18cse136_Assignment5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment- 5\n",
    "## 1. Write a python code to find the dissimilarities matrix of nominal attributes.\n",
    "## 2. Write a python code to find the dissimilarities matrix of numerical attributes.\n",
    "## 3. Write a python code to find the dissimilarities matrix of binary attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.spatial import distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</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>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/Anasuya-Sahoo/DMDW-Lab/main/student-mat.csv\"\n",
    "data = pd.read_csv(url)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.  Dissimilarities matrix of nominal attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mjob      Fjob  reason guardian\n",
       "0  at_home   teacher  course   mother\n",
       "1  at_home     other  course   father\n",
       "2  at_home     other   other   mother\n",
       "3   health  services    home   mother\n",
       "4    other     other    home   father"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal = data[['Mjob','Fjob','reason','guardian']]\n",
    "nominal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mjob      Fjob  reason guardian\n",
       "0    home   teacher  course   mother\n",
       "1    home     other  course   father\n",
       "2    home     other   other   mother\n",
       "3  health  services    home   mother\n",
       "4   other     other    home   father"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal = nominal.replace('at_home','home')\n",
    "nominal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal = nominal.astype('category')\n",
    "type(nominal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nominal['guardian'] = lb.fit_transform(nominal['guardian'])\n",
    "nominal['Mjob'] = lb.fit_transform(nominal['Mjob'])\n",
    "nominal['Fjob'] = lb.fit_transform(nominal['Fjob'])\n",
    "nominal['reason'] = lb.fit_transform(nominal['reason'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mjob  Fjob  reason  guardian\n",
       "0     1     4       0         1\n",
       "1     1     2       0         0\n",
       "2     1     2       2         1\n",
       "3     0     3       1         1\n",
       "4     2     2       1         0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nominal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom1 = np.array(nominal)\n",
    "nom1.reshape(-1,2)\n",
    "nom1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom2 = np.array(nominal)\n",
    "nom2.reshape(-1,2)\n",
    "nom2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 2.23606798, 2.82842712, ..., 2.44948974, 2.82842712,\n",
       "        3.31662479],\n",
       "       [2.23606798, 0.        , 2.23606798, ..., 2.23606798, 2.23606798,\n",
       "        1.41421356],\n",
       "       [2.82842712, 2.23606798, 0.        , ..., 2.44948974, 2.82842712,\n",
       "        2.64575131],\n",
       "       ...,\n",
       "       [2.44948974, 2.23606798, 2.44948974, ..., 0.        , 1.41421356,\n",
       "        2.23606798],\n",
       "       [2.82842712, 2.23606798, 2.82842712, ..., 1.41421356, 0.        ,\n",
       "        1.73205081],\n",
       "       [3.31662479, 1.41421356, 2.64575131, ..., 2.23606798, 1.73205081,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_matrix = distance.cdist(nom1,nom2)\n",
    "dist_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dist_matrix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Dissimilarities matrix of numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</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>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  Medu  Fedu  traveltime  studytime  failures\n",
       "0   18     4     4           2          2         0\n",
       "1   17     1     1           1          2         0\n",
       "2   15     1     1           1          2         3\n",
       "3   15     4     2           1          3         0\n",
       "4   16     3     3           1          2         0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = data[['age','Medu','Fedu','traveltime','studytime','failures']]\n",
    "numerical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num1 = np.array(numerical[['age','failures']])\n",
    "num1.reshape(-1,2)\n",
    "num1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num2 = np.array(numerical[['Fedu','Medu']])\n",
    "num2.reshape(-1,2)\n",
    "num2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14.56021978, 17.02938637, 17.02938637, ..., 17.02938637,\n",
       "        16.2788206 , 17.02938637],\n",
       "       [13.60147051, 16.03121954, 16.03121954, ..., 16.03121954,\n",
       "        15.29705854, 16.03121954],\n",
       "       [11.04536102, 14.14213562, 14.14213562, ..., 14.14213562,\n",
       "        13.        , 14.14213562],\n",
       "       ...,\n",
       "       [17.02938637, 20.09975124, 20.09975124, ..., 20.09975124,\n",
       "        19.        , 20.09975124],\n",
       "       [14.56021978, 17.02938637, 17.02938637, ..., 17.02938637,\n",
       "        16.2788206 , 17.02938637],\n",
       "       [15.5241747 , 18.02775638, 18.02775638, ..., 18.02775638,\n",
       "        17.2626765 , 18.02775638]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_matrix1 = distance.cdist(num1,num2)\n",
    "dist_matrix1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dist_matrix1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Dissimilarities matrix of binary attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</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>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>internet</th>\n",
       "      <th>higher</th>\n",
       "      <th>romantic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  schoolsup famsup paid activities nursery internet higher romantic\n",
       "0       yes     no   no         no     yes       no    yes       no\n",
       "1        no    yes   no         no      no      yes    yes       no\n",
       "2       yes     no  yes         no     yes      yes    yes       no\n",
       "3        no    yes  yes        yes     yes      yes    yes      yes\n",
       "4        no    yes  yes         no     yes       no    yes       no"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary = data[['schoolsup','famsup','paid','activities','nursery','internet','higher','romantic']]\n",
    "binary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>internet</th>\n",
       "      <th>higher</th>\n",
       "      <th>romantic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   schoolsup  famsup  paid  activities  nursery  internet  higher  romantic\n",
       "0          1       0     0           0        1         0       1         0\n",
       "1          0       1     0           0        0         1       1         0\n",
       "2          1       0     1           0        1         1       1         0\n",
       "3          0       1     1           1        1         1       1         1\n",
       "4          0       1     1           0        1         0       1         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary = binary.replace('no',0)\n",
    "binary = binary.replace('yes',1)\n",
    "binary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num1 = np.array(binary[['schoolsup','famsup']])\n",
    "num1 = num1.reshape(-1,2)\n",
    "num1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num2 = np.array(binary[['schoolsup','famsup']])\n",
    "num2 = num2.reshape(-1,2)\n",
    "num2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 395)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_matrix2 = distance.cdist(num1,num2)\n",
    "dist_matrix2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.41421356, 0.        , ..., 1.        , 1.        ,\n",
       "        1.        ],\n",
       "       [1.41421356, 0.        , 1.41421356, ..., 1.        , 1.        ,\n",
       "        1.        ],\n",
       "       [0.        , 1.41421356, 0.        , ..., 1.        , 1.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [1.        , 1.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [1.        , 1.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [1.        , 1.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_matrix2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dist_matrix2)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}