Retail_Customer_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the path I use on my hard drive, but you should change this to your favorite directory path instead.\n",
    "\n",
    "my_path = \"/users/bnsheehy/OneDrive/Python_Projects/Retail_Clustering\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Locate and name source files for deal offers and customer transactions.\n",
    "\n",
    "file_offers = my_path + \"/offers.csv\"\n",
    "file_transactions = my_path + \"/transactions.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>Offer #</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
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       "      <td>48</td>\n",
       "      <td>France</td>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)       Origin  \\\n",
       "0   January              Malbec                 72            56       France   \n",
       "1   January          Pinot Noir                 72            17       France   \n",
       "2  February           Espumante                144            32       Oregon   \n",
       "3  February           Champagne                 72            48       France   \n",
       "4  February  Cabernet Sauvignon                144            44  New Zealand   \n",
       "\n",
       "   Past Peak  Offer #  \n",
       "0      False        1  \n",
       "1      False        2  \n",
       "2       True        3  \n",
       "3       True        4  \n",
       "4       True        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload deal offers file to Pandas dataframe.\n",
    "\n",
    "df_offers = pd.read_csv (file_offers)\n",
    "df_offers['Offer #'] = df_offers.index + 1\n",
    "df_offers.head()\n",
    "#df_offers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Last Name</th>\n",
       "      <th>Offer #</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Williams</td>\n",
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       "      <th>6</th>\n",
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       "      <td>22</td>\n",
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       "      <th>7</th>\n",
       "      <td>Williams</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brown</td>\n",
       "      <td>7</td>\n",
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       "      <th>9</th>\n",
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      "text/plain": [
       "  Customer Last Name  Offer #\n",
       "0              Smith        2\n",
       "1              Smith       24\n",
       "2            Johnson       17\n",
       "3            Johnson       24\n",
       "4            Johnson       26\n",
       "5           Williams       18\n",
       "6           Williams       22\n",
       "7           Williams       31\n",
       "8              Brown        7\n",
       "9              Brown       29"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload customer transactions file to Pandas dataframe.\n",
    "\n",
    "df_transactions = pd.read_csv (file_transactions)\n",
    "df_transactions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Offer #              1    2    3    4    5    6    7    8    9    10  ...  \\\n",
       "Customer Last Name                                                    ...   \n",
       "Adams               0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "Allen               0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...   \n",
       "Anderson            0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "Bailey              0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...   \n",
       "Baker               0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  ...   \n",
       "Barnes              0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...   \n",
       "Bell                0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "Bennett             0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...   \n",
       "Brooks              0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...   \n",
       "Brown               0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "Offer #              23   24   25   26   27   28   29   30   31   32  \n",
       "Customer Last Name                                                    \n",
       "Adams               0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "Allen               0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Anderson            0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Bailey              0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "Baker               0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "Barnes              0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "Bell                0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Bennett             0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "Brooks              0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Brown               0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot the transactions file to show a matrix with customer names in the row index and offer numbers\n",
    "# as column headers across the top. Then fill NaN's with zeros.\n",
    "\n",
    "df_pivot = pd.pivot_table(df_transactions, values = 'Offer #', index = 'Offer #', \n",
    "                          columns = ['Customer Last Name'], aggfunc = len)\n",
    "df_pivot_tp = df_pivot.transpose()\n",
    "df_pivot_tp.fillna(0, inplace=True)\n",
    "df_pivot_tp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m8bNZ+NXmsEsSkRhTp1Bw943u/gt3P8rdB7n79e6+MdrFSf1LTkrk75cdTesWzfjxUzls2LYz7JJEJIbU2H1kZg+4+w1mNpGqF7w5N2qVSdR0apvMuMuzufjvn3HR3z6lqKSUwk07SE9N4dbhfRg5aK8F70SkiahtTKF8DOH+aBciDWtgZioXZWfw7NRvV0DN31TE6Am5AAoGkSaqxu4jd58e3B3o7pMr34CB0S9PounDhev3aisqKWXM24tCqEZEYkFdB5qvqKLtynqsQ0JQsKlon9pFpPGrbUzhB8ClQA8ze63SQ22Ar6NZmERfemoK+VUEwEGtNL2VSFNV25HCp8CfgAXBz/LbzcCZ0S1Nou3W4X1ISUrcrc0MNmzbyQ3PzWTTdp2ZJNLU1Hik4O4rzSwP2BaMI0gjUj6YPObtRRRsKiI9NYWbTu9F3sYd/OX9JXy67Gvu+V4/hvXtFHKlItJQar2i2d1LzWy7mbVzdy2008iMHJRR5ZlGpx7WkVtemM3VT+Zw0dFd+M2Iw2mbnBRChSLSkOo6zcUOINfM3gW2lTe6+y+iUpWE7siMdrz285N4aNISHp28jE+Wrufe7/Xn5N5pYZcmIlFU17OP3gB+A3wETK90k0asebMEbhnehwnXDKZVi2Zc/o/PGT0hl62afluk0arzIjtm1hzoHfy6yN1LolZVHWmRnYazo6SUP7+7mHEfLycjNYX7LuzP4J4dwi5LRPZDfSyycwqwBHgE+Cuw2MxOrrcKJeYlJyUy+uzDePFnJ5CUmMClj03jzlfnsn2njhpEGpO6dh/9CTjD3Ye4+8nAcODP0StLYtXR3drz5i++w5WDs3jqs5Wc9eDHfLFiQ9hliUg9qWsoJLl7xdwH7r4Y0KkoTVRK80T+79wjeG7U8ZS5c/HfP+P3r89nR0lp2KWJyAGqayjkmNkTZnZKcHuMWgaazSzTzD4wswVmNs/Mrg/ax5jZQjObY2Yvm1lqpX1Gm9lSM1tkZsP3/21JQzi+x8G8df3J/M9xXXn8ky85+6GPmblKM6qLxLM6DTSbWQvgWuAkwIichfSIu1d7yauZdQY6u/sMM2tDJERGAl2A9919l5ndC+DuvzSzw4H/AMcC6cB7QG93r/brpwaaY8cnS9Zz24uz+WrzDn46pCc3nNaLFs0Sa99RRBrcAQ80Az9z97HufoG7n+/ufwauqWkHdy909xnB/S1EpsrIcPd33L18dHIqkZAAOA94zt2L3f1LYCmRgJA4cFKvDrx148lcdHQmj364jBF/+YTcPF3rKBJvGmSWVDPLAgYB0/Z46Grgv8H9DGB1pcfygrY9n2uUmeWYWc66devqWoI0gLbJSdx7YX/+eeUxfFNUwsi/TmHsu4vZuass7NJEpI5qDAUz+0Gw6lp3M3ut0u1D6jhLqpm1Bl4CbnD3zZXafwXsAp4tb6pi96pWexvn7tnunp2WpqtrY9HQvh1554YhnDcgnYcmLWHkI1NYUKj1oEXiQW3TXHwKFAIdiJyWWm4LMKe2JzezJCKB8Ky7T6jUfgXwXeBU/3ZQIw/IrLR7F6CgtteQ2NSuZRJjvz+QM488hDtezuXchz/h+lN78bMhPWmWWNcDVBFpaLWtvLbS3T8ETgM+DmZKLSTyB7uqb/YVzMyAJ4AF7j62UvuZwC+Bc919e6VdXgMuMbMWZtYd6AV8vu9vSWLJGUccwjs3DmH4EYdw/zuLueDRT1myZkvYZYlINer6le0jINnMMoBJwFXAk7XscyJwGTDMzGYFt7OBh4ks0vNu0PY3AHefB4wH5gNvAdfWdOaRxI/2rZrz8KVH8cilR7F6w3bO+csn/H3yMkrL6jbFiog0nLqekjrD3Y8ys+uAFHe/z8xmuvug6JdYPZ2SGn/WbSnmVy/n8s78NRzVNZX7LxpAj7TWYZcl0qTUdEpqXafONjM7Afgf4Ef7uK9IhbQ2Lfj7ZUfz6qwCfvvqXM568GNuO7Mv7VOSuP/dxRWL/dw6vE+V6zyISHTV9Q/7DcBo4GV3n2dmPYAPoleWNGZmxshBGZzQ82Buf2kO/+/1+SQYlPcm5W8qYvSEXAAFg0gDq9OYgrtPdvdz3b38CuTlWmBHDlSntsn848pjSG2ZxJ7DC0UlpYx5e1HVO4pI1NR4pGBmD7j7DcG1ClVdM3Bu1CqTJsHM+GZ71UtzFGwqauBqRKS27qN/BT/vj3Yh0nSlp6aQX0UAJCQYr87KZ0T/dBISajwDWkTqSW3XKUwPfk4mcqro/KAraXLQJnLAbh3eh5Sk3SfPa56YQMfWzbn+uVmc85dPeH/hGuq6SqCI7L/aprkwM/s/M1sPLCSy4to6M/ttw5QnTcHIQRncfUE/MlJTMKhY7nPK7afy4CUD2b5zF1c/mcNFf/uMacvrNLuKiOynGq9TMLMbgbOBUcHMpQRnHj0KvBXMlhoaXafQNJSUljE+ZzUPTVrCms3FDOmdxq3D+3BkRruwSxOJSzVdp1BbKMwETnf39Xu0pwHv6OI1aUg7Skp56tMVPDp5GZu2l3BOv87cdEZveuriN5F9ciDrKSTtGQgA7r4OLccpDSw5KZGfDunJR7cN5RfDDuWDRWs5fexkbntxdpUD1SKy72oLhWpXVqvlMZGoaZucxE1n9OGj24Zy5eDuvDKzgKFjPuR3E+ezfmtx2OWJxLXauo9KgW1VPQQku3uoRwvqPhKIXAH90HtLeGH6alKSEvnRSd358ck9aJusg1mRquz3mEKsUyhIZcvWbWXsO4t5I7eQ1JZJXDOkJ1cMziI5SWtFi1SmUJAmJTfvG+5/ZxGTF6+jU9sWXDesF98/JpMkLe4jAhzYQLNI3OnXpR1PXX0sz486ni4HteTXr8zltLGTeXVWPmVaw0GkRgoFabSO63EwL/7sBP5xZTYpSYlc/9wszn7oY96br6ujRaqjUJBGzcwY1rcTb/7iOzx4yUB2lJTy46dz+N6jn/LZMl0dLbInhYI0CQkJxnkDM3j3piH88fx+FGzawQ8em8plT0xjTt6msMsTiRkaaJYmaUdJKf/6bCV//XApG7eXcNaRh3DzGb2Zm7+ZMW8v0gpw0qjp7CORamzZUcLjH3/J4x8vZ9vOUhLNKK30byIlKZG7L+inYJBGRWcfiVSjTXISN57em49uG0qrFom7BQJoBThpehQKIsDBrVuwvbi0ysfyNxVRqlNZpYlQKIgE0lNTqn1syJgPeOyj5dUuHSrSWCgURAJVrQCXnJTAVSd2Iz01hT+8uYDj757Er1/JZenarSFVKRJdta3RLNJklA8mV3f20dz8b3jy0xWM/yKPZ6au4ju9OnD1id0Z0jtNa0hLoxG1s4/MLBN4GjgEKAPGufuDZtYeeB7IAlYAF7v7RjMz4EEiK71tB6509xk1vYbOPpIwrN9azH+mreJfU1eydksx3Tu04ooTunFhdiatW+h7lsS+UE5JNbPOQGd3n2FmbYDpwEjgSmCDu99jZrcDB7n7L83sbOA6IqFwHPCgux9X02soFCRMO3eV8d+5hfxzygpmrd5EmxbNuCg7kysGd6Pbwa3CLk+kWjFxnYKZvQo8HNxOcffCIDg+dPc+Zvb34P5/gu0XlW9X3XMqFCRWzFy1kSc/XcEbcwopdefUvh256sTuDO55MJGDYJHYUVMoNMixrpllAYOAaUCn8j/0QTB0DDbLAFZX2i0vaNstFMxsFDAKoGvXrlGtW6SuBnU9iEFdD+KOsw/jmakr+fe0Vby3YBq9O7XmysHdOX9QBinNta6DxL6on31kZq2Bl4Ab3H1zTZtW0bbXYYy7j3P3bHfPTktLq68yRepFp7bJ3HxGH6bcPowxF/anWUICd7ycywn3TOKe/y6kQGtJS4yL6pGCmSURCYRn3X1C0LzGzDpX6j5aG7TnAZmVdu8CFESzPpFoSU5K5KLsTC48ugtfrNjIP6d8ybiPlvHYx8sZfkQnrjqxO9ndDlLXksScqIVCcDbRE8ACdx9b6aHXgCuAe4Kfr1Zq/7mZPUdkoPmbmsYTROKBmXFs9/Yc2709eRu386+pK3nu89W8mfsVR2a05crB3RkxoDMtmqlrSWJDNM8+Ogn4GMglckoqwB1ExhXGA12BVcBF7r4hCJGHgTOJnJJ6lbvXOIqsgWaJR9t37uLlmfk8OWUFS9ZupUPr5lx6XDd+eFxXOrZNBuCVmfmarVWiJibOPooGhYLEM3dnytKv+eeUL3l/0VqaJRjn9OtMj7RWPPrhcopKvp2LSbO1Sn0K/ewjEdmbmXFSrw6c1KsDK9Zv46nPVvBCTh5bZ+3aa9vy2VoVChJtmvtIJAZkdWjFnSOO4LPRw6rdRmcuSUNQKIjEkDbJSWRUM1urGYyekMuny9ZrKm+JGoWCSIyparbW5okJDMxM5dVZ+Vz62DROuHsSd02cx4xVG4nncUGJPRpTEIkxNc3WWrSzlEkL1zBxdgHPTlvFP6esICM1hRED0hkxoDOHd26rax/kgOjsI5E4tXlHCe/OW8PEOQV8smQ9u8qcHmmtGNE/nRED0jm0Y+uwS5QYpVNSRRq5Ddt28tbcr5g4u4CpX36NOxzeuS0jBqTz3f6dyWzfMuwSJYYoFESakDWbd/DGnEImzilg5qpNAAzqmsqI/umc078znYIL5KTpUiiINFGrN2zn9TmFTJxdwPzCzZjBcd3bM2JAOmcd2Zn2rZqHXaKEQKEgIixdu5XX5xTw2uwClq/bRmKCcdKhHTh3QDqnH9GJtslJYZcoDUShICIV3J0FhVuYOKeAibMLyNtYRPNmCQztk8aIAemc2rcTKc0TNf9SI6ZQEJEquTszV29i4uwC3phTyNotxbRsnkjfTm2YW7CZnaVlFdtq/qXGQ6EgIrUqLXM+/3IDE+cU8Nznq6jqoumM1BSm3F79VBwSH2oKBV3RLCIAJCYYJ/Q8mD+e34/qvitq/qXGT6EgIntJr2b+JQduen4Wy9dtbdiCpMEoFERkL1XNv5TcLIGhfTrw5txCThs7mZvGz+LL9dtCqlCiRXMficheapp/ad2WYsZ9tIx/TV3JKzPzGTkog+uG9aJ7h1YhVy31QQPNIrJfKofDzl1lCoc4orOPRCRqFA7xR6EgIlGncIgfCgURaTBrt+xg3OTlPDNtJSWlzsiBGVw37FCyFA4xQ6EgIg1O4RC7FAoiEhqFQ+xRKIhI6BQOsUOhICIxQ+EQvlDmPjKzf5jZWjObW6ltoJlNNbNZZpZjZscG7WZmD5nZUjObY2ZHRasuEQlXxzbJ/Pq7h/PRbUO5anAWr88p4NSxk7nlhdms0BXSoYvmNBdPAmfu0XYfcJe7DwR+G/wOcBbQK7iNAh6NYl0iEgPKw+HjXw7lysFZTJytcIgFUZvmwt0/MrOsPZuBtsH9dkBBcP884GmP9GVNNbNUM+vs7oXRqk9EYkPHNsn85ruH89MhPfj75OU8M3UlL8/M5/xBGfx86KHMWr1Ji/00oIae++gG4G0zu5/IUcrgoD0DWF1pu7ygba9QMLNRRI4m6Nq1a1SLFZGGU1U4vDQ9jwQzSoOxz/xNRYyekAugYIiShp4l9RrgRnfPBG4EngjarYptqxwBd/dx7p7t7tlpaWlRKlNEwlIeDh//cigtWyRWBEK5opJS7nt7YUjVNX4NHQpXABOC+y8Axwb384DMStt14duuJRFpgjq2SWZ7cWmVjxVs2sFlT0zjgfcW88mS9Wwr3tXA1TVeDd19VAAMAT4EhgFLgvbXgJ+b2XPAccA3Gk8QkfTUFPKrWO2tVfNE1m0p5sFJS3CHBIPD09uS3a09R3c7iOysg+jcruqFgqRmUQsFM/sPcArQwczygDuBnwAPmlkzYAfB2ADwJnA2sBTYDlwVrbpEJH7cOrwPoyfkUlTy7RFDSlIifzi/HyMHZbB5RwkzVm5k+sqN5KzYyPNfrObJT1cAkfWkywPi6G4H0feQtiQmVNVTLZXp4jURiWmvzMyv89lHJaVlLCjcTM6KIChWbmDN5mIAWrdoxqCuqRzd7SCOyWrPwMxUWrVomuuM6YpmEWmS3J28jUUVAZGzYiOL1mzBHRITjMM6t2mSXU4KBRGRwJ5dTrNWb6ronqqpy2lfjlhinUJBRKQadelyatW8Gbujr78AAAkRSURBVO8vXMvO0rKK/VKSErn7gn5xGQwKBRGROqqqy2nhV1uq3DYjNYUptw9r4AoPXE2h0DRHWUREqmFmZLZvSWb7lhVHAd1vf6PKq2nzNxUxe/Um+ndph1njOLOpoS9eExGJO+mp1Q9An/fIFM568GP+8cmXbNy2swGrig6FgohILW4d3oeUpMTd2iJjCkfy+5FH0rxZAr97fT7H/XES1/57Bh8tXkdZWXx2zav7SESkFuXdSNWdffTD47uxoHAzz3+xmldm5fPGnEIyUlO48OguXJTdhS4HtQyz/H2igWYRkXpUvKuUd+ev4fkvVvPJ0vUAnHRoBy7OzuSMIzrRolliLc8QfTr7SEQkBHkbt/Pi9DxeyMkjf1MRqS2TGDkwg+8fk8lhndvW/gRRolAQEQlRWZkzZdl6nv9iNe/MW8PO0jL6ZbTj4mMyOXdAOu1Skhq0HoWCiEiM2LhtJ6/Myuf5L1az8KsttGiWwNn9OnNxdibH92jfIKe2KhRERGKMu5Ob/w3Pf7Ga12YVsKV4F90ObsnF2Zl876guHNIuOWqvrVAQEYlhRTtL+e/cQsbnrGbq8g0kGJzSpyMXZ2dy6mEdSUqs36sHFAoiInFixfptvDB9NS9Oz2PN5mI6tG7OBUd14eLsTA7t2LpeJuZTKIiIxJldpWV8tGQdz3+xmkkL1rKrzMk6uCX5m4ooKf327/b+TMynuY9EROJMs8QEhvXtxLC+nVi3pZiXZ+Zx31uL2LXHldJFJaWMeXtRvc3WqmkuRERiXFqbFow6uSel1UydUVDFOtb7S6EgIhInqpuYr6YJ+/aVQkFEJE5UNzHfrcP71NtraExBRCRO1DYxX31QKIiIxJGRgzKiugSouo9ERKSCQkFERCooFEREpIJCQUREKigURESkQlzPfWRm64CVYddxgDoA68MuIobo89idPo9v6bPY3YF8Ht3cPa2qB+I6FBoDM8upbmKqpkifx+70eXxLn8XuovV5qPtIREQqKBRERKSCQiF848IuIMbo89idPo9v6bPYXVQ+D40piIhIBR0piIhIBYWCiIhUUCiExMwyzewDM1tgZvPM7PqwawqbmSWa2Uwzez3sWsJmZqlm9qKZLQz+Hzkh7JrCZGY3Bv9O5prZf8wsOeyaGpKZ/cPM1prZ3Ept7c3sXTNbEvw8qD5eS6EQnl3Aze5+GHA8cK2ZHR5yTWG7HlgQdhEx4kHgLXfvCwygCX8uZpYB/ALIdvcjgUTgknCranBPAmfu0XY7MMndewGTgt8PmEIhJO5e6O4zgvtbiPyjj94k6THOzLoA5wCPh11L2MysLXAy8ASAu+90903hVhW6ZkCKmTUDWgIFIdfToNz9I2DDHs3nAU8F958CRtbHaykUYoCZZQGDgGnhVhKqB4DbgLKwC4kBPYB1wD+D7rTHzaxV2EWFxd3zgfuBVUAh8I27vxNuVTGhk7sXQuRLJtCxPp5UoRAyM2sNvATc4O6bw64nDGb2XWCtu08Pu5YY0Qw4CnjU3QcB26inroF4FPSVnwd0B9KBVmb2w3CrarwUCiEysyQigfCsu08Iu54QnQica2YrgOeAYWb2TLglhSoPyHP38iPHF4mERFN1GvClu69z9xJgAjA45JpiwRoz6wwQ/FxbH0+qUAiJmRmRPuMF7j427HrC5O6j3b2Lu2cRGUB8392b7DdBd/8KWG1mfYKmU4H5IZYUtlXA8WbWMvh3cypNeOC9kteAK4L7VwCv1seTNquPJ5H9ciJwGZBrZrOCtjvc/c0Qa5LYcR3wrJk1B5YDV4VcT2jcfZqZvQjMIHLW3kya2JQXZvYf4BSgg5nlAXcC9wDjzexHRILzonp5LU1zISIi5dR9JCIiFRQKIiJSQaEgIiIVFAoiIlJBoSAiIhUUChLTzMzN7E+Vfr/FzP6vnp77STO7sD6eq5bXuSiY6fSDaNZlZllmdum+VyjyLYWCxLpi4AIz6xB2IZWZWeI+bP4j4H/dfWi06glkAfsUCvv4PqQJUChIrNtF5EKlG/d8YM9v1Ga2Nfh5iplNNrPxZrbYzO4xs/8xs8/NLNfMelZ6mtPM7ONgu+8G+yea2Rgz+8LM5pjZTys97wdm9m8gt4p6fhA8/1wzuzdo+y1wEvA3MxtTxT63BfvMNrN7qnh8RXkgmlm2mX0Y3B9iZrOC20wza0PkYqbvBG031vV9mFkrM3sjqGGumX2/Lv9hpHHSFc0SDx4B5pjZffuwzwDgMCLTDS8HHnf3Y4PFjK4Dbgi2ywKGAD2BD8zsUOByIjNxHmNmLYApZlY+K+exwJHu/mXlFzOzdOBe4GhgI/COmY1099+Z2TDgFnfP2WOfs4hMd3ycu283s/b78P5uAa519ynBpIo7iEyad4u7l4fbqLq8DzP7HlDg7ucE+7XbhzqkkdGRgsS8YPbYp4kstFJXXwRrVhQDy4DyP4a5RIKg3Hh3L3P3JUTCoy9wBnB5MP3INOBgoFew/ed7BkLgGODDYNK2XcCzRNZEqMlpwD/dfXvwPvecL78mU4CxZvYLIDV4zT3V9X3kEjliutfMvuPu3+xDHdLIKBQkXjxApG++8roCuwj+Hw4mSmte6bHiSvfLKv1exu5HyHvO8+KAAde5+8Dg1r3S/P3bqqnP6vpG9tintnlmKt4jULEEpbvfA/wYSAGmmlnfap6/1vfh7ouJHOHkAncHXV7SRCkUJC4E36LHEwmGciuI/DGDyHz7Sfvx1BeZWUIwztADWAS8DVwTTG2OmfWuwyI304AhZtYhGLz9ATC5ln3eAa42s5bB61TVfbSCb9/j98obzaynu+e6+71ADpEjnC1Am0r71ul9BF1f2939GSKL2TTlabqbPI0pSDz5E/DzSr8/BrxqZp8TWaO2um/xNVlE5I93J+Bn7r7DzB4n0sU0IzgCWUctSx26e6GZjQY+IPIN/U13r3EqY3d/y8wGAjlmthN4E7hjj83uAp4wszvYfWW+G8xsKFBKZFrt/xI5CtplZrOJrOn7YB3fRz9gjJmVASXANTXVLY2bZkkVEZEK6j4SEZEKCgUREamgUBARkQoKBRERqaBQEBGRCgoFERGpoFAQEZEK/x/URyHNEV6y2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate scree plot to show where the \"elbow\" lies\n",
    "# in terms of the number of clusters generated.\n",
    "# It looks like there is a faint elbow at 3 clusters\n",
    "# and again at 8 clusters.\n",
    "\n",
    "distortions = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(\n",
    "        n_clusters=i, init='random',\n",
    "        n_init=10, max_iter=300,\n",
    "        tol=1e-04, random_state=0\n",
    "    )\n",
    "    km.fit(df_pivot_tp)\n",
    "    distortions.append(km.inertia_)\n",
    "\n",
    "# plot\n",
    "plt.plot(range(1, 11), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.09174871508750351\n",
      "For n_clusters = 3 The average silhouette_score is : 0.1107183912025193\n",
      "For n_clusters = 4 The average silhouette_score is : 0.12349204708263416\n",
      "For n_clusters = 5 The average silhouette_score is : 0.11482891379977885\n",
      "For n_clusters = 6 The average silhouette_score is : 0.11879508142787866\n",
      "For n_clusters = 7 The average silhouette_score is : 0.10874624428071616\n",
      "For n_clusters = 8 The average silhouette_score is : 0.14097216560635834\n"
     ]
    }
   ],
   "source": [
    "# Compute and list average silhouette scores for each number\n",
    "# of clusters.\n",
    "\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(df_pivot_tp)\n",
    "    silhouette_avg = silhouette_score(df_pivot_tp, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.09174871508750351\n",
      "For n_clusters = 3 The average silhouette_score is : 0.1107183912025193\n",
      "For n_clusters = 4 The average silhouette_score is : 0.12349204708263416\n",
      "For n_clusters = 5 The average silhouette_score is : 0.11482891379977885\n",
      "For n_clusters = 6 The average silhouette_score is : 0.11879508142787866\n",
      "For n_clusters = 7 The average silhouette_score is : 0.10874624428071616\n",
      "For n_clusters = 8 The average silhouette_score is : 0.14097216560635834\n"
     ]
    },
    {
     "data": {
      "image/png": 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+2+m4pe5v26nwGKiWXrtg33Yg95+b2T6STlU4b+0t6RBJ55nZXHdfVmQe3d2ntjlP9PQ8FeP9fJG4pFCFumgiE/fZ+xSOz2cUqpwu7WK6SAlKYhIUGyHuL4Vy7XjH5Zn49aYeJjBdeSR2dzaz+TGOoxSqt0jh0btSW0IywUIj1Cq1u6iOd+qWxo//UKgStcDdFyj8yHyi4IJ3c+wObRfP5oL3hd89orYT9F2Fd18Unlj16SLLmL8z9BV3313SMQon4J7YX20n8T3c/Y0qsZQs3o37Qvx4lULd4n9LKnyM8MGSfuDuF7r7wZJuiP3fXGTSB0n6nbu/z93frLA+JGnPHpR85feHQQr1hWVm8xRKQaS2/aHH3P0yhbt+Uki87jOzIUVG+Z/Y/ZdCgteR/IMvaiRdWrBvLFSoMnlnXBc7x+Gudve9JL1DPbxwNrOdw+L4x9z9RIUf480KFw9v6mS0wv340nxJlIVG1DOLzC5//M2Kw49pPw8zGyFpV4VqVWe4+z5qu1ud33/KcXxtr+fVljSdbMEwSceWMG6+HZQULoJkZsMLxn28m6WDr2OhgfmT7n6Zux8p6cT41RSF9d3jc0IPHGNmQ+JNrpNiv60K7Uw6kj8uXNIZBdv0MIVt+r1Oxiu8OD1Veq2kvf1+nb8BIElHuvt8SZ/sYHqd7XdzFJIOSVri7vuq7QmdpXggdvPnvD9I+r3Ctcx5sd9vVdxrsZlZ+9+jJOUfAz3TzN6e72lmxW5MFd44zJfOndh+IDPbU9Iqd/8vdz9Wbb+RwyTlH6iQXy/5/S2vp/tUft6lnKc6MkLhWCv2mtbFvA9SWK+7Sfq1pANIYPoXkphknSnpTxae3vM3M/uXwh0KKVaVKIPPKVxIm6Tfm9k/1Hah+Lykr8f3v47dOoU73Y+r4wu1D8fucZL+HYuKVyokN+8vGC6fnJ1k4clM+fnkG81K0jfM7E9mdkqsavPF2P/G+DSUJ8xsjcKF9RFFljFfFepcM3syzqO+yPDFFP6Xyt/N7GlJH+rG+F9X+HHI/1je2e4i65uS1saqZH9VSHbaz7e9GyWttvCkmcckfSn2X65QXao77lJIQCXp7ri+/qxwYd6o4nfzS+buVyrUrZbCD9f98eKzo2EfkDRe4QKxswvSuxT2SZP0oIUnRT2tUF/+pwp3adcorBNJ+qiFJxb9RaG6Wk+8SdLzsZpivsF3PhnrcHvFH8x8tcK3KVTH+JvCMXhokXnlj78DzOzhOP32d1gnKFSnWR3PH8+o7aIgH085jq/t4u6bFeroSyFxflGhJHdqCeO+IOlr8ePFZvZ8HHdnhWqkRZ8iVaL3SXrFwhOUHlNbVbVNCutze88J3TFd4Vz6ouJNBoWLwc2dDH+rwgNWhkt6Ku4XzylUF/5ftSUQ24jrNX8x+n4ze1ahFLn9Rf6Tajt+7o/H1C16vQaFUiop7F8Pm9l743Jsiv1vi8dCR08q7Ez+4ndkjONPaqvaNCx2H+hiGs8UvH8yHhM7dSOGcvmc2qoW3mNmz5nZCwqlVZ15TqGxvCR91sx+q7YbZ4XeLml5rB75mNpKr/PVvaS29TJe0jNxvQxWD/epAqWcp17H3R9wd+vidW0X8/61QrsmxTjvj8v1JzN7axfjIgVIYpJ1pUJ7mAaFuyiTFE5KNyg8VanXxdKTBQoXz+sU7ow1KDzx56BYN13u/osY30qFIt9n1MEFgrvfo1CV6TcKCc9uCncK/1fb3s29UuEHJ6NQXW6POP5qhYuGZQqlQfsrrAdJuiR+94TCiXBHhYu/L6j43Z/FCj92WxUuMi9VD//Y0d1/Kek/FdbDYIX1cFE3xl+nbRPSO9oN8jWFak5jFaqTNSg0oH5Hkcneo5BoDFdYjxsV9qOjY+lPyTy00XiT2pLbXRQuMu6RtMDdlxcZvVvc/TqFtgVSKIH6RUdVH+KwjR7aOnQ2rWaFJOCzChd5Oyv8+D4p6XpJ/4jr4mSFUoesQmL2Lm3btqE7/qrQuLpZYT8fHvudE/eTzrxP0nvisEMUGlE/rW3bArT3CYVqkesUErJvSbq73TCrFZLkf8dhZihcZH9SoTSqXMdXb7hS4eJofYzrf9WWnLSv2tbeBQpJw1MKF/mDFI73I9z9Z70Q208U2lQMUji+WhQaBB/t7uu295zQTTcrJOwjFaqgfUFFEjUPbTgWKJzPVynspyMUjoEr1FbNsiPnKuxjmxXaP3xBsX1MwfSfkbREIXGsU7jRcbraicfeeQo3xgYr3O2f4e5rFUp6nlK4/siorRpYKQpLWZ5w9yZ3f1Vtx5JL+r8upnGfQhum1QrHzP5quxmRmHisLlC4KbVM4ZgeoXBTprNxWiWdpnBuqVbYbid3MOjv4nRMoQpyjcJTI0+O21QKx9/3FI7JXRTWS/V27lNSCeepMipsg7mvti3FGV/meaMCrJvXPAC6ycL/S3xG0iOx6gkwoJnZRElb3X19/DxY4aJod0kPuXun/6EzEFjbf+V8tIS7zQAwINGwHygTMztJ4S7l0bHXpxIMB+hLDlBot/SIQknifIUSola1VTsE+o1YfemqTr7+SSypBtANJDFA+eyp8LjkRkmfdPfvJhwP0Fe8pNBIf0+FqlJrFNrm3ejuDyUZGFAm49X2FLb2numkP4AiqE4GAAAAIFVo2A8AAAAgVUhiAAAAAKQKSQwAAACAVCGJAQAAAJAqJDEAAAAAUoUkBgAAAECqkMQAAAAASBWSGAAAAACpQhIDAAAAIFVIYgAAAACkCkkMAAAAgFQhiQEAAACQKiQxAAAAAFKFJAYAAABAqpDEAAAAAEgVkhgAAAAAqUISAwAAACBVSGIAAAAApApJDAAAAIBUIYkBAAAAkCokMQASZ2bXmtmdZZ7HwWb2bMHnpWZ2eKXm3xeY2aFmtrwXp3eQmT1nZk1m9rbemm67eby2nfoSM3uXmf0i6TgkycwWm9kfko4DACqJJAZA2cWL3PwrZ2ZbCj6/qxIxuPvv3X1OJeZVKjO73cyub9evT1y0dxRbBz4m6VZ3H+buP6zQPPsEd/+Wux+RdBy9yczczGYnHQcAlIIkBkDZxYvcYe4+TNLLko4r6PetpONDj82Q9GRPRjSzml6OpWLSHHu5sE4AVBpJDIC+os7MvmFmG83sSTPbL/+FmU0xs++ZWYOZvWRm7+tsImZ2jJk9Faezwswui/27qkpVbP67mdkDZrYufnd8wXcPmNm5BZ+3qdpjZrua2S/NbI2ZPWtmb4/9z5f0LkmXxxKpH5vZNyXtIOnHsd/lcdgFZvZgnP8TZnZokeVfamYfietgrZl93czqOxm2w+XqKLYOxn1B0k4FsQ6K2+lHcVmfN7PzCoa/1sy+a2Z3mtkGSYvbTa/YPPcys7+Z2Xozu6dweczsWDN7PC7Dg2a2ZyfL+kUz+3S7fvea2Qfi+w+b2Qtx+z9lZicWDLfYzP5oZv9tZmskXdvBdj7QzB6JMT5iZge22yaHF3x+rfqimdXHdbI6LsMjZjaxk2WYbmbfj8fBajO7tYNhZsYSlZqCfq/to2Y228x+F+NsNLN7Yv//i4M/Edf/aV2t37hc/2lmf5O0ycxq4ucVcT0+a2aLOloWANheJDEA+orjJd0taZSkH0m6VZLMrErSjyU9IWmqpEWSLjWzIzuZzm2SLnD34ZLmSfrNds6/Ns7/F5ImSHqvpG+ZWZdV08xsqKRfSvp2HPd0SZ83s93d/cuSviXpU7FE6jh3P1PbllR9ysymSvqJpOsljZF0maTvmdn4IrN+l6QjJc2StIukKzuIrdPl6ii29uO7+6x2sTZLukvScklTJJ0i6YZ2F7EnSPquwjr+VrvpFZvn2yUdJWlHSXsqJkBmto+kr0m6QNJYSV+S9CMzG9TBOvm2pNPMzOK4oyUdobDNJekFSQdLGinpo5LuNLPJBePvL+nFuK4+3m5djlHYRjfHOD4r6SdmNraDONo7O85zehz3Qklb2g9kZtWS7pP0L0kzFY6Fu9sPV4LrFLb5aEnTJN0iSe5+SPz+DXH931Pi+j1d0lsVtuksSZdImh+PvyMlLe1BjADQJZIYAH3FH9z9p+6elfRNSW+I/edLGu/uH3P3jLu/KOkrkt7RyXRaJM01sxHuvtbd/7Kd818gaZikG+P8f6NwMXl6CdM8VtJSd/+6u7fGWL6ncIFfqjMk/TTGlnP3X0p6VNIxRca51d2XufsahQvujmLdnuV6HTObLmmhpP90963u/rikr0o6s2Cwh9z9h3E5XnehXsTN7r4yLs+PJe0V+58n6Uvu/rC7Z939DknNcdna+70kV0hUpLANHnL3lZLk7v8b55Fz93skPSfpjQXjr3T3W+J2bB/7WyU95+7fjN/fJekZSa9L/jrQopAgzI7L8Ji7b+hguDcqJIcfcvdNcR33pDF/i0I1wCklTKOU9Xtz3Ne2SMpKGqRw/NW6+1J3f6EHMQJAl0hiAPQVrxS83yypPlaJmSFpSqzOss7M1km6QlKHVW4knaxwgf+vWG3mgO2c/xRJy9w9V/D9vxTuhHdlhqT928X+LkmTSowpP41T201joaTJRcZZ1i7WKR0Msz3L1ZEpkta4+8Yi01umnmm/bYbF9zMkfbDdupmuDpbX3V2h5CKfpL1TBaVBZnZWQbWpdQqleONKjH2KwrIWKnVdflPSzyXdbWYrzexTsZSsvemS/uXurSVMs5jLJZmkP1uoQrikyLClrN/X1ou7Py/pUknXSlplZnebWUf7HgBsN5IYAH3dMkkvufuogtdwd++wJMLdH3H3ExSq/fxQ0ne2c/4rJU2P1drydpC0Ir7fJGlIwXeFCcoySb9rF/swd78oH25Hi9Du8zJJ32w3jaHufmORmKe3i3VlD5aro9iKWSlpjJkN72R6pUyzu/NcJunj7dbNkFgS0pG7JJ1iZjMUqod9T5Li568oVIUa6+6jJP1D4WK/lNhWKlzwFyppH3H3Fnf/qLvPlXSgQundWZ0s6w7WdQP6TbHb2fxecffz3H2KQjWxz1vnTyQrZf1us17c/dvuvlBhfbikT3YRLwD0CEkMgL7uz5I2xAbDg82s2szmmdn89gOaWZ2F/+8Y6e4tkjYoVHHZHg8rXBhebma1FhrVH6e29giPSzrJzIbEi8FzCsa9T9IuZnZmHLfWzOab2W7x+1cVGscXat/vTknHmdmRcdnrLTykYFqRmC82s2mxrcYVku7pwXJ1FFun3H2ZpAclfSLGuKfCuujO0+e6NU+FxONCM9vfgqFm9tZ2iVRhjH+V1KBQze3n7r4ufjVU4YK7QZLM7N0KJTGl+qnCdn5nbNx+mqS5CttfCvvIO+J63k8F1QnN7DAz2yO2edmgUN2ro332z5L+LenGuJz1ZnZQB8vYoJA8nRH3lyUKbVXy8zu1YN9ZG5c7P7/2679b69fM5pjZm2Obma0KbXu29/gDgA6RxADo02IbleMU2kG8JKlR4SJ0ZCejnClpqYUnYF2o0KZke+afUWj0f3Sc9+clneXuz8RB/ltSRuEC8A4VXLTHqlVHKLTfWalQLeqTCu0GpPAQgrmxqk7+f1Y+IenK2O+ymBycoJCMNCjcHf+Qip+/v63QePvF+Hrdf6+UsFwdxdaV0xUana+U9ANJ18Q2PKXq1jzd/VGFdhu3KlyQP692Tz3rwF2SDldYR/npPCXpM5IeUtiOe0j6Y6lBu/tqhRKUD0parVBl61h3b4yDXKWQSKxVeGjAtwtGn6TwsIMNkp6W9DuFxLX9PPLHwWyFByosl3RaJyGdp7CPrJa0u0JymTdf0sNm1qTwAIv/cPeX4nfXSrojrv+392D9DpJ0o8L+9IpCaegV0mt/Dtqjx3EDQEcsVBMGAPQHZrZU0rnu/qukYwEAoFwoiQEAAACQKiQxAAAAAFKF6mQAAAAAUoWSGAAAAACp0tXz5ntk3LhxPnPmzHJMGgAAAEA/8NhjjzW6+/iejFuWJGbmzJl69NFHyzFppN2zz4bunDnJxgEAAIBEmdm/ejpuWZIYoFMXXBC6DzyQaBgAAABIL9rEAAAAAEgVkhgAAAAAqUISAwAAACBVSGIAAAAApAoN+1FZV16ZdAQAAABIOZIYVNbhhycdAQAAAFKO6mSorMcfDy8AAGoUvnoAACAASURBVACghyiJQWVdemno8j8xAAAA6CFKYgAAAACkCkkMAAAAgFQhiQEAAACQKiQxAAAAAFKFhv2orBtuSDoCAAAApBxJDCrrwAOTjgAAAAApR3UyVNaDD4YXAAAA0EOUxKCyrrgidPmfGAAAAPQQJTEAAAAAUoUkBgAAAECqkMQAAAAASBWSGAAAAACpQsN+VNZNNyUdAQAAAFKOJAaVtddeSUcAAACAlKM6GSrrV78KLwAAAKCHKIlBZV1/fegefniycQAAACC1KIkBAAAAkCokMQAAAABShSQGAAAAQKqQxAAAAABIFRr2o7K+9KWkIwAAAEDKkcRU2O/X/l7Pb3k+6TCSMzx2Vz6YaBhATx06+lDtOHjHpMMAAGBAI4mpsC+v+LIeb3pcdVV1SYeSiIN/v06S9PuDRyUcCfB6Oc9pc26ztmS3qNVbNaRqiAZXD9bY2rGaPXi29hi2h6qtOukwAQAY8EhiEjCudpyG1QxLOoxEnHPXUknSC4fPTTQODGw5z6k516zNuc1qzjWr1mrlcpmZdh26q3YfurvmDp2rHQfvqBn1MzS6ZrTMLOmwAQBARBIDYEBozbVqdetqValKWc9q8qDJ2mf4Ptpj2B6aNXiWZtTP0ORBk1VlPO8EAIC+jiQGwIDQ2NKoY8YdozMmnaEd6nfQoKpBSYcEAAB6iCQGQL+X9axMpgumXqApg6YkHQ4AANhO1JsA0O+tblmtQ0cfSgIDAEA/QUkMKuqzH9876RAwwLi7cp7TGZPOSDoUAADQS0hiUFGNkwYnHQIGmI3ZjZpRP0N7DNsj6VAAAEAvoToZKmrhz1dq4c9XJh0GBoCWXIsaMg3a0LpBZ08+m0ckAwDQj1ASg4o65jtLJUl/OJK2Cehd7q6tua1a37peVValOqvTojGLdPjow3XI6EOSDg8AAPQikhgAqZXznJqyTdqU3aQqVWls3VidMekMHTL6EM0bOk+1VbVJhwgAAMqAJAZAKq1vXa/N2c3adciuOnrs0Tpw1IGaWT+TamMAAAwAJDEAUmlLdotu2uUmLRy1MOlQAABAhdGwH0DqbMpu0vi68Tpg5AFJhwIAABJASQwq6sZP75d0COgHNrZu1Hk7nKdqq046FAAAkACSGFTUhtF1SYeAfqDaqjW9fnrSYQAAgIRQnQwVtejeZVp077Kkw0DKmUz1VfVJhwEAABJCEoOKWvSjZVr0I5IYbL8hVUOSDgEAACSEJAZAquQ8p1Zv1YzBM5IOBQAAJIQkBkCqbMlt0fT66RpaPTTpUAAAQEJo2A+gT2v1Vm3KbtKm7CbVWq2yntVJ409KOiwAAJAgkhgAfUbOc9qc26xN2U2SS1VWpSpVae7Qudp/xP7afdju2mXILhpbOzbpUAEAQIJIYlBRH711/6RDQB+yNbdVTa1NavGW10pZZgyeoaPHHK29hu+lXYbsoh3qd1CVUfMVAAC0IYlBRTUP5s8JBzp318bsRm3ObtaImhE6dPSh2m/4fpozdI5mD56t+moenQwAAIojiUFFHXPPUknST0+bmWgcqDx31/rW9dqa26ppg6bpyplX6rDRh6mmitMQAADoHq4eUFELf7FSEknMQOLuWtu6VplcRrMHz9ZF0y7SwlELqSIGAAB6jCQGQNlkchk1tjRq7tC5et/092m/4fvJzJIOCwAApBxJDICyWNsSSl8umXaJzpp8lqqN9lAAAKB3kMQA6FU5z+nVzKuaXDdZn5z9Sc0dNjfpkAAAQD9DEgOgV23IbtAuQ3bRV3b7ioZWD006HAAA0A+RxKCirrjtwKRDQJm5u8bXjieBAQAAZcPjgQAAAACkCkkMKurEO17QiXe8kHQYKDMa8QMAgHIiiUFFzf+/VzX//15NOgyUUdazGlY9LOkwAABAP0YSA6BX5ZTTkOohSYcBAAD6MZIYAL2q1Vs1vnZ80mEAAIB+jCQGQK+qUY1mDJ6RdBgAAKAf4xHLqKhMPQ2++7sqq9KUuilJhwEAAPoxkhhU1LWf2z/pEFBmLteImhFJhwEAAPoxqpMB6HUmSzoEAADQj5HEoKJO+/I/ddqX/5l0GCgTd1fOc0mHAQAA+jmSGFTUGx5u1Bsebkw6DJTJqswq7T5sd02sm5h0KAAAoB8jiQHQKxqaG7RD/Q66aZebVFNFczsAAFA+AyaJWbJkiSZMmKB58+YlHQrQb+Q8pw2tG7SieYVG147W53f9vEbWjEw6LAAA0M8NmCRm8eLFuv/++5MOA0g1d1dTtkn/bv63GjINWpVZpR3qd9B7pr5HX5v7NU2om5B0iAAAYAAYMHU+DjnkEC1dujTpMAa8jaPqkg4B3eDu2pLbog2tG1RlVcp5TjMHz9TJ40/W/BHzNW/YPA2tHpp0mAAAYIAZMEkM+oZPfGa/pENACdxdDZkG5ZTT5LrJOmriUVowYoH2GLaHRtWOSjo8AAAwwJHEAHidta1rNWPwDN0651aqiAEAgD5nwLSJQd9w1s1P66ybn046DBSR9ayac826aserSGAAAECfREkMKmrXJ9YmHQKKyHpW/27+t06ccKL2GLZH0uEAAAB0aMCUxJx++uk64IAD9Oyzz2ratGm67bbbkg4J6FM2ZTfp1cyretv4t+myHS5LOhwAAIBODZiSmLvuuivpEIA+yd3V0NKgQTZIn579aR025rCkQwIAAChqwCQxAF5vQ+sGbWrdpH1G7KPrdrpOEwdNTDokAACALpHEoKIaJw5OOgRIamptUlO2SZPqJumqmVfpsDGHqdqqkw4LAACgJCQxqKjP3rB30iEMaE3ZJjW1Nmli3UR9eMaHtWjMItVUcRoAAADpwtULMABkPauGTIPG1o7VZTtdpiPHHEnyAgAAUourGFTUuZ96UpL01ct3TziSgaOptUkbWjfo9Emn673T36tBVYOSDgkAAGC7kMSgonZ6dn3SIQwY7q5VmVUaWj1UN8+5WQeNOijpkAAAAHoFSQzQT63KrNJew/fSDbNu0Li6cUmHAwAA0GsGzJ9dAgNNlVXp3VPeTQIDAAD6HZIYoJ9yuWbUz0g6DAAAgF5HdTJU1IoZw5IOYUBY37peI6pHaFLdpKRDAQAA6HUkMaioz129Z9Ih9HubspvUkmvRF3b7gqqMwlYAAND/kMQA/UhzrlkbWzfqpl1u0u7DeIw1AADon7hNi4q6+GN/08Uf+1vSYfRba1vW6r3T3svjlAEAQL9GSQwqauq/mpIOoV+rtmpNr5+edBgAAABlRUkM0I+4POkQAAAAyo6SGCDlsp7VutZ1as21akj1EE2rn5Z0SAAAAGVFEgOkkLurKdukpmyTqlSlBSMX6JQJp2jByAWqq6pLOjwAAICyIolBRb04Z2TSIaRa1rNqyDTIZJpaP1UXTb1Ii8Ys0ri6cUmHBgAAUDEkMaior17OY3+3R2NLo/Ydsa8unX6p5gyZIzNLOiQAAICKI4kBUsTdNXfIXO06dNekQwEAAEgMTydDRX3gir/qA1f8NekwAAAAkGKUxKCixr26JekQAAAAkHKUxAAAAABIFZIYICVaci1qzjUnHQYAAEDiqE4G9EHurmZvVlNrk1q9VVVWpRqr0cJRC3XwqIOTDg8AACBRJDGoqGfeMDrpEPqknOe0ObdZTa1NqrIq5TynMbVjtGjMIr1xxBs1b9g8zaifoWqrTjpUAACAxJHEoKK+8b7dkg4hMe6urLJqybUo4xllchm1eItqrVZZz2rm4Jk6btxx2mf4Ppo7dK7G147nf2AAAAA6QBID9AJ3V9azryUnGc+oJdeimqoaVataLlert2pQ1SCNrx2vyYMma+qgqZpRP0Nzh4b/fRlaPTTpxQAAAEgFkhhU1Ec++Kgk6ROf2S/hSErnHhKQwgQl61nVqEZVViWXq8VbNKRqiCbUTXgtQdlh0A6aUDdBY2vHalzdOI2tHashVUMoXQEAANhOJDGoqOHrMkmHUFRLrkWrW1aryqpUparXSlCGVg8NCUrdZE2vn67pg6ZrfN34kKDUhgRlcPXgpMMHAAAYEEhiAElN2SY1tTap1mp10oSTtGDEgm1KUAZVDUo6RAAAAEQkMRiwcp7TmpY1ynpWE+om6OIZF+vIsUdqeM3wpEMDAABAESQxGHBavVWNmUZJ0kEjD9K7Jr9L+w7fV1XGf78CAACkAUkMKuqJ/cclNu+sZ9XY0iiT6eQJJ+vsyWdr8qDJicUDAACAniGJQUXdc/4uFZ9nznNqbGlUznM6auxRunDqhZpaP7XicQAAAKB3kMSgX9ua3arVLav1ptFv0iXTLtGsIbOSDgkAAADbiSQGFXXtxQ+H7uf2L/u83F1rWtfoIzM/olMnnlr2+QEAAKAySGJQUXVbsxWb15rWNZozZI5OmnBSxeYJAACA8uNxTOiXmnPNasm16Kodr1K1VScdDgAAAHoRSQz6na250A7myplXarehuyUdDgAAAHoZ1cnQr2zJbtG61nX62I4f01vHvzXpcAAAAFAGJDGoqEcOmVi2aecTmOtnXa+jxh5VtvkAAAAgWSQxqKgfnF2eRxznE5gbZt2gI8YeUZZ5AAAAoG+gTQxSL5/AfHzWx0lgAAAABgBKYlBRN5zzoCTpitsO7JXpZT2rta1r9fFZH9eRY4/slWkCAACgb6MkBqnWkGnQCeNOoA0MAADAAEISg9RqzjWrrqpO75n2nqRDAQAAQAVRnQx9Ws5zyuQyavZmbc1tVS6XU01V2G1bvVUf2uFDGlc3LuEoAQAAUEkkMUhcq7eqOdes5lxIVKpVrWqrVk455Tyn8XXjNat+lmYNnqUd63fU5EGTNXnQZE2sm6ih1UOTDh8AAAAVRhKDisjkMtqa26r7F41Q1rNa1bxKZqasZ1VbVaupg6ZqZv1MzR48W9Prp2vyoMmaVDdJ42vHv1byAgAAAEgkMaiAxkyjaqxGswfP1pYLF2n24Nm6sW7yayUqI6pHyMySDhMAAAApQRKDstqc3awaq9Hd8+7W1Pqp0ubN4YshQ5INDAAAAKlFEpOAjGe0Jbsl6TAqYl3LOn1650+HBEaSjjkmdB94ILGYAAAAkG4kMRW2x/A9tD67PukwKuZt49+mw8YclnQYAAAA6EdIYirs8hmX6/IZlycdBgAAAJBa/NklAAAAgFQhiQEAAACQKlQnQ2UtXpx0BAAAAEg5khhUFkkMAAAAthPVyVBZjY3hBQAAAPQQJTGorFNOCV3+JwYAAAA9REkMAAAAgFQhiQEAAACQKiQxAAAAAFKFJAYAAABAqtCwH5V10UVJRwAAAICUI4lBZZ12WtIRAAAAIOWoTobKWrYsvAAAAIAeoiQGlXXmmaHL/8QAAACghyiJAQAAAJAqJDEAAAAAUoUkBgAAAECqkMQAAAAASBUa9qOyPvjBpCMAAABAypHEoLKOOy7pCAAAAJByVCdDZT37bHgBAAAAPdRpSYyZbZTk+Y+x6/G9u/uIMseG/uiCC0KX/4kBAABAD3WaxLj78EoGAgAAAAClKKk6mZktNLN3x/fjzGzH8oYFAAAAAB3rMokxs2sk/aekj8RedZLuLGdQAAAAANCZUkpiTpR0vKRNkuTuKyVR1QwAAABAIkp5xHLG3d3MXJLMbGiZY0J/duWVSUcAAACAlCslifmOmX1J0igzO0/SEklfKW9Y6LcOPzzpCAAAAJByXSYx7v5pM3uLpA2SdpF0tbv/suyRoX96/PHQ3WuvZOMAAABAapVSEiNJf5c0WOF/Yv5evnDQ7116aejyPzEAAADooS6TGDM7V9LVkn6j8EeXt5jZx9z9a+UOrj/6r//6L/32t79NOoxEDB8+XPc2N6t+0KCkQwEAAECKlVIS8yFJe7v7akkys7GSHpREEtMDL7/8smprazV06MB5PkJra6tWrVqld7zjHRr0/e8nHQ4AAABSrpQkZrmkjQWfN0paVp5wBoaqqipVV1cnHUZFtLa2qrGxUeeff74uuugiGUkMAAAAtlOnSYyZfSC+XSHpYTO7V6FNzAmS/lyB2NAPNDY26uyzzw4JjFnS4QAAAKAfKFYSk/9DyxfiK+/e8oWD/qa6ulp77713WwJzww3JBgQAAIDU6zSJcfePVjIQDBAHHph0BAAAAEi5Up5ONl7S5ZJ2l1Sf7+/uby5jXOhHamoKdrMHHwxdkhkAAAD0UCkN+78l6R5Jx0q6UNLZkhrKGRT6l7Fjx7Z9uOKK0OV/YgAAANBDVSUMM9bdb5PU4u6/c/clkhaUOS70E9lsVhMmTEg6DAAAAPQjpZTEtMTuv83srZJWSppWvpDQH2zdulVr167V7NmzNWrUqKTDAQAAQD9SShJzvZmNlPRBSbdIGiHp/WWNCqmVzWbV0NCgQYMG6YMf/KBOPvlkHq0MAACAXtVlEuPu98W36yUdVt5wkFbursbGRuVyOZ100km64IILNGbMmKTDAgAAQD9U7M8ub1H4c8sOufv7yhIRUsfd9corr2i33XbTNddco9mzZ3c+8E03VS4wAAAA9EvFSmIerVgUSK18ArP33nvrf/7nfzRkyJDiI+y1V2UCAwAAQL9V7M8u76hkIEiffAKz77776qabbtLgwYO7HulXvwrdww8vb3AAAADot0pp2A90qLGxUbvttlvpCYwkXX996JLEAAAAoIdK+Z8Y4HUymYzcXdddd13pCQwAAADQC4omMWZWbWY8Thmvs3r1al144YWaOXNm0qEAAABggCmaxLh7VtIJFYoFKZLL5XT66acnHQYAAAAGoFLaxPzRzG6VdI+kTfme7v6XskWFPs/MVFNDkyoAAABUXilXoQfG7scK+rmkN/d+OOj3vvSlpCMAAABAynWZxLj7YZUIBOmRzWZVW1ur6urq7o88Z07vBwQAAIABpcunk5nZRDO7zcx+Fj/PNbNzyh8a+qpMJqNx48bJzLo/8o9/HF4AAABAD5XyiOXbJf1c0pT4+Z+SLi1XQOj7Nm/erClTpnQ9YEc+85nwAgAAAHqolCRmnLt/R1JOkty9VVK2rFGhT8pms3rllVdUX1+vc889N+lwAAAAMECV0rB/k5mNVWjMLzNbIGl9WaNCn7N+/Xpt3rxZxx9/vP7jP/5DI0eOTDokAAAADFClJDEfkPQjSbPM7I+Sxks6taxRoU9ZtWqVxo0bp89+9rPab7/9kg4HAAAAA1wpScyTkt4kaY4kk/SsSquGhn4gk8movr5e3/3udzV48OCkwwEAAABKSmIecvd9FJIZSZKZ/UXSPmWLCn3G2rVrdcIJJ/ReAvPNb/bOdAAAADBgdZrEmNkkSVMlDTazvRVKYSRphKQhFYgNCctkMspmszriiCN6b6LTp/fetAAAADAgFSuJOVLSYknTJH1GbUnMRklXlDcsJMnd1djYKHfXJZdcon333bf3Jn7PPaF72mm9N00AAAAMKJ0mMe5+h6Q7zOxkd/9eBWNCgrZs2aK1a9dqr7320tVXX60ZM2b07gy+8IXQJYkBAABAD5XSJmaamY1QKIH5ikJbmA+7+y/KGhkqbt26dZKka665Rscee6yqqnh+AwAAAPqeUq5Sl7j7BklHSJog6d2SbixrVEjE1q1bddlll+n4448ngQEAAECfVcqVar4tzDGSvu7uTxT0Qz9SU1OjHXfcMekwAAAAgKJKSWIeM7NfKCQxPzez4ZJy5Q0LSWhtbdW0adOSDgMAAAAoqpQ2MedI2kvSi+6+2czGKlQpQz/j7uX/Q8vvfre80wcAAEC/V0oSszB29zSjFhm207hxSUcAAACAlCsliflQwft6SW+U9JikN5clIvRvt98euosXJxkFAAAAUqzLJMbdjyv8bGbTJX2qbBGhfyOJAQAAwHbqyXN0l0ua19uBAAAAAEApuiyJMbNbJHn8WKXQyP+JcgaFysrlcmpoaNDQoUNVXV2ddDgAAABAUaW0iXm04H2rpLvc/Y9ligcV5O5avXq1Wlpa9Ja3vEWXXHKJ6urqkg4LAAAAKKqUNjF3VCIQVNaGDRvU1NSkN7zhDbrssss0d+7cpEMCAAAAStJpEmNmf1dbNbJtvpLk7r5n2aJCWW3atElmpptuukkLFy5URR+d/dOfVm5eAAAA6JeKlcQcW7EoUFEtLS3ac889dfDBB1d+5kOGVH6eAAAA6FeKJTG1kia2b/9iZgdLWlnWqFBW7p5c25fPfz503/OeZOYPAACA1Cv2iOWbJG3soP+W+B1Sqrm5WePHj09m5t/5TngBAAAAPVQsiZnp7n9r39PdH5U0s2wRoaxyuZwk6dRTT004EgAAAKBniiUx9UW+G9zbgaAy1qxZo/nz52vnnXdOOhQAAACgR4olMY+Y2Xnte5rZOZIeK19IKJeWlha1tLTowgsvTDoUAAAAoMeKNey/VNIPzOxdakta9pNUJ+nEcgeG3uXuamho0AUXXKA99+Tp2AAAAEivTpMYd39V0oFmdpikebH3T9z9NxWJDL1q1apVWrBggc4555xkA3nggWTnDwAAgNQrVhIjSXL330r6bQViQZm4u7LZrK677jpVV1cnHQ4AAACwXYq1iUE/kX+k8pgxY5IORfr0p8MLAAAA6CGSmAFg69at2nHHHZMOI7jvvvACAAAAeogkZgDYsmWL9t9//6TDAAAAAHoFSUw/l8lkNGjQIJ14Ig+UAwAAQP9AEtOP5R+r/M53vlMjR45MOhwAAACgV3T5dDKk05YtW7R27VrNmzdP73znO5MOp83gwUlHAAAAgJQjielnstmsGhoaNGTIEF199dU67rjjVFXVhwrcfvazpCMAAABAypHE9CNNTU3auHGjjjvuOL3vfe/T6NGjkw4JAAAA6HUkMf3Ipk2b9KEPfUinnXZa0qF07rrrQveqq5KNAwAAAKnVh+oZYXtVVVVpwoQJSYdR3K9/HV4AAABAD5HE9BPurtbWVtXX1ycdCgAAAFBWVCfrB1pbW7Vq1SrtvffemjdvXtLhAAAAAGVFEpNy+cb855xzjs4//3zV1LBJAQAA0L9xxZtia9euVU1NjW655RYdcMABSYdTmrFjk44AAAAAKUcSk1LurubmZt12222aM2dO0uGU7nvfSzoCAAAApBwN+1Nq3bp1mjdvXroSGAAAAKAXkMSkVCaT0VlnnZV0GN33kY+EFwAAANBDVCdLqerqas2cOTPpMLrvoYeSjgAAAAApR0kMAAAAgFQhiQEAAACQKiQxAAAAAFKFNjGorGnTko4AAAAAKUcSk0Ktra1qbW1VTU0KN9+ddyYdAQAAAFKO6mQp09LSoldffVXnnnuuplGqAQAAgAEohbfyB66WlhY1NDTo4osv1pIlS2RmSYfUfZdeGro33ZRsHAAAAEgtkpgUaWxs1CWXXKLFixenM4GRpMcfTzoCAAAApBzVyVIik8lo6NChOuuss9KbwAAAAAC9gCQmJdauXavDDz9c1dXVSYcCAAAAJGrAJDH333+/5syZo9mzZ+vGG29MOpxuMzO9+c1vTjoMAAAAIHEDok1MNpvVxRdfrF/+8peaNm2a5s+fr+OPP15z585NOrSSuLtyuZz22GOPpEPZfrvsknQEAAAASLkBkcT8+c9/1uzZs7XTTjtJkt7xjnfo3nvvTU0Ss2nTJu2www4aMWJE0qFsvy9/OekIAAAAkHIDojrZihUrNH369Nc+T5s2TStWrEgwou7ZtGmT5s+fn3QYAAAAQJ8wIJIYd39dvzQ94au1tVVTp05NOozecf754QUAAAD00ICoTjZt2jQtW7bstc/Lly/XlClTEoyoe6qrqzV69Oikw+gd//xn0hEAAAAg5QZEScz8+fP13HPP6aWXXlImk9Hdd9+t448/PumwSlZdXa0xY8YkHQYAAADQJwyIkpiamhrdeuutOvLII5XNZrVkyRLtvvvuSYdVEndXNpvVrrvumnQoAAAAQJ8wIJIYSTrmmGN0zDHHJB1Gt23ZskWTJk2iJAYAAACIBkwSk0bZbFZr167VqaeemnQovWevvZKOAAAAAClHEtNHtbS0aNWqVTrzzDN1wQUXJB1O77nppqQjAAAAQMqRxPRBmUxGDQ0Nuvjii7VkyZJUPQ4aAAAAKDeSmD5o9erVOv/883XOOeckHUrvO+OM0L3zzmTjAAAAQGqRxPRB1dXVOvzww5MOozyWL086AgAAAKTcgPifmDTJZDKqr6/XTjvtlHQoAAAAQJ9EEtPHrF+/XgcddJCqqtg0AAAAQEe4Uu5jcrmcjjzyyKTDAAAAAPos2sT0IdlsVmam/fbbL+lQyueAA5KOAAAAAClHEtOHNDc3a8qUKRo6dGjSoZTPJz6RdAQAAABIOaqT9TE1NeSVAAAAQDEkMX2Iu/f/P7Y8+eTwAgAAAHqIJKYPaWpq6t/tYSRp9erwAgAAAHqIJKaPyGQyqqmp0ZIlS5IOBQAAAOjTSGL6iDVr1ujMM8/UuHHjkg4FAAAA6NNIYvqAfFuYt73tbUmHAgAAAPR5PAqrD8hkMhoxYoQmTZqUdCjlt2hR0hEAAAAg5Uhi+oCmpiYtXLiw/z+ZTJKuuirpCAAAAJByVCfrA5qbmzVr1qykwwAAAABSgSSmjxg1alTSIVTG0UeHFwAAANBDVCfrA6qrqzVixIikw6iMLVuSjgAAAAApR0lMH1BVVaXJkycnHQYAAACQCiQxCXN3ZbNZzZgxI+lQAAAAgFQgiUnYmjVrNGrUqIHTJgYAAADYTrSJSUhLS4saGho0c+ZMXX/99QPj8cqSdOyxSUcAAACAlCOJqTB31+rVq1VfX6/zzz9f7373u1VXV5d0WJVz2WVJRwAAAICUI4mpsGXLlmn06NH66le/qp133jnpcAAAAIDUIYmpsJqaGh177LEDN4E59NDQfeCBJKMAAABAitGwPwEDpv0LAAAAUAYkMRWUyWRUW1ur+vr6pEMBAAAAUoskpkIymYwuv/xyLV++XIccckjS4QAAAACpRRJTAfkE5g9/+IOGDRum6urqMqIzdgAAEv1JREFUpEMCAAAAUouG/WX20ksv6ZprrtHTTz+tSZMmafXq1UmHlKy3vz3pCAAAAJByJDFl0tLSojvvvFNf/OIXVVVVpYkTJ9KgX5Le856kIwAAAEDKkcSUwTPPPKOrr75aL774osaOHTuw/syyK5s3h+6QIcnGAQAAgNQiiellmzZt0jnnnCNJmjRpEqUv7R1zTOjyPzEAAADoIRr297IVK1Yol8tpzJgxJDAAAABAGZDE9LJ8EgMAAACgPEhietny5cvV2tqadBgAAABAv0US08tefvll1dbWJh0GAAAA0G/RsL+XLV++nKeRFbN4cdIRAAAAIOVIYnrRihUr9MQTT2j48OFJh9J3kcQAAABgO1GdrJdkMhldfvnlcndKYoppbAwvAAD+f3v3H2RXWd9x/PPZH/mBxCRmd7MolHQsoui0oWIV1BEx46gtP5wG0QGVTlpjOigoaJEygIRhDFTK6IASWyZWrUlkkGIAQ4xJWQPkh02AAKZmog35RXbzgySEQHbz7R/niV6W/XH3Zveee3bfr5kz55x7z/Oc7z33mbv3u8/z3AMAFaInZpDccccd2rBhg1pbW/MOpbZNn56tuU8MAAAAKkRPzCBYvHixfvjDH6q5uZl7wwAAAABDjCTmGK1fv17XX3+9Jk6cqIYGOrYAAACAoUYScwy2b9+uyy+/XKNGjdLYsWPzDgcAAAAYEUhiKtTR0aGZM2fqpZde0vjx4/MOBwAAABgxGP9Ugd27d2vmzJnauXOnmpub8w6nWGbNyjsCAAAAFBxJzADt379fs2bN0pYtW9TS0pJ3OMVz0UV5RwAAAICCYzjZAESEvv71r2vTpk0kMJV67rlsAQAAACpET8wALFiwQMuWLeNeMMfi05/O1twnBgAAABWiJ6ZM69ev12233aampibV1XHZAAAAgLzwbbwMBw4c0Fe/+lWNHj1ao0aNyjscAAAAYEQjielHROiWW25Re3u7JkyYkHc4AAAAwIhHEtOPtrY2PfDAA0zkBwAAAGoEE/v7sXr1atXX1zMPZrBceWXeEQAAAKDgSGL60d7ersbGxrzDGD7OPTfvCAAAAFBwdC/0Y9euXWpoINcbNBs2ZAsAAABQIb6d92Pfvn2qr6/PO4zhY+bMbM19YgAAAFAhemL6ceDAAZIYAAAAoIaQxPQhIkhiAAAAgBpDEtOH9vZ2HTx4kDkxAAAAQA0hienDs88+q7q6OtnOOxQAAAAACV0Mvejq6tJDDz2kiMg7lOHl2mvzjgAAAAAFRxLTg87OTs2ePVtLlizR5MmT8w5neJk2Le8IAAAAUHAkMd0cPnxY1113nR5++GG1traqro4Rd4Nq3bpsPXVqvnEAAACgsEhiurn99ttJYIbSFVdka+4TAwAAgArxLb3EwYMHdd9996m5uZkEBgAAAKhRfFMvsWLFCh0+fFiNjY15hwIAAACgFyQxJRYtWkQCAwAAANQ4kpgSGzdu1HHHHZd3GAAAAAD6wMT+pKurSzt37lRLS0veoQxvN9+cdwQAAAAoOJIYSS+++KLmzp2rI0eOMKF/qJ11Vt4RAAAAoOBGdBITEWpra9NNN92kPXv20AtTDY8+mq1JZgAAAFChEZvEbNu2TXPmzNGvfvUrjRs3Tq2trXmHNDJcc0225j4xAAAAqNCISmK6urq0Zs0aLVy4UG1tbbLNTS0BAACAghkRScyOHTv04IMPav78+dq7d6/q6urU1NSk+vr6vEMDAAAAMEDDNomJCK1YsULz58/XqlWrFBEaP368Jk+enHdoAAAAAI7BsE1ili9friuvvFJjxoxRc3MzQ8YAAACAYWJYJzGNjY2aNGlS3qGg1O235x0BAAAACm5YJjFHjhxRW1ubxo8fn3co6G7q1LwjAAAAQMENuzFWO3bs0GWXXaZ9+/Zp9OjReYeD7n7xi2wBAAAAKjRsemIiQosWLdKcOXN0+PBh7vtSq266KVtPm5ZvHAAAACisYZHEtLe368Ybb9Rjjz2mCRMmaOLEiXmHBAAAAGCIFD6JWblypa6++modPHhQra2tsp13SAAAAACGUGGTmM7OTs2dO1d33323xo0bp5aWlrxDAgAAAFAFhUliurq6tGfPHnV0dKijo0Pz5s3T2rVr1dLSooaGwrwMAAAAAMeoJr79d3V1affu3ero6NCuXbvU3t6u7du3a/Pmzdq6dauef/557dmzR7ZVX1+viJBtnXDCCQwfK5q77so7AgAAABTckCcxnZ2d2r179x+Sk127dmnr1q3avHmztm3b9ocEpb6+XnV1dYoIdXZ2yrZGjRqlxsZGjR49Wi0tLaqrG3a/CD3ynHpq3hEAAACg4IYkidmyZYsuvvhi7dy5U7t27dIrr7zyqqWurk6NjY0aNWrUHxKViHhVHRGhQ4cO6dChQ9q/f/9QhJmLrq6uvEPI189+lq3PPTffOAAAAFBYQ5LEjBkzRuecc85QVF14DQ0NOvHEE/MOIz/f/Ga2JokBAABAhYYkiWlqatKMGTOGomoAAAAAIxyTTAAAAAAUCkkMAAAAgEIhiQEAAABQKDVxnxiMID/4Qd4RAAAAoOBIYlBdJ52UdwQAAAAoOIaToboWLMgWAAAAoEL0xKC6vvOdbH3RRfnGAQAAgMKiJwYAAABAoZDEAAAAACgUkhgAAAAAhUISAwAAAKBQmNiP6rrnnrwjAAAAQMGRxKC6mpryjgAAAAAFx3AyVNe8edkCAAAAVIgkBtVFEgMAAIBjRBIDAAAAoFBIYgAAAAAUCkkMAAAAgEIhiQEAAABQKPzEMqrrwQfzjgAAAAAFRxKD6jruuLwjAAAAQMExnAzVdeed2QIAAABUiCQG1bVwYbYAAAAAFSKJAQAAAFAoJDEAAAAACoUkBgAAAEChkMQAAAAAKBRHxOBXau+XtGHQKx4emiR15B0ECod2g0rQblAJ2g0qQbtBJU6NiHGVFByq+8RsiIgzhqjuQrO9hmuDgaLdoBK0G1SCdoNK0G5QCdtrKi3LcDIAAAAAhUISAwAAAKBQhiqJmTtE9Q4HXBtUgnaDStBuUAnaDSpBu0ElKm43QzKxHwAAAACGCsPJAAAAABTKoCQxtt9ge4nt36b1xF6O+7ntvbYXDcZ5a5ntj9jeYHuj7at7eH607QXp+ZW2p1Q/StSaMtrNl20/Y/tJ20ttn5xHnKgt/bWbkuOm2w7b/IIQymo3tj+RPnOetv2f1Y4RtaeMv1N/YnuZ7bXpb9XH8ogTtcX23bZ32l7fy/O2/a3Urp60/Zf91TlYPTFXS1oaEadIWpr2e3KrpE8P0jlrlu16SXdI+qik0yR9yvZp3Q6bIWlPRPyZpH+VNKe6UaLWlNlu1ko6IyL+XNI9km6pbpSoNWW2G9keJ+mLklZWN0LUonLaje1TJH1N0nsj4u2Srqh6oKgpZX7eXCtpYUScLumTku6sbpSoUfMkfaSP5z8q6ZS0fE7Sd/qrcLCSmPMlfT9tf1/SBT0dFBFLJe0fpHPWsr+StDEiNkXEK5LmK7tGpUqv2T2SPmTbVYwRtaffdhMRyyLiYNp9XNKJVY4RtaeczxtJmq0s6T1UzeBQs8ppN/8g6Y6I2CNJEbGzyjGi9pTTbkLS69P2eEnbqhgfalREPCJpdx+HnC/pPyLzuKQJtk/oq87BSmImR8T2FOR2SS2DVG9RvUnScyX7W9JjPR4TEZ2SXpA0qSrRoVaV025KzZD00JBGhCLot93YPl3SSREx7IfyomzlfN68RdJbbK+w/bjtvv6LipGhnHZzg6RLbG+R9KCkL1QnNBTcQL8DqaHcmm3/QlJrD0/9c7l1jCA99ah0/xm4co7ByFJ2m7B9iaQzJH1gSCNCEfTZbmzXKRuyemm1AkIhlPN506BsaMfZynp922y/IyL2DnFsqF3ltJtPSZoXEd+0faakH6R2c2Tow0OBDfh7cdlJTERM6/Ws9vO2T4iI7anrZ6R3OW+RdFLJ/ol6bXfq0WO22G5Q1uXaVzcbhr9y2o1sT1P2z4MPRMTLVYoNtau/djNO0jskLU8jVlsl3W/7vIhYU7UoUWvK/Tv1eEQclvQ72xuUJTWrqxMialA57WaG0tyHiHjM9hhJTeK7IfpW1negUoM1nOx+SZ9N25+V9F+DVG9RrZZ0iu0/tT1K2cS2+7sdU3rNpkv6ZXDTnpGu33aThgXdJek8xqcj6bPdRMQLEdEUEVMiYoqyuVQkMCjn79R9kj4oSbablA0v21TVKFFrymk3myV9SJJsv03SGEntVY0SRXS/pM+kXyl7j6QXjk5V6U3ZPTH9+IakhbZnKGu8F0pS+hnPz0fE36f9NklvlXR8Gis5IyIWD1IMNSMiOm1fJmmxpHpJd0fE07ZvlLQmIu6X9O/Kulg3KuuB+WR+EaMWlNlubpV0vKSfpP+qb46I83ILGrkrs90Ar1Jmu1ks6cO2n5HUJekrEbErv6iRtzLbzZWSvmf7S8qGA13KP2lh+8fKhqY2pRzgekmNkhQR31U2f+pjkjZKOijp7/qtk3YFAAAAoEgGazgZAAAAAFQFSQwAAACAQiGJAQAAAFAoJDEAAAAACoUkBgAAAEChkMQAQD9sT7K9Li07bG9N23vTz88O5rneaPuetH227UVp+wbbVw3muXo49wW2TyvZv9T2G4fynH3EcqHtZ20vS/s/tv2k7S/ZvjHd9LW3smfY/tYxnPuaSsv2UNfv0z1WAACDaLDuEwMAw1a6N8ZUKUsmJB2IiH+xPUXSokE+1zZlN8DNwwXKXs/RxOxSSevVz12Th8gMSf8YEctst0o6KyJOLqdgupHnsdzM8xpJNx9DeQDAEKMnBgCOTb3t79l+2vbDtsdKku032/657V/bbrP91u4FbX+gpIdnre1xtqfYXt/LuU6zvdz2JttfLKnny7bXp+WK9Nir6rF9VUrAeozN9lmSzpN0a4rnnySdIelHaX+s7Xfa/u9UbrHtE3p4TZNt/9T2E2k5q7cY0+OX2F6VznGX7Xrb10l6n6Tv2r5V0sOSWtIx77c9z/b0VP5dth9N51qVrmFpD9brbN9te3W6xuenxy+1fW+6Dr+1fUt6/BuSxqZz/ajba5t19LiSOr6dtu9L1+Vp25/r4boM6P1Ij1+YrtcTth/ppU0AwMgUESwsLCwsZS6SbpB0VdqeIqlT0tS0v1DSJWl7qaRT0va7Jf2yh7p+Jum9aft4Zb3jUyStT4+dLWlRyXkflTRaUpOkXcrudvxOSU9Jel2q42lJp5fWk8pfJemGvmKTNE/S9JIyyyWdkbYb0/mb0/5Fyu7W3f01LZB0RdqulzS+jxjflq5BYzr+Tkmf6eHc3V/LPGW9VaMkbZL0rvT469M1LL1uN5e8JxMk/W+K49JUdrykMZL+T9JJ6bgDvbz3zZI2luw/JOl9afsNaT1WWe/VpLT/+/R+VfJ+PCXpTUdjz7vts7CwsNTSwnAyADg2v4uIdWn715Km2D5e0lmSfmL76HGjeyi7QtJt6T/+90bElpLje/JARLws6WXbOyVNVtZj8dOIeFGSbN8r6f2S7u+pggHE1t2pkt4haUkqVy9pew/HnSPpM5IUEV2SXrDdW4xHlCU4q1OdYyXtLCOW0pi2R8TqdL59qf7SYz4s6Tz/cT7RGEl/kraXRsQLqcwzkk6W9FxvJ4uI9tQL9h5Jv03nX5Ge/qLtj6ftkySdoizR7FM/78cKSfNsL5R0b391AcBIQhIDAMfm5ZLtLmVfxOsk7Y2IqX0VjIhv2H5A0sckPe5ssvqhAZyrQVJvWU+nXj1keExalxVbDyzp6Yg4c4Dljpbt7fHvR8TXKqjzaPko45i/jYgNr3rQfrd6vp79WSDpE5J+oywxC9tnS5om6cyIOGh7uf54vY8a8PsREZ9Pcf61pHW2p0Y2PwsARjzmxADAIEs9Ar+zfaEkOfMX3Y+z/eaIeCoi5iibiP6aeTNleETSBbaPs/06SR+X1CbpeWXzSCbZHi3pb8qIbb+kcSV1l+5vkNRs+8xUrtH223uIZ6mkWemYetuv7yPGpZKm225Jx7/BdlmT95PfSHqj7Xel8uNsd09EFkv6glM3h+3Ty6j3sO3GXp67V9kPIHxKWUIjZUPS9qQE5q2S3tNDuQG/H6l9rIyI6yR1KOvhAQCIJAYAhsrFkmbYfkLZHJDzezjmiqMTtyW9pGyOxYBExP8omyOyStJKSf8WEWsj4rCkG9Nji5R94e8vtvmSvpImwL851ftd2+uUDR+bLmlOKrdO2TCo7i6X9EHbTykbXvf2PmJ8RtK1kh62/aSkJZJe82MBfbz2V5TNzfl2immJXtsDMlvZfJ4n08T62WVUPTcd/6PuT0TEHmW/3nZyRKxKD/9cUkN6DbMlPd5DuUrej1ttP5XifkTSE2XEDgAjgiP664kHAAAAgNpBTwwAAACAQiGJAQAAAFAoJDEAAAAACoUkBgAAAEChkMQAAAAAKBSSGAAAAACFQhIDAAAAoFBIYgAAAAAUyv8DzYUDdyki+OIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DG+3+O+S0o93fdlHjMdACuW3Tw0ZK6YcR8Rjg+eTz1qOBE4BXRcThKaU7h1nGju5T250ndvY8VcT7qWHiglyFeshEprgh1+hLwL8X/+83wrzVBCyJKVHRCPEYyOXaxR2Xm4q3N+9kAjOSXxb9R0TE0UUczyDfmYD86F3oT0gWRm6EWmHARXVxp25V8fJ6cpWoY1NKx5K/ZD7UcMG7pehPHxDPlob/G9/7Jf0n6K803n0hP7Hqo8OsY/3O0GdTSkcAzySfgHfGMfSfxB+ZUnocoywlK+7G/Vvx8l3kusX3AI2PEX4i8M2U0mtTSk8EPlgMf/Iwsz4OuCql9PcppSeTtwfA8p0o+arvD+3AaQARcSS5FAT694edllJ6E/muH+TE69KImDbMJB8v+n8iJ3iDqT/4ohU4q2HfOJ5cZfKiYls8ohjv3SmlFcBfspMXzhHxiLw66X0ppeeSv4y3kC8enjTEZI378Vn1kqjIjaiXDrO4+vF3UDH+3IHLiIiZwKHkalUvSSk9hv671fX9ZzyOr111K/1J02mRdQKnjGLaejsoyBdBRMSMhmmv28HSwYeJ3MD8hpTSm1JKTweeW7y1N3l77/Q5YSc8MyKmFTe5nlcM6yJXjRlM/bhIwEsaPtMTyZ/pJUNM13hx+nzYVtI+cL+u3wAAeHpK6Wjgw4PMb6j9bhk56QA4M6V0FP1P6ByNK4t+/Zz3v8D/kK9lXlUM+ynD2xZbRAz8PipT/THQSyPiBfWBETHcjanGG4f10rnnDhwpIpYDa1JK70gpnUL/d2QnUH+gQn271Pe3up3dp+rLHs15ajAzycfacN2SEZb93oiY3zDoLxv+XzXctGoOJjHleinwf5Gf3vO7iPgT+Q4FFFUlxsG/ki+kA/ifiLie/gvFW4EvFP//pOhPId/pvo7BL9T+seifCtxTFBWvJp8g3tAwXj05e17kJzPVl1NvNAvwnxHxfxFxelHVpn7H5JziaSi/jYj7yRfWJw2zjvWqUK+MiBuKZXQMM/5wGn9L5fcR8QfgzTsw/RfIXw71L8uLBlxkXQg8UFQl+w052Rm43IHOAdZHftLMtcCni+F3katL7YivkBNQgK8W2+sX5AvzdQx/N3/UUkrvJNethvzF9YPi4nOwca8EFpAvEIe6IP0KeZ8M4GeRnxT1B3J9+cvId2nvp/+xmu+N/MSiX5Orq+2MJwG3FtUU6w2+68nYoJ9XSmkV/dUKn0OujvE78jG4cphl1Y+/x0fEz4v5D7zDupBcnWZ9cf64if6Lgno843F87ZKU0hZyHX3IifPt5JLcfUYx7W3A54uXr4+IW4tpH0GuRjrsU6RG6e+BeyM/Qela+quqbSZvz109J+yIfcnn0tspbjKQLwa3DDH+J8mNl2cANxb7xR/J1YW/Tn8CsZ1iu9YvRt8QETeTS5EHXuTfQP/x84PimDqfh1tLLqWCvH/9PCL+rliPzcXw/yiOhcGeVDiU+sXvrCKO/6O/alNn0b9yhHnc1PD/DcUxceAOxDBe/pX+qoUXR8QfI+I2cmnVUP5IbiwP8LGI+Cn9N84avQC4q6geeS39pdf16l7Qv10WADcV22UqO7lPNRjNeephUkpXppRihO7sEZb9buC+YlveSn+1z3vx0dt7BJOYcr2T3B5mLfkuyiLySemD5Kcqjbmi9ORY8sXzBvKdsbXkA/q4om46KaUfFfGtJhf53sQgFwgppYvJVZmuICc8h5HvFH6d7e/mvpP8hdNDri73yGL69eSLhjvJpUHHkLcDwN8W7/2WfCI8gHzx928Mf/fnDPKXXRf5IvMsdvKHHVNKPwbeSt4OU8nb4W92YPoNbJ+QfnHAKJ8nV3OaR65OtpZc5P2XDO1icqIxg7wdHyLvRycXpT+jlnIbjSfRn9weQr7IuBg4NqU0Zj8KllJ6P7ltAeQSqB8NVvWhGHddym0dhppXNzkJ+Bj5Iu8R5C/fG4APANcX2+I0cqlDlZyYvZjt2zbsiN+QG1d3k/fzGcWwVxT7yVD+HnhdMe40ciPqP7B9W4CBPkSuFrmBnJB9ifxUnUbryUnyPcU4+5Mvsj9MLo0ar+NrLLyTfHH0YBHX1+lPTgZWbRvoNeSk4UbyRX47+Xg/KaX0/TGI7XvkNhXt5OOrF7icfHxt2NVzwg76BDlhn0WugvZvDJOopdyG41jy+XwNeT+dST4G3k5/NcvBvJK8j20ht3/4N4r2MQ3zvwk4k5w4TiHf6PgrBiiOvVeRb4xNJd/t3z+l9AC5pOdG8vVHD/3VwEajsZTltymlTSml++g/lhLw3yPM41Lyxex68jFzDP03I0pTHKvHkm9K3Uk+pmeSb8oMNU0f8ELyuaWF/LmdNsioVxXzCXIV5FbyUyNPKz5TyMffJeRj8hDydmnZxX0KRnGeGkfvIK/nTPJNklvJN28eW1wLqcnFDl7zSNpBkX9f4lzgl0XVE2lSi4i9gK6U0oPF66nki6IjgGtSSkP+hs5kEP2/lfPeUdxtlqRJyYb90jiJiOeR71KeXAz6SInhSBPJ48ntln5JLkk8mlxC1Ed/tUNpjxERz6K/uvBA3ytKqiXtAJMYafwsJz8ueR3w4ZTSN0qOR5oo7iA30l9Orip1P7lt3jkppWvKDEwaJwvofwrbQDcNMVzSMKxOJkmSJKmp2LBfkiRJUlMxiZEkSZLUVExiJEmSJDUVkxhJkiRJTcUkRpIkSVJTMYmRJEmS1FRMYiRJkiQ1FZMYSZIkSU3FJEaSJElSUzGJkSRJktRUTGIkSZIkNRWTGEmSJElNxSRGkiRJUlMxiZEkSZLUVExiJEmSJDUVkxhJkiRJTcUkRpIkSVJTMYmRJEmS1FRMYiRJkiQ1FZMYSZIkSU3FJEZS6SLi7Ii4aJyX8cSIuLnh9aqIeOruWv5EEBErI+KuMZzfcRHxx4jYFBHPGav5DljGts9pIomIF0fEj8qOAyAizoiI/y07DknanUxiJI274iK33tUiYmvD6xfvjhhSSv+TUlq2O5Y1WhFxQUR8YMCwCXHRPlhsg3gf8MmUUmdK6Vu7aZkTQkrpSymlk8qOYyxFRIqIg8uOQ5JGwyRG0rgrLnI7U0qdwJ+BUxuGfans+LTT9gdu2JkJI6J1jGPZbZo59vHiNpG0u5nESJoopkTEf0bEQxFxQ0Q8tv5GROwdEZdExNqIuCMi/n6omUTEMyPixmI+d0fEm4rhI1WlGm75h0XElRGxoXjvLxreuzIiXtnweruqPRFxaET8OCLuj4ibI+IFxfBXAy8G3lKUSH03Ii4E9gO+Wwx7SzHusRHxs2L5v42IlcOs/6qIeFuxDR6IiC9ERMcQ4w66XoPFNsi0twEHNsTaXnxO3ynW9daIeFXD+GdHxDci4qKI2AicMWB+wy1zRUT8LiIejIiLG9cnIk6JiOuKdfhZRCwfYl3/PSI+OmDYtyPijcX//xgRtxWf/40R8dyG8c6IiKsj4l8i4n7g7EE+5ydExC+LGH8ZEU8Y8Jk8teH1tuqLEdFRbJP1xTr8MiL2GmId9o2I/yqOg/UR8clBxllalKi0Ngzbto9GxMERcVUR57qIuLgY/t/F6L8ttv8LR9q+xXq9NSJ+B2yOiNbi9d3Fdrw5Ip4y2LpI0q4yiZE0UfwF8FVgNvAd4JMAEVEBvgv8FtgHeApwVkQ8fYj5/AfwmpTSDOBI4IpdXH5bsfwfAQuBvwO+FBEjVk2LiOnAj4EvF9P+FfCpiDgipfQZ4EvAR4oSqVNTSi9l+5Kqj0TEPsD3gA8Ac4E3AZdExIJhFv1i4OnAQcAhwDsHiW3I9RostoHTp5QOGhBrN/AV4C5gb+B04IMDLmKfDXyDvI2/NGB+wy3zBcAzgAOA5RQJUEQ8Bvg88BpgHvBp4DsR0T7INvky8MKIiGLaOcBJ5M8c4DbgicAs4L3ARRGxuGH6Y4Dbi231TwO25VzyZ/SJIo6PAd+LiHmDxDHQy4tl7ltM+1pg68CRIqIFuBT4E7CUfCx8deB4o/B+8mc+B1gCnA+QUjqheP9Rxfa/eJTb96+AZ5E/04OAvwWOLo6/pwOrdiJGSRqRSYykieJ/U0qXpZSqwIXAo4rhRwMLUkrvSyn1pJRuBz4L/OUQ8+kFDo+ImSmlB1JKv97F5R8LdALnFMu/gnwx+VejmOcpwKqU0hdSSn1FLJeQL/BH6yXAZUVstZTSj4FfAc8cZppPppTuTCndT77gHizWXVmvh4mIfYHjgbemlLpSStcBnwNe2jDaNSmlbxXr8bAL9WF8IqW0ulif7wIriuGvAj6dUvp5SqmaUvoi0F2s20D/AyRyogL5M7gmpbQaIKX09WIZtZTSxcAfgcc1TL86pXR+8TkOjP1ZwB9TShcW738FuAl4WPI3iF5ygnBwsQ7XppQ2DjLe48jJ4ZtTSpuLbbwzjfl7ydUA9x7FPEazfT9R7GtbgSrQTj7+2lJKq1JKt+1EjJI0IpMYSRPFvQ3/bwE6iiox+wN7F9VZNkTEBuDtwKBVboDTyBf4fyqqzTx+F5e/N3BnSqnW8P6fyHfCR7I/cMyA2F8MLBplTPV5PH/API4HFg8zzZ0DYt17kHF2Zb0Gszdwf0rpoWHmdyc7Z+Bn01n8vz/wDwO2zb4Msr4ppUQuuagnaS+ioTQoIl7WUG1qA7kUb/4oY9+bvK6NRrstLwR+CHw1IlZHxEeKUrKB9gX+lFLqG8U8h/MWIIBfRK5CeOYw445m+27bLimlW4GzgLOBNRHx1YgYbN+TpF1mEiNporsTuCOlNLuhm5FSGrQkIqX0y5TSs8nVfr4FfG0Xl78a2Leo1la3H3B38f9mYFrDe40Jyp3AVQNi70wp/U093MFWYcDrO4ELB8xjekrpnGFi3ndArKt3Yr0Gi204q4G5ETFjiPmNZp47usw7gX8asG2mFSUhg/kKcHpE7E+uHnYJQPH6s+SqUPNSSrOB68kX+6OJbTX5gr/RqPaRlFJvSum9KaXDgSeQS+9eNsS67hcjN6DfXPSHWt69KaVXpZT2JlcT+1QM/USy0Wzf7bZLSunLKaXjydsjAR8eIV5J2ikmMZImul8AG4sGw1MjoiUijoyIoweOGBFTIv9+x6yUUi+wkVzFZVf8nHxh+JaIaIvcqP5U+tsjXAc8LyKmFReDr2iY9lLgkIh4aTFtW0QcHRGHFe/fR24c32jgsIuAUyPi6cW6d0R+SMGSYWJ+fUQsKdpqvB24eCfWa7DYhpRSuhP4GfChIsbl5G2xI0+f26FlkhOP10bEMZFNj4hnDUikGmP8DbCWXM3thymlDcVb08kX3GsBIuKvySUxo3UZ+XN+UdG4/YXA4eTPH/I+8pfFdn4sDdUJI+LEiHhk0eZlI7m612D77C+Ae4BzivXsiIjjBlnHteTk6SXF/nImua1KfXnPb9h3HijWu768gdt/h7ZvRCyLiCcXbWa6yG17dvX4k6RBmcRImtCKNiqnkttB3AGsI1+EzhpikpcCqyI/Aeu15DYlu7L8HnKj/5OLZX8KeFlK6aZilH8BesgXgF+k4aK9qFp1Ern9zmpytagPk9sNQH4IweFFVZ3676x8CHhnMexNRXLwbHIyspZ8d/zNDH/+/jK58fbtRfew314ZxXoNFttI/orc6Hw18E3gPUUbntHaoWWmlH5FbrfxSfIF+a0MeOrZIL4CPJW8jerzuRE4F7iG/Dk+Erh6tEGnlNaTS1D+AVhPrrJ1SkppXTHKu8iJxAPkhwZ8uWHyReSHHWwE/gBcRU5cBy6jfhwcTH6gwl3AC4cI6VXkfWQ9cAQ5uaw7Gvh5RGwiP8Di/6WU7ijeOxv4YrH9X7AT27cdOIe8P91LLg19O2z7cdCdehy3JA0mcjVhSdKeICJWAa9MKV1ediySJI0XS2IkSZIkNRWTGEmSJElNxepkkiRJkpqKJTGSJEmSmspIz5vfKfPnz09Lly4dj1lLkiRJ2gNce+2161JKC3Zm2nFJYpYuXcqvfvWr8Zi1BDffnPvLlpUbhyRJknZaRPxpZ6cdlyRGGleveU3uX3llqWFIkiSpHLaJkSRJktRUTGIkSZIkNRWTGEmSJElNxSRGkiRJUlOxYb+azzvfWXYEkiRJKpFJjJrPU59adgSSJEkqkdXJ1Hyuu1EAyvkAACAASURBVC53kiRJmpQsiVHzOeus3Pd3YiRJkiYlS2IkSZIkNRWTGEmSJElNxSRGkiRJUlMxiZEkSZLUVGzYr+bzwQ+WHYEkSZJKZBKj5vOEJ5QdgSRJkkpkdTI1n5/9LHeSJEmalCyJUfN5+9tz39+JkSRJmpQsiZEkSZLUVExiJEmSJDUVkxhJkiRJTcUkRpIkSVJTsWG/ms9555UdgSRJkkpkEqPms2JF2RFIkiSpRFYnU/O5/PLcSZIkaVKyJGZ3u+vH8OAtZUfR3N5WVCdbfFa5cUjaMdMWwwHPKzsKSdIewCRmd7vlP+H+30KlvexImtfWe3P/5gtKDUPSDkhVaJ9rEiNJGhMmMWVonwdtnWVH0bwqt+f+tEXlxiFp9Ko9OZGRJGkM2CZGkjS+ar3QvQ5mHVJ2JJKkPYQlMZKk8dP9ANR64NBXwbIzy45GkrSHMIlR8/n75WVHIGk0utbB1AVw9Idg7hFlRyNJ2oOYxKj5LLE9kdQUatVcAmMCI0kaYyYxaj7/d1/uH7tXuXFIerhUzY34az1Q3VJ2NJKkPZRJjJrPf92W+yYx0u6VarmRfrU7Jym1HqAClRYgIPUBLbkK2bQDYfoSmPvIkoOWJO2JJlUSU61WeexjH8s+++zDpZdeWnY4kjRxpJQTlFpPf0kKCSqt5ASlmsfpmAczD8gJSud++QcsOxZAx/ycvLTNhIiy10aStIebVEnMxz/+cQ477DA2btxYdiiSNLF0rcslKp1LYfo+OUGZvk9OTDrm5659LoRP5pcklW/SJDF33XUX3/ve93jHO97Bxz72sbLDkaSJJVXh4JfCEa8vOxJJkkY0aW6pnXXWWXzkIx+hUpk0qyxJkiTtkSbFFf2ll17KwoULOeqoo8oORWPhzY/OnSRJkialSVGd7Oqrr+Y73/kOl112GV1dXWzcuJGXvOQlXHTRRWWHpp2xYGrZEUh7llo1N+qXJKlJTIqSmA996EPcddddrFq1iq9+9as8+clPNoFpZletzp2kHVPrg96HYOsa2HIPdK3N/3ffD7OXwZwjy45QkqRRmRQlMdrDfG9V7j9p71LDkCaklCD1Ql8XVLfm33aptOWG+5U2mHkgzFoGsw/LTyDr3BemLvSpY5KkpjLpkpiVK1eycuXKssOQpCyl4jdYag39GlCFWg2oDy9+pyUqRcJRGfB7LKmYVw3apsPMg2DOYTDrEOjcHzqXQPs8f8NFkrRHmHRJjCTtlHpysV2yUX34MOrJRktOGLaVcFSAxLZkg1rRT9DSAa1Ti25a0Z+eu7bOhm5G/7gt7dAytXjdkfstHf3TmKxIkvZgJjGSJq/uB6DWTU4wYvCSDYqSEipFsjA1l3S01BOO6fl123RonQFTZhQJyNSGxKIhyRiYhFTaTDgkSdpBJjGSJq9Ug2Vn5qpXLUWC0tK+fQJST0Ki1WRDkqQJwiRGzecdjy07Au1JFj4e5q8oOwpJkrQDTGLUfGZNKTsCSZIklchnaqr5/PjO3EmSJGlSsiRGzaeewDxt33Lj0O61rZF9w1PCqA3/OtXyNNseSxxs14C/1ufvo0iS1IRMYiTtvG2PCh4ukaiPU214nbb/rZMokov+GQ+Yf+r/0caW9qLxfXtD4/upDY8pbnxEcfG6pQNapkClPfdb2ov/2/PvqEiSpKZiEiNNVtVu6NnQn2xEkJOKomRisMQi1ftFCQcJop5YdPQnDK2NjxIemFhM63/EcGVKf1JSTzAah9XHqQ/z6WCSJAmTGGlySjXoXg9LnwMdCxpKK9q3Ty4GSyi2lWi0+xsnkiSpFCYx0mS0dQ0seQY8+p0mIZIkqemYxKj5vO+YsiMoX6pB6oNatWhrMuB/ACpQaWFblbDGtinT94MV/2gCI0mSmpJJjJpPR0vZEey6XU1CoiW3L5kyE9pm5P6UWTBlDrTPLYZ35q51OrRNL9qjTM/DrAYmSZKamEmMms+lq3L/lKW7Z3nbPWmrysOfvlXd/nVA/1O36o/1hdwgPhVJSKVIMmaYhEiSJO0gkxg1n/9enfv1JKb++F0GJBODJRj1caIld42/GbJN/bdIipIPov+JW60dDU/banjiVmPC8bAndE1/eDLSMmU3bSxJkqQ9j0mMylPthr6tgycgjT9OuO1RvwEkqPXm6bfeV5SMUDwta2CSMbXoOovkod6vP+K3o//RvvWEo/EJXfXXFQ8TSZKkicSrM5WnZwPMOQKm7tVfQlFPNhqTjMaEoqUd5rwsJzjP+n6RZFi1SpIkaTIxiVF5ogWO/H8wf8WOTdfSnvtTZo59TJIkSZrwTGJ2t7YZ0PsQ9G0pO5LyRZiISJIkaYeZxOxujz8X+jaXHcUEUYGOuTs+2ZVXjnkkkiRJah4mMbtbvbG5JEmSpJ1SKTsAaYd99KO5kyRJ0qRkEqPmc+mluZMkSdKkZBIjSZIkqamYxEiSJElqKiYxkiRJkpqKTydT85nq090kSZImM5MYNZ/vf7/sCCRJklQiq5NJkiRJaiomMWo+739/7iRJkjQpmcSo+fzkJ7mTJEnSpGQSI0mSJKmpmMRIkiRJaiomMZIkSZKaio9YVvOZN6/sCCRJklQikxg1n0suKTsCSZIklcjqZJIkSZKaikmMms/b3pY7SZIkTUpWJ1PzueaasiOQJElSiSyJkSRJktRUTGIkSZIkNRWTGEmSJElNxTYxaj5LlpQdgSRJkkpkEqPmc9FFZUcgSZKkEpnE7GZf4Av8gl+UHcaYOJqjOZMzyw5DkiRJk4xJzG52BVdwEzcxlallh7JTuujiAR5gOct5HI8rJ4izzsr9884rZ/mSJEkqlUlMCTqLv2ZQpUoPPfTSyxa20E477+f9nM7ptJa1+1x3XTnLlSRJ0oRgEjNJJRK99G5LUHrooUqVVlqpUCGRqFKlhRYWsIClLOVQDuUMzmA+88sOX5IkSZOYScweqEp1W2JST1KCoIUWgqBGjSpVZjCDhSxkEYtYUvzNZz5zmct85jOPeUxnOkGUvUqSJEnSNiYxe4BuutnIRmrUAKhQYR7z2Jd92Zu92Zd9WcQi5jX8zWEObbSVHLkkSZK040ximlAPPWxkI330EQSddPJUnspxHMdylrM3e+/ZpSeHHFJ2BJIkSSqRScwEVqVKd/HXRRdBUKHCVKZyAidwPMfzKB7Fvuy7ZyctA33mM2VHIEmSpBKZxJSsjz666NqWrLQUf1WqVKiwmMUsZSkHczBLWcrhHM5Slk6upEWSJElqYBKzG1SpchM3sZrV/Jk/cx/3MZvZVKnSTjtLWMIBHMDBHMy+7Mvi4m8e80xWBvPqV+e+JTKSJEmT0qRIYrq6ujjhhBPo7u6mr6+P008/nfe+973jvtw++riKqzif87mHewDYwAbeyBtZyUoWs5gZzDBR2VG33FJ2BJIkSSrRpEhi2tvbueKKK+js7KS3t5fjjz+ek08+mWOPPXZcltdHH1dwBZ/kk9zDPUxjGgtYsK1Ny0pWsoxl47JsSZIkaU83KZKYiKCzsxOA3t5eent7iRj70o/NbOYH/IDP83nWsIZpTGMRi8Z8OZIkSdJkNimSGIBqtcpRRx3Frbfeyutf/3qOOeaYMZv3XdzF1/k63+Ab9NBDJ50mL5IkSdI4mTRJTEtLC9dddx0bNmzguc99Ltdffz1HHnnkLs3zRm7kM3yGq7kagLnMZQpTxiJcDWfFirIjkCRJUokmTRJTN3v2bFauXMkPfvCDnU5i1rOe8zmfS7mUVlpZyEIqVMY4Ug3pvPPKjkCSJEklmhRX3mvXrmXDhg0AbN26lcsvv5xDDz10h+fTSy9f5as8m2dzGZexkIUsYIEJjCRJkrQbTYqSmHvuuYeXv/zlVKtVarUaL3jBCzjllFN2eD6f5/P8O//OfOYzhznjEKlG5SUvyf2LLio3DkmSJJViUiQxy5cv5ze/+c0uz+dyLmc2s+mgYwyi0k67666yI5AkSVKJrAc1SrdwC3dwB9OZXnYokiRJ0qRmEjMKP+SHvJyX00EHwdj/vowkSZKk0ZsU1cl2Vg89fIJP8BW+whzmMJWpZYckSZIkTXomMUO4ndt5G2/jNm5jL/aihZayQ1Ld4x9fdgSSJEkqkUnMADVqfI2vcR7nUaHCIhZZhWyi+dCHyo5AkiRJJZp0SUwXXaxv+FvHOu4q/u7hHu7lXh7kQeYxj3bayw5XkiRJ0gB7TBKTSGxi03bJyTrW8Wf+zN3czT3cw1rWspnNtNFGECQSffRRoUIbbUwp/haz2NKXiey003L/kkvKjUOSJEmlaNok5mqu5nIu5y7u4j7uYy1r6aOPFloIgho1+uijldZtycl0pjOTmSYozW79+rIjkCRJUomaMom5iqt4E2+ihZZtCcoc5tj4XpIkSZoEmi6J+QW/4M28mZnMZBrTyg5HkiRJ0m7WdD92+R7ew7TiT5IkSdLk01QlMZvZzFrWsohFZYeiMj3lKWVHIEmSpBJN2CQmkXiAB7i7+FvFKn7H74jiT5PYu95VdgSSJEkqUelJzBa2cDd3s5rV3Mmd3MRN3MZt/Ik/bXvaWJUqNWp00ME85pUdsiRJkqQS7dYkpo8+vs/3uZEb+SN/ZBWr2MAG2mjb9pstbbTRQQezmEVr+TmWJqKTT87973+/3DgkSZJUit2aJVzGZbybd9NOOx3F3yIWWT1MO2br1rIjkCRJUol2WxLzEA9xHucxj3k+WUySJEnSThuXJOZO7uQMzmAjG9lU/HXRxRSmsIAF47FISZIkSZPEuCQxW9jCKlbRQguttDKb2VSoWG1MkiRJ0i4blyQmkeiiazxm3fT66Cs7hOZ3yillRyBJkqQSjUsSs4QlnMu54zHrptdCCwdxUNlhNLc3vansCCRJklSicUliOunkOI4bj1lLkiRJmuQqZQcg7bCVK3MnSZKkSckkRpIkSVJTMYmRJEmS1FRMYiRJkiQ1FZMYSZIkSU1lXJ5OJo2rF7yg7AgkSZJUIpMYNZ/Xva7sCCRJklQiq5Op+WzZkjtJkiRNSpbEqPk885m5f+WVpYYhSZKkclgSI0mSJKmpmMRIkiRJaiomMZIkSZKaikmMJEmSpKZiw341nzPOKDsCSZIklcgkRs3HJEaSJGlSszqZms+6dbmTJEnSpGRJjJrP6afnvr8TI0mSNClZEiNJkiSpqZjESJIkSWoqJjGSJEmSmopJjCRJkqSmYsN+NZ+/+ZuyI5AkSVKJTGLUfF74wrIjkCRJUolMYnaz/117J7dvfrDsMJratNX3ArBl70VjMr+DO2fzhPlLxmRekiRJGn8mMbvZp2+/jt9sWMOUis2RdtZn3vRhAF790bfu0nwe7O1mRusUPnDkCWMRliRJknYTk5gSLGifSmfrlLLDaFrtlRYA9pk6Y6emr6Yaa7q3csTM+fzzo05k2Yx5YxmeJEmSxplJjPZo1ZTY0tfLlmovABFBAM/e+2DeeMjRTDeZlCRJajomMdpj1FJiS7WXzX29JKAlgpQSB8+Yy6Nn78XyWQs4uHMO+02bSavV+SRJkpqWSYyaSi2l3JFY07WFvlSjrUhIailx4PTZrFi0FytmL+TgzjnsP20mbUX1M0mSJO0ZTGI0oaSU6Es1umtVuqtVumtVWqJCS0CN3J7lmy88lXntU3n2Po/gwOmzWDy1k8UdnSydPospJiySJEl7PJMYlWZrtZeNvT3USLRFCwnoS1U6W6ewT0cn+0+fxUGds9ln6gwWdUxnr/bpLGifRutTrQomSZI0mZnEaLfqq9VY37OVIJgzpYMX7nsYB06fzaKp07clKtNa24afyc035/6yZeMfsCRJkiacIZOYiHgISPWXRT8V/6eU0sxxjk17iJQSD/Z201Wr0hYVTl50IM/Z5xAeNXshlYiRZzDQa16T+1deOaZxSpIkqTkMmcSklHbuRzikAdb2bOHgzrm8eN/DedLCfX2ssSRJknbJqKqTRcTxwCNSSl+IiPnAjJTSHeMbmvYULVHhrcuOYfnshWWHIkmSpD3AiC2kI+I9wFuBtxWDpgAXjWdQ2rNUU2JmW3vZYUiSJGkPMZrHPD0X+AtgM0BKaTVgVTONyqa+HuZO6WDJVHcZSZIkjY3RVCfrSSmliEgAETF9nGPSHmRTXy//7xGPpbUyho9Ffuc7x25ekiRJajqjSWK+FhGfBmZHxKuAM4HPjm9Yana1lLivazMHTJ/NMxYdOLYzf+pTx3Z+kiRJaiojJjEppY9GxNOAjcAhwLtTSj8e98jUtLZW+7i/ZyunLj6Ytx56LB0tY/xzRNddl/srVoztfCVJktQURnt1+XtgKvl3Yn4/fuGomVVTjXXdW2mNCu874ok8a/FBxM78DsxIzjor9/2dGEmSpElpxCQmIl4JvBu4gvxDl+dHxPtSSp8f7+DUHGopsbZ7K5A4edGBvPqgFexjQ35JkiSNk9GUxLwZeHRKaT1ARMwDfgaYxIj13VvpSTVWLtiX1x/8GA6YPrvskCRJkrSHG00ScxfwUMPrh4A7xyccNYtqSqzpzg3333fE8Rw2c37ZIUmSJGmSGDKJiYg3Fv/eDfw8Ir5NbhPzbOAXuyE2TVBbq3080NPFs/d5BG9ZdszYN9yXJEmShjHc1We9UcNtRVf37fELRxNdSon7e7by3iOeyCnj1XB/JB/84O5fpiRJkiaMIZOYlNJ7d2cgag4P9nazfNZCTt374PKCeMITylu2JEmSSjeap5MtAN4CHAF01IenlJ48jnFpAkopsbXax4v2O7zcQH72s9w3mZEkSZqURtOY4UvAxcApwGuBlwNrxzMoTUzrerpYNnMeJy7cr9xA3v723Pd3YiRJkialyijGmZdS+g+gN6V0VUrpTODYcY5rTN15552ceOKJHHbYYRxxxBF8/OMfLzukprO12kcl4JxHPom2SkvZ4UiSJGkSG01JTG/RvycingWsBpaMX0hjr7W1lXPPPZfHPOYxPPTQQxx11FE87WlP4/DDS64W1STWd2+lRuIDR5zAvtNmlh2OJEmSJrnRJDEfiIhZwD8A5wMzgTeMa1RjbPHixSxevBiAGTNmcNhhh3H33XebxIygmmqs6drCftNn8c/LV3JQ55yyQ5IkSZJGTmJSSpcW/z4InDi+4Yy/VatW8Zvf/IZjjjmm7FAmvDVdW3jOPofwpmWP87dgJEmSNGEM92OX55N/3HJQKaW/H5eIxtGmTZs47bTTOO+885g502pRw+mpVZnW2sZbDj2GKROtDcx555UdgSRJkko03O31X+22KHaD3t5eTjvtNF784hfzvOc9r+xwJrz7e7o4bZ9lEy+BAVixouwIJEmSVKLhfuzyi7szkPGUUuIVr3gFhx12GG984xvLDqcpBMGKOQvLDmNwl1+e+099arlxSJIkqRSjecRy07v66qu58MILueKKK1ixYgUrVqzgsssuKzusCa0SwczWKWWHMbgPfCB3kiRJmpQmRWvt448/npSGbN6jQaSUmN8+rewwJEmSpIcZtiQmIloioqkep6xd113tY3prGwf7SGVJkiRNQMMmMSmlKvDs3RSLJogHert5ysL9qUSUHYokSZL0MKOpTnZ1RHwSuBjYXB+YUvr1uEWl0mzp66Wj0sorDlxediiSJEnSoEaTxDyh6L+vYVgCnjz24agsKSU2V3vZ2NvDBx/5JBZ1dJYd0tA+/emyI5AkSVKJRkxiUkon7o5AtPv11qo82NtNNSUScMD0WbzigEdx0l5Lyw5teMuWlR2BJEmSSjRiEhMRewEfBPZOKZ0cEYcDj08p/ce4R6cxlVJiU18vm/p6aK1UaIsWVi7cjxMX7M9RcxYxr31q2SGOzne/m/unnlpuHJIkSSrFaKqTXQB8AXhH8foWcvsYk5gJrpYSm/p62NzXS2ulQjUlDpg+i5fsdQTHztubQ2fMo7XShD8VdO65uW8SI0mSNCmNJomZn1L6WkS8DSCl1BcR1XGOSzuhmmo81NfL1mofrRHUUuKQGXM5bv4+PGb2Ig6bOY+Zbe1lhylJkiTtktEkMZsjYh65MT8RcSzw4LhGpVFLKbG+Zyu1lIgIjpy1gOPnL2HF7IUcOmMeHS2T4vdMJUmSNImM5gr3jcB3gIMi4mpgAfD8cY1Ko9JV7eP+ni4OnzmPNy87hsNmzqOt0lJ2WJIkSdK4Gk0ScwPwJGAZEMDNjPAjmRpfKSXWdG+hrdLCPx56LM9bcggt4UciSZKkyWE0Scw1KaXHkJMZACLi18Bjxi0qDamvVmNN9xYeN3cx7zniuIn9ey7j5cILy45AkiRJJRoyiYmIRcA+wNSIeDS5FAZgJjBtN8SmAbZW+3igp4uX7X8Erz/4qOZ8sthY2HffsiOQJElSiYYriXk6cAawBDiX/iTmIeDt4xuWBrOuewsfeuSTOHnxQWWHUq6LL879F76w3DgkSZJUiiGTmJTSF4EvRsRpKaVLdmNMGkIlKiYwAP/2b7lvEiNJkjQpjaY+0pKImBnZ5yLi1xFx0rhHJkmSJEmDGE0Sc2ZKaSNwErAQ+GvgnHGNSpIkSZKGMJokpt4W5pnAF1JKv20YJkmSJEm71WiSmGsj4kfkJOaHETEDqI1vWGpU/12YA6bPKjsUSZIkqXSj+Z2YVwArgNtTSlsiYh65Spl2g5QS93Vv4RGdc/jkY55WdjgTwze+UXYEkiRJKtFokpjji/7yCGuR7U6b+3rZ0NvFsXP34Z8ftZLprVPKDmlimD+/7AgkSZJUotEkMW9u+L8DeBxwLfDkcYlIbOrrYVNfL/OmdPDuw4/jWYsPoq3SUnZYE8cFF+T+GWeUGYUkSZJKMmISk1I6tfF1ROwLfGTcIpqEUkp01apsqfbSU6uxYMpU3nj443j6ogOYYvLycCYxkiRJk9poSmIGugs4cqwDmSx6ajU29fWypa+PWqrRUqnQlxIL26fy6Nn7sHLB/py0aKklL5IkSdIQRkxiIuJ8IBUvK+RG/r8dz6D2FA/2dnPbpge4bdMGfvfgGn7/4Do29HTR0dLKMxYdwCNnLuDAztkcMH2W7V0kSZKkURpNScyvGv7vA76SUrp6nOLZY1y34T5ed+2PSCR6U422aGFaSytdtT6++LhnsWzGvLJDlCRJkprSaNrEfHF3BLIn6ar28a7r/4fWSoVZbe3bvbel2ldSVJIkSdKeYcgkJiJ+T381su3eAlJKafm4RTWBpZTYXO3lod4eNvZ181BvDw/15e7B3m7Wdm/hlofu596uzSzqmF52uHumyy4rOwJJkiSVaLiSmFN2WxQl6alVt0tCHurtZmNfDw/19nB/z1bWdm9lfc9WHujp4oGeLjb2dbOprwcIWiII8u/mJBLVlKilREsErVFhQfvUclduTzZtWtkRSJIkqUTDJTFtwF4D279ExBOB1eMa1S5IKXHjxnWs6d7Cxt5cOrKuZwvrurdyf89WHujpZkNvFxt7e+ipVWmJoFIkJAmopUQ11QigtVKhNYquUqG90sK09ulU/NHPcn3qU7n/uteVG4ckSZJKMVwScx7w9kGGby3eO3WQ90qzvnsrP7z3Dr705xtY272ViJyQNJaO9CclwZwp7VQIwoSk+Xzta7lvEiNJkjQpDZfELE0p/W7gwJTSryJi6bhFtAOqqcavH7iPr915E1et/TMJ6GxtY2H7VJMTSZIkaQ81XBLTMcx7497go6val9ui9HZxf9EmZV33Fu7euol7ujaxtnsL93ZtpqvaRyWC+e1TaYnKeIclSZIkqWTDJTG/jIhXpZQ+2zgwIl4BXLszC0spsamvJyclDcnJfV2buXvrJu7r3szari2s69lKd7WPlkqFFoJaQ8P5tkqFtqjQVsm/uzLwEcaSJEmS9mzDJTFnAd+MiBfTn7Q8FpgCPHekGf9p84N88+5bWN21iTVdm1lTNKzvqlbpTVV6azV6a7lfI9EWLUypVJhSaaGtaLtSrdWoNswzgL5ajT5qbG3S31vpS4M9tVqSJEnSaA2ZxKSU7gOeEBEnAkcWg7+XUrpiNDOeUmlhv2kz2W/azDEIc8/REhX2nzar7DCa25VXlh2BJEmSSjRcSQwAKaWfAj/d0RkvntrJ85Ys26mgJEmSJGkotoRX8/noR3MnSZKkSckkRs3n0ktzJ0mSpEnJJEaSJElSUzGJkSRJktRUTGIkSZIkNZURn04mTThTp5YdgSRJkkpkEqPm8/3vlx2BJEmSSmR1MkmSJElNxSRGzef978+dJEmSJiWTGDWfn/wkd5IkSZqUTGIkSZIkNRWTGEmSJElNxSRGkiRJUlPxEctqPvPmlR2BJEmSSmQSo+ZzySVlRyBJkqQSWZ1MkiRJUlMxiVHzedvbcidJkqRJyepkaj7XXFN2BJIkSSqRJTGSJEmSmopJjCRJkqSmYhIjSZIkqanYJkbNZ8mSsiOQJElSiUxi1HwuuqjsCCRJklQik5jd7A9/6OGuu/rKDmNSOeqodubObSk7DEmSJI0Rk5jd7JxzNvC73/UwZUrZkTSvN/zpbBLw4UXvYevWxNatCYD29qC9PejoCDo7K+y3XysHHNDKAQe0mcRIkiTtQUxidrNaDWbPrtDZ6TMVRlKrJbq7+7taDVpbg0O23EBKMG9eC0uW5ETl4IPb2GefVhYtamHRohZmz64QEWWvgiRJksaBSYx2m1ot0dcHfX2J3t7czx20tkKlEkRASjnZSymx114tLFvWxkEHtbF0aSuLF7dy+BumMGVK8JOf7F32KkmSJKkEJjHaafWkpLe3MTlJVKs5KWlpiWK8PC7AjBkV5sypMG9eCwsWtLBwYQsLF1aYNSuXnsycWWHWrNzNmBGDl6Z0WMIiSZI0mZnEaFi1WmLTpsTmzTVaW3NJSWNSMmtWhblzW5g3r7ItKVmwoMKcOS3bJSSzZlXo7BwiKZEkSZJ2gEmMHqavL/HggzV6e3OismxZGyefPIMDD2zbLimZPr2kpOSQQ3b/MiVJkjRhc+Nk5AAAGzxJREFUTJok5swzz+TSSy9l4cKFXH/99WWHM+H09SXWr68SEVQqcOyx7Zx00lSOOaZj4j3Z6zOfKTsCSZIklWjSJDFnnHEGf/u3f8vLXvayskOZkNasqXLccR286EWdrFgxhY4On54mSZKkiWnSXKmecMIJzJ07t+wwJrQTTujg2GM7Jn4C8+pX506SJEmT0qQpiZmMUup/atjAfv7Nle0fa9zW1iSN7m+5pewIJEmSVCKTmCZRq+VHF2//Gyu5HwEtLRDRn5BUq3mazs7cCH/27Pxo4/nz8yON587NTw+bObPCjBm5v/feE6ztiyRJkjQIk5gJZN26Kn19iUolN66HekKSnxLW+f/bu/fouuo67+Of7zknbVLSJG1zadO0DWlaLm1DhRZKZ4CiOGB1FUVaLvaRaplZozja8cEZxnGNomuNOjg+I6g44yPizCiXYWBkGLkNYxX0oaAPUChDASnYlotpbVPS5Jycy3f+2Ls1LWl7miZnn53zfq2117lkXz7n5LeSfLL3Prs2KCMNDcF1ViZPTqi5OanJk5OaONH2l5J9xaS21pRIxGTvCgAAAFAkSkwZyeVcn/pUgxobDywlEydG+HHGAAAAQJmpmBJz2WWXaf369dqxY4fa2tp07bXXau3atVHH2q9QCA4LW7nyOMrKkSxcGHUCAAAARKhiSswtt9wSdYQh9fcX1NNTkJm0ePF4Ckwx/u7vok4AAACACFVMiSknmYyrtzenREKqr09qzZqJOu+8Gs2dWxV1NAAAAKDsUWJGWV9fQa+8ktMrr+T0/PNZ7dqVVz4vXXJJrc4/f4Lmzavi5PujtXp1cPvP/xxtDgAAAESCEjNCcjnXtm05vfxyTr/6VVbPPDOg557Lqrs7r1TKVCi43KWenoLuumuq5s0bF3Xk+Nq2LeoEAAAAiBAl5ii4u3bvLmjHjoK6u/PaujWrp5/O6tlnB7RtWz78WOTg+i3V1aaamoSmTk0ecJ5LKpVXincdAAAAGDb+nA65u/r6XN3deXV357VjR0Gvv57Tr3+d09ateb32Wk7d3XlJpmQyuH5LLueqqjLV1JiamhIcFgYAAACUQMWUmIEBD8tJfn9ReeWVnLZuzenVV/N64428BgZcqVRw1ft83pXPS+PGWThJTU1JigoAAAAQsTFbYtxdmzdn9cgjad17b59eeSW3v6AUCsFelFQqKCfjxpnq6xNKJsVHHMfBmWdGnQAAAAARGlMlJpNxPflkRuvX9+uBB/rV2+sqFFy1tW89NwUx9sUvRp0AAAAAEYp1iXF3bd+e11NPDWj9+n498khahULwfH19Us3NiagjAgAAABhhsSsxO3YEpeXnP0/r4YfT2r07L0lKJk0NDQmlUuxtGfPe//7g9l//NdocAAAAiETZl5hdu/LatCmrDRvS+slP0nrttZzMgnNX6uoSam7mMLGKs3Nn1AkAAAAQobIqMQMDrpdeymrz5qw2bMjoyScz6u7OK5EILhZZV5dQSwulBQAAAKhkkZUYd9frr+e1eXNWGzcO6LHH0nr++ZwSieCjjVMp03HHGaUFAAAAwAFGvcS4u3btKmjbtuCikS++OKBnn83q+eez6utzSa5CQTruuIQaGxNKJiksAAAAAA5tVErM7t0FffGLu/Tcc1lt2ZJVf7+USgUXkHSXamoSqqkx1dby6WEYhne8I+oEAAAAiJC5+4ivdNy4Lp869d9VU5NQdbUpVVZn3kQrn5duu61ZJ5wwLuooAAAAQGTM7Jfuvmg4y45KvWhtTeozn5k0GquOvVRKamuj1QEAAADDNSp/TTc2JnXllXWjsWpAete7gtt77402BwAAACLBLgHET39/1AkAAAAQIc6sBwAAABArlBgAAAAAsUKJAQAAABArnBOD+HnPe6JOAAAAgAhRYhA/V18ddQIAAABEiMPJAAAAAMQKJQbxs2xZMAEAAKAiUWIAAAAAxAolBgAAAECsUGIAAAAAxAolBgAAAECs8BHLiJ9Vq6JOAAAAgAhRYhA/H/1o1AkAAAAQIQ4nQ/z09QUTAAAAKhJ7YhA/y5cHt+vXRxoDAAAA0WBPDAAAAIBYocQAAAAAiBVKDAAAAIBYocQAAAAAiBVO7Ef8rFkTdQIAAABEiBKD+KHEAAAAVDQOJ0P87NgRTAAAAKhI7IkpsZdffllvvPFG1DFi7aSPfESS9N833hhxElSiZDKphQsXKpXixycAAFHht3CJXXvttdq4caPGjRsXdZTY+saLL0qS1q1bF3ESVIp0Oq09e/aooaFBp556qr7yla+ovr4+6lgAAFQsSkyJ5XI51dXVqba2NuoosVVVVSVJamxsjDgJxjJ31+7duzUwMKDa2lp94AMf0IUXXqjOzk6ZWdTxAACoaJQYADiIu+vVV19VV1eXLr/8cp199tmqrq6OOhYAAAhRYgDgIOl0Wk1NTfrud7/LXhcAAMoQJQax86P29qgjYIzr6enRypUrKTAAAJQpSgxi55HW1qgjYIxLJBJasmRJ1DEAAMAhcJ0YxE5jf78a+/ujjoExqlAoyN11wgknRB0FAAAcAntiEDuffOIJSdKnly6NOAnirFAoKJ1Oq7+/X5lM5oDrvpxxxhlqbm6OMB0AADgcSgyAMc3d95eVdDq9/yO6C4WCWltbtWjRIs2bN08dHR2aNWuWpk2bxoUsAQAoc/ymBjDmZLNZ7dy5U4lEQoVCQS0tLerq6tKCBQvU0dGhmTNnavr06Vx0FgCAmKLEABgT3F29vb3au3evqqqqdNFFF2nFihXq7OzU+PHjo44HAABGECUGQOzt3LlT2WxWM2bM0Lp163TeeeeptrY26lgAAGCUUGIQO3fNnh11BJSRXC6nQqGg73znO1qwYAHXdgEAoAJQYhA7j7e0RB0BZWTPnj1asmSJurq6oo4CAABKhOvEIHam9/Zqem9v1DFQJjKZjM4+++yoYwAAgBKixCB2rtq4UVdt3Bh1DJSJVCrFNV0AAKgwHE4GIDbcXQMDA8pkMspkMspms5Kkurq6iJMBAIBSqpgSc9999+kTn/iE8vm8rrzySl1zzTVRRwJwEHdXNps9oKgkEgklk0m5u/L5vCZPnqyOjg61t7fr+OOPV1tbm0466aSoowMAgBKqiBKTz+d11VVX6cEHH1RbW5sWL16sFStW6OSTT446GlBxCoWCMpmM+vv7lclkZGZKJpOSgotU1tXVqbW1VTNnzlRnZ6daW1vV0tKilpYWNTc3q6qqKuJXAAAAolYRJeaxxx5TZ2enOjo6JEmXXnqpfvjDH1JigFHk7kqn0+rv71c6nVYqlZKZKZ/Pa9q0aVqwYIFmz56tmTNn7i8pLS0tqqmpiTo6AAAocxVRYrZv364ZM2bsf9zW1qYNGzZEmAjH4rY5c6KOgEHcff+elXQ6rWQyKTNTLpdTS0uLFixYoPnz5+8vLG1tbRo3blzUsQEAQIxVRIlx97c8xwXx4uuppqaoI1S8dDqt3bt3K5FIKJfLqampSaeeeuoBZWXGjBmqrq6OOioAABiDKqLEtLW1aevWrfsfb9u2Ta2trREmwrE4vqdHkrSlvj7iJJXD3dXb26ve3l4lk0lNnDhRF110kc4991x1dXVpwoQJUUcEAAAVpCJKzOLFi/XCCy9oy5Ytmj59um699Vb94Ac/iDoWhukPN22SJH166dKIk4xdhUJB/f396uvrUz6flyR1dHToiiuu0NKlSzV37lz2ZgIAgMhURIlJpVL6+te/rvPPP1/5fF4f/vCHNW/evKhjAWXh4MKSTCZVKBTU1tamZcuW6bTTTtPpp5+uJg7jAwAAZaIiSowkLV++XMuXL486BhApd1d/f7/27t2rfD6vVCqlXC6nGTNmaNmyZTrllFM0Z84cdXR0cD4LAAAoWxVTYoBK5e7q6+vTm2++KTPT1KlTdc4551BYAABAbFFigDGqv79fPT09MjO1tLRo9erVevvb365Zs2ZxPgsAAIg1Sgxi5x9PPDHqCGWjUCgok8kok8loYGBAuVxOVVVVcnc1NDToQx/6kN75zneqs7OT4gIAAMYMSgxi57nJk6OOUDKHKilmwZXv9+1lmTNnjtrb29Xe3q6pU6eqtbWV4gIAAMYsSgxi58Tf/lbS2Coz7q7u7u4hS8rUqVPfUlKam5vV0tKiSZMmUVQAAEDFocQgdj743HOSxtZ1YjKZjKqrq3XVVVftLynNzc2UFAAAgCFQYoAyMXHiRK1atSrqGAAAAGWPEgOUmLsrk8mor69P6XRaqVRK7q4FCxZEHQ0AACAWKDHAKMrn8+rr61NfX58KhcL+i0s2Njbq9NNPV1dX1/7zXVpbW6OOCwAAEAuUGGCE7d27V729vTIzmZlmz56t+fPna968eero6FB7e7tqa2ujjgkAABBblBjEzrfnzYs6wiH9NvzktM9+9rNauHChpk2bpkQiEXEqAACAsYUSg9jZUl8fdYS32PcRyVOmTNH111+vzs7OqCMBAACMWZQYxM4p3d2SpKeamiJO8jvd3d3q7OzUDTfcoMlj6Po1AAAA5YgSg9i55IUXJJVPidm1a5cmTZqkr33taxQYAACAEqDEAMcgn88rk8nopptuUmNjY9RxAAAAKgJnHAPHoFAoqLa2VnPnzo06CgAAQMWgxADD5O7au3dv1DEAAAAqDoeTAUehUChoz549SqfTcnd1dHRo9erVUccCAACoKJQYxM43urpKur18Pq+enh5ls1lJ0vz58/Xud79bS5cu1bRp00qaBQAAAJQYxND2El/tvru7WwsWLNDKlSu1ZMkSTZo0qaTbBwAAwIEoMYidxW+8IUl6vKWlJNtLJBJatWqVLrjggpJsDwAAAIdHiUHsvO9Xv5JUmhKTyWSUTqeVTCZHfVsAAAAoDiUGGMLAwIB27typ8ePH62Mf+5jOOuusqCMBAAAgRIlBRXN3FQoFZbNZ5XI5ZbNZZTIZVVVVae3atbr88stVX18fdUwAAAAMQonBmHNwKdl36+5KpVIyM5mZCoWC8vm8qqqq1NDQoJaWFk2ZMkUnnXSSVq1apcmTJ0f9UgAAADAESgzKXqFQUC6X219GsrmcvFDQ9u3blUql9p+v4u7K5XJKJBJqaGhQU1OTGhsb1dTUpJaWFjU2NmrSpEmqr69XfX29GhoaVFdXp+rq6ohfIQAAAI4GJabEGhsbtWnTJvX09EQdJTYSiYTq6uo0efJkTZkyRQ8tXKgpU6boz044QQ0NDWpoaNhfTOrr6zVhwgSZWdSxAQAAMEooMSX21a9+VYVCIeoYsZJIJCglAAAA2I8SU2Jmxsf1HqvbbgtuL7kk2hwAAACIBCUG8XPjjcEtJQYAAKAiJaIOAAAAAABHgxIDAAAAIFYoMQAAAABihRIDAAAAIFY4sR/xc8cdUScAAABAhCgxiJ/GxqgTAAAAIEIcTob4ufnmYAIAAEBFosQgfigxAAAAFY0SAwAAACBWKDEAAAAAYoUSAwAAACBWKDEAAAAAYoWPWEb8/OhHUScAAABAhCgxiJ8JE6JOAAAAgAhxOBni55vfDCYAAABUJEoM4uf224MJAAAAFYkSAwAAACBWKDEAAAAAYoUSAwAAACBWKDEAAAAAYsXcfeRXavampM0jvuKxoVHSjqhDIHYYNxgOxg2Gg3GD4WDcYDhOcPeJw1lwtK4Ts9ndF43SumPNzH7Be4OjxbjBcDBuMByMGwwH4wbDYWa/GO6yHE4GAAAAIFYoMQAAAABiZbRKzD+M0nrHAt4bDAfjBsPBuMFwMG4wHIwbDMewx82onNgPAAAAAKOFw8kAAAAAxMqIlBgzm2xmD5rZC+HtpEPMd5+Z7Taze0Ziu+XMzC4ws81m9qKZXTPE18eb2W3h1zeYWXvpU6LcFDFuPmlmz5rZRjN7yMxmRZET5eVI42bQfBebmZsZnyCEosaNma0Kf+ZsMrMflDojyk8Rv6dmmtmPzeyJ8HfV8ihyoryY2U1m9hsze+YQXzczuz4cVxvN7NQjrXOk9sRcI+khd58j6aHw8VCuk/S/RmibZcvMkpK+Ieldkk6WdJmZnXzQbGsl7XL3Tkn/R9KXS5sS5abIcfOEpEXu3iXpDkl/U9qUKDdFjhuZ2URJH5e0obQJUY6KGTdmNkfSX0j6PXefJ2ldyYOirBT58+Yzkm5397dJulTSN0ubEmXqZkkXHObr75I0J5z+SNKNR1rhSJWYCyV9L7z/PUnvHWomd39I0psjtM1ydrqkF939JXcfkHSrgvdosMHv2R2S3mFmVsKMKD9HHDfu/mN37wsfPiqprcQZUX6K+XkjSV9QUHrTpQyHslXMuPlDSd9w912S5O6/KXFGlJ9ixo1Lqgvv10t6tYT5UKbc/aeSfnuYWS6U9I8eeFRSg5lNO9w6R6rEtLj7a2HI1yQ1j9B642q6pK2DHm8LnxtyHnfPSeqRNKUk6VCuihk3g62VdO+oJkIcHHHcmNnbJM1w9zF/KC+KVszPm7mS5prZz8zsUTM73H9RURmKGTefk7TazLZJ+pGkPylNNMTc0f4NpFSxazaz/5Q0dYgv/WWx66ggQ+1ROfhj4IqZB5Wl6DFhZqslLZJ0zqgmQhwcdtyYWULBIatrShUIsVDMz5uUgkM7linY6/uwmc13992jnA3lq5hxc5mkm939b83sTEn/FI6bwujHQ4wd9d/FRZcYdz/vkFs1e8PMprn7a+Gun0rf5bxN0oxBj9v01t2p++bZZmYpBbtcD7ebDWNfMeNGZnaegn8enOPumRJlQ/k60riZKGm+pPXhEatTJd1tZivc/RclS4lyU+zvqUfdPStpi5ltVlBqHi9NRJShYsbNWoXnPrj7/zOzakmN4m9DHF5RfwMNNlKHk90t6Yrw/hWSfjhC642rxyXNMbPjzWycghPb7j5onsHv2cWS/su5aE+lO+K4CQ8L+ntJKzg+HaHDjht373H3Rndvd/d2BedSUWBQzO+pf5N0riSZWaOCw8teKmlKlJtixs2vJb1DkszsJEnVkrpLmhJxdLekD4afUrZEUs++U1UOpeg9MUfwJUm3m9laBYN3pSSFH+P5x+5+Zfj4YUknSqoNj5Vc6+73j1CGsuHuOTP7mKT7JSUl3eTum8zs85J+4e53S/qOgl2sLyrYA3NpdIlRDoocN9dJqpX0L+F/1X/t7isiC43IFTlugAMUOW7ul/QHZvaspLykT7n7zuhSI2pFjpv/LenbZvanCg4HWsM/aWFmtyg4NLUx7ACflVQlSe7+LQXnTy2X9KKkPkkfOuI6GVcAAAAA4mSkDicDAAAAgJKgxAAAAACIFUoMAAAAgFihxAAAAACIFUoMAAAAgFihxADAEZjZFDN7MpxeN7Pt4f3d4cfPjuS2Ws3sjvD+MjO7J7z/OTO7eiS3NcS232tmJw96vMbMWkdzm4fJstLM/tvMfhw+vsXMNprZn5rZ58OLvh5q2UVmdv0xbPvTw112iHW9HF5jBQAwgkbqOjEAMGaF18ZYKAVlQlKvu3/FzNol3TPC23pVwQVwo/BeBa9nXzFbI+kZHeGqyaNkraSPuvuPzWyqpKXuPquYBcMLeR7LxTw/Lemvj2F5AMAoY08MABybpJl928w2mdkDZlYjSWY228zuM7NfmtnDZnbiwQua2TmD9vA8YWYTzazdzJ45xLZONrP1ZvaSmX180Ho+aWbPhNO68LkD1mNmV4cFbMhsZrZU0gpJ14V5/lzSIknfDx/XmNlpZvaTcLn7zWzaEK+pxczuMrOnwmnpoTKGz682s8fCbfy9mSXN7K8k/b6kb5nZdZIekNQcznOWmd1sZheHyy82s5+H23osfA8H78E6zsxuMrPHw/f4wvD5NWZ2Z/g+vGBmfxM+/yVJNeG2vn/Qa/vIvvkGreOG8P6/he/LJjP7oyHel6P6foTPrwzfr6fM7KeHGBMAUJncnYmJiYmpyEnS5yRdHd5vl5STtDB8fLuk1eH9hyTNCe+fIem/hljXv0v6vfB+rYK94+2SngmfWybpnkHb/bmk8ZIaJe1UcLXj0yQ9Lem4cB2bJL1t8HrC5a+W9LnDZZN0s6SLBy2zXtKi8H5VuP2m8PElCq7WffBruk3SuvB+UlL9YTKeFL4HVeH835T0wSG2ffBruVnB3qpxkl6StDh8vi58Dwe/b3896HvSIOn5MMeacNl6SdWSXpE0I5yv9xDf+yZJLw56fK+k3w/vTw5vaxTsvZoSPn45/H4N5/vxtKTp+7JHPfaZmJiYymnicDIAODZb3P3J8P4vJbWbWa2kpZL+xcz2zTd+iGV/Jumr4X/873T3bYPmH8p/uHtGUsbMfiOpRcEei7vcfa8kmdmdks6SdPdQKziKbAc7QdJ8SQ+GyyUlvTbEfG+X9EFJcve8pB4zO1TGgoKC83i4zhpJvykiy+BMr7n74+H29oTrHzzPH0haYb87n6ha0szw/kPu3hMu86ykWZK2Hmpj7t4d7gVbIumFcPs/C7/8cTN7X3h/hqQ5CormYR3h+/EzSTeb2e2S7jzSugCgklBiAODYZAbdzyv4Qzwhabe7Lzzcgu7+JTP7D0nLJT1qwcnq6aPYVkrSoVpPTgceMlwd3haVbQgmaZO7n3mUy+1b9lDPf8/d/2IY69y3vBcxz/vdffMBT5qdoaHfzyO5TdIqSc8pKGZuZssknSfpTHfvM7P1+t37vc9Rfz/c/Y/DnO+W9KSZLfTg/CwAqHicEwMAIyzcI7DFzFZKkgVOOXg+M5vt7k+7+5cVnIj+lvNmivBTSe81swlmdpyk90l6WNIbCs4jmWJm4yW9p4hsb0qaOGjdgx9vltRkZmeGy1WZ2bwh8jwk6SPhPEkzqztMxockXWxmzeH8k82sqJP3Q89JajWzxeHyE83s4CJyv6Q/sXA3h5m9rYj1Zs2s6hBfu1PBByBcpqDQSMEhabvCAnOipCVDLHfU349wfGxw97+StEPBHh4AgCgxADBaPiBprZk9peAckAuHmGfdvhO3JfUrOMfiqLj7/1dwjshjkjZI+r/u/oS7ZyV9PnzuHgV/8B8p262SPhWeAD87XO+3zOxJBYePXSzpy+FyTyo4DOpgn5B0rpk9reDwunmHyfispM9IesDMNkp6UNJbPizgMK99QMG5OTeEmR7UW/eAfEHB+TwbwxPrv1DEqv8hnP/7B3/B3Xcp+PS2We7+WPj0fZJS4Wv4gqRHh1huON+P68zs6TD3TyU9VUR2AKgI5n6kPfEAAAAAUD7YEwMAAAAgVigxAAAAAGKFEgMAAAAgVigxAAAAAGKFEgMAAAAgVigxAAAAAGKFEgMAAAAgVigxAAAAAGLlfwB30thxqRWu9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yjLggV6EeLZGpvxB4Y0qpD+iLiN+RS+0Xb2HaagGWxJQoIv4lIo6CXK5dXHG5pfh67VYmMFvy26K7T0Q8sYjj2eTqLZAb7sJQQrIwInoiosKwk+riSt3S4uPvyVWijk4pHU3+kzmv7oR3XdGdOSyedXXv67/7LUMH6K/UX30hNxj+yBjLWLsy9JmU0kHAc8gH4K1xFEMH8UNSSkfSYClZcTXuP4qP7ybXLb4PqL+N8FOAb6WUXpNSegq5cTPAcWNM+snAL1JKb0wpHUdeHwCHbkXJV2176AROBYiIg8mlIDC0PWy1lNJbyVf9ICdel0ZE9xij/FvRvYuc4I3kqqLbBpxdt20cS64yeXGxLvYphntPSukw4G/YyhPniNgnL056X0rpFPKf8TryycPTRhmtfjs+u1YSFRFzImLxGLOr7X+PLYafN3weETEb2J9crerFKaXDGbpaXdt+JmL/2la3M5Q0nRrZLOCkBsattYOCfBJERPTUjXv9OEsHHyXy7e1vSim9NaX0LOCU4qtF5PW91ceErfCciOguLnI9v+i3gdzOZCS1/SKRb/RR+02fTv5NvznKePUnpy+ATSXtw7fr2gUAyDcfeSL5xijDjbbd7UdOOgBenlI6gqE7dDbi8qJbO+b9knzjjgrwqqLfzxnbptgiYvj/UZlqt4FeHBGn13pGxFgXpuovHNZK504ZPlBEHAo8kFJ6V0rpJIb+I2eRq13B0HqpbW81W7tN1ebdyHFqJLPJ+9pYr11HGzmldBe5mink/8a24vhbK3X848hjqpWYxJTrJcCvI2J1RNwYEXeRr1BAUVViAvw7+UQ6gP+NiN8zdKJ4O/mOLJDv7gX5D+tacmO8kU7U/k/RPRm4rygqXkZObt5UN1wtOXt+RFwTEbX51BrNAnwxIn4dEacVVW3+s+j/oYi4KyJuiIiHySfWJ4yxjLWqUK+MiJuKeXSNMfxY6p+l8ruI+APwtnGM/3nyn0Ptz/LiYSdZFwEriqpk15GTneHzHe5DwEMRcXtEXAN8quh/D7m61Hh8hZyAAny1WF9XkU/MH2Tsq/kNSymdQ65bDfmP64fFyedIw14O7Eg+QRzthPQr5G0ygCsj4qbit3mE3IhzMXld3FMM/97iCty15OpqW+NpwO1FNcVag+9aMjbi75VSWspQtcK/JlfHuJG8Dy4ZY161/e9JEfGbYvrDr7AuJFeneag4ftzC0ElBLZ6J2L+2SUppHbmOPuTE+U/kktzHNDDuHcB/FR9fHxG3F+PuQ65Ges52CPGNwP0RcWexf9Wqqq0lr89tPSaMx27kY+mfKC4ykE8G140y/CfIdf97gJuL7eI2cnXhrzOUQGymWK+1k9E3RcSt5FLk4Sf5NzG0//yw2Kc+zqMtJ5dSQd6+fhMRbyiWY23R/3PFvjDSrdZHUzv5nVPE8WuGqjbNKrqXb2Eat9S9v6nYJ/YaRwwT5d8Zqup0SUTcFhF3kEurRnMbubE8wEcj4ucMXTirdzpwT1E98hqGSq9r1b1gaL3sCNxSrJcZbOU2VaeR49SjpJQuTynFFl7nbmHebycnXwcxdJxZTD4mfnD00dQqTGLKdQ65Pcxy8lWUnckHpQ+S76q03RWlJ0eTT55Xkq+MLSff8efJRd10Uko/LuJbRi7yvYURThBSSpeQqzL9jJzwHEC+Uvh1Nr+aew75D6eXXF3ukGL8h8gnDXeTS4OOIq8HyHVW30iu7rKQXI3gPvJBeqyrP2eS/+w2kE8yz2YrH+yYUvoJQ7eLnEFeD68dx/gr2Twh/cKwQf6LXM1pPvlAu5zcgPpvxpjsJeREo4e8HleTt6MTi9KfhqXcRuNpDCW3+5JPMi4Bjk4p3TPG6OOSUvon8p8K5BKoH49U9aEY9sGU2zqMNq2N5CTgo+STvH3If743kW8l+vtiXZxKLnUYICdmL2Lztg3jcR25cfVG8nbeU/R7RbGdjOaNwOuKYbvJ9bL/wOZtAYY7j1wtciX5T/dLwFeHDfMQOUm+rxhmD/JJ9j+TS6Mmav/aHs4hnxw9UsT1dYaSk+FV24Z7NTlpuJl8kt9J3t9PSCn9YDvE9n1ym4pO8v7VR77t+YkppZXbekwYp4+RE/Y55Cpo/8EYiVrRhuNo8vH8AfJ2Opu8D7yToWqWI3kleRtbR27/8B8U7WPqpn8L8HLyyWAH+ULH344QRyKXjNxOXkdHAnuklFaQS3puJp9/9DJUDawR9aUsN6SU1qSU/sLQvpTIt1Afy6XkNkwPkfeZoxi6GFGaYl89mnxR6m7yPj2bfFFmtHH6gTPIx5Yq+Xc7dYRBf1FMJ8glEW3ku0aeWvymkPe/b5L3yX3J66W6jdsUNHCcmigppe+QS2l/Tf5/qJLvgnlkSunXEzlvNUeM85xH0jhFxJvJ7UJ+W1Q9kaa1iNgJ2JBSeqT4PIN8UnQQ+RlKx5QZX9li6Gny723garMkTUs27JcmSEQ8n3yV8sSi14dLDEeaTJ5Ebrf0W3JJ4hPJJUT9DFU7lKaMiHguQ9WFh/t+UVItaRxMYqSJcyj5dskPAv+cUvpGyfFIk8Wd5Eb6h5KrSj1Mbpv3oZTSr8oMTJogOzJ0F7bhbhmlv6QxWJ1MkiRJUkuxYb8kSZKklmISI0mSJKmlmMRIkiRJaikmMZIkSZJaikmMJEmSpJZiEiNJkiSppZjESJIkSWopJjGSJEmSWopJjCRJkqSWYhIjSZIkqaWYxEiSJElqKSYxkiRJklqKSYwkSZKklmISI0mSJKmlmMRIkiRJaikmMZIkSZJaikmMJEmSpJZiEiNJkiSppZjESJIkSWopJjGSJEmSWopJjKTSRcS5EXHxBM/jKRFxa93npRHxjGbNfzKIiCURcc92nN6TI+K2iFgTEX+9vaY7bB6bfqfJJCJeFBE/LjsOgIg4MyJ+WXYcktRMJjGSJlxxklt7DUbE+rrPL2pGDCml/00p7deMeTUqIi6MiPcP6zcpTtpHim0E7wM+kVKalVL6dpPmOSmklL6UUjqh7Di2p4hIEbF32XFIUiNMYiRNuOIkd1ZKaRbwZ+Dkun5fKjs+bbU9gJu2ZsSIaNvOsTRNK8c+UVwnkprNJEbSZNEREV+MiNURcVNEPKH2RUQsiohvRsTyiLgzIt442kQi4jkRcXMxnXsj4q1F/y1VpRpr/gdExOURsbL47q/qvrs8Il5Z93mzqj0RsX9E/CQiHo6IWyPi9KL/WcCLgLcXJVLfi4iLgN2B7xX93l4Me3REXFnM/4aIWDLG8i+NiH8s1sGKiPh8RHSNMuyIyzVSbCOMewewV12sncXv9N1iWW+PiFfVDX9uRHwjIi6OiFXAmcOmN9Y8D4uIGyPikYi4pH55IuKkiLi+WIYrI+LQUZb1PyPiI8P6fSci3ly8/z8RcUfx+98cEafUDXdmRFwREf8aEQ8D547wOx8TEb8tYvxtRBwz7Dd5Rt3nTdUXI6KrWCcPFcvw24jYaZRl2C0i/rvYDx6KiE+MMMziokSlra7fpm00IvaOiF8UcT4YEZcU/f9fMfgNxfo/Y0vrt1iud0TEjcDaiGgrPt9brMdbI+L4kZZFkraVSYykyeKvgK8Cc4HvAp8AiIgK8D3gBuAxwPHA2RHxrFGm8zng1SmlHuBg4GfbOP/2Yv4/BhYCbwC+FBFbrJoWETOBnwBfLsb9W+CTEXFQSunTwJeADxclUienlF7C5iVVH46IxwDfB94PzAPeCnwzInYcY9YvAp4FPBbYFzhnhNhGXa6RYhs+fkrpscNi3Qh8BbgHWAScBnxw2Ens84BvkNfxl4ZNb6x5ng48G9gTOJQiAYqIw4H/Al4NzAc+BXw3IjpHWCdfBs6IiCjG3QE4gfybA9wBPAWYA7wXuDgidqkb/yjgT8W6+sCwdTmP/Bt9rIjjo8D3I2L+CHEM97JinrsV474GWD98oIioApcCdwGLyfvCV4cP14B/Iv/mOwC7Ah8HSCk9tfj+ccX6v6TB9fu3wHPJv+ljgb8Hnljsf88Clm5FjJK0RSYxkiaLX6aULkspDQAXAY8r+j8R2DGl9L6UUm9K6U/AZ4C/GWU6fcCBETE7pbQipXTtNs7/aGAW8KFi/j8jn0z+bQPTPAlYmlL6fEqpv4jlm+QT/Ea9GLisiG0wpfQT4GrgOWOM84mU0t0ppYfJJ9wjxboty/UoEbEbcCzwjpTShpTS9cBngZfUDfarlNK3i+V41In6GD6WUlpWLM/3gMOK/q8CPpVS+k1KaSCl9AVgY7Fsw/0vkMiJCuTf4FcppWUAKaWvF/MYTCldAtwGHFk3/rKU0seL33F47M8FbkspXVR8/xXgFuBRyd8I+sgJwt7FMlyTUlo1wnBHkpPDt6WU1hbreGsa8/eRqwEuamAajazfjxXb2npgAOgk73/tKaWlKaU7tiJGSdoikxhJk8X9de/XAV1FlZg9gEVFdZaVEbESeCcwYpUb4FTyCf5dRbWZJ23j/BcBd6eUBuu+v4t8JXxL9gCOGhb7i4CdG4ypNo0XDJvGscAuY4xz97BYF40wzLYs10gWAQ+nlFaPMb272TrDf5tZxfs9gLcMWze7McLyppQSueSilqS9kLrSoIh4aV21qZXkUrwFDca+iLys9RpdlxcBPwK+GhHLIuLDRSnZcLsBd6WU+huY5ljeDgRwVeQqhC8fY9hG1u+m9ZJSuh04GzgXeCAivhoRI217krTNTGIkTXZ3A3emlObWvXpSSiOWRKSUfptSeh652s+3ga9t4/yXAbsV1dpqdgfuLd6vBbrrvqtPUO4GfjEs9lkppdfWwh1pEYZ9vhu4aNg0ZqaUPjRGzLsNi3XZVizXSLGNZRkwLyJ6RpleI9Mc7zzvBj4wbN10FyUhI/kKcFpE7EGuHvZNgOLzZ8hVoeanlOYCvyef7DcS2zLyCX+9hraRlFJfSum9KaUDgWPIpXcvHWVZd48tN6BfW3RHm9/9KaVXpZQWkauJfTJGvyNZI+t3s/WSUvpySulY8vpIwD9vIV5J2iomMZImu6uAVUWD4RkRUY2IgyPiicMHjIiOyM/vmJNS6gNWkau4bIvfkE8M3x4R7ZEb1Z/MUHuE64HnR0R3cTL4irpxLwX2jYiXFOO2R8QTI+KA4vu/kBvH1xve72Lg5Ih4VrHsXZFvUrDrGDG/PiJ2LdpqvBO4ZCuWa6TYRpVSuhu4EjiviPFQ8roYz93nxjVPcuLxmog4KrKZEfHcYYlUfYzXAcvJ1dx+lFJaWXw1k3zCvRwgIv6OXBLTqMvIv/MLi8btZwAHkn9/yNvI3xTr+QnUVSeMiKdHxCFFm5dV5OpeI22zVwH3AR8qlrMrIp48wjIuJydPLy62l5eT26rU5veCum1nRbHctfkNX//jWr8RsV9EHFe0mdlAbtuzrfufJI3IJEbSpFa0UTmZ3A7iTuBB8knonFFGeQmwNPIdsF5DblOyLfPvJTf6P7GY9yeBl6aUbikG+Vegl3wC+AXqTtqLqlUnkNvvLCNXi/pncrsByDchOLCoqlN7zsp5wDlFv7cWycHzyMnIcvLV8bcx9vH7y+TG238qXo969koDyzVSbFvyt+RG58uAbwH/t2jD06hxzTOldDW53cYnyCfktzPsrmcj+ArwDPI6qk3nZuB84Ffk3/EQ4IpGg04pPUQuQXkL8BC5ytZJKaUHi0HeTU4kVpBvGvDlutF3Jt/sYBXwB+AX5MR1+Dxq+8He5Bsq3AOcMUpIryJvIw8BB5GTy5onAr+JiDXkG1j8Q0rpzuK7c4EvFOv/9K1Yv53Ah8jb0/3k0tB3wqaHg27V7bglaSSRqwlLkqaCiFgKvDKl9NOyY5EkaaJYEiNJkiSppZjESJIkSWopVieTJEmS1FIsiZEkSZLUUrZ0v/mtsmDBgrR48eKJmLQkSZKkKeCaa655MKW049aMOyFJzOLFi7n66qsnYtLS5m69NXf326/cOCRJkjQuEXHX1o47IUmM1DSvfnXuXn55qWFIkiSpeWwTI0mSJKmlmMRIkiRJaikmMZIkSZJaikmMJEmSpJZiw361tnPOKTsCSZIkNZlJjFrbM55RdgSSJElqMquTqbVdf31+SZIkadqwJEat7eyzc9fnxEiSJE0blsRIkiRJaikmMZIkSa4h18YAACAASURBVJJaikmMJEmSpJZiEiNJkiSppdiwX63tgx8sOwJJkiQ1mUmMWtsxx5QdgSRJkprM6mRqbVdemV+SJEmaNiyJUWt75ztz1+fESJIkTRuWxEiSJElqKSYxkiRJklqKSYwkSZKklmISI0mSJKml2LBfre2CC8qOQJIkSU1mEtNsGzfAhnVlRzF17Ll77j7y8PjG65wBXTO2fzySJEmacCYxzfYvb4bfXwWVatmRTA33P5K7O88Z+fvBAejdmN9XqjCjG7q64ZCj4I0faE6MkiRJ2q5MYppt7Wro7oHuWWVHMjX84vbcPXAf6O+DDetzSVelAhEQFdhjX9jnENj3UNh1L3jMntAzStIjSZKkSc8kRq2vvw8euh86OmGP/WD/x8GeB+RkZZfdob2j7AglSZK0HZnEqHWtXQ19vTCzBz70Zdh971z6IkmSpCnNJEatp78PVj4IO+wIu+2dq4btsU/ZUUmSJKlJfE6MWkdKsGI5rF4Bp74KLviWbVskSZKmIUti1Bo2boBVD+fG+a9+D+y6Z+7/qU+VG5ckSZKaziRGk9+qFflWyWe9G57+V/nOYzX77VdeXJIkSSqFSYwmrzQIDy+HeTvC2/4VFu/76GG+973cPfnk5sYmSZKk0pjEaHJJCdauys97CeDwp8Jr3jN625fzz89dkxhJkqRpwyRG5Usp3y55w9r8cMo994enPw8OfwrMX1h2dJIkSZpkTGJUrt4N+XbJex2U27s84Wkwf6eyo5IkSdIkZhKj8vT3wyMPw+veB0usDiZJkqTG+JwYlWOgH1Y8AC94tQmMJEmSxsWSGDVfX2+uQva8l+WHVm6Liy7aPjFJkiSpZZjEqLk2rIfVK+Hl74ATXgAR2za93XbbPnFJkiSpZZjEqHn6+2D1CviH8+CYE7bPNC+5JHfPOGP7TE+SJEmTnkmMmmNwEB5+AF74hu2XwAD8x3/krkmMJEnStGHDfk28lOCh++FpJ8Pzziw7GkmSJLU4kxhNvIcfgIOeAGe9a9vbwEiSJGnaM4nRxFq9EhYugrd8BNo7yo5GkiRJU4BJjCZW7wZ44RthZk/ZkUiSJGmKsGG/Js76tTl5OfzYiZvHN74xcdOWJEnSpGQSo4kxMABrHoE3f3hiq5EtWDBx05YkSdKkZHUyTYwVD8CJfwtHHT+x87nwwvySJEnStGESo4lz+msmfh4mMZIkSdOOSYy2v5Tywy07Z5QdiSRJkqagadUmZvHixfT09FCtVmlra+Pqq68uO6Spqb8PZs2GarXsSCRJkjQFTaskBuDnP/85C2wMPrHWPAJHP6PsKCRJkjRFWZ1M29/gIBz25LKjkCRJ0hQ1rZKYiOCEE07giCOO4NOf/nTZ4UxdlQp0djVnXpddll+SJEmaNqZVdbIrrriCRYsW8cADD/DMZz6T/fffn6c+9allh6Vt0d1ddgSSJElqsmlVErNo0SIAFi5cyCmnnMJVV11VckTaZp/8ZH5JkiRp2pg2SczatWtZvXr1pvc//vGPOfjgg0uOaopKqXnz+trX8kuSJEnTxrSpTvaXv/yFU045BYD+/n5e+MIX8uxnP7vkqKaggYGcxOy+d9mRSJIkaYqaNknMXnvtxQ033FB2GFPb4AA8dD8c99ew825lRyNJkqQpatokMZoAA/2wbg1sWA9tbZAGYf/D4LSzyo5MkiRJU5hJjBrX1wtrV0N/L1Tb8q2U930cPP7JsO+hsMe+zbu1siRJkqYtkxg1Zu1q6NuYH2J52DGw98Gw6165BKZMl19e7vwlSZLUdCYxasyGtfD698GxJ5YdiSRJkqY5kxiNbGAgJy7r1+VqYz07wJHHlR3Vo33kI7n71reWG4ckSZKaxiRGMDgIG9bB+rUQQAQQuY3LAYfDPofkNi8dnWVH+miXXpq7JjGSJEnThknMdNTfB2tX5W6lmpOYXfeCAw/PDfX32Ad23r389i6SJEnSCDxLnQ7SYL4V8vp1UK3m16FHwxOeBov3h133hPaOsqOUJEmSGmISM1UNDsKK5VAJGEyweF846ng46Imw5/6WskiSJKlleSY7Va1YntuznPRi2O9xMLOn7IgmxowZZUcgSZKkJjOJmaoCOOO1OYGZyn7wg7IjkCRJUpNVyg5AE2D1Spi/c76rmCRJkjTFmMRMNf190LsR3vjB/HyXqe6f/im/JEmSNG1YnazVpJQTlf5e6Cu6AwP5jmNRhYH+XI1sn4PLjrQ5/ud/cvfd7y43DkmSJDWNScxkkRIMDgwlJrVuVIoEJfL3g4Mwa06uLjZ/J1j4GNhpV9hhAcyZB3Pmw6I9yl4aSZIkacKYxDRTSvmuYatW5IdNVttyla/BwVyC0tkFO+wI83aChYtycjJvIcydn5OTufOhZ663R5YkSdK05tnwRFm7Gu5dCsuWwp9uhtt+D/fckb/bZXc47q9h0eKiBGV+LkXp8nbBkiRJ0paYxGyrgQF44F64+w64+3b4441w5y25tKXanquABdDZnauBrV4Jb/4X2HO/siOfGubPLzsCSZIkNZlJzHj09cF9d8E9f4I7boI/XAd/vi1XB4uA/qJKWFd3brMSUXbEU983v1l2BJIkSWoyk5jR9PXlkpU/3w633Qi33gj33pnbsKTB3L6lqxt6dsgN7yVJkiQ1hUnMcGtXwy8uhe98HtasKqqDVWBGd250Px2evdJK/vEfc/e888qNQ5IkSU1jElOz/D744SXwk69Dby/Mmp2TFk1uv/pV2RFIkiSpyUxiAG6+Fj7wulzqMnsezO4oOyJJkiRJozCJgfzsljSYG+NLkiRJmtSmbxLT1wv3/Tk/x+XqX+SG+pIkSZImvamfxKQEKx/Kdxa79878HJc7boK/3FvcaSzlamQ9O5QdqbbGrruWHYEkSZKabOolMQMD8LvfwM3X5ITlrttgw1qotMFAH7S151sjz9sx33VMre3ii8uOQJIkSU02dZKYvl74zf/A1/4Tli8beo5LVzfM7Ck7OkmSJEnbSesnMRs3wM+/A//9WVi9Erpm2kB/Ojn77Ny94IJy45AkSVLTtH4S88sfwGc+kJ/pYvIy/Vx/fdkRSJIkqclav1FIf107F0mSJElTXusnMXffUXYEkiRJkpqotZOYq34OP/kGzFtYdiSSJEmSmmTi28SklKt89fVBf2/u9vWO8X6EfhvXw4b1udu7ITfm37gebvgVzJoL1dZv2qOttO++ZUcgSZKkJpuYs/+lf4SXPSUnIAP9QEAlIGLo2SwRjx4vpaI7mN+nQRhMQ+NVKrkbkd93zYSuGROyCGoRn/502RFIkiSpySYmiRnoh7Wrh5INUk5G8pfjm1Yt2UmDMDC4+Xcb18PqFdsabXMNjHP5JUmSJG1mYpKY+QvhhW+YkEm3vGob7LRr2VFMHWedlbuWyEiSJE0bE5PEzF0Az3/FhExa2swf/1h2BJIkSWqy1r47mSRJkqRpxyRGkiRJUksxiZEkSZLUUnzAilrbYYeVHYEkSZKazCRGre2CC8qOQJIkSU1mdTJJkiRJLcUkRq3txS/OL0mSJE0bVidTa7vnnrIjkCRJUpNZEiNJkiSppZjESJIkSWopJjGSJEmSWoptYtTanvSksiOQJElSk5nEqLWdd17ZEUiSJKnJrE4mSZIkqaWYxKi1nXpqfkmSJGnasDqZWttDD5UdgSRJkprMkhhJkiRJLcUkRpIkSVJLMYmRJEmS1FJsE6PWdvzxZUcgSZKkJjOJUWt797vLjkCSJElNZnUySZIkSS3Fkpgmu7//w6xJV5QdxnYSVKOHnjiOBdW/KyeEE0/M3R/8oJz5S5IkqelMYppsffod/Wk5FbrLDqUhg2xgkHUkNgIBVGiPnZkRB9Edh9MV+9IVB5QX4Pr15c1bkiRJpTCJKUHQSSVmlB3GiFJK9LOcIEj00xY7MSueTHc8ns7Yh854LG0xt+wwJUmSNI2ZxGiTwdRLPw8yq3IsCyqvoDP2pBo9ZYclSZIkbcYkRgAMpEcYjA3sVHkz8yovJCLKDkmSJEkakUnMNFerPlaN2SyufpIZlYPLDml8Tjqp7AgkSZLUZCYx01hKg/THX+iKA9i1+lHaY8eyQxq/t7617AgkSZLUZD4nZhrri7/QE89kj+pnWjOBkSRJ0rRkEjNNDaTVtDGPXarnUImussPZekuW5JckSZKmjWmVxAwMDPD4xz+ek6ZpO4qU+ulPD9PH/QzGOnapvJtqzCw7LEmSJGlcplWbmH/7t3/jgAMOYNWqVWWH0hQpDTLIagZZB7QRUWVmHMmsWMLMyuF0xG5lhyhJkiSN27RJYu655x6+//3v8653vYuPfvSjZYczoVLqo5+HgKArDqSn8nRmxhPoiv2ImDY/uSRJkqaoaXNGe/bZZ/PhD3+Y1atXlx3KhOtnBT2VZ7BL9Ryri0mSJGnKmRZtYi699FIWLlzIEUccUXYoEy6lBDHInMoJ0yOBOf30/JIkSdK0MS1KYq644gq++93vctlll7FhwwZWrVrFi1/8Yi6++OKyQ9uuBlMv/TxIdxxCdxxZdjjN8brXlR2BJEmSmmxalMScd9553HPPPSxdupSvfvWrHHfccVMugelPDzMQK1hY/Xv2qH52epTCAKxbl1+SJEmaNqZFScxUl9IAiV72bLuYrtiv7HCa6znPyd3LLy81DEmSJDXPtEtilixZwpIp93DERCW6pl8CI0mSpGlpWlQnm/oS/pSSJEmaLjzznQISiaCj7DAkSZKkpjCJmQIGWUuFGWWHIUmSJDXFtGsTM5WkNEBfLKc95rNL9T1lh1OOM88sOwJJkiQ1mUlMixpIqxmMNewQp7Ow+vrpc0vl4UxiJEmSph2TmBaUUmKQtexa/Sg9laeWHU65HnwwdxcsKDcOSZIkNY1JTAsaYCUdsRez4illh1K+007LXZ8TI0mSNG2YxLSAWsnLIKshqlRiJjtV3kxElB2aJEmS1HQmMZNUSgMMsIoUGyESHSxmduVF9FSOpZN9ifDGcpIkSZqeTGImkcHUywArgQQRdMfhzI4TmVU5kvbYuezwJEmSpEnBJKZEuZrYOgZZBVSpRBdz4jnMrhzPjHj89L3jmCRJkjQGk5iSDKQ1DMZq2lnEvMoLmBVPoSsOIKJadmit5bWvLTsCSZIkNZlJTEkGeIQdK69lQeWVNtDfFmecUXYEkiRJajKTmCZJqZ9+HmIjdzHACgA6Yy8TmG119925u9tu5cYhSZKkpjGJ2Q5y25Y19LOc/rScPh6gLy2jN91Fb7qHPu5jIK2AaAP66Y7HMatyHN1xRNmht76XvCR3fU6MJEnStGESMw696V42pFvpS3+hN91FH3fTm+6ln7+Q6CfI7VkSA5AGCToJOgg6aWNngqA/Pcgu7e+mK/YreWkkSZKk1mQS06CBtIal/S9jgNWQ+oE2KkWCUmWHRzfIt5aYJEmSNCFMYhr08OCX6WcF7Wnk57WkNNjglNL2C0qSJEmahkxiGrQmXUnQTn88tE3TqTCDCj7/RZIkSdpaJjEN2rPtwrJD0Eje8payI5AkSVKTmcSotZ18ctkRSJIkqckqZQcgbZNbb80vSZIkTRujlsRExGqGWqHX7rWVivcppTR7gmOTtuzVr85dnxMjSZI0bYyaxKSUepoZiCRJkiQ1oqHqZBFxbET8XfF+QUTsObFhSZIkSdLItpjERMT/Bd4B/GPRqwO4eCKDkiRJkqTRNFIScwrwV8BagJTSMsCqZpIkSZJK0cgtlntTSikiEkBE+KRGTR7nnFN2BJIkSWqyRpKYr0XEp4C5EfEq4OXAZyY2LKlBz3hG2RFIkiSpybaYxKSUPhIRzwRWAfsC70kp/WTCI5Macf31uXvYYeXGIUmSpKZppCQG4HfADPJzYn43ceFI43T22bnrc2IkSZKmjUbuTvZK4Crg+cBpwK8j4uUTHZgkSZIkjaSRkpi3AY9PKT0EEBHzgSuB/5rIwCRJkiRpJI3cYvkeYHXd59XA3RMTjiRJkiSNbdSSmIh4c/H2XuA3EfEdcpuY55Grl0mSJElS041Vnaz2QMs7ilfNdyYuHGmcPvjBsiOQJElSk42axKSU3tvMQKStcswxZUcgSZKkJttiw/6I2BF4O3AQ0FXrn1I6bgLjkhpz5ZW5azIjSZI0bTRyd7IvAZcAJwGvAV4GLJ/IoKSGvfOduetzYiRJkqaNRu5ONj+l9DmgL6X0i5TSy4GjJzguSZIkSRpRIyUxfUX3voh4LrAM2HXiQpIkSZKk0TWSxLw/IuYAbwE+DswG3jShUUmSJEnSKLaYxKSULi3ePgI8fWLDmfpuXH0j92y8p+wwWkoQPG2Hp9Fd7S47FEmSJE0CYz3s8uPkh1uOKKX0xgmJaAJs2LCBpz71qWzcuJH+/n5OO+003vvecu4gff6fz+eGNTfQUekoZf5lSynRl/rYOLiRjYMb6U29BEFHdNBR6aA92umodDC7OpuFHQvZpXMXduvajaPnHD1yEnPBBc1fCEmSJJVqrJKYq5sWxQTr7OzkZz/7GbNmzaKvr49jjz2WE088kaOPbv79CQbTIDu07cCstllNn3czDKZB1g6spTf10jvYC0BbtBEEg2mQQQaZ1zZvswTlMR2PYUHHAua3z2dBe+7OqM5obIaHHTaBSyNJkqTJaKyHXX6hmYFMpIhg1qycNPT19dHX10dElBzV1PRA7wM8dsZj2XPGnuzWuRu7dO6yKTFZ0LGAHdp2oL3Svv1m+NOf5u4znrH9pilJkqRJrZGG/VPCwMAARxxxBLfffjuvf/3rOeqoo8oOacrZMLiB7mo3nz/w842XpGyr978/d01iJEmSpo1pk8RUq1Wuv/56Vq5cySmnnMLvf/97Dj744LLDmrRSSgwwQH/qpz/1M5CG3venfqpUqVChEpVcVYxB+lM/f7/r3zcvgZEkSdK0NGYSExFV4I0ppX9tUjwTbu7cuSxZsoQf/vCH0yKJGUyDmyUgw99Xo0o1qgS5el0i5XEYYGZlJj1tPezYviNz2+Yyr20e8zrmMa9tHnPb59JT7WFWdRY91R562nqYWZ3JDm07lLzEkiRJmurGTGJSSgMR8TygpZOY5cuX097ezty5c1m/fj0//elPecc73lF2WNtN72Avq/pX0Z/6aYv8kw6Sk5cgmFWdxey22cxtm5uTkfZ5zG+fz/z2+fS09QwlI21DScnM6kwqUSl5ySRJkqRHa6Q62RUR8QngEmBtrWdK6doJi2o7u++++3jZy17GwMAAg4ODnH766Zx00kllh7VVUkr0pl5W96+mP/XnmxZUZ/G0HZ7GMXOOYfeu3ZndNntTMtJV6fImBpIkSZpSGklijim676vrl4Djtn84E+PQQw/luuuuKzuMbbJmYA1r+3MOOadtDsfPO54nzX4Sh/Ycyq6du07fROVTnyo7AkmSJDXZFpOYlNLTmxGIxrZ+YD2vesyreM7857Coc9H0TVqG22+/siOQJElSk22x0UNE7BQRn4uIHxSfD4yIV0x8aKoZTIMkEk+a8yQe0/UYE5h63/tefkmSJGnaaKQ62YXA54F3FZ//SG4f87kJimla60/9rBtYx7qBdUQEFSoMpkH26d6HRZ2Lyg5v8jn//Nw9+eRy45AkSVLTNJLELEgpfS0i/hEgpdQfEQMTHNeUl1KiL/WxbmAd6wfXU40qAFWq7NO9D4/reRwHzTyIvWbsxR5de9BR6Sg5YkmSJGlyaCSJWRsR88mN+YmIo4FHJjSqKSalxPK+5dy+7nb+vPHP3N97P7Ors5lZnckhsw7hsFmHsf/M/dlrxl4s6lzkrY0lSZKkMTSSxLwZ+C7w2Ii4AtgReMGERtXCUkos27iMO9bfwS3rbuHa1dfyh7V/YP3AeipRYUX/Ct65+J08d/5zmd8+3/YtkiRJ0jg1ksTcBDwN2A8I4FYauCHAdLFuYB1/WPsHblhzA1euvJI/rPtDfn4LQX/qZ0Z1Bt2Vbma3zQYgCI6efTQLOhaUHLkkSZLUmhpJYn6VUjqcnMwAEBHXAodPWFST2CP9j3DTmpu4bvV1/PKRX3L7utupRpX+1E93tZvZ1dm0VRpZrdouLrqo7AgkSZLUZKOebUfEzsBjgBkR8XhyKQzAbKC7CbFNGiv6VvDZZZ/lipVXcOf6O1k9sJoNgxuYVZ3FzOpMBlK+z8Ga/jWsYc2Y0xpksBkhTx+77VZ2BJIkSWqysYoMngWcCewKnM9QErMaeOfEhjW5VKLC7l27s9tO237CXJuWtpNLLsndM84oNw5JkiQ1TaSUxh4g4tSU0jfHM9EnPOEJ6eqrr96mwKSGLFmSu5dfXmYUkiRJGqeIuCal9IStGbeRBvq7RsTsyD4bEddGxAlbMzNJkiRJ2laNJDEvTymtAk4AFgJ/B3xoQqOSJEmSpFE0ksTU2sI8B/h8SumGun6SJEmS1FSNJDHXRMSPyUnMjyKiB7zFliRJkqRyNPJAk1cAhwF/Simti4j55CplUvm+8Y2yI5AkSVKTNZLEHFt0D42wFpkmmQULyo5AkiRJTdZIEvO2uvddwJHANcBxExKRNB4XXpi7Z55ZZhSSJElqoi0mMSmlk+s/R8RuwIcnLCJpPExiJEmSpp1GGvYPdw9w8PYORJIkSZIascWSmIj4OJCKjxVyI/8bJjIoSZIkSRpNI21irq573w98JaV0xQTFI0mSJEljaqRNzBeaEYgkSZIkNWLUJCYifsdQNbLNvgJSSunQCYtKatRll5UdgSRJkppsrJKYk5oWhbS1urvLjkCSJElNNlYS0w7sNLz9S0Q8BVg2oVFJjfrkJ3P3da8rNw5JkiQ1zVi3WL4AWD1C//XFd1L5vva1/JIkSdK0MVYSszildOPwnimlq4HFExaRJEmSJI1hrCSma4zvZmzvQCRJkiSpEWMlMb+NiFcN7xkRrwCumbiQJEmSJGl0YzXsPxv4VkS8iKGk5QlAB3DKRAcmSZIkSSMZNYlJKf0FOCYing4cXPT+fkrpZ02JTGrE5ZeXHYEkSZKabKySGABSSj8Hft6EWCRJkiRpi7aYxGj7ev/v/sT/PrCy7DCmjNO+9kUAvnH6S0uORNJE62lv43NHH8icDv+6JGm685+gyW5dtY61AwN0V6tlhzIlHPnrXwLwdZMYacr789r1rOnvN4mRJJnElKE9gs7qWDeGU6MqkbuuT2lqSykBUXYYkqRJwjM/SdKk90jfALvO7GSnro6yQ5EkTQImMZKkSS2lxPqBAd52wB60VfzbkiRZnUwtrrezs+wQJE2w/pSYWa1y9II5ZYciSZokpkUSc/fdd/PSl76U+++/n0qlwllnncU//MM/lB2WtoNz//nfyw5B0gRJKTGQoHcwERFE2CZGkpRNiySmra2N888/n8MPP5zVq1dzxBFH8MxnPpMDDzyw7NAkacpJKTEIDAwmBlLtRd37/KpEUI0gKJrsB6QEgwkGSaQE3W0VZrZVOWRuT7kLJUmaVKZFErPLLruwyy67ANDT08MBBxzAvffeaxIzBZzxxU8DcMlLzyo5Eqn1bUo+6hKPwZToH0wMFv36EwSJaiWoEJvdL2wQiuGgq5qTj1ntVXra2pjVXmV2WxtzOtqY097G3I42ZrZVmdlWZUa1ysy2Ct1tVbqrVbrbKnRXq3RVK5a+SJJGNC2SmHpLly7luuuu46ijjio7FG0Hj7v2KsAkRtpWy9ZvhATtlWBGNScfs9qq9LRVmd3exuwi8ZjdXqWnPScgOeEYSjq6NyUlFSomH5KkCTStkpg1a9Zw6qmncsEFFzB79uyyw5GkSaM9gi8fezB79XSXHYokSVs0be5V2dfXx6mnnsqLXvQinv/855cdjiRNOt6+WJLUKqbFP1ZKiVe84hUccMABvPnNby47HEmSJEnbYFokMVdccQUXXXQRP/vZzzjssMM47LDDuOyyy8oOS9vB6tlzWD3bZ0dI22LDwCD9KZUdhiRJDZsWbWKOPfZYkn/QU9J57zu/7BCkljOYEmv6B1jbP0AlgjntVU7ffSd2mdFRdmiSJDVkWiQxkjRd1W6R3J8Sa/sHSAkScMCcbp65y3yOXjCHx86a4a2MJUktxSRGLe2ln/kYAF981RtLjkSaWLWn1/ennJDkxGSQ/uKBktW6B0em4jWQEhWC2R1Vduho5+gFczh+53kcMW82czo8/EuSWpf/Ympp+990Y9khSOOWaolIXSlJrZsSVCOoBETdE+wHUqKrWmF2exsLOtvZoaON+Z3t7NjZwfzOduYUz3KZ3d5GT/Esl9ntVTorPjBSkjT1mMRIUhM90tvPqr5+5nS0Mbe9jbkz2tiho535ne0s7Opgh4425rS309OeHzLZ0zaUkHgLZEmSMpMYSWqigZQ4cdECznv83mWHIklSy/KyniRJkqSWYkmMWtqDO+5UdgiSJElqMpMYtbSPvusDZYegaSSllO/8lWCQVHRz/0Hy7YwTuSF+IuVu8V2tu7Z/oMxFkCRpSjCJkTTpDU8ENksiRuo3SoJRIYhgUxcgyHcBY9jzcGu3KU5FQlJLTqoRtFfyq6NSoaMSdFSquVut0BkVOqtBZ7VCZ6VKZzXoqlborFTobqvSWQkO26GnWatOkqQpySRGLe2Vn/gXAD77928rOZKpr/acksGUGGDo/WAau0QCEpXi+SVRJA+bEgeANJRBbJ44DJVmBBSJQ2VT8jCjUqG9UqGzljxUKkXiUNmUNHRVN3911I1fS0TaY6hfWyXoGDaftti8X8XbFUuSVDqTGLW0vW6/tewQJqX6ZGJT4jHsfe27BFSDoUSjyCmGEoqhko4ZbRVmVKvMrFbpbsvdrrZcGjGjWt2USMyoFt3ic3td0jCUQFRojxH6VYL2yIlJ7ftqhM86kSRJm5jESCVLdaUWmyUb1N6nTQ87rD2ZvRK5VGPo9oJRV+Upj99RCbqqVbrbKnRX25jVlt/Pamvb9AySWcUzSGYUJRUz1o71SQAAGwxJREFUqlVmVCvMaKvQVakwo634XM3VpUwkJEnSZGASo2lpUwPt4v3gsOpQI7a9qGtnMVSFaqiqVIVcTypXmxpfO4tKQFe1Qne1ysy2odestuqmpKOWeHQXiUV90rF5ApLbXfhgREmSNFWZxKiphicJI929qdEG2pWA3tzoguUbegE2tbtIddlDLXGAzRuBVwLao0JHdahtREdRDarWYLv2vtamYlM7i6KaVFcxfK72tHk7i45q7o7WzqK+GpXtLCRJkhpnEtNkXdUKa/oH2DA4WHYoEyMNJQybkoeUGKBIHKDuZD6f2HcUiUJOGIYSh+ENs+s/15KMHQ85iErAhx6/96MaaG/W1mKEJKNaMXGQJElqRSYxTXbeYXvzUG9f2WFMqFrCUF/6MGGJw5e+CMCu23eqkiRJmsRMYppsQVcHC7o6yg5DkiRJalm2/FVrO+us/Pr/7d17cJz1fe/xz3e1ut9t3WxLlmxZlozAFywTm7qQBCaJTSHp4CR4wqEu7knbQ0JokrYpZShDMpSU5CTTpLkeMuRkEi5lSEMolyRufJLC4ZZgbj51YAKn2CWkEGPLkixpd7/943nkCCNLa3m1zz7a92vmmb3ouXx392d5P/r9fs8DAACAokFPDOLtF7+IugIAAADkGT0xAAAAAGKFEAMAAAAgVggxAAAAAGKFOTGIt7Vro64AAAAAeUaIQbx9/vNRVwAAAIA8YzgZAAAAgFghxCDeLr00WAAAAFA0GE6GeNu/P+oKAAAAkGf0xAAAAACIFUIMAAAAgFghxAAAAACIFebEIN42bYq6AgAAAOQZIQbx9rd/G3UFAAAAyDOGkwEAAACIFUIM4u3ii4MFAAAARYPhZIi3116LugIAAADkGT0xAAAAAGKFEAMAAAAgVggxAAAAAGKFOTF5dujQuA4fTkddxrxRN3CuJOnwS0cjrgSFJJk0LVpUHnUZAABgjhBi8uwv/uIX+vnPB5VMWtSlzBNbg5sHn4y2DETKXRocTEmS6uqSWrasUl//+mmqruZXHAAA8xH/w+fZ8HBGNTUJ1dTw1gOnwt01OJjW0FBaiYSpu7tKW7c26dxzG9XXVy0z/lAAAMB8xTdpxNp1j3w4uH3LFyKuBPng7hoayujIkbTcXT091brggiZt3tyozs4KggsAAEWCEINYK0uPRl0C8mB0NKODB8dlZmprK9flly/R+ecvZN4LAABFihADoGANDqY0NJRRdXWJtm9fpK1bm9XbW0WPCwAARY4QA6DgjI1l9NprKTU3l+raa7t1zjmNSiY5IzwAAAgQYgAUhEzGNTSU1pEjGZWWmq64ol3bty9SRUVJ1KUBAIACQ4hBrD3WujnqEjAL7q6xMdeRI2mNjWWUTCaUybi6u6u0cWO9LrmkTa2tzHcBAABTI8Qg1r7bfVnUJSAL7kFgGRxMK5k0uQfXc9m8uUEbNtRr1apq9fRUq7ycIWMAAGBmhBgAc8bddehQWiMjGXV1VWjbtkatWVOn3t4qNTeXMUEfAADMCiEGsXbDQx+UJF199tcirgTHGxxM6ciRILx85CNLtXlzI6EFAADkBCEGQE65u155ZVytrWW65pqletvbFqqkhPACAAByhxADIGfcXb/61ZjOPLNOn/tcr6qr+RUDAAByj28YAE7J+HhGQ0NpDQ9nZGbauLFBn/nMSk6NDAAA5gwhBkDWJgeW0lJTJiNVVCTU31+r9evrtGpVtc46q15lZZxlDAAAzJ2iCTGXX3657rnnHrW0tOiZZ56JuhzkyL8uPj/qEuYtd9fwcEaHD6eUTAaBpbIyodNPDwJLX1+1VqyoUmsrZxkDAAD5VTQhZseOHfrQhz6kyy7juiLzyb1d74u6hHklk3EdPpzW0aMZmUltbeXatq1Va9bUasWKKrW0EFgAAED0iibEnHPOOXrxxRejLgM5Vp4ekSSNllRGXEl8ubtee21c6XTw+IwzarRlS5M2bmzQkiXlhBYAAFBwiibEYH76m0c+IonrxJyKQ4dSam0t15VXLtXAQL3q6vi1AAAAChvfVoAi5y4tX16pt799YdSlAAAAZIVTCAFFLpVyrucCAABihRADFLlMRtqypSnqMgAAALJWNCFm+/bt2rRpk/bt26f29nbdfPPNUZcERGJ8PKNDh1J6+eVRvfLKmBYuLNVZZ9VFXRYAAEDWimYMya233hp1CZgDuzp+L+oSCpa7a3TUNTwcnDK5tDShTMZVXp5Qb2+V1q6t1apVNTrjjBolk0Xz9wwAADAPFE2Iwfy0q+OiqEsoCJmMa2Qko+HhtFIpKZk0pVKupqZSbdxYrzVratXTU63lyyu51gsAAIg9QgxirW7soCTpcFljxJXkTzrtGhpKa3g4LTNTImHKZFwdHRU699xGnXFGcGHKZcsqVVvLP3EAADD/8A0HsfaJx/9S0vy9Tsz4eEZDQxmNjKRVUhL0niQSpp6eKq1ZU6v+/hp1d1eqs7NSZWUMCQMAAMWBEAMUiFQqmL8yNJRRSUlw/ZaKioROO61aZ55Zp76+ai1fXqUlS8qVSDAcDAAAFC9CDBCBTCYYEjY0NDEkLOhh6eur1sBAnfr7a9TTU6VFi8qZvwIAAHAcQgyQZ0ePZvSb34yrr69a69fXafXqYA5LR0fFsSFjAAAAODFCDJBHIyNpvf56Stdf360LLmiJuhwAAIBYIsQg1u7t2hZ1CVPKZFyjo5ljSyYjJZMJubs+9akevetdTVGXCAAAEFuEGMTavy5+RyTHdXelUhNBJbgtKTGVlASnO3aXFi0q16pVFerurlJnZ4UWLy5XR0eFFi+uiKRmAACA+YIQg1hrGvmVJOnVyrY5O8bYWEZDQ2mNjARXvZeCM4lVVSXU0VGhrq5KdXdXqqOjUq2tZVq0qFwLF5ZyBjEAAIA5QohBrH30iWsl5e46MRMXkhwaSiuZtGOnOV67tk4bNtSpu7tKbW3lamsrU00N/3wAAACiwLcwFCV319hYMAzs6NFgzsrEmcFWrqzShg3BWcNWrqxWW1sZpzkGAAAoIIQYzEuTQ8rYWDBvpawsIbPgIpLptGvhwlKtWFGlZcsqtW5dEFiWLatUMpmIunwAAABMgxCDWJoIKam0yzOuAwdGTxhSOjuDeSuLFlWotbVMLS1lamoqJawAAADEFCEGsTAxuf7oUVcyaUqnXQ0NSVVWJlRRkdBVVy0lpAAAABQJQgwKTirlGh5Oa2goo2RSkybX12pgoF6rVlWrp6daCxaUSt8PJvZ3Xbgk4qoBAACQL4QY5F067Roby2hsLLgdH/c3XGMlkTD19VVrw4Y6nX56jXp6pplcf+GF+X8BAAAAiBQhBjk1MVdlIqSMjmaUSJiSySCApFJBYGltLVNPT7na2yvU2Vmh1tZyNTeXqrm5TG1t5cfOFDajffuC297eOXpFAAAAKDSEGMzK+HhGQ0MZjYykVVJiSiSCXpRMRlqwoFSdnZVqb6/Q0qXBleqDeSplam4uU21tSe5OWfzHfxzc7t6dm/0BAACg4BFiMK2JnpXh4d9Oqs9kXJWVCfX31xw7NXFrazCZfsECJtQDAABgbhFiICkIK+PjwfCv4NoqrtJSUzotNTQkNTBQr7Vra9XbW63u7ko1N3MBSAAAAESDEFNEMpnfhpTR0eAq9aWlCbkHc1Vqa0vU3l6pZcsq1N8fTKhfvrxSjY2lUZcOAAAAHEOIicDoqKukJD1n+0+nXUePBr0pE2f9SqddktTaWqbe3hp1d1eqq6tSbW3lWrSoTK2t5aqqKpmzmgAAAIBcIcTk2Zo1tRocTM3pMaqrS9TVVanu7mByfVtbudragon1icQ8GwJ2zTVRVwAAAIA8M3fP+U4HBgb88ccfz/l+AQAAAMwPZvYzdx+YzbacRgrxtmdPsAAAAKBoMJwM8XbVVcEt14kBAAAoGvTEAAAAAIgVQgwAAACAWCHEAAAAAIgVQgwAAACAWGFiP+LthhuirgAAAAB5RohBvJ19dtQVAAAAIM8YToZ4e+ihYAEAAEDRoCcG8Xb11cEt14kBAAAoGvTEAAAAAIgVQgwAAACAWCHEAAAAAIgVQgwAAACAWGFiP+Lt85+PugIAAADkGSEG8bZ2bdQVAAAAIM8YToZ4+9GPggUAAABFg54YxNunPhXcnn9+tHUAAAAgb4qmJ+b+++9Xb2+vVqxYoRtvvDHqcgAAAADMUlGEmHQ6rSuuuEL33Xef9u7dq1tvvVV79+6NuiwAAAAAs1AUIebRRx/VihUrtHz5cpWVlemSSy7R9773vajLAgAAADALRRFiDhw4oI6OjmOP29vbdeDAgQgrAgAAADBbRTGx393f9JyZRVCJtHv3bj3//PORHLtQrFu3TuvXr8/Nzr761dzsBwAAALFRFCGmvb1dL7300rHH+/fv1+LFiyOp5eabb9aePXtUVlYWyfFPhbtrfHxcqVTqDbeZTEalpaVKJpMqLS09dr+qqkqNjY1asGCBmpub1dLSotbWVlVWVuauqN7e3O0LAAAAsVAUIWbDhg167rnn9MILL2jJkiW67bbb9J3vfCeyepqamlRTUxPZ8Y+XyWQ0PDys0dFRjY+PK51OK5lMKpFIyMzk7kqlUiopKVFzc7MaGxvV1NSk5uZmtbW1aeHChWpsbFR9fb3q6+vV0NCg+vr6/AS1738/uL3wwrk/FgAAAApCUYSYZDKpL37xi3rnO9+pdDqtyy+/XP39/VGXFZnx8XEdOXJER48eVTKZVDqdVldXl3p6etTW1qaWlhY1NDQcCyMTS1VVVWTD8E7os58NbgkxAAAARaMoQowkbd26VVu3bo26jLxzd42MjGhwcFBSMBeovLxc69at08aNG9Xf36+VK1eqqqoq4koBAACA7BRNiCk26XRar776qtxdS5Ys0XnnnaeBgQH19fWpo6Oj8HpUAAAAgCwRYuaZ0dFRHTx4UIlEQlu2bNEHPvABrVy5MuqyAAAAgJwhxMRQKpXS2NiYxsbGNDo6emwi/sRQsZ07d+riiy9Wc3Nz1KUCAAAAOUeIKTATpzGeCChjY2MyM5WUlEgKhomVlpaqtbVV7e3tWrp0qTo7O9XS0qKWlhYtX75cFRUVEb+KPPrWt6KuAAAAAHlGiMmzRCKhgwcPamho6NhzZqZEInEswDQ0NGjJkiVqb2/XsmXLtHjxYrW0tKi5uVnNzc2qqalhTsuEjo6oKwAAAECeEWLy7GMf+9gbLrwpSWVlZcdCSlNTUywvhBmZ228Pbt///mjrAAAAQN4QYvJs9erVWr16ddRlzB9f/nJwS4gBAAAoGomoCwAAAACAk0GIAQAAABArhBgAAAAAsUKIAQAAABArTOxHvN15Z9QVAAAAIM8IMYi3pqaoKwAAAECeMZwM8XbLLcECAACAokGIQbwRYgAAAIoOIQYAAABArBBiAAAAAMQKIQYAAABArBBiAAAAAMQKp1hGvN17b9QVAAAAIM8IMYi3qqqoKwAAAECeMZwM8falLwULAAAAigYhBvF2xx3BAgAAgKJBiAEAAAAQK4QYAAAAALFCiAEAAAAQK4QYAAAAALFi7p77nZoNStqX8x3PD02SXo26CMQO7QazQbvBbNBuMBu0G8xGr7vXzmbDubpOzD53H5ijfceamT3Oe4OTRbvBbNBuMBu0G8wG7QazYWaPz3ZbhpMBAAAAiBVCDAAAAIBYmasQ87U52u98wHuD2aDdYDZoN5gN2g1mg3aD2Zh1u5mTif0AAAAAMFcYTgYAAAAgVnISYsxsgZn90MyeC28bT7De/Wb2upndk4vjFjIze5eZ7TOz583sE1P8vNzMbg9//oiZdeW/ShSaLNrNR81sr5k9ZWa7zKwzijpRWGZqN5PW22ZmbmacQQhZtRsze1/4O+dZM/tOvmtE4cni/6mlZvZjM3si/L9qaxR1orCY2TfM7Ndm9swJfm5m9vdhu3rKzM6caZ+56on5hKRd7t4jaVf4eCo3SfpvOTpmwTKzEkn/IGmLpNMkbTez045bbaekg+6+QtLnJH06v1Wi0GTZbp6QNODuqyXdKenv8lslCk2W7UZmVivpSkmP5LdCFKJs2o2Z9Uj6K0m/4+79kq7Ke6EoKFn+vrlG0h3uvk7SJZK+lN8qUaBukfSuaX6+RVJPuHxQ0pdn2mGuQsy7JX0zvP9NSe+ZaiV33yVpMEfHLGRnSXre3X/p7mOSblPwHk02+T27U9J5ZmZ5rBGFZ8Z24+4/dvfh8OHDktrzXCMKTza/byTpkwpC79F8FoeClU27+e+S/sHdD0qSu/86zzWi8GTTblxSXXi/XtJ/5LE+FCh3/4mk30yzyrsl/W8PPCypwcwWTbfPXIWYVnd/OSzyZUktOdpvXC2R9NKkx/vD56Zcx91Tkg5JWpiX6lCosmk3k+2UdN+cVoQ4mLHdmNk6SR3uPu+H8iJr2fy+WSlppZk9aGYPm9l0f0VFccim3Vwn6VIz2y/pXkkfzk9piLmT/Q6kZLZ7NrMfSWqb4kd/ne0+ishUPSrHnwYum3VQXLJuE2Z2qaQBSefOaUWIg2nbjZklFAxZ3ZGvghAL2fy+SSoY2vFWBb2+PzWz09399TmuDYUrm3azXdIt7v5ZM9sk6Vthu8nMfXmIsZP+Xpx1iHH38094VLNXzGyRu78cdv0Ue5fzfkkdkx63683dqRPr7DezpIIu1+m62TD/ZdNuZGbnK/jjwbnuPpqn2lC4Zmo3tZJOl7Q7HLHaJuluM7vI3R/PW5UoNNn+P/Wwu49LesHM9ikINY/lp0QUoGzazU6Fcx/c/f+aWYWkJvHdENPL6jvQZLkaTna3pD8I7/+BpO/laL9x9ZikHjNbZmZlCia23X3cOpPfs22S/sW5aE+xm7HdhMOCvirpIsanIzRtu3H3Q+7e5O5d7t6lYC4VAQbZ/D/1T5LeJklm1qRgeNkv81olCk027ebfJZ0nSWa2SlKFpP/Ma5WIo7slXRaepWyjpEMTU1VOJOuemBncKOkOM9upoPG+V5LC03j+ibv/Ufj4p5L6JNWEYyV3uvsDOaqhYLh7ysw+JOkBSSWSvuHuz5rZ9ZIed/e7Jd2soIv1eQU9MJdEVzEKQZbt5iZJNZL+Mfyr+r+7+0WRFY3IZdlugDfIst08IOkdZrZXUlrSn7v7a9FVjahl2W4+JunrZvZnCoYD7eCPtDCzWxUMTW0KM8DfSCqVJHf/ioL5U1slPS9pWNIfzrhP2hUAAACAOMnVcDIAAAAAyAtCDAAAAIBYIcQAAAAAiBVCDAAAAIBYIcQAAAAAiBVCDADMwMwWmtmecPmVmR0I778enn42l8dabGZ3hvffamb3hPevM7OP5/JYUxz7PWZ22qTHO8xs8Vwec5pa3mtm/8/Mfhw+vtXMnjKzPzOz68OLvp5o2wEz+/tTOPbVs912in29GF5jBQCQQ7m6TgwAzFvhtTHWSkGYkHTE3T9jZl2S7snxsf5DwQVwo/AeBa9nIpjtkPSMZrhq8hzZKel/uPuPzaxN0tnu3pnNhuGFPE/lYp5XS7rhFLYHAMwxemIA4NSUmNnXzexZM/uBmVVKkpl1m9n9ZvYzM/upmfUdv6GZnTuph+cJM6s1sy4ze+YExzrNzHab2S/N7MpJ+/momT0TLleFz71hP2b28TCATVmbmZ0t6SJJN4X1/KWkAUnfDh9Xmtl6M/s/4XYPmNmiKV5Tq5l918yeDJezT1Rj+PylZvZoeIyvmlmJmV0rabOkr5jZTZJ+IKklXOd3zewWM9sWbr/BzB4Kj/Vo+B5O7sGqNrNvmNlj4Xv87vD5HWZ2V/g+PGdmfxc+f6OkyvBY3z7utf3pxHqT9vGF8P4/he/Ls2b2wSnel5P6PMLn3xu+X0+a2U9O0CYAoDi5OwsLCwtLlouk6yR9PLzfJSklaW34+A5Jl4b3d0nqCe+/RdK/TLGv70v6nfB+jYLe8S5Jz4TPvVXSPZOO+5CkcklNkl5TcLXj9ZKellQd7uNZSesm7yfc/uOSrpuuNkm3SNo2aZvdkgbC+6Xh8ZvDx+9XcLXu41/T7ZKuCu+XSKqfpsZV4XtQGq7/JUmXTXHs41/LLQp6q8ok/VLShvD5uvA9nPy+3TDpM2mQ9Iuwjh3htvWSKiT9f0kd4XpHTvDZN0t6ftLj+yRtDu8vCG8rFfReLQwfvxh+XrP5PJ6WtGSi9qjbPgsLC0shLQwnA4BT84K77wnv/0xSl5nVSDpb0j+a2cR65VNs+6Ck/xn+xf8ud98/af2p/LO7j0oaNbNfS2pV0GPxXXcfkiQzu0vS70q6e6odnERtx+uVdLqkH4bblUh6eYr13i7pMkly97SkQ2Z2ohozCgLOY+E+KyX9OotaJtf0srs/Fh7vcLj/yeu8Q9JF9tv5RBWSlob3d7n7oXCbvZI6Jb10ooO5+3+GvWAbJT0XHv/B8MdXmtnvh/c7JPUoCJrTmuHzeFDSLWZ2h6S7ZtoXABQTQgwAnJrRSffTCr6IJyS97u5rp9vQ3W80s3+WtFXSwxZMVj96EsdKSjpR6knpjUOGK8LbrGqbgkl61t03neR2E9ue6PlvuvtfzWKfE9t7Futc7O773vCk2Vs09fs5k9slvU/SvykIZm5mb5V0vqRN7j5sZrv12/d7wkl/Hu7+J2GdF0jaY2ZrPZifBQBFjzkxAJBjYY/AC2b2XkmywJrj1zOzbnd/2t0/rWAi+pvmzWThJ5LeY2ZVZlYt6fcl/VTSKwrmkSw0s3JJv5dFbYOSaifte/LjfZKazWxTuF2pmfVPUc8uSX8arlNiZnXT1LhL0jYzawnXX2BmWU3eD/2bpMVmtiHcvtbMjg8iD0j6sIXdHGa2Lov9jptZ6Ql+dpeCEyBsVxBopGBI2sEwwPRJ2jjFdif9eYTt4xF3v1bSqwp6eAAAIsQAwFz5gKSdZvakgjkg755inasmJm5LGlEwx+KkuPvPFcwReVTSI5L+l7s/4e7jkq4Pn7tHwRf+mWq7TdKfhxPgu8P9fsXM9igYPrZN0qfD7fYoGAZ1vI9IepuZPa1geF3/NDXulXSNpB+Y2VOSfijpTScLmOa1jymYm/OFsKYf6s09IJ9UMJ/nqXBi/Sez2PXXwvW/ffwP3P2ggrO3dbr7o+HT90tKhq/hk5IenmK72XweN5nZ02HdP5H0ZBa1A0BRMPeZeuIBAAAAoHDQEwMAAAAgVggxAAAAAGKFEAMAAAAgVggxAAAAAGKFEAMAAAAgVggxAAAAAGKFEAMAAAAgVggxAAAAAGLlvwAFnvi8UvVo6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since the scree plot is not as definitive as we like to see, generate Silhouette scores and plots for each \n",
    "# potential number of clusters and see if we get a better read.\n",
    "# It looks like eight clusters is where we get the highest Silhouette score and most of the clusters will\n",
    "# exceed the average Silhouette level in the plot graphs.\n",
    "\n",
    "\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    # Create a subplot with 1 row and 2 columns\n",
    "    fig, (ax1) = plt.subplots()\n",
    "    #fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(14, 7)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(df_pivot_tp) + (n_clusters + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(df_pivot_tp)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(df_pivot_tp, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(df_pivot_tp, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "   \n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % n_clusters),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the number of clusters at 8 and run the KMeans algorithm.\n",
    "\n",
    "kmclusters = 8\n",
    "\n",
    "kmeans = KMeans(n_clusters=kmclusters, random_state=0).fit(df_pivot_tp)"
   ]
  },
  {
   "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>Customer Last Name</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barnes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bell</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bennett</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brooks</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brown</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Butler</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Campbell</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Carter</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Clark</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Collins</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Last Name  cluster\n",
       "0               Adams        4\n",
       "1               Allen        6\n",
       "2            Anderson        0\n",
       "3              Bailey        7\n",
       "4               Baker        3\n",
       "5              Barnes        3\n",
       "6                Bell        0\n",
       "7             Bennett        4\n",
       "8              Brooks        1\n",
       "9               Brown        7\n",
       "10             Butler        1\n",
       "11           Campbell        0\n",
       "12             Carter        7\n",
       "13              Clark        1\n",
       "14            Collins        1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a list customers with cluster assignments.\n",
    "\n",
    "df_cluster_map = pd.DataFrame()\n",
    "df_cluster_map['Customer Last Name'] = df_pivot_tp.index.values\n",
    "df_cluster_map['cluster'] = kmeans.labels_\n",
    "df_cluster_map.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Customer Last Name</th>\n",
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       "      <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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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>3</td>\n",
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       "      <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>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barnes</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bell</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bennett</td>\n",
       "      <td>4</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",
       "      <td>1.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brooks</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brown</td>\n",
       "      <td>7</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Butler</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Campbell</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Carter</td>\n",
       "      <td>7</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Clark</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Collins</td>\n",
       "      <td>1</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",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cook</td>\n",
       "      <td>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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cox</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cruz</td>\n",
       "      <td>7</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",
       "      <td>0.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Davis</td>\n",
       "      <td>6</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",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Diaz</td>\n",
       "      <td>7</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Edwards</td>\n",
       "      <td>4</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",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Evans</td>\n",
       "      <td>2</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",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Fisher</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Flores</td>\n",
       "      <td>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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Last Name  cluster    1    2    3    4    5    6    7    8  ...  \\\n",
       "0               Adams        4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "1               Allen        6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2            Anderson        0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "3              Bailey        7  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...   \n",
       "4               Baker        3  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...   \n",
       "5              Barnes        3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "6                Bell        0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "7             Bennett        4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...   \n",
       "8              Brooks        1  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...   \n",
       "9               Brown        7  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...   \n",
       "10             Butler        1  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...   \n",
       "11           Campbell        0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "12             Carter        7  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...   \n",
       "13              Clark        1  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...   \n",
       "14            Collins        1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "15               Cook        0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "16             Cooper        6  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...   \n",
       "17                Cox        0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "18               Cruz        7  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "19              Davis        6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "20               Diaz        7  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  ...   \n",
       "21            Edwards        4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...   \n",
       "22              Evans        2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "23             Fisher        1  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "24             Flores        0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "     23   24   25   26   27   28   29   30   31   32  \n",
       "0   0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1   0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2   0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3   0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "5   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "6   0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "7   0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "8   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9   0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "10  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  \n",
       "11  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "12  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "13  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  \n",
       "14  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "15  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "16  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "17  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "18  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "19  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "20  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "21  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "22  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "23  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  \n",
       "24  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[25 rows x 34 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the transaction matrix to the cluster assignments so they can be summed up for each cluster.\n",
    "\n",
    "df_cluster_map = df_cluster_map.merge(df_pivot_tp, on = 'Customer Last Name', how = 'right')\n",
    "df_cluster_map.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send cluster transaction matrix to CSV for double checking the count if desired.\n",
    "\n",
    "# df_cluster_map.to_csv(path_or_buf = my_path + \"/df_cluster_map.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cluster</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster    1    2    3    4    5    6    7    8    9  ...   23   24   25  \\\n",
       "0        4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1        6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "2        0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  1.0  0.0   \n",
       "3        7  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4        3  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "    26   27   28   29   30   31   32  \n",
       "0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "1  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get rid of the customer names since they are not needed for the counts.\n",
    "\n",
    "df_cluster_map = df_cluster_map.iloc[:,1:34]\n",
    "df_cluster_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "          1    2    3    4    5    6    7     8    9    10  ...   23    24  \\\n",
       "cluster                                                     ...              \n",
       "0        0.0  4.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...  0.0  12.0   \n",
       "1        4.0  3.0  1.0  2.0  0.0  0.0  0.0   1.0  1.0  0.0  ...  0.0   0.0   \n",
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       "4        0.0  0.0  0.0  0.0  0.0  1.0  1.0  13.0  0.0  1.0  ...  0.0   0.0   \n",
       "\n",
       "          25   26   27   28   29   30    31   32  \n",
       "cluster                                           \n",
       "0        0.0  8.0  0.0  0.0  0.0  0.0   0.0  0.0  \n",
       "1        1.0  0.0  1.0  4.0  1.0  4.0   4.0  1.0  \n",
       "2        0.0  0.0  5.0  0.0  0.0  0.0   1.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  0.0  0.0  11.0  2.0  \n",
       "4        0.0  0.0  1.0  0.0  6.0  8.0   0.0  0.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the matrix table by cluster number and sum up the transactions for each cluster column-wise.\n",
    "\n",
    "df_cluster_counts = df_cluster_map.groupby('cluster').sum()\n",
    "df_cluster_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\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>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster     0     1    2     3     4    5    6     7\n",
       "Offer #                                             \n",
       "1         0.0   4.0  0.0   1.0   0.0  1.0  4.0   0.0\n",
       "2         4.0   3.0  0.0   0.0   0.0  0.0  3.0   0.0\n",
       "3         0.0   1.0  2.0   2.0   0.0  0.0  0.0   1.0\n",
       "4         0.0   2.0  4.0   1.0   0.0  1.0  4.0   0.0\n",
       "5         0.0   0.0  0.0   0.0   0.0  2.0  2.0   0.0\n",
       "6         0.0   0.0  5.0   3.0   1.0  2.0  1.0   0.0\n",
       "7         0.0   0.0  1.0   3.0   1.0  0.0  0.0  14.0\n",
       "8         0.0   1.0  0.0   3.0  13.0  0.0  0.0   3.0\n",
       "9         0.0   1.0  0.0   2.0   0.0  1.0  6.0   0.0\n",
       "10        0.0   0.0  0.0   3.0   1.0  1.0  1.0   1.0\n",
       "11        0.0  11.0  0.0   0.0   0.0  0.0  2.0   0.0\n",
       "12        0.0   1.0  0.0   0.0   0.0  0.0  3.0   1.0\n",
       "13        0.0   0.0  0.0   0.0   3.0  0.0  0.0   3.0\n",
       "14        0.0   0.0  0.0   1.0   0.0  7.0  1.0   0.0\n",
       "15        0.0   2.0  0.0   1.0   0.0  3.0  0.0   0.0\n",
       "16        0.0   2.0  0.0   0.0   0.0  0.0  3.0   0.0\n",
       "17        7.0   0.0  0.0   0.0   0.0  0.0  0.0   0.0\n",
       "18        0.0   0.0  0.0   1.0  10.0  0.0  0.0   3.0\n",
       "19        0.0   0.0  1.0   4.0   0.0  0.0  0.0   0.0\n",
       "20        0.0   2.0  0.0   1.0   0.0  1.0  2.0   0.0\n",
       "21        0.0   0.0  1.0   2.0   1.0  0.0  0.0   0.0\n",
       "22        0.0   7.0  4.0   5.0   0.0  4.0  1.0   0.0\n",
       "23        0.0   0.0  0.0   0.0   0.0  3.0  2.0   0.0\n",
       "24       12.0   0.0  0.0   0.0   0.0  0.0  0.0   0.0\n",
       "25        0.0   1.0  0.0   0.0   0.0  1.0  4.0   0.0\n",
       "26        8.0   0.0  0.0   0.0   0.0  1.0  6.0   0.0\n",
       "27        0.0   1.0  5.0   0.0   1.0  0.0  2.0   0.0\n",
       "28        0.0   4.0  0.0   0.0   0.0  0.0  2.0   0.0\n",
       "29        0.0   1.0  0.0   0.0   6.0  0.0  1.0   9.0\n",
       "30        0.0   4.0  0.0   0.0   8.0  1.0  1.0   8.0\n",
       "31        0.0   4.0  1.0  11.0   0.0  1.0  0.0   0.0\n",
       "32        0.0   1.0  0.0   2.0   0.0  0.0  1.0   0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the cluster counts so we can add the offer details back into the matrix.\n",
    "\n",
    "df_cluster_counts_tp = df_cluster_counts.transpose()\n",
    "df_cluster_counts_tp.index.name = \"Offer #\"\n",
    "df_cluster_counts_tp.head(33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</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>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>May</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>June</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>19</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>July</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>July</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>August</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>September</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</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>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>September</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "0        1    January              Malbec                 72            56   \n",
       "1        2    January          Pinot Noir                 72            17   \n",
       "2        3   February           Espumante                144            32   \n",
       "3        4   February           Champagne                 72            48   \n",
       "4        5   February  Cabernet Sauvignon                144            44   \n",
       "5        6      March            Prosecco                144            86   \n",
       "6        7      March            Prosecco                  6            40   \n",
       "7        8      March           Espumante                  6            45   \n",
       "8        9      April          Chardonnay                144            57   \n",
       "9       10      April            Prosecco                 72            52   \n",
       "10      11        May           Champagne                 72            85   \n",
       "11      12        May            Prosecco                 72            83   \n",
       "12      13        May              Merlot                  6            43   \n",
       "13      14       June              Merlot                 72            64   \n",
       "14      15       June  Cabernet Sauvignon                144            19   \n",
       "15      16       June              Merlot                 72            88   \n",
       "16      17       July          Pinot Noir                 12            47   \n",
       "17      18       July           Espumante                  6            50   \n",
       "18      19       July           Champagne                 12            66   \n",
       "19      20     August  Cabernet Sauvignon                 72            82   \n",
       "20      21     August           Champagne                 12            50   \n",
       "21      22     August           Champagne                 72            63   \n",
       "22      23  September          Chardonnay                144            39   \n",
       "23      24  September          Pinot Noir                  6            34   \n",
       "24      25    October  Cabernet Sauvignon                 72            59   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "26      27    October           Champagne                 72            88   \n",
       "27      28   November  Cabernet Sauvignon                 12            56   \n",
       "28      29   November        Pinot Grigio                  6            87   \n",
       "29      30   December              Malbec                  6            54   \n",
       "30      31   December           Champagne                 72            89   \n",
       "31      32   December  Cabernet Sauvignon                 72            45   \n",
       "\n",
       "          Origin  Past Peak     0     1    2     3     4    5    6     7  \n",
       "0         France      False   0.0   4.0  0.0   1.0   0.0  1.0  4.0   0.0  \n",
       "1         France      False   4.0   3.0  0.0   0.0   0.0  0.0  3.0   0.0  \n",
       "2         Oregon       True   0.0   1.0  2.0   2.0   0.0  0.0  0.0   1.0  \n",
       "3         France       True   0.0   2.0  4.0   1.0   0.0  1.0  4.0   0.0  \n",
       "4    New Zealand       True   0.0   0.0  0.0   0.0   0.0  2.0  2.0   0.0  \n",
       "5          Chile      False   0.0   0.0  5.0   3.0   1.0  2.0  1.0   0.0  \n",
       "6      Australia       True   0.0   0.0  1.0   3.0   1.0  0.0  0.0  14.0  \n",
       "7   South Africa      False   0.0   1.0  0.0   3.0  13.0  0.0  0.0   3.0  \n",
       "8          Chile      False   0.0   1.0  0.0   2.0   0.0  1.0  6.0   0.0  \n",
       "9     California      False   0.0   0.0  0.0   3.0   1.0  1.0  1.0   1.0  \n",
       "10        France      False   0.0  11.0  0.0   0.0   0.0  0.0  2.0   0.0  \n",
       "11     Australia      False   0.0   1.0  0.0   0.0   0.0  0.0  3.0   1.0  \n",
       "12         Chile      False   0.0   0.0  0.0   0.0   3.0  0.0  0.0   3.0  \n",
       "13         Chile      False   0.0   0.0  0.0   1.0   0.0  7.0  1.0   0.0  \n",
       "14         Italy      False   0.0   2.0  0.0   1.0   0.0  3.0  0.0   0.0  \n",
       "15    California      False   0.0   2.0  0.0   0.0   0.0  0.0  3.0   0.0  \n",
       "16       Germany      False   7.0   0.0  0.0   0.0   0.0  0.0  0.0   0.0  \n",
       "17        Oregon      False   0.0   0.0  0.0   1.0  10.0  0.0  0.0   3.0  \n",
       "18       Germany      False   0.0   0.0  1.0   4.0   0.0  0.0  0.0   0.0  \n",
       "19         Italy      False   0.0   2.0  0.0   1.0   0.0  1.0  2.0   0.0  \n",
       "20    California      False   0.0   0.0  1.0   2.0   1.0  0.0  0.0   0.0  \n",
       "21        France      False   0.0   7.0  4.0   5.0   0.0  4.0  1.0   0.0  \n",
       "22  South Africa      False   0.0   0.0  0.0   0.0   0.0  3.0  2.0   0.0  \n",
       "23         Italy      False  12.0   0.0  0.0   0.0   0.0  0.0  0.0   0.0  \n",
       "24        Oregon       True   0.0   1.0  0.0   0.0   0.0  1.0  4.0   0.0  \n",
       "25     Australia      False   8.0   0.0  0.0   0.0   0.0  1.0  6.0   0.0  \n",
       "26   New Zealand      False   0.0   1.0  5.0   0.0   1.0  0.0  2.0   0.0  \n",
       "27        France       True   0.0   4.0  0.0   0.0   0.0  0.0  2.0   0.0  \n",
       "28        France      False   0.0   1.0  0.0   0.0   6.0  0.0  1.0   9.0  \n",
       "29        France      False   0.0   4.0  0.0   0.0   8.0  1.0  1.0   8.0  \n",
       "30        France      False   0.0   4.0  1.0  11.0   0.0  1.0  0.0   0.0  \n",
       "31       Germany       True   0.0   1.0  0.0   2.0   0.0  0.0  1.0   0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the offer details back into the cluster transaction count matrix so we can start sorting\n",
    "# by cluster to find insights.\n",
    "\n",
    "df_offer_clusters = df_offers.merge(df_cluster_counts_tp, on='Offer #', how='right')\n",
    "df_offer_clusters = df_offer_clusters[['Offer #'] + [ col for col in df_offer_clusters.columns if col != 'Offer #' ]]\n",
    "df_offer_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>September</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>July</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "23      24  September          Pinot Noir                  6            34   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "16      17       July          Pinot Noir                 12            47   \n",
       "1        2    January          Pinot Noir                 72            17   \n",
       "30      31   December           Champagne                 72            89   \n",
       "29      30   December              Malbec                  6            54   \n",
       "28      29   November        Pinot Grigio                  6            87   \n",
       "27      28   November  Cabernet Sauvignon                 12            56   \n",
       "26      27    October           Champagne                 72            88   \n",
       "24      25    October  Cabernet Sauvignon                 72            59   \n",
       "\n",
       "         Origin  Past Peak     0    1    2     3    4    5    6    7  \n",
       "23        Italy      False  12.0  0.0  0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "25    Australia      False   8.0  0.0  0.0   0.0  0.0  1.0  6.0  0.0  \n",
       "16      Germany      False   7.0  0.0  0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "1        France      False   4.0  3.0  0.0   0.0  0.0  0.0  3.0  0.0  \n",
       "30       France      False   0.0  4.0  1.0  11.0  0.0  1.0  0.0  0.0  \n",
       "29       France      False   0.0  4.0  0.0   0.0  8.0  1.0  1.0  8.0  \n",
       "28       France      False   0.0  1.0  0.0   0.0  6.0  0.0  1.0  9.0  \n",
       "27       France       True   0.0  4.0  0.0   0.0  0.0  0.0  2.0  0.0  \n",
       "26  New Zealand      False   0.0  1.0  5.0   0.0  1.0  0.0  2.0  0.0  \n",
       "24       Oregon       True   0.0  1.0  0.0   0.0  0.0  1.0  4.0  0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 0. Observe that they all love Pinot Noir.\n",
    "\n",
    "df_offer_cluster0 = df_offer_clusters.sort_values(by=0, ascending=False)\n",
    "df_offer_cluster0.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
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       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>May</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
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       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>June</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>19</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>August</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "10      11       May           Champagne                 72            85   \n",
       "21      22    August           Champagne                 72            63   \n",
       "0        1   January              Malbec                 72            56   \n",
       "30      31  December           Champagne                 72            89   \n",
       "29      30  December              Malbec                  6            54   \n",
       "27      28  November  Cabernet Sauvignon                 12            56   \n",
       "1        2   January          Pinot Noir                 72            17   \n",
       "14      15      June  Cabernet Sauvignon                144            19   \n",
       "3        4  February           Champagne                 72            48   \n",
       "19      20    August  Cabernet Sauvignon                 72            82   \n",
       "\n",
       "    Origin  Past Peak    0     1    2     3    4    5    6    7  \n",
       "10  France      False  0.0  11.0  0.0   0.0  0.0  0.0  2.0  0.0  \n",
       "21  France      False  0.0   7.0  4.0   5.0  0.0  4.0  1.0  0.0  \n",
       "0   France      False  0.0   4.0  0.0   1.0  0.0  1.0  4.0  0.0  \n",
       "30  France      False  0.0   4.0  1.0  11.0  0.0  1.0  0.0  0.0  \n",
       "29  France      False  0.0   4.0  0.0   0.0  8.0  1.0  1.0  8.0  \n",
       "27  France       True  0.0   4.0  0.0   0.0  0.0  0.0  2.0  0.0  \n",
       "1   France      False  4.0   3.0  0.0   0.0  0.0  0.0  3.0  0.0  \n",
       "14   Italy      False  0.0   2.0  0.0   1.0  0.0  3.0  0.0  0.0  \n",
       "3   France       True  0.0   2.0  4.0   1.0  0.0  1.0  4.0  0.0  \n",
       "19   Italy      False  0.0   2.0  0.0   1.0  0.0  1.0  2.0  0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 1. Observe that they appear to like French wines the most.\n",
    "\n",
    "df_offer_cluster1 = df_offer_clusters.sort_values(by=1, ascending=False)\n",
    "df_offer_cluster1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
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       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>July</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign   Varietal  Minimum Qty (ltr)  Discount (%)       Origin  \\\n",
       "5        6     March   Prosecco                144            86        Chile   \n",
       "26      27   October  Champagne                 72            88  New Zealand   \n",
       "3        4  February  Champagne                 72            48       France   \n",
       "21      22    August  Champagne                 72            63       France   \n",
       "2        3  February  Espumante                144            32       Oregon   \n",
       "30      31  December  Champagne                 72            89       France   \n",
       "6        7     March   Prosecco                  6            40    Australia   \n",
       "20      21    August  Champagne                 12            50   California   \n",
       "18      19      July  Champagne                 12            66      Germany   \n",
       "0        1   January     Malbec                 72            56       France   \n",
       "\n",
       "    Past Peak    0    1    2     3    4    5    6     7  \n",
       "5       False  0.0  0.0  5.0   3.0  1.0  2.0  1.0   0.0  \n",
       "26      False  0.0  1.0  5.0   0.0  1.0  0.0  2.0   0.0  \n",
       "3        True  0.0  2.0  4.0   1.0  0.0  1.0  4.0   0.0  \n",
       "21      False  0.0  7.0  4.0   5.0  0.0  4.0  1.0   0.0  \n",
       "2        True  0.0  1.0  2.0   2.0  0.0  0.0  0.0   1.0  \n",
       "30      False  0.0  4.0  1.0  11.0  0.0  1.0  0.0   0.0  \n",
       "6        True  0.0  0.0  1.0   3.0  1.0  0.0  0.0  14.0  \n",
       "20      False  0.0  0.0  1.0   2.0  1.0  0.0  0.0   0.0  \n",
       "18      False  0.0  0.0  1.0   4.0  0.0  0.0  0.0   0.0  \n",
       "0       False  0.0  4.0  0.0   1.0  0.0  1.0  4.0   0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 2. Observe that they like sparkling wines the most - Champagne, Espumante, Prosecco\n",
    "\n",
    "df_offer_cluster2 = df_offer_clusters.sort_values(by=2, ascending=False)\n",
    "df_offer_cluster2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Offer #</th>\n",
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       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
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       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>July</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "30      31  December           Champagne                 72            89   \n",
       "21      22    August           Champagne                 72            63   \n",
       "18      19      July           Champagne                 12            66   \n",
       "5        6     March            Prosecco                144            86   \n",
       "6        7     March            Prosecco                  6            40   \n",
       "7        8     March           Espumante                  6            45   \n",
       "9       10     April            Prosecco                 72            52   \n",
       "31      32  December  Cabernet Sauvignon                 72            45   \n",
       "2        3  February           Espumante                144            32   \n",
       "8        9     April          Chardonnay                144            57   \n",
       "\n",
       "          Origin  Past Peak    0    1    2     3     4    5    6     7  \n",
       "30        France      False  0.0  4.0  1.0  11.0   0.0  1.0  0.0   0.0  \n",
       "21        France      False  0.0  7.0  4.0   5.0   0.0  4.0  1.0   0.0  \n",
       "18       Germany      False  0.0  0.0  1.0   4.0   0.0  0.0  0.0   0.0  \n",
       "5          Chile      False  0.0  0.0  5.0   3.0   1.0  2.0  1.0   0.0  \n",
       "6      Australia       True  0.0  0.0  1.0   3.0   1.0  0.0  0.0  14.0  \n",
       "7   South Africa      False  0.0  1.0  0.0   3.0  13.0  0.0  0.0   3.0  \n",
       "9     California      False  0.0  0.0  0.0   3.0   1.0  1.0  1.0   1.0  \n",
       "31       Germany       True  0.0  1.0  0.0   2.0   0.0  0.0  1.0   0.0  \n",
       "2         Oregon       True  0.0  1.0  2.0   2.0   0.0  0.0  0.0   1.0  \n",
       "8          Chile      False  0.0  1.0  0.0   2.0   0.0  1.0  6.0   0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 3. Observe that many of these guys also like sparkling wines. Maybe this group\n",
    "# should be combined with cluster 2?\n",
    "\n",
    "df_offer_cluster3 = df_offer_clusters.sort_values(by=3, ascending=False)\n",
    "df_offer_cluster3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
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       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
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       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign      Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "7        8     March     Espumante                  6            45   \n",
       "17      18      July     Espumante                  6            50   \n",
       "29      30  December        Malbec                  6            54   \n",
       "28      29  November  Pinot Grigio                  6            87   \n",
       "12      13       May        Merlot                  6            43   \n",
       "9       10     April      Prosecco                 72            52   \n",
       "26      27   October     Champagne                 72            88   \n",
       "20      21    August     Champagne                 12            50   \n",
       "6        7     March      Prosecco                  6            40   \n",
       "5        6     March      Prosecco                144            86   \n",
       "\n",
       "          Origin  Past Peak    0    1    2    3     4    5    6     7  \n",
       "7   South Africa      False  0.0  1.0  0.0  3.0  13.0  0.0  0.0   3.0  \n",
       "17        Oregon      False  0.0  0.0  0.0  1.0  10.0  0.0  0.0   3.0  \n",
       "29        France      False  0.0  4.0  0.0  0.0   8.0  1.0  1.0   8.0  \n",
       "28        France      False  0.0  1.0  0.0  0.0   6.0  0.0  1.0   9.0  \n",
       "12         Chile      False  0.0  0.0  0.0  0.0   3.0  0.0  0.0   3.0  \n",
       "9     California      False  0.0  0.0  0.0  3.0   1.0  1.0  1.0   1.0  \n",
       "26   New Zealand      False  0.0  1.0  5.0  0.0   1.0  0.0  2.0   0.0  \n",
       "20    California      False  0.0  0.0  1.0  2.0   1.0  0.0  0.0   0.0  \n",
       "6      Australia       True  0.0  0.0  1.0  3.0   1.0  0.0  0.0  14.0  \n",
       "5          Chile      False  0.0  0.0  5.0  3.0   1.0  2.0  1.0   0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 4. Observe that these folks appear to prefer lower quantity discount deals.\n",
    "\n",
    "df_offer_cluster4 = df_offer_clusters.sort_values(by=4, ascending=False)\n",
    "df_offer_cluster4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
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       "      <th>7</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>September</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</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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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>June</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>19</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</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>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>August</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "13      14       June              Merlot                 72            64   \n",
       "21      22     August           Champagne                 72            63   \n",
       "22      23  September          Chardonnay                144            39   \n",
       "14      15       June  Cabernet Sauvignon                144            19   \n",
       "4        5   February  Cabernet Sauvignon                144            44   \n",
       "5        6      March            Prosecco                144            86   \n",
       "0        1    January              Malbec                 72            56   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "24      25    October  Cabernet Sauvignon                 72            59   \n",
       "19      20     August  Cabernet Sauvignon                 72            82   \n",
       "\n",
       "          Origin  Past Peak    0    1    2    3    4    5    6    7  \n",
       "13         Chile      False  0.0  0.0  0.0  1.0  0.0  7.0  1.0  0.0  \n",
       "21        France      False  0.0  7.0  4.0  5.0  0.0  4.0  1.0  0.0  \n",
       "22  South Africa      False  0.0  0.0  0.0  0.0  0.0  3.0  2.0  0.0  \n",
       "14         Italy      False  0.0  2.0  0.0  1.0  0.0  3.0  0.0  0.0  \n",
       "4    New Zealand       True  0.0  0.0  0.0  0.0  0.0  2.0  2.0  0.0  \n",
       "5          Chile      False  0.0  0.0  5.0  3.0  1.0  2.0  1.0  0.0  \n",
       "0         France      False  0.0  4.0  0.0  1.0  0.0  1.0  4.0  0.0  \n",
       "25     Australia      False  8.0  0.0  0.0  0.0  0.0  1.0  6.0  0.0  \n",
       "24        Oregon       True  0.0  1.0  0.0  0.0  0.0  1.0  4.0  0.0  \n",
       "19         Italy      False  0.0  2.0  0.0  1.0  0.0  1.0  2.0  0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 5. This one is a bit noisy, but you can observe that these guys appear\n",
    "# to like both sparkling wine and red wine.\n",
    "\n",
    "df_offer_cluster5 = df_offer_clusters.sort_values(by=5, ascending=False)\n",
    "df_offer_cluster5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Offer #</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
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       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
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       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</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>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "8        9     April          Chardonnay                144            57   \n",
       "25      26   October          Pinot Noir                144            83   \n",
       "24      25   October  Cabernet Sauvignon                 72            59   \n",
       "0        1   January              Malbec                 72            56   \n",
       "3        4  February           Champagne                 72            48   \n",
       "11      12       May            Prosecco                 72            83   \n",
       "15      16      June              Merlot                 72            88   \n",
       "1        2   January          Pinot Noir                 72            17   \n",
       "4        5  February  Cabernet Sauvignon                144            44   \n",
       "27      28  November  Cabernet Sauvignon                 12            56   \n",
       "\n",
       "         Origin  Past Peak    0    1    2    3    4    5    6    7  \n",
       "8         Chile      False  0.0  1.0  0.0  2.0  0.0  1.0  6.0  0.0  \n",
       "25    Australia      False  8.0  0.0  0.0  0.0  0.0  1.0  6.0  0.0  \n",
       "24       Oregon       True  0.0  1.0  0.0  0.0  0.0  1.0  4.0  0.0  \n",
       "0        France      False  0.0  4.0  0.0  1.0  0.0  1.0  4.0  0.0  \n",
       "3        France       True  0.0  2.0  4.0  1.0  0.0  1.0  4.0  0.0  \n",
       "11    Australia      False  0.0  1.0  0.0  0.0  0.0  0.0  3.0  1.0  \n",
       "15   California      False  0.0  2.0  0.0  0.0  0.0  0.0  3.0  0.0  \n",
       "1        France      False  4.0  3.0  0.0  0.0  0.0  0.0  3.0  0.0  \n",
       "4   New Zealand       True  0.0  0.0  0.0  0.0  0.0  2.0  2.0  0.0  \n",
       "27       France       True  0.0  4.0  0.0  0.0  0.0  0.0  2.0  0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 6. Observe that this group is also pretty noisy. But except for the Chardonnay\n",
    "# at the top of the list, they also appear to like both red wine and bubbly. Maybe this group\n",
    "# should be combined with cluster 5?\n",
    "\n",
    "df_offer_cluster6 = df_offer_clusters.sort_values(by=6, ascending=False)\n",
    "df_offer_cluster6.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
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       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign      Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "6        7     March      Prosecco                  6            40   \n",
       "28      29  November  Pinot Grigio                  6            87   \n",
       "29      30  December        Malbec                  6            54   \n",
       "17      18      July     Espumante                  6            50   \n",
       "7        8     March     Espumante                  6            45   \n",
       "12      13       May        Merlot                  6            43   \n",
       "9       10     April      Prosecco                 72            52   \n",
       "2        3  February     Espumante                144            32   \n",
       "11      12       May      Prosecco                 72            83   \n",
       "25      26   October    Pinot Noir                144            83   \n",
       "\n",
       "          Origin  Past Peak    0    1    2    3     4    5    6     7  \n",
       "6      Australia       True  0.0  0.0  1.0  3.0   1.0  0.0  0.0  14.0  \n",
       "28        France      False  0.0  1.0  0.0  0.0   6.0  0.0  1.0   9.0  \n",
       "29        France      False  0.0  4.0  0.0  0.0   8.0  1.0  1.0   8.0  \n",
       "17        Oregon      False  0.0  0.0  0.0  1.0  10.0  0.0  0.0   3.0  \n",
       "7   South Africa      False  0.0  1.0  0.0  3.0  13.0  0.0  0.0   3.0  \n",
       "12         Chile      False  0.0  0.0  0.0  0.0   3.0  0.0  0.0   3.0  \n",
       "9     California      False  0.0  0.0  0.0  3.0   1.0  1.0  1.0   1.0  \n",
       "2         Oregon       True  0.0  1.0  2.0  2.0   0.0  0.0  0.0   1.0  \n",
       "11     Australia      False  0.0  1.0  0.0  0.0   0.0  0.0  3.0   1.0  \n",
       "25     Australia      False  8.0  0.0  0.0  0.0   0.0  1.0  6.0   0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 7. Observe that, except for the Pino Grigio, these folks also like red wine\n",
    "# and sparkling wine, but at lower quantities.\n",
    "\n",
    "df_offer_cluster7 = df_offer_clusters.sort_values(by=7, ascending=False)\n",
    "df_offer_cluster7.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Offer #</th>\n",
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       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "  Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "0       1   January              Malbec                 72            56   \n",
       "1       2   January          Pinot Noir                 72            17   \n",
       "2       3  February           Espumante                144            32   \n",
       "3       4  February           Champagne                 72            48   \n",
       "4       5  February  Cabernet Sauvignon                144            44   \n",
       "\n",
       "        Origin  Past Peak    0    1    2    3  \n",
       "0       France      False  8.0  1.0  0.0  1.0  \n",
       "1       France      False  4.0  0.0  0.0  6.0  \n",
       "2       Oregon       True  0.0  6.0  0.0  0.0  \n",
       "3       France       True  4.0  8.0  0.0  0.0  \n",
       "4  New Zealand       True  1.0  3.0  0.0  0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since we ended up with a couple of clusters that appeared to be very similar to each other,\n",
    "# let's reduce the number of clusters to 4 and re-run the KMeans algorithm.\n",
    "\n",
    "kmclusters = 4\n",
    "kmeans = KMeans(n_clusters=kmclusters, random_state=0).fit(df_pivot_tp)\n",
    "\n",
    "# Generate a list customers with cluster assignments.\n",
    "df_cluster_map = pd.DataFrame()\n",
    "df_cluster_map['Customer Last Name'] = df_pivot_tp.index.values\n",
    "df_cluster_map['cluster'] = kmeans.labels_\n",
    "\n",
    "# Add the transaction matrix to the cluster assignments so they can be summed up for each cluster.\n",
    "df_cluster_map = df_cluster_map.merge(df_pivot_tp, on = 'Customer Last Name', how = 'right')\n",
    "df_cluster_map = df_cluster_map.iloc[:,1:34]\n",
    "\n",
    "# Group the matrix table by cluster number and sum up the transactions for each cluster column-wise.\n",
    "df_cluster_counts = df_cluster_map.groupby('cluster').sum()\n",
    "\n",
    "# Transpose the cluster counts so we can add the offer details back into the matrix.\n",
    "df_cluster_counts_tp = df_cluster_counts.transpose()\n",
    "df_cluster_counts_tp.index.name = \"Offer #\"\n",
    "\n",
    "# Add the offer details back into the cluster transaction count matrix so we can start sorting\n",
    "# by cluster to find insights.\n",
    "df_offer_clusters = df_offers.merge(df_cluster_counts_tp, on='Offer #', how='right')\n",
    "df_offer_clusters = df_offer_clusters[['Offer #'] \n",
    "                                      + [ col for col in df_offer_clusters.columns if col != 'Offer #' ]]\n",
    "df_offer_clusters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>May</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "10      11       May           Champagne                 72            85   \n",
       "21      22    August           Champagne                 72            63   \n",
       "0        1   January              Malbec                 72            56   \n",
       "29      30  December              Malbec                  6            54   \n",
       "24      25   October  Cabernet Sauvignon                 72            59   \n",
       "27      28  November  Cabernet Sauvignon                 12            56   \n",
       "1        2   January          Pinot Noir                 72            17   \n",
       "3        4  February           Champagne                 72            48   \n",
       "8        9     April          Chardonnay                144            57   \n",
       "30      31  December           Champagne                 72            89   \n",
       "\n",
       "    Origin  Past Peak     0     1     2    3  \n",
       "10  France      False  10.0   3.0   0.0  0.0  \n",
       "21  France      False   9.0  12.0   0.0  0.0  \n",
       "0   France      False   8.0   1.0   0.0  1.0  \n",
       "29  France      False   6.0   0.0  16.0  0.0  \n",
       "24  Oregon       True   6.0   0.0   0.0  0.0  \n",
       "27  France       True   5.0   0.0   1.0  0.0  \n",
       "1   France      False   4.0   0.0   0.0  6.0  \n",
       "3   France       True   4.0   8.0   0.0  0.0  \n",
       "8    Chile      False   4.0   6.0   0.0  0.0  \n",
       "30  France      False   3.0  14.0   0.0  0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 0. Observe a lot of Champagne and red wine fans.\n",
    "\n",
    "df_offer_cluster0 = df_offer_clusters.sort_values(by=0, ascending=False)\n",
    "df_offer_cluster0.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign    Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "30      31  December   Champagne                 72            89   \n",
       "21      22    August   Champagne                 72            63   \n",
       "5        6     March    Prosecco                144            86   \n",
       "7        8     March   Espumante                  6            45   \n",
       "26      27   October   Champagne                 72            88   \n",
       "3        4  February   Champagne                 72            48   \n",
       "6        7     March    Prosecco                  6            40   \n",
       "2        3  February   Espumante                144            32   \n",
       "8        9     April  Chardonnay                144            57   \n",
       "9       10     April    Prosecco                 72            52   \n",
       "\n",
       "          Origin  Past Peak    0     1     2    3  \n",
       "30        France      False  3.0  14.0   0.0  0.0  \n",
       "21        France      False  9.0  12.0   0.0  0.0  \n",
       "5          Chile      False  1.0  11.0   0.0  0.0  \n",
       "7   South Africa      False  0.0   9.0  11.0  0.0  \n",
       "26   New Zealand      False  0.0   8.0   0.0  1.0  \n",
       "3         France       True  4.0   8.0   0.0  0.0  \n",
       "6      Australia       True  0.0   7.0  12.0  0.0  \n",
       "2         Oregon       True  0.0   6.0   0.0  0.0  \n",
       "8          Chile      False  4.0   6.0   0.0  0.0  \n",
       "9     California      False  0.0   6.0   0.0  1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 1. Observe a lot of real bubbly fans here.\n",
    "\n",
    "df_offer_cluster1 = df_offer_clusters.sort_values(by=1, ascending=False)\n",
    "df_offer_cluster1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>August</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "29      30  December              Malbec                  6            54   \n",
       "28      29  November        Pinot Grigio                  6            87   \n",
       "6        7     March            Prosecco                  6            40   \n",
       "17      18      July           Espumante                  6            50   \n",
       "7        8     March           Espumante                  6            45   \n",
       "12      13       May              Merlot                  6            43   \n",
       "27      28  November  Cabernet Sauvignon                 12            56   \n",
       "19      20    August  Cabernet Sauvignon                 72            82   \n",
       "20      21    August           Champagne                 12            50   \n",
       "21      22    August           Champagne                 72            63   \n",
       "\n",
       "          Origin  Past Peak    0     1     2    3  \n",
       "29        France      False  6.0   0.0  16.0  0.0  \n",
       "28        France      False  0.0   1.0  16.0  0.0  \n",
       "6      Australia       True  0.0   7.0  12.0  0.0  \n",
       "17        Oregon      False  0.0   3.0  11.0  0.0  \n",
       "7   South Africa      False  0.0   9.0  11.0  0.0  \n",
       "12         Chile      False  0.0   0.0   6.0  0.0  \n",
       "27        France       True  5.0   0.0   1.0  0.0  \n",
       "19         Italy      False  2.0   4.0   0.0  0.0  \n",
       "20    California      False  0.0   4.0   0.0  0.0  \n",
       "21        France      False  9.0  12.0   0.0  0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 2. Observe a misture of varietals but mostly low volume buyers.\n",
    "\n",
    "df_offer_cluster2 = df_offer_clusters.sort_values(by=2, ascending=False)\n",
    "df_offer_cluster2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>September</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>July</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>September</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign    Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "25      26    October  Pinot Noir                144            83   \n",
       "23      24  September  Pinot Noir                  6            34   \n",
       "16      17       July  Pinot Noir                 12            47   \n",
       "1        2    January  Pinot Noir                 72            17   \n",
       "9       10      April    Prosecco                 72            52   \n",
       "26      27    October   Champagne                 72            88   \n",
       "22      23  September  Chardonnay                144            39   \n",
       "15      16       June      Merlot                 72            88   \n",
       "11      12        May    Prosecco                 72            83   \n",
       "0        1    January      Malbec                 72            56   \n",
       "\n",
       "          Origin  Past Peak    0    1    2     3  \n",
       "25     Australia      False  2.0  1.0  0.0  12.0  \n",
       "23         Italy      False  0.0  0.0  0.0  12.0  \n",
       "16       Germany      False  0.0  0.0  0.0   7.0  \n",
       "1         France      False  4.0  0.0  0.0   6.0  \n",
       "9     California      False  0.0  6.0  0.0   1.0  \n",
       "26   New Zealand      False  0.0  8.0  0.0   1.0  \n",
       "22  South Africa      False  1.0  3.0  0.0   1.0  \n",
       "15    California      False  1.0  3.0  0.0   1.0  \n",
       "11     Australia      False  3.0  1.0  0.0   1.0  \n",
       "0         France      False  8.0  1.0  0.0   1.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 3. Here they are again, our ever reliable Pinot Noir fans!\n",
    "\n",
    "df_offer_cluster3 = df_offer_clusters.sort_values(by=3, ascending=False)\n",
    "df_offer_cluster3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What if we use a hierarchical clustering technique, like Agglomerative Clustering with cosine distance\n",
    "# measurements, instead of Kmeans which uses Euclidean distance?\n",
    "# We start by producing a Dendrogram chart instead of a Skree plot to find the optimal cluster number.\n",
    "# This dendrogram suggests that we should start with just 3 clusters.\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "dendrogram = sch.dendrogram(sch.linkage(df_pivot_tp, method  = \"ward\"))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Euclidean distances')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='cosine', compute_full_tree='auto',\n",
       "                        connectivity=None, distance_threshold=None,\n",
       "                        linkage='average', memory=None, n_clusters=4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With the Kmeans algo, we got more than three good observations so going for only three\n",
    "# clusters doesn't seem like enough. Let's star with four clusters and see what we get.\n",
    "\n",
    "aggclusters = 4\n",
    "\n",
    "model = AgglomerativeClustering(n_clusters=aggclusters, linkage=\"average\", affinity=\"cosine\")\n",
    "model.fit(df_pivot_tp)"
   ]
  },
  {
   "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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>May</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>June</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>19</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>July</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>July</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>August</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>September</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>September</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>October</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "0        1    January              Malbec                 72            56   \n",
       "1        2    January          Pinot Noir                 72            17   \n",
       "2        3   February           Espumante                144            32   \n",
       "3        4   February           Champagne                 72            48   \n",
       "4        5   February  Cabernet Sauvignon                144            44   \n",
       "5        6      March            Prosecco                144            86   \n",
       "6        7      March            Prosecco                  6            40   \n",
       "7        8      March           Espumante                  6            45   \n",
       "8        9      April          Chardonnay                144            57   \n",
       "9       10      April            Prosecco                 72            52   \n",
       "10      11        May           Champagne                 72            85   \n",
       "11      12        May            Prosecco                 72            83   \n",
       "12      13        May              Merlot                  6            43   \n",
       "13      14       June              Merlot                 72            64   \n",
       "14      15       June  Cabernet Sauvignon                144            19   \n",
       "15      16       June              Merlot                 72            88   \n",
       "16      17       July          Pinot Noir                 12            47   \n",
       "17      18       July           Espumante                  6            50   \n",
       "18      19       July           Champagne                 12            66   \n",
       "19      20     August  Cabernet Sauvignon                 72            82   \n",
       "20      21     August           Champagne                 12            50   \n",
       "21      22     August           Champagne                 72            63   \n",
       "22      23  September          Chardonnay                144            39   \n",
       "23      24  September          Pinot Noir                  6            34   \n",
       "24      25    October  Cabernet Sauvignon                 72            59   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "26      27    October           Champagne                 72            88   \n",
       "27      28   November  Cabernet Sauvignon                 12            56   \n",
       "28      29   November        Pinot Grigio                  6            87   \n",
       "29      30   December              Malbec                  6            54   \n",
       "30      31   December           Champagne                 72            89   \n",
       "31      32   December  Cabernet Sauvignon                 72            45   \n",
       "\n",
       "          Origin  Past Peak     0    1     2     3  \n",
       "0         France      False   6.0  4.0   0.0   0.0  \n",
       "1         France      False   5.0  0.0   0.0   5.0  \n",
       "2         Oregon       True   5.0  0.0   1.0   0.0  \n",
       "3         France       True   9.0  3.0   0.0   0.0  \n",
       "4    New Zealand       True   0.0  4.0   0.0   0.0  \n",
       "5          Chile      False   8.0  3.0   1.0   0.0  \n",
       "6      Australia       True   4.0  0.0  15.0   0.0  \n",
       "7   South Africa      False   4.0  0.0  16.0   0.0  \n",
       "8          Chile      False   3.0  7.0   0.0   0.0  \n",
       "9     California      False   3.0  2.0   2.0   0.0  \n",
       "10        France      False  10.0  3.0   0.0   0.0  \n",
       "11     Australia      False   3.0  0.0   1.0   1.0  \n",
       "12         Chile      False   0.0  0.0   6.0   0.0  \n",
       "13         Chile      False   1.0  8.0   0.0   0.0  \n",
       "14         Italy      False   2.0  4.0   0.0   0.0  \n",
       "15    California      False   3.0  1.0   0.0   1.0  \n",
       "16       Germany      False   0.0  0.0   0.0   7.0  \n",
       "17        Oregon      False   1.0  0.0  13.0   0.0  \n",
       "18       Germany      False   5.0  0.0   0.0   0.0  \n",
       "19         Italy      False   4.0  2.0   0.0   0.0  \n",
       "20    California      False   3.0  0.0   1.0   0.0  \n",
       "21        France      False  18.0  3.0   0.0   0.0  \n",
       "22  South Africa      False   1.0  4.0   0.0   0.0  \n",
       "23         Italy      False   0.0  0.0   0.0  12.0  \n",
       "24        Oregon       True   5.0  1.0   0.0   0.0  \n",
       "25     Australia      False   1.0  3.0   0.0  11.0  \n",
       "26   New Zealand      False   6.0  2.0   1.0   0.0  \n",
       "27        France       True   6.0  0.0   0.0   0.0  \n",
       "28        France      False   2.0  0.0  15.0   0.0  \n",
       "29        France      False   6.0  0.0  16.0   0.0  \n",
       "30        France      False  15.0  2.0   0.0   0.0  \n",
       "31       Germany       True   4.0  0.0   0.0   0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Like above, map cluster numbers to customers, then to transactions, then sum the transactions\n",
    "# by cluster and match up to offer attributes for sorting.\n",
    "\n",
    "df_agglom_map = pd.DataFrame()\n",
    "df_agglom_map['Customer Last Name'] = df_pivot_tp.index.values\n",
    "df_agglom_map['cluster'] = model.labels_\n",
    "\n",
    "df_agglom_map = df_agglom_map.merge(df_pivot_tp, on = 'Customer Last Name', how = 'right')\n",
    "df_agglom_map = df_agglom_map.iloc[:,1:34]\n",
    "\n",
    "df_agglom_counts = df_agglom_map.groupby('cluster').sum()\n",
    "\n",
    "df_agglom_counts_tp = df_agglom_counts.transpose()\n",
    "df_agglom_counts_tp.index.name = \"Offer #\"\n",
    "\n",
    "df_offer_aggloms = df_offers.merge(df_agglom_counts_tp, on='Offer #', how='right')\n",
    "df_offer_aggloms = df_offer_aggloms[['Offer #'] + [ col for col in df_offer_aggloms.columns if col != 'Offer #' ]]\n",
    "df_offer_aggloms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>May</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "21      22    August           Champagne                 72            63   \n",
       "30      31  December           Champagne                 72            89   \n",
       "10      11       May           Champagne                 72            85   \n",
       "3        4  February           Champagne                 72            48   \n",
       "5        6     March            Prosecco                144            86   \n",
       "0        1   January              Malbec                 72            56   \n",
       "29      30  December              Malbec                  6            54   \n",
       "27      28  November  Cabernet Sauvignon                 12            56   \n",
       "26      27   October           Champagne                 72            88   \n",
       "1        2   January          Pinot Noir                 72            17   \n",
       "\n",
       "         Origin  Past Peak     0    1     2    3  \n",
       "21       France      False  18.0  3.0   0.0  0.0  \n",
       "30       France      False  15.0  2.0   0.0  0.0  \n",
       "10       France      False  10.0  3.0   0.0  0.0  \n",
       "3        France       True   9.0  3.0   0.0  0.0  \n",
       "5         Chile      False   8.0  3.0   1.0  0.0  \n",
       "0        France      False   6.0  4.0   0.0  0.0  \n",
       "29       France      False   6.0  0.0  16.0  0.0  \n",
       "27       France       True   6.0  0.0   0.0  0.0  \n",
       "26  New Zealand      False   6.0  2.0   1.0  0.0  \n",
       "1        France      False   5.0  0.0   0.0  5.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 0. Observe that these folks appear to love French Champagne and they\n",
    "# also like French red wine.\n",
    "\n",
    "df_offer_agglom0 = df_offer_aggloms.sort_values(by=0, ascending=False)\n",
    "df_offer_agglom0.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
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       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>April</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>57</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>June</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>19</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>September</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>144</td>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "13      14       June              Merlot                 72            64   \n",
       "8        9      April          Chardonnay                144            57   \n",
       "0        1    January              Malbec                 72            56   \n",
       "14      15       June  Cabernet Sauvignon                144            19   \n",
       "22      23  September          Chardonnay                144            39   \n",
       "4        5   February  Cabernet Sauvignon                144            44   \n",
       "5        6      March            Prosecco                144            86   \n",
       "21      22     August           Champagne                 72            63   \n",
       "3        4   February           Champagne                 72            48   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "\n",
       "          Origin  Past Peak     0    1    2     3  \n",
       "13         Chile      False   1.0  8.0  0.0   0.0  \n",
       "8          Chile      False   3.0  7.0  0.0   0.0  \n",
       "0         France      False   6.0  4.0  0.0   0.0  \n",
       "14         Italy      False   2.0  4.0  0.0   0.0  \n",
       "22  South Africa      False   1.0  4.0  0.0   0.0  \n",
       "4    New Zealand       True   0.0  4.0  0.0   0.0  \n",
       "5          Chile      False   8.0  3.0  1.0   0.0  \n",
       "21        France      False  18.0  3.0  0.0   0.0  \n",
       "3         France       True   9.0  3.0  0.0   0.0  \n",
       "25     Australia      False   1.0  3.0  0.0  11.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 1. Observe that this group is pretty noisy. It's hard to find a clear\n",
    "# pattern here.\n",
    "\n",
    "df_offer_agglom1 = df_offer_aggloms.sort_values(by=1, ascending=False)\n",
    "df_offer_agglom1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>March</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>Australia</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>July</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>May</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>April</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>March</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>144</td>\n",
       "      <td>86</td>\n",
       "      <td>Chile</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>August</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #  Campaign      Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "29      30  December        Malbec                  6            54   \n",
       "7        8     March     Espumante                  6            45   \n",
       "6        7     March      Prosecco                  6            40   \n",
       "28      29  November  Pinot Grigio                  6            87   \n",
       "17      18      July     Espumante                  6            50   \n",
       "12      13       May        Merlot                  6            43   \n",
       "9       10     April      Prosecco                 72            52   \n",
       "2        3  February     Espumante                144            32   \n",
       "5        6     March      Prosecco                144            86   \n",
       "20      21    August     Champagne                 12            50   \n",
       "\n",
       "          Origin  Past Peak    0    1     2    3  \n",
       "29        France      False  6.0  0.0  16.0  0.0  \n",
       "7   South Africa      False  4.0  0.0  16.0  0.0  \n",
       "6      Australia       True  4.0  0.0  15.0  0.0  \n",
       "28        France      False  2.0  0.0  15.0  0.0  \n",
       "17        Oregon      False  1.0  0.0  13.0  0.0  \n",
       "12         Chile      False  0.0  0.0   6.0  0.0  \n",
       "9     California      False  3.0  2.0   2.0  0.0  \n",
       "2         Oregon       True  5.0  0.0   1.0  0.0  \n",
       "5          Chile      False  8.0  3.0   1.0  0.0  \n",
       "20    California      False  3.0  0.0   1.0  0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 2. Observe that this group does not like to buy in high quantity\n",
    "# volume, and except for the Pinot Grigio, they seem to like sparkling wines\n",
    "# and red wine.\n",
    "\n",
    "df_offer_agglom2 = df_offer_aggloms.sort_values(by=2, ascending=False)\n",
    "df_offer_agglom2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
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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>Offer #</th>\n",
       "      <th>Campaign</th>\n",
       "      <th>Varietal</th>\n",
       "      <th>Minimum Qty (ltr)</th>\n",
       "      <th>Discount (%)</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Past Peak</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>September</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>Italy</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>October</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>July</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May</td>\n",
       "      <td>Prosecco</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>Australia</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>June</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>California</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>November</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>November</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>October</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>December</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Offer #   Campaign            Varietal  Minimum Qty (ltr)  Discount (%)  \\\n",
       "23      24  September          Pinot Noir                  6            34   \n",
       "25      26    October          Pinot Noir                144            83   \n",
       "16      17       July          Pinot Noir                 12            47   \n",
       "1        2    January          Pinot Noir                 72            17   \n",
       "11      12        May            Prosecco                 72            83   \n",
       "15      16       June              Merlot                 72            88   \n",
       "28      29   November        Pinot Grigio                  6            87   \n",
       "27      28   November  Cabernet Sauvignon                 12            56   \n",
       "26      27    October           Champagne                 72            88   \n",
       "29      30   December              Malbec                  6            54   \n",
       "\n",
       "         Origin  Past Peak    0    1     2     3  \n",
       "23        Italy      False  0.0  0.0   0.0  12.0  \n",
       "25    Australia      False  1.0  3.0   0.0  11.0  \n",
       "16      Germany      False  0.0  0.0   0.0   7.0  \n",
       "1        France      False  5.0  0.0   0.0   5.0  \n",
       "11    Australia      False  3.0  0.0   1.0   1.0  \n",
       "15   California      False  3.0  1.0   0.0   1.0  \n",
       "28       France      False  2.0  0.0  15.0   0.0  \n",
       "27       France       True  6.0  0.0   0.0   0.0  \n",
       "26  New Zealand      False  6.0  2.0   1.0   0.0  \n",
       "29       France      False  6.0  0.0  16.0   0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by cluster 3. Observe that we have some big time Pinot Noir fans here.\n",
    "\n",
    "df_offer_agglom3 = df_offer_aggloms.sort_values(by=3, ascending=False)\n",
    "df_offer_agglom3.head(10)"
   ]
  }
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