7th September - cluster analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "customers_df = pd.read_csv( r\"C:\\Users\\Home\\Desktop\\MLAP - Getting Started with Python\\k mean and clustering\\Clustering_ Segmentation - KMeans & Hierarchical\\Income Data.csv\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>income</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41100.0</td>\n",
       "      <td>48.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54100.0</td>\n",
       "      <td>28.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47800.0</td>\n",
       "      <td>46.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19100.0</td>\n",
       "      <td>40.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18200.0</td>\n",
       "      <td>35.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    income    age\n",
       "0  41100.0  48.75\n",
       "1  54100.0  28.10\n",
       "2  47800.0  46.75\n",
       "3  19100.0  40.25\n",
       "4  18200.0  35.80"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.head( 5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.lmplot( \"age\", \"income\", data=customers_df, fit_reg = False, size = 4 );\n",
    "#plt.title( \"Fig 1: Customer Segments Based on Income and Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n",
    "clusters = KMeans( 3 )\n",
    "clusters.fit( customers_df )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_df[\"clusterid\"] = clusters.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>income</th>\n",
       "      <th>age</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41100.0</td>\n",
       "      <td>48.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54100.0</td>\n",
       "      <td>28.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47800.0</td>\n",
       "      <td>46.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19100.0</td>\n",
       "      <td>40.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18200.0</td>\n",
       "      <td>35.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    income    age  clusterid\n",
       "0  41100.0  48.75          2\n",
       "1  54100.0  28.10          0\n",
       "2  47800.0  46.75          2\n",
       "3  19100.0  40.25          1\n",
       "4  18200.0  35.80          1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 339.375x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "markers = ['+','^','.']\n",
    "sn.lmplot( \"age\", \"income\",\n",
    "data=customers_df,\n",
    "hue = \"clusterid\",\n",
    "fit_reg=False,\n",
    "markers = markers,\n",
    "size = 4 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.3701637 ,  0.09718548],\n",
       "       [-1.3791283 ,  0.90602749],\n",
       "       [ 1.10388844,  0.51405021],\n",
       "       [ 0.23849387, -1.27162408],\n",
       "       [-0.35396857, -1.32762083]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "scaled_customers_df = scaler.fit_transform(customers_df[[\"age\", \"income\"]] )\n",
    "scaled_customers_df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "clusters_new = KMeans( 3, random_state=42 )\n",
    "clusters_new.fit( scaled_customers_df )\n",
    "customers_df[\"clusterid_new\"] = clusters_new.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 365x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "markers = ['+','^','.']\n",
    "sn.lmplot( \"age\", \"income\",\n",
    "data=customers_df,\n",
    "hue = \"clusterid_new\",\n",
    "fit_reg=False,\n",
    "markers = markers,\n",
    "size = 4 );\n",
    "#plt.title( \"Fig 3: Customer segments created after normalization\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.46756522e+04, 3.17004348e+01],\n",
       "       [1.81447917e+04, 3.91744792e+01],\n",
       "       [4.30539326e+04, 4.64191011e+01]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusterid</th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>31.700435</td>\n",
       "      <td>6.122122</td>\n",
       "      <td>54675.652174</td>\n",
       "      <td>2362.224320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.174479</td>\n",
       "      <td>3.626068</td>\n",
       "      <td>18144.791667</td>\n",
       "      <td>6745.241906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>46.419101</td>\n",
       "      <td>2.289620</td>\n",
       "      <td>43053.932584</td>\n",
       "      <td>3613.769632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusterid        age                  income             \n",
       "                  mean       std          mean          std\n",
       "0         0  31.700435  6.122122  54675.652174  2362.224320\n",
       "1         1  39.174479  3.626068  18144.791667  6745.241906\n",
       "2         2  46.419101  2.289620  43053.932584  3613.769632"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.groupby( 'clusterid' )['age','income'].agg( [\"mean\",'std'] ).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "beer_df = pd.read_csv( r\"C:\\Users\\Home\\Desktop\\MLAP - Getting Started with Python\\k mean and clustering\\Clustering_ Segmentation - KMeans & Hierarchical\\beer.csv\" )"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "6             Augsberger       175      24      5.5  0.40\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaled_beer_df = scaler.fit_transform( beer_df[['calories','sodium','alcohol','cost']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sn.cubehelix_palette(as_cmap=True, rot=-.3, light=1)\n",
    "sn.clustermap(scaled_beer_df, cmap=cmap, linewidths=.2,\n",
    "figsize = (8,8) );\n",
    "#plt.title( \"Fig 4: Dendrogram of Beer Dataset\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'ix'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-46e98a444eea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbeer_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m16\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ix'"
     ]
    }
   ],
   "source": [
    "beer_df.ix[[10, 16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  calories  sodium  alcohol  cost\n",
       "10  Coors       140      18      4.6  0.44\n",
       "16  Hamms       139      19      4.4  0.43"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df.iloc[[10, 16]]  # .ix is deprecated. Change ix by .loc it will work corrcetly. \n",
    "# https://stackoverflow.com/questions/59991397/attributeerror-dataframe-object-has-no-attribute-ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  calories  sodium  alcohol  cost\n",
       "2             Lowenbrau       157      15      0.9  0.48\n",
       "18  Olympia_Goled_Light        72       6      2.9  0.46"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df.iloc[[2,18]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_range = range( 1, 10 )\n",
    "cluster_errors = []\n",
    "\n",
    "for num_clusters in cluster_range:\n",
    "    clusters = KMeans( num_clusters )\n",
    "    clusters.fit( scaled_beer_df )\n",
    "    cluster_errors.append( clusters.inertia_ )\n",
    "    \n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot( cluster_range, cluster_errors, marker = \"o\" );\n",
    "#plt.title( \"Fig 5: Elbow Diagram\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaled_beer_df = scaler.fit_transform( beer_df[['calories','sodium','alcohol','cost']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3\n",
    "clusters = KMeans( k, random_state = 42 )\n",
    "clusters.fit( scaled_beer_df )\n",
    "beer_df[\"clusterid\"] = clusters.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  calories  sodium  alcohol  cost  clusterid\n",
       "2             Lowenbrau       157      15      0.9  0.48          0\n",
       "8           Miller_Lite        99      10      4.3  0.43          0\n",
       "9       Budweiser_Light       113       8      3.7  0.40          0\n",
       "11          Coors_Light       102      15      4.1  0.46          0\n",
       "12       Michelob_Light       135      11      4.2  0.50          0\n",
       "15    Pabst_Extra_Light        68      15      2.3  0.38          0\n",
       "18  Olympia_Goled_Light        72       6      2.9  0.46          0\n",
       "19        Schlitz_Light        97       7      4.2  0.47          0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.clusterid == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  clusterid\n",
       "0              Budweiser       144      15      4.7  0.43          1\n",
       "1                Schlitz       151      19      4.9  0.43          1\n",
       "5          Old_Milwaukee       145      23      4.6  0.28          1\n",
       "6             Augsberger       175      24      5.5  0.40          1\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42          1\n",
       "10                 Coors       140      18      4.6  0.44          1\n",
       "16                 Hamms       139      19      4.4  0.43          1\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43          1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.clusterid == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  calories  sodium  alcohol  cost  clusterid\n",
       "3   Kronenbourg       170       7      5.2  0.73          2\n",
       "4      Heineken       152      11      5.0  0.77          2\n",
       "13        Becks       150      19      4.7  0.76          2\n",
       "14        Kirin       149       6      5.0  0.79          2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.clusterid == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_clusters = AgglomerativeClustering( 3 )\n",
    "h_clusters.fit( scaled_beer_df )\n",
    "beer_df[\"h_clusterid\"] = h_clusters.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "      <th>h_clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  calories  sodium  alcohol  cost  clusterid  \\\n",
       "2             Lowenbrau       157      15      0.9  0.48          0   \n",
       "8           Miller_Lite        99      10      4.3  0.43          0   \n",
       "9       Budweiser_Light       113       8      3.7  0.40          0   \n",
       "11          Coors_Light       102      15      4.1  0.46          0   \n",
       "12       Michelob_Light       135      11      4.2  0.50          0   \n",
       "15    Pabst_Extra_Light        68      15      2.3  0.38          0   \n",
       "18  Olympia_Goled_Light        72       6      2.9  0.46          0   \n",
       "19        Schlitz_Light        97       7      4.2  0.47          0   \n",
       "\n",
       "    h_clusterid  \n",
       "2             0  \n",
       "8             0  \n",
       "9             0  \n",
       "11            0  \n",
       "12            0  \n",
       "15            0  \n",
       "18            0  \n",
       "19            0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.h_clusterid == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "      <th>h_clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  clusterid  \\\n",
       "0              Budweiser       144      15      4.7  0.43          1   \n",
       "1                Schlitz       151      19      4.9  0.43          1   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28          1   \n",
       "6             Augsberger       175      24      5.5  0.40          1   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42          1   \n",
       "10                 Coors       140      18      4.6  0.44          1   \n",
       "16                 Hamms       139      19      4.4  0.43          1   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43          1   \n",
       "\n",
       "    h_clusterid  \n",
       "0             1  \n",
       "1             1  \n",
       "5             1  \n",
       "6             1  \n",
       "7             1  \n",
       "10            1  \n",
       "16            1  \n",
       "17            1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.h_clusterid == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>clusterid</th>\n",
       "      <th>h_clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
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       "      <td>0.77</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  calories  sodium  alcohol  cost  clusterid  h_clusterid\n",
       "3   Kronenbourg       170       7      5.2  0.73          2            2\n",
       "4      Heineken       152      11      5.0  0.77          2            2\n",
       "13        Becks       150      19      4.7  0.76          2            2\n",
       "14        Kirin       149       6      5.0  0.79          2            2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer_df[beer_df.h_clusterid == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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