96f96cc",
   "metadata": {},
   "source": [
    "The `iloc` method can be used to index a dataframe based on integer positions. The method provides a means to access specific rows and columns within the dataset, utilizing integer-based referencing.\n",
    "\n",
    "Indexing is defined by two parameters: the `start_index` and the `end_index`.\n",
    "\n",
    "The `start_index` denotes the initial position from which the indexing operation commences, inclusively, and the end index signifies the position where the operation concludes, exclusively.\n",
    "\n",
    "Taking a look at the first 10 rows of the whisky dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "928fb8a9",
   "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>RowID</th>\n",
       "      <th>Distillery</th>\n",
       "      <th>Body</th>\n",
       "      <th>Sweetness</th>\n",
       "      <th>Smoky</th>\n",
       "      <th>Medicinal</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Honey</th>\n",
       "      <th>Spicy</th>\n",
       "      <th>Winey</th>\n",
       "      <th>Nutty</th>\n",
       "      <th>Malty</th>\n",
       "      <th>Fruity</th>\n",
       "      <th>Floral</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Aberfeldy</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tPH15 2EB</td>\n",
       "      <td>286580</td>\n",
       "      <td>749680</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Aberlour</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tAB38 9PJ</td>\n",
       "      <td>326340</td>\n",
       "      <td>842570</td>\n",
       "      <td>Speyside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>AnCnoc</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tAB5 5LI</td>\n",
       "      <td>352960</td>\n",
       "      <td>839320</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ardbeg</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>\\tPA42 7EB</td>\n",
       "      <td>141560</td>\n",
       "      <td>646220</td>\n",
       "      <td>Islay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ardmore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>\\tAB54 4NH</td>\n",
       "      <td>355350</td>\n",
       "      <td>829140</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>ArranIsleOf</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>KA27 8HJ</td>\n",
       "      <td>194050</td>\n",
       "      <td>649950</td>\n",
       "      <td>Islands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Auchentoshan</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>G81 4SJ</td>\n",
       "      <td>247670</td>\n",
       "      <td>672610</td>\n",
       "      <td>Lowlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Auchroisk</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>\\tAB55 3XS</td>\n",
       "      <td>340754</td>\n",
       "      <td>848623</td>\n",
       "      <td>Speyside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Aultmore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>\\tAB55 3QY</td>\n",
       "      <td>340754</td>\n",
       "      <td>848623</td>\n",
       "      <td>Speyside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Balblair</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>\\tIV19 1LB</td>\n",
       "      <td>270820</td>\n",
       "      <td>885770</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowID    Distillery  Body  Sweetness  Smoky  Medicinal  Tobacco  Honey  \\\n",
       "0      1     Aberfeldy     2          2      2          0        0      2   \n",
       "1      2      Aberlour     3          3      1          0        0      4   \n",
       "2      3        AnCnoc     1          3      2          0        0      2   \n",
       "3      4        Ardbeg     4          1      4          4        0      0   \n",
       "4      5       Ardmore     2          2      2          0        0      1   \n",
       "5      6   ArranIsleOf     2          3      1          1        0      1   \n",
       "6      7  Auchentoshan     0          2      0          0        0      1   \n",
       "7      8     Auchroisk     2          3      1          0        0      2   \n",
       "8      9      Aultmore     2          2      1          0        0      1   \n",
       "9     10      Balblair     2          3      2          1        0      0   \n",
       "\n",
       "   Spicy  Winey  Nutty  Malty  Fruity  Floral     Postcode   Latitude  \\\n",
       "0      1      2      2      2       2       2   \\tPH15 2EB     286580   \n",
       "1      3      2      2      3       3       2   \\tAB38 9PJ     326340   \n",
       "2      0      0      2      2       3       2    \\tAB5 5LI     352960   \n",
       "3      2      0      1      2       1       0   \\tPA42 7EB     141560   \n",
       "4      1      1      2      3       1       1   \\tAB54 4NH     355350   \n",
       "5      1      1      0      1       1       2     KA27 8HJ     194050   \n",
       "6      1      0      2      2       3       3      G81 4SJ     247670   \n",
       "7      1      2      2      2       2       1   \\tAB55 3XS     340754   \n",
       "8      0      0      2      2       2       2   \\tAB55 3QY     340754   \n",
       "9      2      0      2      1       2       1   \\tIV19 1LB     270820   \n",
       "\n",
       "    Longitude     Region  \n",
       "0      749680  Highlands  \n",
       "1      842570   Speyside  \n",
       "2      839320  Highlands  \n",
       "3      646220      Islay  \n",
       "4      829140  Highlands  \n",
       "5      649950    Islands  \n",
       "6      672610   Lowlands  \n",
       "7      848623   Speyside  \n",
       "8      848623   Speyside  \n",
       "9      885770  Highlands  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_df.iloc[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06eb1653",
   "metadata": {},
   "source": [
    "The `iloc` method can also be applied in examining the columns within a dataframe.\n",
    "\n",
    "In the dataset, a subset of the columns contain the data for flavor attributes of whisky. Utilizing the `.columns` attribute on a dataframe object yields the column names associated with that particular dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "299fb30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowID', 'Distillery', 'Body', 'Sweetness', 'Smoky', 'Medicinal',\n",
       "       'Tobacco', 'Honey', 'Spicy', 'Winey', 'Nutty', 'Malty', 'Fruity',\n",
       "       'Floral', 'Postcode', ' Latitude', ' Longitude', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bf853e",
   "metadata": {},
   "source": [
    "Starting from index 2 for the first flavor attribute `Body` and including index 14 for `Floral` gives the column index locations for the flavors.\n",
    "\n",
    "Next, the flavor attributes for the first ten whiskies in the dataframe are examined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bdaa83f9",
   "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>Body</th>\n",
       "      <th>Sweetness</th>\n",
       "      <th>Smoky</th>\n",
       "      <th>Medicinal</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Honey</th>\n",
       "      <th>Spicy</th>\n",
       "      <th>Winey</th>\n",
       "      <th>Nutty</th>\n",
       "      <th>Malty</th>\n",
       "      <th>Fruity</th>\n",
       "      <th>Floral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Body  Sweetness  Smoky  Medicinal  Tobacco  Honey  Spicy  Winey  Nutty  \\\n",
       "0      2          2      2          0        0      2      1      2      2   \n",
       "1      3          3      1          0        0      4      3      2      2   \n",
       "2      1          3      2          0        0      2      0      0      2   \n",
       "3      4          1      4          4        0      0      2      0      1   \n",
       "4      2          2      2          0        0      1      1      1      2   \n",
       "5      2          3      1          1        0      1      1      1      0   \n",
       "6      0          2      0          0        0      1      1      0      2   \n",
       "7      2          3      1          0        0      2      1      2      2   \n",
       "8      2          2      1          0        0      1      0      0      2   \n",
       "9      2          3      2          1        0      0      2      0      2   \n",
       "10     4          3      2          0        0      2      1      3      3   \n",
       "\n",
       "    Malty  Fruity  Floral  \n",
       "0       2       2       2  \n",
       "1       3       3       2  \n",
       "2       2       3       2  \n",
       "3       2       1       0  \n",
       "4       3       1       1  \n",
       "5       1       1       2  \n",
       "6       2       3       3  \n",
       "7       2       2       1  \n",
       "8       2       2       2  \n",
       "9       1       2       1  \n",
       "10      0       1       2  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_df.iloc[0:11,2:14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3c5313",
   "metadata": {},
   "source": [
    "## Exploring Correlations\n",
    "---\n",
    "\n",
    "The next step is to examine the correlation among various flavor attributes.\n",
    "\n",
    "To accomplish this, the `corr` method is used to calculate correlations across the columns of our whisky dataframe. The method, by default, employs the `Pearson Correlation` technique, which looks for linear correlations within the data.\n",
    "\n",
    "In the scenario where attributes have been measured for two variables, the Pearson Correlation coefficient tends to approach 1 as the data points in the scatterplot converge towards a straight upward line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99290fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "flavors = whisky_df.iloc[:,2:14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5401f7e2",
   "metadata": {},
   "source": [
    "The flavor attributes for the whiskies are stored within the dataframe named `flavors`.\n",
    "\n",
    "To compute the pairwise correlation of columns, the `corr` method is employed and the resulting output is stored in the dataframe named `corr_flavors`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "161ec746",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Body  Sweetness     Smoky  Medicinal   Tobacco     Honey  \\\n",
      "Body       1.000000  -0.136518  0.524032   0.354050  0.168718  0.082031   \n",
      "Sweetness -0.136518   1.000000 -0.405897  -0.392017 -0.147871  0.132558   \n",
      "Smoky      0.524032  -0.405897  1.000000   0.686071  0.365501 -0.195318   \n",
      "Medicinal  0.354050  -0.392017  0.686071   1.000000  0.425106 -0.396629   \n",
      "Tobacco    0.168718  -0.147871  0.365501   0.425106  1.000000 -0.275490   \n",
      "Honey      0.082031   0.132558 -0.195318  -0.396629 -0.275490  1.000000   \n",
      "Spicy      0.188500  -0.054200  0.231745   0.044903  0.054068  0.139563   \n",
      "Winey      0.408576   0.115727 -0.028190  -0.202651  0.009097  0.362021   \n",
      "Nutty      0.126323  -0.032493 -0.023132  -0.113671 -0.117717  0.188492   \n",
      "Malty     -0.116859  -0.001516 -0.192875  -0.258959 -0.059347  0.310184   \n",
      "Fruity    -0.013205   0.019820 -0.312970  -0.330975 -0.235145  0.108822   \n",
      "Floral    -0.461203   0.144987 -0.431663  -0.511323 -0.212375  0.183029   \n",
      "\n",
      "              Spicy     Winey     Nutty     Malty    Fruity    Floral  \n",
      "Body       0.188500  0.408576  0.126323 -0.116859 -0.013205 -0.461203  \n",
      "Sweetness -0.054200  0.115727 -0.032493 -0.001516  0.019820  0.144987  \n",
      "Smoky      0.231745 -0.028190 -0.023132 -0.192875 -0.312970 -0.431663  \n",
      "Medicinal  0.044903 -0.202651 -0.113671 -0.258959 -0.330975 -0.511323  \n",
      "Tobacco    0.054068  0.009097 -0.117717 -0.059347 -0.235145 -0.212375  \n",
      "Honey      0.139563  0.362021  0.188492  0.310184  0.108822  0.183029  \n",
      "Spicy      1.000000  0.092704 -0.042856  0.036303  0.144714  0.034663  \n",
      "Winey      0.092704  1.000000  0.198467  0.112368  0.090694 -0.126932  \n",
      "Nutty     -0.042856  0.198467  1.000000  0.066157  0.071765  0.018302  \n",
      "Malty      0.036303  0.112368  0.066157  1.000000  0.207288  0.106309  \n",
      "Fruity     0.144714  0.090694  0.071765  0.207288  1.000000  0.262336  \n",
      "Floral     0.034663 -0.126932  0.018302  0.106309  0.262336  1.000000  \n"
     ]
    }
   ],
   "source": [
    "corr_flavors = pd.DataFrame.corr(flavors)\n",
    "\n",
    "print(corr_flavors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68951360",
   "metadata": {},
   "source": [
    "The correlation matrix is visually represented using the `pcolor` function to plot its contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0fb3ac5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.pcolor(corr_flavors)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfefa5bd",
   "metadata": {},
   "source": [
    "Continuing our analysis, the next step involves examining the correlations among different whiskies based on their flavor attributes. This can be accomplished by transposing the dataset.\n",
    "\n",
    "Doing this helps in uncovering how the flavor profiles of whiskies from various distilleries correlate with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b8cb48a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_whisky = pd.DataFrame.corr(flavors.transpose())\n",
    "plt.figure(figsize = (10,7))\n",
    "plt.pcolor(corr_whisky)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58e998b",
   "metadata": {},
   "source": [
    "## Clustering Whisky by Flavor profile\n",
    "---\n",
    "\n",
    "Employing the `spectral co-clustering` technique available in the `scikit-learn` machine learning module, the aim is to discover an approximate solution to the given problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "98a9b587",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import SpectralCoclustering as SpectralCC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2565f17",
   "metadata": {},
   "source": [
    "The provided adjacency matrix showcases a relationship where rows correspond to variable 1 and columns correspond to variable 2. Each element within the matrix represents the frequency with which variable 1 is associated with variable 2.\n",
    "\n",
    "By manipulating this matrix and considering eigenvalues and eigenvectors, an approximate solution to the clustering problem can be found.\n",
    "\n",
    "The term `spectral` is tied to the use of eigenvalues and eigenvectors. This co-clustering technique addresses the clustering of both rows and columns simultaneously, regardless of their individual meanings.\n",
    "\n",
    "Employing this method helps in identifying the clusters of whiskies within the correlation matrix of flavor attributes. This approach essentially restructures the order of rows and columns in the correlation matrix, enabling clear identification of blocks representing groups of whiskies.\n",
    "\n",
    "Given that the dataset encompasses whiskies from six distinct regions, the clustering algorithm is applied to detect six distinct blocks within the matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "75e25e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SpectralCoclustering(n_clusters=6, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SpectralCoclustering</label><div class=\"sk-toggleable__content\"><pre>SpectralCoclustering(n_clusters=6, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SpectralCoclustering(n_clusters=6, random_state=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SpectralCC(n_clusters = 6, random_state = 0)\n",
    "model.fit(corr_whisky)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "193b38d0",
   "metadata": {},
   "source": [
    "After constructing the model object, the model is trained using the data extracted from the `corr_whisky` correlation matrix.\n",
    "\n",
    "The resulting output is an array that possesses dimensions equal to the number of row clusters, which is 6 in this context, multiplied by the number of rows in the data matrix.\n",
    "\n",
    "This amounts to 86 enries, representing individual whiskies. Each entry within this array is marked either as `True` or `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "332a7801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 86)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rows_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb194c3",
   "metadata": {},
   "source": [
    "By summing the columns of this array, the totla number of observatoins associated with each cluster can be determined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3f173703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19,  5, 24, 18, 14,  6])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(model.rows_,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9537599",
   "metadata": {},
   "source": [
    "The resulting output provides information about the distribution of whiskies across various clusters. Observing the row labels with the array reveals that they are represented by numbers ranging from 0 to 5, corresponding to the six specified clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "29c3dc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, 5, 2, 0, 3, 2, 4, 3, 2, 0, 2, 0, 4, 4, 3, 2, 0, 1, 3, 5,\n",
       "       3, 5, 3, 3, 2, 2, 3, 2, 3, 4, 0, 0, 0, 4, 2, 3, 0, 1, 0, 3, 2, 2,\n",
       "       2, 3, 4, 0, 0, 3, 3, 2, 2, 2, 0, 1, 2, 5, 5, 0, 3, 4, 2, 4, 3, 2,\n",
       "       1, 2, 1, 0, 2, 4, 0, 4, 2, 4, 3, 5, 4, 3, 0, 4, 0, 0, 2, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.row_labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf0fe7c",
   "metadata": {},
   "source": [
    "## Comparing Correlation Matrices\n",
    "---\n",
    "\n",
    "The process to visualize the cluster involves three tasks:\n",
    "\n",
    "- Extracting the group labels from the model and add them as a new column to the whisky table\n",
    "\n",
    "- Rearrange the rows of the table in ascending order based on the group labels\n",
    "\n",
    "- Resetting the index of the dataframe to ensure proper alignment of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7e685b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# task 1: adding cluster group labels to whisky data\n",
    "whisky_df['Group'] = pd.Series(model.row_labels_, index = whisky_df.index)\n",
    "\n",
    "# task 2: sorting the whiskies according to increasing order of group lables\n",
    "whisky_df = whisky_df.iloc[np.argsort(model.row_labels_)]\n",
    "\n",
    "# task 3: resetting the index of whisky dataframe\n",
    "whisky_df = whisky_df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7b4f09f4",
   "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>RowID</th>\n",
       "      <th>Distillery</th>\n",
       "      <th>Body</th>\n",
       "      <th>Sweetness</th>\n",
       "      <th>Smoky</th>\n",
       "      <th>Medicinal</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Honey</th>\n",
       "      <th>Spicy</th>\n",
       "      <th>Winey</th>\n",
       "      <th>Nutty</th>\n",
       "      <th>Malty</th>\n",
       "      <th>Fruity</th>\n",
       "      <th>Floral</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Region</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>Tullibardine</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>PH4 1QG</td>\n",
       "      <td>289690</td>\n",
       "      <td>708850</td>\n",
       "      <td>Highlands</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>GlenElgin</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>IV30 3SL</td>\n",
       "      <td>322640</td>\n",
       "      <td>861040</td>\n",
       "      <td>Speyside</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>GlenDeveronMacduff</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AB4 3JT</td>\n",
       "      <td>372120</td>\n",
       "      <td>860400</td>\n",
       "      <td>Speyside</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>GlenSpey</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>AB38 7AU</td>\n",
       "      <td>327760</td>\n",
       "      <td>849140</td>\n",
       "      <td>Speyside</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>Glenkinchie</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>EH34 5ET</td>\n",
       "      <td>344380</td>\n",
       "      <td>666690</td>\n",
       "      <td>Lowlands</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowID          Distillery  Body  Sweetness  Smoky  Medicinal  Tobacco  \\\n",
       "0     86        Tullibardine     2          3      0          0        1   \n",
       "1     34           GlenElgin     2          3      1          0        0   \n",
       "2     33  GlenDeveronMacduff     2          3      1          1        1   \n",
       "3     41            GlenSpey     1          3      1          0        0   \n",
       "4     48         Glenkinchie     1          2      1          0        0   \n",
       "\n",
       "   Honey  Spicy  Winey  Nutty  Malty  Fruity  Floral   Postcode   Latitude  \\\n",
       "0      0      2      1      1      2       2       1    PH4 1QG     289690   \n",
       "1      2      1      1      1      1       2       3   IV30 3SL     322640   \n",
       "2      1      1      2      0      2       0       1    AB4 3JT     372120   \n",
       "3      0      1      1      1      2       0       2   AB38 7AU     327760   \n",
       "4      1      2      0      0      2       2       2   EH34 5ET     344380   \n",
       "\n",
       "    Longitude     Region  Group  \n",
       "0      708850  Highlands      0  \n",
       "1      861040   Speyside      0  \n",
       "2      860400   Speyside      0  \n",
       "3      849140   Speyside      0  \n",
       "4      666690   Lowlands      0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0dd39a2",
   "metadata": {},
   "source": [
    "Given the reshuffling of rows and columns in the whsiky dataframe, it is recommended to recalculate the correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e090c624",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_whisky = pd.DataFrame.corr(whisky_df.iloc[:,2:14].transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e93b35d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_whisky_np = np.array(correlation_whisky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c7610791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "plt.subplot(121)\n",
    "plt.pcolor(corr_whisky)\n",
    "plt.title(\"Original\")\n",
    "plt.axis(\"tight\")\n",
    "plt.subplot(122)\n",
    "plt.pcolor(correlation_whisky)\n",
    "plt.title(\"Rearranged\")\n",
    "plt.axis(\"tight\");\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f844e2bd",
   "metadata": {},
   "source": [
    "Here is the plot.\n",
    "\n",
    "To the left, the initial correlation matrix is showcased, detailing the correlation computed among 86 whiskies based on their flavor profiles.\n",
    "\n",
    "To the right, a corresponding correlation matrix is shown, but with its rows and columns reorganized.\n",
    "\n",
    "By applying the spectral co-clustering technique, six distinct clusters of whiskies were identified. Following the diagonal line from the bottom-left corner to the top-right corner on the right matrix, the six clusters of whiskies become visually evident.\n",
    "\n",
    "It is resonable to anticipate that whiskies within the same cluster share similarities in their flavor profiles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d77da99",
   "metadata": {},
   "source": [
    "## Bokeh Plot for Whisky Classification\n",
    "---\n",
    "\n",
    "The aim of this case study is to plot the correlations among distillery flavor profiles and generating an interactive geographical map of distilleries. \n",
    "\n",
    "The code employs `Bokeh`, a powerful visualization library to create interactive visualizations that enchance the understanding of the intricate relationships between distilleries, their flavor attributes and their geographic locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fabc73bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import SpectralCoclustering as CC\n",
    "import numpy as np, pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "63034f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_whisky = os.path.join(relative_path,\"grouped_whisky.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e88b03d",
   "metadata": {},
   "source": [
    "The CSV file containing the whisky data is stored into a dataframe named `whisky`.\n",
    "\n",
    "The correlation among the flavor attributes are calculated after extracting the columns that correspond to the attributes and transposing them. The correlation values are stored as numpy array in `correlations`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "62375ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky = pd.read_csv(grouped_whisky, index_col=0)\n",
    "\n",
    "# cleaning up the whisky dataframe\n",
    "whisky = whisky.drop(whisky.columns[0], axis = 1)\n",
    "whisky.index = [x for x in range(len(whisky))]\n",
    "\n",
    "correlations = pd.DataFrame.corr(whisky.iloc[:,2:14].transpose())\n",
    "\n",
    "# setting integer-based row and column index\n",
    "correlations.index = [x for x in range(len(correlations))]\n",
    "correlations.columns = [x for x in range(len(correlations))]\n",
    "\n",
    "correlations = np.array(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec32418",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "---\n",
    "\n",
    "Providing an introductory demonstration of an interactive grid plot using `Bokeh`.\n",
    "\n",
    "- Writing the code to plot an example Bokeh plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "74ddf000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, a tool is imported to enable the display of text when the \n",
    "# cursor is positioned over a plot element. Additionally, a\n",
    "# data structure is imported to store plot specifications for utilizing\n",
    "# in Bokeh visualizations\n",
    "  \n",
    "from bokeh.models import HoverTool, ColumnDataSource\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d087870e",
   "metadata": {},
   "source": [
    "Importing 'product' function from 'itertools' module. The function is used to create a list containing all possible combinations of elements from 'plot_values' list to form a grid-like structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b496ccac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5)]\n"
     ]
    }
   ],
   "source": [
    "# Plotting a simple 5x5 grid of squares, alternating between two colors\n",
    "plot_values = [1,2,3,4,5]\n",
    "plot_colors = ['#0173b2', '#de8f05']\n",
    "\n",
    "from itertools import product\n",
    "\n",
    "grid = list(product(plot_values, plot_values))\n",
    "print(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "83033751",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5)\n",
      "(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5)\n"
     ]
    }
   ],
   "source": [
    "# The first value is the x coordinate, and the second value is the y coordinate.\n",
    "# The coordinate values are stored in seperate lists\n",
    "\n",
    "xs, ys = zip(*grid)\n",
    "print(xs)\n",
    "print(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d2218386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2', '#de8f05', '#0173b2']\n"
     ]
    }
   ],
   "source": [
    "# Making a list of colors, alternating between red and blue.\n",
    "\n",
    "colors = [plot_colors[i%2] for i in range(len(grid))]\n",
    "print(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5214c004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lastly, the alpha value for each point is calculated to\n",
    "# indicate its transparency level, with 0 denoting full transparency\n",
    "\n",
    "alphas = np.linspace(0, 1, len(grid))\n",
    "\n",
    "# Bokeh stores each of these values in a special dataframe, called\n",
    "# ColumnDataSource. The values for coordinates, colors, and\n",
    "# alpha values are stored in it.\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data = {\n",
    "        \"x\": xs,\n",
    "        \"y\": ys,\n",
    "        \"colors\": colors,\n",
    "        \"alphas\": alphas,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e809e38d",
   "metadata": {},
   "source": [
    "The `fig.rect()` function is used to draw a rectangular figure on the plot, with the position and size of each rectangle being determined the by `x` and `y` coordinates from `grid`.\n",
    "\n",
    "The color of each rectangle is set based on the `colors` list and the transparency from `alphas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a17671b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, output_file, show\n",
    "\n",
    "# Setting the name of the output file\n",
    "output_file(\"Basic_Example.html\", title=\"Basic Example\")\n",
    "\n",
    "\n",
    "fig = figure(tools=\"hover\")\n",
    "fig.rect(\"x\", \"y\", 0.9, 0.9, source=source, color=\"colors\",alpha=\"alphas\")\n",
    "hover = fig.select(dict(type=HoverTool))\n",
    "hover.tooltips = {\n",
    "    \"Value\": \"@x, @y\",\n",
    "    }\n",
    "\n",
    "# Displaying the plot in the output file\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5463694",
   "metadata": {},
   "source": [
    "![example_bokeh_plot.png](example_bokeh_plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3606cd",
   "metadata": {},
   "source": [
    "### Task 2 \n",
    "---\n",
    "\n",
    "Generating the names and colors required for plotting the correlation matrix of whisky flavors.\n",
    "\n",
    "- Constructing a dictionary named `region_colors` where the keys are the `regions` and the values are the corresponding `cluster_colors`\n",
    "- Displaying the content of `region_colors` dictionary by printing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0597273d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_colors = ['#0173b2', '#de8f05', '#029e73', '#d55e00', '#cc78bc', '#ca9161']\n",
    "regions = [\"Speyside\", \"Highlands\", \"Lowlands\", \"Islands\", \"Campbelltown\", \"Islay\"]\n",
    "\n",
    "region_colors = {regions[i]: cluster_colors[i] for i in range(len(regions))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2548e33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Speyside': '#0173b2',\n",
       " 'Highlands': '#de8f05',\n",
       " 'Lowlands': '#029e73',\n",
       " 'Islands': '#d55e00',\n",
       " 'Campbelltown': '#cc78bc',\n",
       " 'Islay': '#ca9161'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d5cb12",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "---\n",
    "\n",
    "The objective of this task is to create a list called `correlation_colors`, where the values correspond to colors for plotting distillery pairs based on their flavor correlations.\n",
    "\n",
    "- Establishing `correlation_colors` for each distillery pair. When the correlation is below `0.7`, the color `white` is assigned\n",
    "\n",
    "- For distillery pairs with correlations surpassing `0.7`, if they share same whisky group, then the associated color from `cluster_colors` is used. Otherwise, `lightgray` is assigned to `correlation_colors` for the particular distillery pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dfd77cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "distilleries = list(whisky.Distillery)\n",
    "correlation_colors = []\n",
    "\n",
    "for i in range(len(distilleries)):\n",
    "    \n",
    "    for j in range(len(distilleries)):\n",
    "\n",
    "        # for correlation less than 0.7\n",
    "        if correlations[i][j] < 0.7:\n",
    "            \n",
    "            # 'white' is the color appended\n",
    "            correlation_colors.append('white')\n",
    "\n",
    "        else:\n",
    "\n",
    "            # if groups are identical                                         \n",
    "            if correlations[i][j] == 1.0:                  \n",
    "                # the mutual group color is appended\n",
    "                correlation_colors.append(cluster_colors[whisky.Group[i]])\n",
    "\n",
    "            else:\n",
    "                # otherwise, 'lightgray' is appended                                      \n",
    "                correlation_colors.append('lightgray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f00dd5fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7396"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(correlation_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ccafa8",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "---\n",
    "\n",
    "- Assigning the list `correlation_colors` which contains string color values for each pair of distilleries, to the `color` attribute within `ColumnDataSource`\n",
    "\n",
    "- Defining `correlations` attribute within the `source` using the array containing the correlation of whisky flavors. To convert `correlations` from a numpy array to a list, the `flatten` method is employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8b25655",
   "metadata": {},
   "outputs": [],
   "source": [
    "source = ColumnDataSource(\n",
    "    data = {\n",
    "        \"x\": np.repeat(distilleries,len(distilleries)),\n",
    "        \"y\": list(distilleries)*len(distilleries),\n",
    "        \"colors\": correlation_colors,\n",
    "        \"correlations\": list(correlations.flatten())\n",
    "    }\n",
    ")\n",
    "\n",
    "output_file(\"Whisky Correlations.html\", title=\"Whisky Correlations\")\n",
    "fig = figure(title=\"Whisky Correlations\",\n",
    "    x_axis_location=\"above\", x_range=list(reversed(distilleries)), y_range=distilleries,\n",
    "    tools=\"hover,box_zoom,reset\")\n",
    "fig.grid.grid_line_color = None\n",
    "fig.axis.axis_line_color = None\n",
    "fig.axis.major_tick_line_color = None\n",
    "fig.axis.major_label_text_font_size = \"4pt\"\n",
    "fig.xaxis.major_label_orientation = np.pi / 3\n",
    "fig.rect('x', 'y', .9, .9, source=source,\n",
    "     color='colors', alpha='correlations')\n",
    "hover = fig.select(dict(type=HoverTool))\n",
    "hover.tooltips = {\n",
    "    \"Whiskies\": \"@x, @y\",\n",
    "    \"Correlation\": \"@correlations\",\n",
    "}\n",
    "\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50bb43a1",
   "metadata": {},
   "source": [
    "![whisky_correlation_plot.png](whisky_correlation_plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328831a2",
   "metadata": {},
   "source": [
    "### Task 6\n",
    "---\n",
    "\n",
    "The objective of this task is to define a function named `location_plot` that takes two inputs: a string `title` and a list of colors corresponding to each distillery. The function produces a Bokeh plot illustrating the geographic locations of each distillery based on their latitude and longitude.\n",
    "\n",
    "- The `region` column in the dataframe `whisky` contains information about the regional group membership of each distillery. Create a list composed of the values from `region_colors` for each distillery and assigning this list to the variable `region_cols`\n",
    "\n",
    "- Implementing the `location_plot` function to generate a plot for each distillery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bc5354c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def location_plot(title,colors):\n",
    "    \"\"\" \n",
    "    The function creates an interactive scatter plot of whisky\n",
    "    distillery locations using Bokeh.\n",
    "\n",
    "    Inputs:\n",
    "        title (str): Title of the plot and the name of the output HTML file\n",
    "        colors (list): List of color values corresponding to each distillery\n",
    "\n",
    "    Return:\n",
    "        None\n",
    "\n",
    "        The function disaplys an interactive scatter plot and saves\n",
    "        an HTML file\n",
    "    \"\"\"\n",
    "    \n",
    "    # Set up the output HTML file\n",
    "    output_file(title+\".html\")\n",
    "\n",
    "    # Creating ColumnDataSource object with \n",
    "    # location data and attributes\n",
    "    location_source = ColumnDataSource(\n",
    "        data = {\n",
    "            \"x\": whisky[\" Latitude\"],\n",
    "            \"y\": whisky[\" Longitude\"],\n",
    "            \"colors\": colors,\n",
    "            \"regions\": whisky.Region,\n",
    "            \"distilleries\": whisky.Distillery\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Create a figure with hover and save tools\n",
    "    fig = figure(title = title,\n",
    "        x_axis_location = \"above\", tools=\"hover, save\")\n",
    "    fig.plot_width  = 400\n",
    "    fig.plot_height = 500\n",
    "\n",
    "    # Create circles representing distillery locations\n",
    "    fig.circle(\"x\", \"y\", size=9, source=location_source, color='colors', line_color = None)\n",
    "    fig.xaxis.major_label_orientation = np.pi / 3\n",
    "\n",
    "    # Setting up tooltips for hover tool\n",
    "    hover = fig.select(dict(type = HoverTool))\n",
    "    hover.tooltips = {\n",
    "        \"Distillery\": \"@distilleries\",\n",
    "        \"Location\": \"(@x, @y)\"\n",
    "    }\n",
    "\n",
    "    # Displaying the plot\n",
    "    show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "50df8a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Campbelltown', 'Highlands', 'Islands', 'Islay', 'Lowlands', 'Speyside'}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(whisky[\"Region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7b4c05ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#color_array = np.repeat(list(region_colors.values()),len(whisky['Distillery']))\n",
    "region_cols = [region_colors[region] for region in whisky[\"Region\"]]\n",
    "location_plot(\"Whisky Locations and Regions\", region_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f130181",
   "metadata": {},
   "source": [
    "![distillery_locations.png](distillery_locations.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f751928",
   "metadata": {},
   "source": [
    "In this graph, we observe the distribution of whiskies across six distinct regions. Each region showcases a unique blend of flavors and characteristics that define its whiskies. Notably, the distribution of whiskies within the same region tends to follow a consistent pattern, highlighting the influence of regional factors on flavor profiles.\n",
    "\n",
    "The graph offers an intriguing perspective on how different whisky group types are distributed across various regions. While regions play a significant role in shaping whisky flavors, it's fascinating to note that the distribution of group types can vary significantly across these regions. This variation suggests a rich diversity in flavor profiles that transcends geographical boundaries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d40d8158",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "---\n",
    "\n",
    "- Creating the list `classification_cols` by associating each distillery's `Group` membership with the respective color from the `cluster_colors` list\n",
    "\n",
    "- Employing the `location_plot` functino to generate two interactive plots of distilleries: one using `region_cols` for color coding and the other using `classification_cols`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "46ee2257",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_cols = [region_colors[region] for region in whisky['Region']]\n",
    "classification_cols = [cluster_colors[ind] for ind in whisky['Group']]\n",
    "\n",
    "location_plot(\"Whisky Locations and Groups\", classification_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3f8fb8",
   "metadata": {},
   "source": [
    "![distillery_groups.png](distillery_groups.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864c2f42",
   "metadata": {},
   "source": [
    "The interplay between regions and group types becomes even more apparent in this graph. As we examine the distribution of whiskies within the same regions across different group types, a consistent regional pattern emerges. At the same time, the varying proportions of group types across regions highlight the nuanced interactions between regional influences and whisky characteristics.\n",
    "\n",
    "The graph showcases the intricate relation between regions and group types, revealing the complexity and diversity of whisky production. While regions provide a foundation for flavor development, the distribution of group types underscores the artistry of crafting whiskies with distinct characteristics. This visual representation unveils a world of flavors that reflects the ingenuity of distilleries in different regions producing similar flavors.\n",
    "\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}