CustomerSegmentation/4_Hierarchical Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Segmentation using Hierarchical Clustering\n",
    "This script uses the bottom-up agglomerative  approach. Each data point has its own cluster, then combining similar items to form a larger cluster as one moves up the hierarchy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "            0         1         2         3         4         5         6  \\\n",
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       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_path = \"../Outputs/customer_order_train_reduced.csv\"\n",
    "df = pd.read_csv(df_path)\n",
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compare with K-mean and DBSCAN, we choose cluster number equals to 5 for this one.\n",
    "\n",
    "In addition, since we need to group-up the datapoints instead of divide them, we will choose 'Agglomerative Clustering' approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within 'Agglomerative Clustering' we have several methods*, which are slightly different in rules in regards of how we group the datapoints and how to differentiate each group.\n",
    "\n",
    "*method:\n",
    "    1. Single Linkage. In single linkage hierarchical clustering, the distance between two clusters is defined as the shortest distance between two points in each cluster. For example, the distance between clusters “r” and “s” to the left is equal to the length of the arrow between their two closest points.\n",
    "    2. Complete Linkage. In complete linkage hierarchical clustering, the distance between two clusters is defined as the longest distance between two points in each cluster. For example, the distance between clusters “r” and “s” to the left is equal to the length of the arrow between their two furthest points.\n",
    "    3. Average Linkage. In average linkage hierarchical clustering, the distance between two clusters is defined as the average distance between each point in one cluster to every point in the other cluster. For example, the distance between clusters “r” and “s” to the left is equal to the average length each arrow between connecting the points of one cluster to the other.\n",
    "    4. Ward Method. Ward’s method says that the distance between two clusters, A and B, is how much the sum of squares will increase when we merge them. (to minimize the std in each cluster)\n",
    "    \n",
    "\n",
    "We will try each method below and see if it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0: 20164\n",
      "cluster 1: 1\n",
      "cluster 2: 1\n",
      "cluster 3: 1\n",
      "cluster 4: 1\n"
     ]
    }
   ],
   "source": [
    "# Single Linkage\n",
    "# build the model\n",
    "ac_model_single = AgglomerativeClustering(n_clusters=5, linkage='single').fit(df)\n",
    "\n",
    "# print our the number of observations per cluster\n",
    "for c in np.unique(ac_model_single.labels_):\n",
    "        print('cluster {}: {}'.format(c, np.sum(ac_model_single.labels_==c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0: 20046\n",
      "cluster 1: 17\n",
      "cluster 2: 2\n",
      "cluster 3: 22\n",
      "cluster 4: 81\n"
     ]
    }
   ],
   "source": [
    "# Complete Linkage\n",
    "# build the model\n",
    "ac_model_complete = AgglomerativeClustering(n_clusters=5, linkage='complete').fit(df)\n",
    "\n",
    "# print our the number of observations per cluster\n",
    "for c in np.unique(ac_model_complete.labels_):\n",
    "        print('cluster {}: {}'.format(c, np.sum(ac_model_complete.labels_==c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0: 20161\n",
      "cluster 1: 3\n",
      "cluster 2: 2\n",
      "cluster 3: 1\n",
      "cluster 4: 1\n"
     ]
    }
   ],
   "source": [
    "# Average Linkage\n",
    "# build the model\n",
    "ac_model_ave = AgglomerativeClustering(n_clusters=5, linkage='average').fit(df)\n",
    "\n",
    "# print our the number of observations per cluster\n",
    "for c in np.unique(ac_model_ave.labels_):\n",
    "        print('cluster {}: {}'.format(c, np.sum(ac_model_ave.labels_==c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0: 18402\n",
      "cluster 1: 69\n",
      "cluster 2: 1220\n",
      "cluster 3: 234\n",
      "cluster 4: 243\n"
     ]
    }
   ],
   "source": [
    "# Ward Method\n",
    "# build the model\n",
    "ac_model_ward = AgglomerativeClustering(n_clusters=5, linkage='ward').fit(df)\n",
    "\n",
    "# print our the number of observations per cluster\n",
    "for c in np.unique(ac_model_ward.labels_):\n",
    "        print('cluster {}: {}'.format(c, np.sum(ac_model_ward.labels_==c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1728x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the clusters\n",
    "n_clusters_ = 5\n",
    "X = df.values\n",
    "labels = ac_model_ward.labels_\n",
    "plt.figure(figsize=(24, 8))\n",
    "for i in range(n_clusters_):\n",
    "    one_cluster = X[labels == i]\n",
    "    plt.plot(one_cluster[:,0],one_cluster[:,1],'o')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot dendrogram\n",
    "X = df.values\n",
    "linked = linkage(X, 'ward')\n",
    "\n",
    "plt.figure(figsize=(24, 8))\n",
    "dendrogram(linked,\n",
    "            orientation='top',\n",
    "            # labels=labelList,\n",
    "            distance_sort='descending',\n",
    "            show_leaf_counts=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the four methos we use, it looks like that Ward Linkage provide us the best result(instead of having one cluster holds over 99% datapoint, we have several clusters with their cluster size over 100).\n",
    "\n",
    "Next step, we will use the results from Ward Linkage Method and find out what item(s) fall into each cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ac_build_ward_n(data_train, n_cluster, data_predict):\n",
    "    # build model\n",
    "    ac_model_ward = AgglomerativeClustering(n_clusters=5, linkage='ward')\n",
    "    \n",
    "    # predict customer group\n",
    "    cust_group = ac_model_ward.fit_predict(data_predict)\n",
    "    \n",
    "    # print number of customers per cluster\n",
    "    print('Cluster Group | Total Customer')\n",
    "    print(pd.Series(cust_group).value_counts())\n",
    "    \n",
    "    # save model\n",
    "    pickle.dump(ac_model_ward, open(f'ac_model_ward_{n_cluster}.sav', 'wb'))\n",
    "    \n",
    "    return cust_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster Group | Total Customer\n",
      "0    18402\n",
      "2     1220\n",
      "4      243\n",
      "3      234\n",
      "1       69\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "customer_cluster = ac_build_ward_n(df, 5, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>air fresheners candles</th>\n",
       "      <th>asian foods</th>\n",
       "      <th>baby accessories</th>\n",
       "      <th>baby bath body care</th>\n",
       "      <th>baby food formula</th>\n",
       "      <th>bakery desserts</th>\n",
       "      <th>baking ingredients</th>\n",
       "      <th>baking supplies decor</th>\n",
       "      <th>beauty</th>\n",
       "      <th>beers coolers</th>\n",
       "      <th>...</th>\n",
       "      <th>international</th>\n",
       "      <th>meat seafood</th>\n",
       "      <th>missing_y</th>\n",
       "      <th>other_y</th>\n",
       "      <th>pantry</th>\n",
       "      <th>personal care</th>\n",
       "      <th>pets</th>\n",
       "      <th>produce</th>\n",
       "      <th>snacks</th>\n",
       "      <th>organic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
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       "      <td>152</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>85028</th>\n",
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       "      <td>8</td>\n",
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       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>49</td>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>72168</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>93</td>\n",
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       "    <tr>\n",
       "      <th>142535</th>\n",
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       "      <th>13607</th>\n",
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       "      <td>0</td>\n",
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       "      <td>9</td>\n",
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       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
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       "      <th>164881</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
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       "      <th>1918</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>34069</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>20168 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         air fresheners candles  asian foods  baby accessories  \\\n",
       "user_id                                                          \n",
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       "\n",
       "         baby bath body care  baby food formula  bakery desserts  \\\n",
       "user_id                                                            \n",
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       "\n",
       "         baking ingredients  baking supplies decor  beauty  beers coolers  \\\n",
       "user_id                                                                     \n",
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       "...                     ...                    ...     ...            ...   \n",
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       "1918                      0                      0       0              0   \n",
       "34069                     5                      1       0              0   \n",
       "\n",
       "         ...  international  meat seafood  missing_y  other_y  pantry  \\\n",
       "user_id  ...                                                            \n",
       "19510    ...              0             1          0        0       5   \n",
       "147319   ...              3             3          1        0       7   \n",
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       "142535   ...              3             6          0        0       8   \n",
       "...      ...            ...           ...        ...      ...     ...   \n",
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       "1918     ...              1             2          0        0       3   \n",
       "34069    ...              2             6          0        0      18   \n",
       "\n",
       "         personal care  pets  produce  snacks  organic  \n",
       "user_id                                                 \n",
       "19510               15     0       76      14      152  \n",
       "147319               0     0       18       6       23  \n",
       "85028                0     0      173      49       68  \n",
       "72168                3     0       43      31       93  \n",
       "142535               4     0       12       5        1  \n",
       "...                ...   ...      ...     ...      ...  \n",
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       "34069                8     0       61      28        4  \n",
       "\n",
       "[20168 rows x 157 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the original dataset\n",
    "cust_train = pd.read_csv('../Outputs/customer_order_train.csv', index_col='user_id')\n",
    "cust_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         air fresheners candles  asian foods  baby accessories  \\\n",
       "user_id                                                          \n",
       "19510                         0            0                 0   \n",
       "147319                        0            1                 0   \n",
       "85028                         0            8                 0   \n",
       "72168                         0            0                 0   \n",
       "142535                        0            1                 0   \n",
       "\n",
       "         baby bath body care  baby food formula  bakery desserts  \\\n",
       "user_id                                                            \n",
       "19510                      0                  0                0   \n",
       "147319                     0                  0                1   \n",
       "85028                      3                126                0   \n",
       "72168                      0                  0                0   \n",
       "142535                     0                  0                0   \n",
       "\n",
       "         baking ingredients  baking supplies decor  beauty  beers coolers  \\\n",
       "user_id                                                                     \n",
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       "72168                     4                      0       0              0   \n",
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       "\n",
       "         ...  meat seafood  missing_y  other_y  pantry  personal care  pets  \\\n",
       "user_id  ...                                                                  \n",
       "19510    ...             1          0        0       5             15     0   \n",
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       "72168    ...             2          0        0      13              3     0   \n",
       "142535   ...             6          0        0       8              4     0   \n",
       "\n",
       "         produce  snacks  organic  customer type  \n",
       "user_id                                           \n",
       "19510         76      14      152              0  \n",
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       "\n",
       "[5 rows x 158 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feed the cluster back to the dataset \n",
    "cust_train['customer type'] = customer_cluster\n",
    "cust_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_train['customer type'].to_csv('../Outputs/customer_group_HC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>beauty</th>\n",
       "      <th>beers coolers</th>\n",
       "      <th>...</th>\n",
       "      <th>international</th>\n",
       "      <th>meat seafood</th>\n",
       "      <th>missing_y</th>\n",
       "      <th>other_y</th>\n",
       "      <th>pantry</th>\n",
       "      <th>personal care</th>\n",
       "      <th>pets</th>\n",
       "      <th>produce</th>\n",
       "      <th>snacks</th>\n",
       "      <th>organic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer type</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0.176285</td>\n",
       "      <td>1.504836</td>\n",
       "      <td>0.050212</td>\n",
       "      <td>0.056081</td>\n",
       "      <td>0.695305</td>\n",
       "      <td>0.291273</td>\n",
       "      <td>2.819422</td>\n",
       "      <td>0.185415</td>\n",
       "      <td>0.048473</td>\n",
       "      <td>0.364417</td>\n",
       "      <td>...</td>\n",
       "      <td>2.441854</td>\n",
       "      <td>6.149658</td>\n",
       "      <td>0.644550</td>\n",
       "      <td>0.296816</td>\n",
       "      <td>16.282415</td>\n",
       "      <td>3.783882</td>\n",
       "      <td>0.998424</td>\n",
       "      <td>82.799804</td>\n",
       "      <td>25.430062</td>\n",
       "      <td>88.274318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.202899</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>1.739130</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>185.869565</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>6.318841</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>0.144928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.956522</td>\n",
       "      <td>16.318841</td>\n",
       "      <td>2.652174</td>\n",
       "      <td>0.971014</td>\n",
       "      <td>40.811594</td>\n",
       "      <td>12.405797</td>\n",
       "      <td>0.942029</td>\n",
       "      <td>253.362319</td>\n",
       "      <td>85.405797</td>\n",
       "      <td>458.449275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.081967</td>\n",
       "      <td>2.031148</td>\n",
       "      <td>0.424590</td>\n",
       "      <td>0.300820</td>\n",
       "      <td>26.332787</td>\n",
       "      <td>0.295082</td>\n",
       "      <td>4.152459</td>\n",
       "      <td>0.191803</td>\n",
       "      <td>0.077049</td>\n",
       "      <td>0.070492</td>\n",
       "      <td>...</td>\n",
       "      <td>3.360656</td>\n",
       "      <td>10.074590</td>\n",
       "      <td>0.823770</td>\n",
       "      <td>0.495082</td>\n",
       "      <td>22.756557</td>\n",
       "      <td>4.693443</td>\n",
       "      <td>0.309836</td>\n",
       "      <td>135.137705</td>\n",
       "      <td>35.860656</td>\n",
       "      <td>191.227049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>2.316239</td>\n",
       "      <td>0.474359</td>\n",
       "      <td>0.470085</td>\n",
       "      <td>82.730769</td>\n",
       "      <td>0.337607</td>\n",
       "      <td>5.247863</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>0.072650</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>...</td>\n",
       "      <td>4.282051</td>\n",
       "      <td>11.888889</td>\n",
       "      <td>1.918803</td>\n",
       "      <td>0.482906</td>\n",
       "      <td>29.337607</td>\n",
       "      <td>7.311966</td>\n",
       "      <td>0.525641</td>\n",
       "      <td>179.606838</td>\n",
       "      <td>47.414530</td>\n",
       "      <td>288.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>0.201646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.374486</td>\n",
       "      <td>1.275720</td>\n",
       "      <td>0.234568</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.152263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238683</td>\n",
       "      <td>3.016461</td>\n",
       "      <td>0.098765</td>\n",
       "      <td>0.127572</td>\n",
       "      <td>10.061728</td>\n",
       "      <td>0.909465</td>\n",
       "      <td>0.053498</td>\n",
       "      <td>151.662551</td>\n",
       "      <td>23.872428</td>\n",
       "      <td>56.176955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               air fresheners candles  asian foods  baby accessories  \\\n",
       "customer type                                                          \n",
       "0                            0.176285     1.504836          0.050212   \n",
       "1                            0.202899     2.666667          1.739130   \n",
       "2                            0.081967     2.031148          0.424590   \n",
       "3                            0.068376     2.316239          0.474359   \n",
       "4                            0.004115     0.201646          0.000000   \n",
       "\n",
       "               baby bath body care  baby food formula  bakery desserts  \\\n",
       "customer type                                                            \n",
       "0                         0.056081           0.695305         0.291273   \n",
       "1                         0.695652         185.869565         0.913043   \n",
       "2                         0.300820          26.332787         0.295082   \n",
       "3                         0.470085          82.730769         0.337607   \n",
       "4                         0.000000           0.000000         0.374486   \n",
       "\n",
       "               baking ingredients  baking supplies decor    beauty  \\\n",
       "customer type                                                        \n",
       "0                        2.819422               0.185415  0.048473   \n",
       "1                        6.318841               0.449275  0.144928   \n",
       "2                        4.152459               0.191803  0.077049   \n",
       "3                        5.247863               0.358974  0.072650   \n",
       "4                        1.275720               0.234568  0.012346   \n",
       "\n",
       "               beers coolers  ...  international  meat seafood  missing_y  \\\n",
       "customer type                 ...                                           \n",
       "0                   0.364417  ...       2.441854      6.149658   0.644550   \n",
       "1                   0.000000  ...       4.956522     16.318841   2.652174   \n",
       "2                   0.070492  ...       3.360656     10.074590   0.823770   \n",
       "3                   0.025641  ...       4.282051     11.888889   1.918803   \n",
       "4                   0.152263  ...       0.238683      3.016461   0.098765   \n",
       "\n",
       "                other_y     pantry  personal care      pets     produce  \\\n",
       "customer type                                                             \n",
       "0              0.296816  16.282415       3.783882  0.998424   82.799804   \n",
       "1              0.971014  40.811594      12.405797  0.942029  253.362319   \n",
       "2              0.495082  22.756557       4.693443  0.309836  135.137705   \n",
       "3              0.482906  29.337607       7.311966  0.525641  179.606838   \n",
       "4              0.127572  10.061728       0.909465  0.053498  151.662551   \n",
       "\n",
       "                  snacks     organic  \n",
       "customer type                         \n",
       "0              25.430062   88.274318  \n",
       "1              85.405797  458.449275  \n",
       "2              35.860656  191.227049  \n",
       "3              47.414530  288.269231  \n",
       "4              23.872428   56.176955  \n",
       "\n",
       "[5 rows x 157 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = cust_train.groupby('customer type').mean()\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Total</th>\n",
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       "      <th>...</th>\n",
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       "      <th>first aid</th>\n",
       "      <th>specialty wines champagnes</th>\n",
       "      <th>shave needs</th>\n",
       "      <th>kitchen supplies</th>\n",
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       "      <th>baby bath body care</th>\n",
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       "      <th>customer type</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>279.679002</td>\n",
       "      <td>88.274318</td>\n",
       "      <td>82.799804</td>\n",
       "      <td>47.021628</td>\n",
       "      <td>31.631888</td>\n",
       "      <td>31.082763</td>\n",
       "      <td>25.430062</td>\n",
       "      <td>23.245843</td>\n",
       "      <td>19.522389</td>\n",
       "      <td>16.282415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093631</td>\n",
       "      <td>0.08385</td>\n",
       "      <td>0.080752</td>\n",
       "      <td>0.078633</td>\n",
       "      <td>0.06934</td>\n",
       "      <td>0.066134</td>\n",
       "      <td>0.056081</td>\n",
       "      <td>0.050212</td>\n",
       "      <td>0.048473</td>\n",
       "      <td>0.039344</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 157 columns</p>\n",
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      ],
      "text/plain": [
       "                    Total    organic    produce  dairy eggs  fresh fruits  \\\n",
       "customer type                                                               \n",
       "0              279.679002  88.274318  82.799804   47.021628     31.631888   \n",
       "\n",
       "               fresh vegetables     snacks  beverages     frozen     pantry  \\\n",
       "customer type                                                                 \n",
       "0                     31.082763  25.430062  23.245843  19.522389  16.282415   \n",
       "\n",
       "               ...  facial care  first aid  specialty wines champagnes  \\\n",
       "customer type  ...                                                       \n",
       "0              ...     0.093631    0.08385                    0.080752   \n",
       "\n",
       "               shave needs  kitchen supplies  eye ear care  \\\n",
       "customer type                                                \n",
       "0                 0.078633           0.06934      0.066134   \n",
       "\n",
       "               baby bath body care  baby accessories    beauty  frozen juice  \n",
       "customer type                                                                 \n",
       "0                         0.056081          0.050212  0.048473      0.039344  \n",
       "\n",
       "[1 rows x 157 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster 0\n",
    "cluster_0 = df_new.loc[[0]]\n",
    "cluster_0 = cluster_0.sort_values(by=0, ascending=False, axis=1)\n",
    "# cluster_0 \n",
    "cluster_0 .loc[:, cluster_0 .columns != \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>white wines</th>\n",
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       "    <tr>\n",
       "      <th>customer type</th>\n",
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       "      <td>1</td>\n",
       "      <td>458.449275</td>\n",
       "      <td>253.362319</td>\n",
       "      <td>191.652174</td>\n",
       "      <td>185.869565</td>\n",
       "      <td>143.333333</td>\n",
       "      <td>112.144928</td>\n",
       "      <td>86.318841</td>\n",
       "      <td>85.405797</td>\n",
       "      <td>65.594203</td>\n",
       "      <td>58.782609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 156 columns</p>\n",
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      "text/plain": [
       "                  organic     produce      babies  baby food formula  \\\n",
       "customer type                                                          \n",
       "1              458.449275  253.362319  191.652174         185.869565   \n",
       "\n",
       "               dairy eggs  fresh fruits  fresh vegetables     snacks  \\\n",
       "customer type                                                          \n",
       "1              143.333333    112.144928         86.318841  85.405797   \n",
       "\n",
       "               beverages     frozen  ...  ice cream toppings  \\\n",
       "customer type                        ...                       \n",
       "1              65.594203  58.782609  ...            0.086957   \n",
       "\n",
       "               bulk grains rice dried goods  eye ear care  frozen juice  \\\n",
       "customer type                                                             \n",
       "1                                  0.086957      0.086957      0.028986   \n",
       "\n",
       "               spirits  specialty wines champagnes  alcohol  red wines  \\\n",
       "customer type                                                            \n",
       "1                  0.0                         0.0      0.0        0.0   \n",
       "\n",
       "               white wines  beers coolers  \n",
       "customer type                              \n",
       "1                      0.0            0.0  \n",
       "\n",
       "[1 rows x 156 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster 1\n",
    "cluster_1 = df_new.loc[[1]]\n",
    "cluster_1 = cluster_1.sort_values(by=1, ascending=False, axis=1)\n",
    "# cluster_1 \n",
    "cluster_1 .loc[:, cluster_1 .columns != \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer type</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",
       "      <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",
       "      <td>2</td>\n",
       "      <td>191.227049</td>\n",
       "      <td>135.137705</td>\n",
       "      <td>83.715574</td>\n",
       "      <td>56.679508</td>\n",
       "      <td>46.390164</td>\n",
       "      <td>35.860656</td>\n",
       "      <td>31.036885</td>\n",
       "      <td>28.27623</td>\n",
       "      <td>26.332787</td>\n",
       "      <td>26.295082</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>0.096721</td>\n",
       "      <td>0.081967</td>\n",
       "      <td>0.080328</td>\n",
       "      <td>0.078689</td>\n",
       "      <td>0.077049</td>\n",
       "      <td>0.070492</td>\n",
       "      <td>0.055738</td>\n",
       "      <td>0.038525</td>\n",
       "      <td>0.027869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 156 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  organic     produce  dairy eggs  fresh fruits  \\\n",
       "customer type                                                     \n",
       "2              191.227049  135.137705   83.715574     56.679508   \n",
       "\n",
       "               fresh vegetables     snacks     frozen    babies  \\\n",
       "customer type                                                     \n",
       "2                     46.390164  35.860656  31.036885  28.27623   \n",
       "\n",
       "               baby food formula  packaged vegetables fruits  ...  first aid  \\\n",
       "customer type                                                 ...              \n",
       "2                      26.332787                   26.295082  ...   0.098361   \n",
       "\n",
       "               ice cream toppings  air fresheners candles  eye ear care  \\\n",
       "customer type                                                             \n",
       "2                        0.096721                0.081967      0.080328   \n",
       "\n",
       "               kitchen supplies    beauty  beers coolers  \\\n",
       "customer type                                              \n",
       "2                      0.078689  0.077049       0.070492   \n",
       "\n",
       "               specialty wines champagnes  frozen juice   spirits  \n",
       "customer type                                                      \n",
       "2                                0.055738      0.038525  0.027869  \n",
       "\n",
       "[1 rows x 156 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster 2\n",
    "cluster_2 = df_new.loc[[2]]\n",
    "cluster_2 = cluster_2.sort_values(by=2, ascending=False, axis=1)\n",
    "# cluster_2 \n",
    "cluster_2 .loc[:, cluster_2 .columns != \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>organic</th>\n",
       "      <th>produce</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>babies</th>\n",
       "      <th>baby food formula</th>\n",
       "      <th>fresh fruits</th>\n",
       "      <th>fresh vegetables</th>\n",
       "      <th>snacks</th>\n",
       "      <th>frozen</th>\n",
       "      <th>beverages</th>\n",
       "      <th>...</th>\n",
       "      <th>frozen juice</th>\n",
       "      <th>beauty</th>\n",
       "      <th>air fresheners candles</th>\n",
       "      <th>red wines</th>\n",
       "      <th>cocoa drink mixes</th>\n",
       "      <th>kitchen supplies</th>\n",
       "      <th>beers coolers</th>\n",
       "      <th>specialty wines champagnes</th>\n",
       "      <th>white wines</th>\n",
       "      <th>spirits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer type</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",
       "      <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",
       "      <td>3</td>\n",
       "      <td>288.269231</td>\n",
       "      <td>179.606838</td>\n",
       "      <td>116.209402</td>\n",
       "      <td>84.649573</td>\n",
       "      <td>82.730769</td>\n",
       "      <td>78.440171</td>\n",
       "      <td>59.782051</td>\n",
       "      <td>47.41453</td>\n",
       "      <td>40.628205</td>\n",
       "      <td>36.615385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089744</td>\n",
       "      <td>0.07265</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>0.047009</td>\n",
       "      <td>0.042735</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.012821</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 156 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  organic     produce  dairy eggs     babies  \\\n",
       "customer type                                                  \n",
       "3              288.269231  179.606838  116.209402  84.649573   \n",
       "\n",
       "               baby food formula  fresh fruits  fresh vegetables    snacks  \\\n",
       "customer type                                                                \n",
       "3                      82.730769     78.440171         59.782051  47.41453   \n",
       "\n",
       "                  frozen  beverages  ...  frozen juice   beauty  \\\n",
       "customer type                        ...                          \n",
       "3              40.628205  36.615385  ...      0.089744  0.07265   \n",
       "\n",
       "               air fresheners candles  red wines  cocoa drink mixes  \\\n",
       "customer type                                                         \n",
       "3                            0.068376   0.047009           0.042735   \n",
       "\n",
       "               kitchen supplies  beers coolers  specialty wines champagnes  \\\n",
       "customer type                                                                \n",
       "3                      0.029915       0.025641                    0.012821   \n",
       "\n",
       "               white wines  spirits  \n",
       "customer type                        \n",
       "3                 0.008547      0.0  \n",
       "\n",
       "[1 rows x 156 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster 3\n",
    "cluster_3 = df_new.loc[[3]]\n",
    "cluster_3 = cluster_3.sort_values(by=3, ascending=False, axis=1)\n",
    "# cluster_3 \n",
    "cluster_3 .loc[:, cluster_3 .columns != \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>produce</th>\n",
       "      <th>packaged produce</th>\n",
       "      <th>organic</th>\n",
       "      <th>fresh fruits</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>snacks</th>\n",
       "      <th>beverages</th>\n",
       "      <th>packaged vegetables fruits</th>\n",
       "      <th>fresh vegetables</th>\n",
       "      <th>frozen</th>\n",
       "      <th>...</th>\n",
       "      <th>kosher foods</th>\n",
       "      <th>seafood counter</th>\n",
       "      <th>air fresheners candles</th>\n",
       "      <th>deodorants</th>\n",
       "      <th>cat food care</th>\n",
       "      <th>bulk grains rice dried goods</th>\n",
       "      <th>shave needs</th>\n",
       "      <th>baby food formula</th>\n",
       "      <th>baby bath body care</th>\n",
       "      <th>baby accessories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer type</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",
       "      <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",
       "      <td>4</td>\n",
       "      <td>151.662551</td>\n",
       "      <td>69.090535</td>\n",
       "      <td>56.176955</td>\n",
       "      <td>50.588477</td>\n",
       "      <td>30.674897</td>\n",
       "      <td>23.872428</td>\n",
       "      <td>19.99177</td>\n",
       "      <td>17.374486</td>\n",
       "      <td>14.300412</td>\n",
       "      <td>10.218107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 156 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  produce  packaged produce    organic  fresh fruits  \\\n",
       "customer type                                                          \n",
       "4              151.662551         69.090535  56.176955     50.588477   \n",
       "\n",
       "               dairy eggs     snacks  beverages  packaged vegetables fruits  \\\n",
       "customer type                                                                 \n",
       "4               30.674897  23.872428   19.99177                   17.374486   \n",
       "\n",
       "               fresh vegetables     frozen  ...  kosher foods  \\\n",
       "customer type                               ...                 \n",
       "4                     14.300412  10.218107  ...      0.004115   \n",
       "\n",
       "               seafood counter  air fresheners candles  deodorants  \\\n",
       "customer type                                                        \n",
       "4                     0.004115                0.004115         0.0   \n",
       "\n",
       "               cat food care  bulk grains rice dried goods  shave needs  \\\n",
       "customer type                                                             \n",
       "4                        0.0                           0.0          0.0   \n",
       "\n",
       "               baby food formula  baby bath body care  baby accessories  \n",
       "customer type                                                            \n",
       "4                            0.0                  0.0               0.0  \n",
       "\n",
       "[1 rows x 156 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster 4\n",
    "cluster_4 = df_new.loc[[4]]\n",
    "cluster_4 = cluster_4.sort_values(by=4, ascending=False, axis=1)\n",
    "# cluster_4 \n",
    "cluster_4 .loc[:, cluster_4 .columns != \"Total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot purchase pattern heat map\n",
    "# `order_history`: order history data (unscaled, non-dimension-reduction)\n",
    "# `plot_threshold`: percent of purchase for a product category to not display in heat map (0.02 = 2%)\n",
    "#                   [reduce number of categories to plot]\n",
    "from matplotlib import gridspec\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.2)\n",
    "\n",
    "def customer_group_heatmap(order_history, plot_threshold=0.02):\n",
    "    # 1) convert number of purchase to percent\n",
    "    # aisle categories\n",
    "    order_history.iloc[:,0:134] = order_history.iloc[:,0:134].div(order_history.Total, axis=0)\n",
    "    # department categories\n",
    "    order_history.iloc[:,135:156] = order_history.iloc[:,135:156].div(order_history.Total, axis=0)\n",
    "    # organic product\n",
    "    order_history.iloc[:,156] = order_history.iloc[:,156].div(order_history.Total, axis=0)\n",
    "    \n",
    "    # 2) average by customer group\n",
    "    cg_order_history = order_history.groupby('customer type').mean()\n",
    "\n",
    "    cg_aisle = cg_order_history.iloc[:,0:134]\n",
    "    cg_dept = cg_order_history.iloc[:,135:157]\n",
    "    cg_total = cg_order_history.iloc[:,[134]].round(0)\n",
    "    \n",
    "    # 3) threshold for a product category to display in heatmap\n",
    "    cg_aisle = cg_aisle.loc[:,(cg_aisle>plot_threshold).any()]\n",
    "    cg_dept = cg_dept.loc[:,(cg_dept>plot_threshold).any()]\n",
    "    \n",
    "    # 4) Number of customers per group\n",
    "    cg_count = order_history.iloc[:,-2:].groupby('customer type').count()\n",
    "    \n",
    "    # 5) plot heat map\n",
    "    fig = plt.figure(figsize=(10,12))\n",
    "    gridspec.GridSpec(10,1)\n",
    "    \n",
    "    # Map 1: Number of Customer in each cluster\n",
    "    plt.subplot2grid((10,1), (0,0))\n",
    "    plt.title('Number of Customers')\n",
    "    sns.heatmap(cg_count.transpose(), yticklabels=False, cmap=\"Greys\", annot=True, fmt='d', linewidths=0.2)\n",
    "    plt.xlabel(\"\")\n",
    "    \n",
    "    # Map 2: Avearage number of products purchased\n",
    "    plt.subplot2grid((10,1), (1,0))\n",
    "    plt.title('Average # of Items Purchased by Customer Type')\n",
    "    sns.heatmap(cg_total.transpose(), cmap=\"Reds\", annot=True, fmt='0g', linewidths=0.2)\n",
    "    plt.xlabel(\"\")\n",
    "\n",
    "    # Map 3: Average percent of products by aisle bought by customer cluster\n",
    "    plt.subplot2grid((10,1), (2,0), rowspan=4)\n",
    "    plt.title('Percent of Items Purchased by Customer Type (Aisle)')\n",
    "    sns.heatmap(cg_aisle.transpose(), cmap=\"Blues\", linewidths=0.1)\n",
    "    plt.xlabel(\"\")\n",
    "    \n",
    "    # Map 4: Average percent of products by department bought by customer cluster\n",
    "    plt.subplot2grid((10,1), (6,0), rowspan=4)\n",
    "    plt.title('Percent of Items Purchased by Customer Type (Department)')\n",
    "    sns.heatmap(cg_dept.transpose(), cmap=\"Greens\", linewidths=0.1)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../Outputs/Hierarchical_customer_group5.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_group_heatmap(cust_train.copy(), 0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}