T6 - 4 - Clustering - Clustering completo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering con Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('../GitHub/python-ml-course/datasets/wine/winequality-red.csv',sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 10.,   0.,  53.,   0., 681.,   0., 638.,   0., 199.,  18.]),\n",
       " array([3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.884500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.779245</td>\n",
       "      <td>0.693962</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>2.694340</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>12.264151</td>\n",
       "      <td>36.245283</td>\n",
       "      <td>0.996542</td>\n",
       "      <td>3.381509</td>\n",
       "      <td>0.596415</td>\n",
       "      <td>10.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.167254</td>\n",
       "      <td>0.577041</td>\n",
       "      <td>0.243686</td>\n",
       "      <td>2.528855</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>16.983847</td>\n",
       "      <td>56.513950</td>\n",
       "      <td>0.997104</td>\n",
       "      <td>3.304949</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>9.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.347179</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>2.477194</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>15.711599</td>\n",
       "      <td>40.869906</td>\n",
       "      <td>0.996615</td>\n",
       "      <td>3.318072</td>\n",
       "      <td>0.675329</td>\n",
       "      <td>10.629519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.872362</td>\n",
       "      <td>0.403920</td>\n",
       "      <td>0.375176</td>\n",
       "      <td>2.720603</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>14.045226</td>\n",
       "      <td>35.020101</td>\n",
       "      <td>0.996104</td>\n",
       "      <td>3.290754</td>\n",
       "      <td>0.741256</td>\n",
       "      <td>11.465913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.566667</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>2.577778</td>\n",
       "      <td>0.068444</td>\n",
       "      <td>13.277778</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>0.995212</td>\n",
       "      <td>3.267222</td>\n",
       "      <td>0.767778</td>\n",
       "      <td>12.094444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality                                                                 \n",
       "3             8.360000          0.884500     0.171000        2.635000   \n",
       "4             7.779245          0.693962     0.174151        2.694340   \n",
       "5             8.167254          0.577041     0.243686        2.528855   \n",
       "6             8.347179          0.497484     0.273824        2.477194   \n",
       "7             8.872362          0.403920     0.375176        2.720603   \n",
       "8             8.566667          0.423333     0.391111        2.577778   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality                                                                   \n",
       "3         0.122500            11.000000             24.900000  0.997464   \n",
       "4         0.090679            12.264151             36.245283  0.996542   \n",
       "5         0.092736            16.983847             56.513950  0.997104   \n",
       "6         0.084956            15.711599             40.869906  0.996615   \n",
       "7         0.076588            14.045226             35.020101  0.996104   \n",
       "8         0.068444            13.277778             33.444444  0.995212   \n",
       "\n",
       "               pH  sulphates    alcohol  \n",
       "quality                                  \n",
       "3        3.398000   0.570000   9.955000  \n",
       "4        3.381509   0.596415  10.265094  \n",
       "5        3.304949   0.620969   9.899706  \n",
       "6        3.318072   0.675329  10.629519  \n",
       "7        3.290754   0.741256  11.465913  \n",
       "8        3.267222   0.767778  12.094444  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"quality\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalización de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247788</td>\n",
       "      <td>0.397260</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.106845</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.098940</td>\n",
       "      <td>0.567548</td>\n",
       "      <td>0.606299</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.283186</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.116438</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.215548</td>\n",
       "      <td>0.494126</td>\n",
       "      <td>0.362205</td>\n",
       "      <td>0.209581</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283186</td>\n",
       "      <td>0.438356</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.133556</td>\n",
       "      <td>0.197183</td>\n",
       "      <td>0.169611</td>\n",
       "      <td>0.508811</td>\n",
       "      <td>0.409449</td>\n",
       "      <td>0.191617</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.584071</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.105175</td>\n",
       "      <td>0.225352</td>\n",
       "      <td>0.190813</td>\n",
       "      <td>0.582232</td>\n",
       "      <td>0.330709</td>\n",
       "      <td>0.149701</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.247788</td>\n",
       "      <td>0.397260</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.106845</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.098940</td>\n",
       "      <td>0.567548</td>\n",
       "      <td>0.606299</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>0.141593</td>\n",
       "      <td>0.328767</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.075342</td>\n",
       "      <td>0.130217</td>\n",
       "      <td>0.436620</td>\n",
       "      <td>0.134276</td>\n",
       "      <td>0.354626</td>\n",
       "      <td>0.559055</td>\n",
       "      <td>0.149701</td>\n",
       "      <td>0.323077</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.294521</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.089041</td>\n",
       "      <td>0.083472</td>\n",
       "      <td>0.535211</td>\n",
       "      <td>0.159011</td>\n",
       "      <td>0.370778</td>\n",
       "      <td>0.614173</td>\n",
       "      <td>0.257485</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>0.150442</td>\n",
       "      <td>0.267123</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.106845</td>\n",
       "      <td>0.394366</td>\n",
       "      <td>0.120141</td>\n",
       "      <td>0.416300</td>\n",
       "      <td>0.535433</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.359589</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.075342</td>\n",
       "      <td>0.105175</td>\n",
       "      <td>0.436620</td>\n",
       "      <td>0.134276</td>\n",
       "      <td>0.396476</td>\n",
       "      <td>0.653543</td>\n",
       "      <td>0.227545</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>0.123894</td>\n",
       "      <td>0.130137</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.184932</td>\n",
       "      <td>0.091820</td>\n",
       "      <td>0.239437</td>\n",
       "      <td>0.127208</td>\n",
       "      <td>0.397944</td>\n",
       "      <td>0.511811</td>\n",
       "      <td>0.197605</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0          0.247788          0.397260         0.00        0.068493   0.106845   \n",
       "1          0.283186          0.520548         0.00        0.116438   0.143573   \n",
       "2          0.283186          0.438356         0.04        0.095890   0.133556   \n",
       "3          0.584071          0.109589         0.56        0.068493   0.105175   \n",
       "4          0.247788          0.397260         0.00        0.068493   0.106845   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594       0.141593          0.328767         0.08        0.075342   0.130217   \n",
       "1595       0.115044          0.294521         0.10        0.089041   0.083472   \n",
       "1596       0.150442          0.267123         0.13        0.095890   0.106845   \n",
       "1597       0.115044          0.359589         0.12        0.075342   0.105175   \n",
       "1598       0.123894          0.130137         0.47        0.184932   0.091820   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
       "0                0.140845              0.098940  0.567548  0.606299   \n",
       "1                0.338028              0.215548  0.494126  0.362205   \n",
       "2                0.197183              0.169611  0.508811  0.409449   \n",
       "3                0.225352              0.190813  0.582232  0.330709   \n",
       "4                0.140845              0.098940  0.567548  0.606299   \n",
       "...                   ...                   ...       ...       ...   \n",
       "1594             0.436620              0.134276  0.354626  0.559055   \n",
       "1595             0.535211              0.159011  0.370778  0.614173   \n",
       "1596             0.394366              0.120141  0.416300  0.535433   \n",
       "1597             0.436620              0.134276  0.396476  0.653543   \n",
       "1598             0.239437              0.127208  0.397944  0.511811   \n",
       "\n",
       "      sulphates   alcohol  quality  \n",
       "0      0.137725  0.153846      0.4  \n",
       "1      0.209581  0.215385      0.4  \n",
       "2      0.191617  0.215385      0.4  \n",
       "3      0.149701  0.215385      0.6  \n",
       "4      0.137725  0.153846      0.4  \n",
       "...         ...       ...      ...  \n",
       "1594   0.149701  0.323077      0.4  \n",
       "1595   0.257485  0.430769      0.6  \n",
       "1596   0.251497  0.400000      0.6  \n",
       "1597   0.227545  0.276923      0.4  \n",
       "1598   0.197605  0.400000      0.6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm=(df-df.min())/(df.max()-df.min())\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering jerárquico con scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "clus=AgglomerativeClustering(n_clusters=6,linkage=\"ward\").fit(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Número de vinos en cada cluster')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_h=pd.Series(clus.labels_)\n",
    "plt.hist(md_h)\n",
    "plt.title(\"Histograma de los clusters\")\n",
    "plt.xlabel('ID del cluster')\n",
    "plt.ylabel('Número de vinos en cada cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estos son todos y cada uno de los hijos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    4],\n",
       "       [ 135,  140],\n",
       "       [ 750,  751],\n",
       "       ...,\n",
       "       [3179, 3191],\n",
       "       [3192, 3193],\n",
       "       [3194, 3195]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus.children_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram,linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z=linkage(df_norm,\"ward\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "plt.title(\"Dendograma para el clustering jerárquico truncado\")\n",
    "plt.xlabel('ID del vino')\n",
    "plt.ylabel(\"Distancia\")\n",
    "dendrogram(Z,leaf_rotation=90,leaf_font_size=12,truncate_mode=\"lastp\",\n",
    "           p=6,show_leaf_counts=True,show_contracted=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=KMeans(n_clusters=6)\n",
    "model.fit(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 4, 4, 5], dtype=int32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "md_k=pd.Series(model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>clust_h</th>\n",
       "      <th>clust_k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247788</td>\n",
       "      <td>0.397260</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.106845</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.098940</td>\n",
       "      <td>0.567548</td>\n",
       "      <td>0.606299</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.283186</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.116438</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.215548</td>\n",
       "      <td>0.494126</td>\n",
       "      <td>0.362205</td>\n",
       "      <td>0.209581</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283186</td>\n",
       "      <td>0.438356</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.133556</td>\n",
       "      <td>0.197183</td>\n",
       "      <td>0.169611</td>\n",
       "      <td>0.508811</td>\n",
       "      <td>0.409449</td>\n",
       "      <td>0.191617</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.584071</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.105175</td>\n",
       "      <td>0.225352</td>\n",
       "      <td>0.190813</td>\n",
       "      <td>0.582232</td>\n",
       "      <td>0.330709</td>\n",
       "      <td>0.149701</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.247788</td>\n",
       "      <td>0.397260</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.068493</td>\n",
       "      <td>0.106845</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.098940</td>\n",
       "      <td>0.567548</td>\n",
       "      <td>0.606299</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0       0.247788          0.397260         0.00        0.068493   0.106845   \n",
       "1       0.283186          0.520548         0.00        0.116438   0.143573   \n",
       "2       0.283186          0.438356         0.04        0.095890   0.133556   \n",
       "3       0.584071          0.109589         0.56        0.068493   0.105175   \n",
       "4       0.247788          0.397260         0.00        0.068493   0.106845   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
       "0             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
       "1             0.338028              0.215548  0.494126  0.362205   0.209581   \n",
       "2             0.197183              0.169611  0.508811  0.409449   0.191617   \n",
       "3             0.225352              0.190813  0.582232  0.330709   0.149701   \n",
       "4             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
       "\n",
       "    alcohol  quality  clust_h  clust_k  \n",
       "0  0.153846      0.4        2        2  \n",
       "1  0.215385      0.4        2        2  \n",
       "2  0.215385      0.4        2        2  \n",
       "3  0.215385      0.6        3        1  \n",
       "4  0.153846      0.4        2        2  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm[\"clust_h\"]=md_h\n",
    "df_norm[\"clust_k\"]=md_k\n",
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "md_h=pd.Series(clus.labels_)\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "plt.suptitle('Histograma de los clusters')\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.hist(md_h)\n",
    "plt.title(\"Método jerárquico\")\n",
    "plt.xlabel('ID del cluster')\n",
    "plt.ylabel('Número de vinos en cada cluster')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(md_k,color='g')\n",
    "plt.title(\"Método K-means\")\n",
    "plt.xlabel('ID del cluster')\n",
    "plt.ylabel('Número de vinos en cada cluster')\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "baricentro_K=pd.DataFrame(model.cluster_centers_,columns=df_norm.columns.values.tolist()[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.316574</td>\n",
       "      <td>0.277149</td>\n",
       "      <td>0.304574</td>\n",
       "      <td>0.152025</td>\n",
       "      <td>0.126989</td>\n",
       "      <td>0.380482</td>\n",
       "      <td>0.297856</td>\n",
       "      <td>0.539048</td>\n",
       "      <td>0.435927</td>\n",
       "      <td>0.178904</td>\n",
       "      <td>0.214010</td>\n",
       "      <td>0.456151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.576777</td>\n",
       "      <td>0.202416</td>\n",
       "      <td>0.507266</td>\n",
       "      <td>0.131876</td>\n",
       "      <td>0.130315</td>\n",
       "      <td>0.137434</td>\n",
       "      <td>0.091003</td>\n",
       "      <td>0.653138</td>\n",
       "      <td>0.335691</td>\n",
       "      <td>0.224457</td>\n",
       "      <td>0.290605</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263954</td>\n",
       "      <td>0.359043</td>\n",
       "      <td>0.123484</td>\n",
       "      <td>0.091326</td>\n",
       "      <td>0.122836</td>\n",
       "      <td>0.144824</td>\n",
       "      <td>0.107683</td>\n",
       "      <td>0.492112</td>\n",
       "      <td>0.486484</td>\n",
       "      <td>0.155429</td>\n",
       "      <td>0.220165</td>\n",
       "      <td>0.439764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.337168</td>\n",
       "      <td>0.292237</td>\n",
       "      <td>0.481333</td>\n",
       "      <td>0.075571</td>\n",
       "      <td>0.537896</td>\n",
       "      <td>0.209390</td>\n",
       "      <td>0.210718</td>\n",
       "      <td>0.514391</td>\n",
       "      <td>0.233596</td>\n",
       "      <td>0.590020</td>\n",
       "      <td>0.165128</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176106</td>\n",
       "      <td>0.324330</td>\n",
       "      <td>0.089130</td>\n",
       "      <td>0.094163</td>\n",
       "      <td>0.096080</td>\n",
       "      <td>0.248561</td>\n",
       "      <td>0.122922</td>\n",
       "      <td>0.325876</td>\n",
       "      <td>0.577337</td>\n",
       "      <td>0.185160</td>\n",
       "      <td>0.486377</td>\n",
       "      <td>0.610435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.362900</td>\n",
       "      <td>0.159751</td>\n",
       "      <td>0.423295</td>\n",
       "      <td>0.105474</td>\n",
       "      <td>0.105434</td>\n",
       "      <td>0.163500</td>\n",
       "      <td>0.083861</td>\n",
       "      <td>0.408491</td>\n",
       "      <td>0.418635</td>\n",
       "      <td>0.235575</td>\n",
       "      <td>0.491294</td>\n",
       "      <td>0.696899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0       0.316574          0.277149     0.304574        0.152025   0.126989   \n",
       "1       0.576777          0.202416     0.507266        0.131876   0.130315   \n",
       "2       0.263954          0.359043     0.123484        0.091326   0.122836   \n",
       "3       0.337168          0.292237     0.481333        0.075571   0.537896   \n",
       "4       0.176106          0.324330     0.089130        0.094163   0.096080   \n",
       "5       0.362900          0.159751     0.423295        0.105474   0.105434   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
       "0             0.380482              0.297856  0.539048  0.435927   0.178904   \n",
       "1             0.137434              0.091003  0.653138  0.335691   0.224457   \n",
       "2             0.144824              0.107683  0.492112  0.486484   0.155429   \n",
       "3             0.209390              0.210718  0.514391  0.233596   0.590020   \n",
       "4             0.248561              0.122922  0.325876  0.577337   0.185160   \n",
       "5             0.163500              0.083861  0.408491  0.418635   0.235575   \n",
       "\n",
       "    alcohol   quality  \n",
       "0  0.214010  0.456151  \n",
       "1  0.290605  0.550000  \n",
       "2  0.220165  0.439764  \n",
       "3  0.165128  0.466667  \n",
       "4  0.486377  0.610435  \n",
       "5  0.491294  0.696899  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baricentro_K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretación final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>clust_h</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clust_k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.316574</td>\n",
       "      <td>0.277149</td>\n",
       "      <td>0.304574</td>\n",
       "      <td>0.152025</td>\n",
       "      <td>0.126989</td>\n",
       "      <td>0.380482</td>\n",
       "      <td>0.297856</td>\n",
       "      <td>0.539048</td>\n",
       "      <td>0.435927</td>\n",
       "      <td>0.178904</td>\n",
       "      <td>0.214010</td>\n",
       "      <td>0.456151</td>\n",
       "      <td>1.044164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.576777</td>\n",
       "      <td>0.202416</td>\n",
       "      <td>0.507266</td>\n",
       "      <td>0.131876</td>\n",
       "      <td>0.130315</td>\n",
       "      <td>0.137434</td>\n",
       "      <td>0.091003</td>\n",
       "      <td>0.653138</td>\n",
       "      <td>0.335691</td>\n",
       "      <td>0.224457</td>\n",
       "      <td>0.290605</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1.972656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263954</td>\n",
       "      <td>0.359043</td>\n",
       "      <td>0.123484</td>\n",
       "      <td>0.091326</td>\n",
       "      <td>0.122836</td>\n",
       "      <td>0.144824</td>\n",
       "      <td>0.107683</td>\n",
       "      <td>0.492112</td>\n",
       "      <td>0.486484</td>\n",
       "      <td>0.155429</td>\n",
       "      <td>0.220165</td>\n",
       "      <td>0.439764</td>\n",
       "      <td>2.005906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.337168</td>\n",
       "      <td>0.292237</td>\n",
       "      <td>0.481333</td>\n",
       "      <td>0.075571</td>\n",
       "      <td>0.537896</td>\n",
       "      <td>0.209390</td>\n",
       "      <td>0.210718</td>\n",
       "      <td>0.514391</td>\n",
       "      <td>0.233596</td>\n",
       "      <td>0.590020</td>\n",
       "      <td>0.165128</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176106</td>\n",
       "      <td>0.324330</td>\n",
       "      <td>0.089130</td>\n",
       "      <td>0.094163</td>\n",
       "      <td>0.096080</td>\n",
       "      <td>0.248561</td>\n",
       "      <td>0.122922</td>\n",
       "      <td>0.325876</td>\n",
       "      <td>0.577337</td>\n",
       "      <td>0.185160</td>\n",
       "      <td>0.486377</td>\n",
       "      <td>0.610435</td>\n",
       "      <td>3.417391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.362900</td>\n",
       "      <td>0.159751</td>\n",
       "      <td>0.423295</td>\n",
       "      <td>0.105474</td>\n",
       "      <td>0.105434</td>\n",
       "      <td>0.163500</td>\n",
       "      <td>0.083861</td>\n",
       "      <td>0.408491</td>\n",
       "      <td>0.418635</td>\n",
       "      <td>0.235575</td>\n",
       "      <td>0.491294</td>\n",
       "      <td>0.696899</td>\n",
       "      <td>0.108527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "clust_k                                                                 \n",
       "0             0.316574          0.277149     0.304574        0.152025   \n",
       "1             0.576777          0.202416     0.507266        0.131876   \n",
       "2             0.263954          0.359043     0.123484        0.091326   \n",
       "3             0.337168          0.292237     0.481333        0.075571   \n",
       "4             0.176106          0.324330     0.089130        0.094163   \n",
       "5             0.362900          0.159751     0.423295        0.105474   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "clust_k                                                                   \n",
       "0         0.126989             0.380482              0.297856  0.539048   \n",
       "1         0.130315             0.137434              0.091003  0.653138   \n",
       "2         0.122836             0.144824              0.107683  0.492112   \n",
       "3         0.537896             0.209390              0.210718  0.514391   \n",
       "4         0.096080             0.248561              0.122922  0.325876   \n",
       "5         0.105434             0.163500              0.083861  0.408491   \n",
       "\n",
       "               pH  sulphates   alcohol   quality   clust_h  \n",
       "clust_k                                                     \n",
       "0        0.435927   0.178904  0.214010  0.456151  1.044164  \n",
       "1        0.335691   0.224457  0.290605  0.550000  1.972656  \n",
       "2        0.486484   0.155429  0.220165  0.439764  2.005906  \n",
       "3        0.233596   0.590020  0.165128  0.466667  4.900000  \n",
       "4        0.577337   0.185160  0.486377  0.610435  3.417391  \n",
       "5        0.418635   0.235575  0.491294  0.696899  0.108527  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.groupby('clust_k').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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