Transaction_details.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori,association_rules\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "#from apyori import apriori\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"H:\\\\study\\\\Association Rules\\\\transactions_retail1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>'HANGING'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'HOLDER'</th>\n",
       "      <th>'T-LIGHT'</th>\n",
       "      <th>'WHITE'</th>\n",
       "      <th>NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'LANTERN'</td>\n",
       "      <td>'METAL'</td>\n",
       "      <td>'WHITE'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'COAT'</td>\n",
       "      <td>'CREAM'</td>\n",
       "      <td>'CUPID'</td>\n",
       "      <td>'HANGER'</td>\n",
       "      <td>'HEARTS'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'BOTTLE'</td>\n",
       "      <td>'FLAG'</td>\n",
       "      <td>'HOT'</td>\n",
       "      <td>'KNITTED'</td>\n",
       "      <td>'UNION'</td>\n",
       "      <td>'WATER'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'HEART.'</td>\n",
       "      <td>'HOTTIE'</td>\n",
       "      <td>'RED'</td>\n",
       "      <td>'WHITE'</td>\n",
       "      <td>'WOOLLY'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'7'</td>\n",
       "      <td>'BABUSHKA'</td>\n",
       "      <td>'BOXES'</td>\n",
       "      <td>'NESTING'</td>\n",
       "      <td>'SET'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557035</th>\n",
       "      <td>'20'</td>\n",
       "      <td>'NAPKINS'</td>\n",
       "      <td>'OF'</td>\n",
       "      <td>'PACK'</td>\n",
       "      <td>'SPACEBOY'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557036</th>\n",
       "      <td>'APRON'</td>\n",
       "      <td>'CHILDREN''S'</td>\n",
       "      <td>'DOLLY'</td>\n",
       "      <td>'GIRL'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557037</th>\n",
       "      <td>'CHILDRENS'</td>\n",
       "      <td>'CUTLERY'</td>\n",
       "      <td>'DOLLY'</td>\n",
       "      <td>'GIRL'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557038</th>\n",
       "      <td>'CHILDRENS'</td>\n",
       "      <td>'CIRCUS'</td>\n",
       "      <td>'CUTLERY'</td>\n",
       "      <td>'PARADE'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557039</th>\n",
       "      <td>'9'</td>\n",
       "      <td>'BAKING'</td>\n",
       "      <td>'PIECE'</td>\n",
       "      <td>'RETROSPOT'</td>\n",
       "      <td>'SET'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>557040 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          'HANGING'        'HEART'   'HOLDER'    'T-LIGHT'     'WHITE'  \\\n",
       "0         'LANTERN'        'METAL'    'WHITE'          NaN         NaN   \n",
       "1            'COAT'        'CREAM'    'CUPID'     'HANGER'    'HEARTS'   \n",
       "2          'BOTTLE'         'FLAG'      'HOT'    'KNITTED'     'UNION'   \n",
       "3          'HEART.'       'HOTTIE'      'RED'      'WHITE'    'WOOLLY'   \n",
       "4               '7'     'BABUSHKA'    'BOXES'    'NESTING'       'SET'   \n",
       "...             ...            ...        ...          ...         ...   \n",
       "557035         '20'      'NAPKINS'       'OF'       'PACK'  'SPACEBOY'   \n",
       "557036      'APRON'  'CHILDREN''S'    'DOLLY'       'GIRL'         NaN   \n",
       "557037  'CHILDRENS'      'CUTLERY'    'DOLLY'       'GIRL'         NaN   \n",
       "557038  'CHILDRENS'       'CIRCUS'  'CUTLERY'     'PARADE'         NaN   \n",
       "557039          '9'       'BAKING'    'PIECE'  'RETROSPOT'       'SET'   \n",
       "\n",
       "             NA  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2       'WATER'  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "557035      NaN  \n",
       "557036      NaN  \n",
       "557037      NaN  \n",
       "557038      NaN  \n",
       "557039      NaN  \n",
       "\n",
       "[557040 rows x 6 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>'HANGING'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'HOLDER'</th>\n",
       "      <th>'T-LIGHT'</th>\n",
       "      <th>'WHITE'</th>\n",
       "      <th>NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>557040</td>\n",
       "      <td>538818</td>\n",
       "      <td>525799</td>\n",
       "      <td>436182</td>\n",
       "      <td>222350</td>\n",
       "      <td>83150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>949</td>\n",
       "      <td>1177</td>\n",
       "      <td>1099</td>\n",
       "      <td>856</td>\n",
       "      <td>489</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>'BAG'</td>\n",
       "      <td>'HEART'</td>\n",
       "      <td>'RED'</td>\n",
       "      <td>'OF'</td>\n",
       "      <td>'SET'</td>\n",
       "      <td>'SET'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>41795</td>\n",
       "      <td>16186</td>\n",
       "      <td>18724</td>\n",
       "      <td>20683</td>\n",
       "      <td>14738</td>\n",
       "      <td>16194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       'HANGING'  'HEART' 'HOLDER' 'T-LIGHT' 'WHITE'     NA\n",
       "count     557040   538818   525799    436182  222350  83150\n",
       "unique       949     1177     1099       856     489    184\n",
       "top        'BAG'  'HEART'    'RED'      'OF'   'SET'  'SET'\n",
       "freq       41795    16186    18724     20683   14738  16194"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HANGING'      'COAT'\n",
       "'HEART'       'CREAM'\n",
       "'HOLDER'      'CUPID'\n",
       "'T-LIGHT'    'HANGER'\n",
       "'WHITE'      'HEARTS'\n",
       "NA                NaN\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gather All Items of Each Transactions into Numpy Array\n",
    "transaction = []\n",
    "for i in range(0,df.shape[0]):\n",
    "    for j in range(0,df.shape[1]):\n",
    "        transaction.append(df.values[i,j])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction = np.array(transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", ..., \"'RETROSPOT'\", \"'SET'\",\n",
       "       'nan'], dtype='<U23')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform into dataframe\n",
    "df_transaction = pd.DataFrame(transaction,columns=[\"Items\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'LANTERN'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'METAL'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'WHITE'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342235</th>\n",
       "      <td>'BAKING'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342236</th>\n",
       "      <td>'PIECE'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342237</th>\n",
       "      <td>'RETROSPOT'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342238</th>\n",
       "      <td>'SET'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342239</th>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3342240 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Items\n",
       "0          'LANTERN'\n",
       "1            'METAL'\n",
       "2            'WHITE'\n",
       "3                nan\n",
       "4                nan\n",
       "...              ...\n",
       "3342235     'BAKING'\n",
       "3342236      'PIECE'\n",
       "3342237  'RETROSPOT'\n",
       "3342238        'SET'\n",
       "3342239          nan\n",
       "\n",
       "[3342240 rows x 1 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transaction['incident_count'] = 1 # Put 1 to Each Item For Making Countable Table, to be able to perform Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexNames = df_transaction[df_transaction['Items'] == \"nan\" ].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transaction.drop(indexNames,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Final Step: Make a New Appropriate Pandas DataFrame for Visualizations  \n",
    "df_table = df_transaction.groupby('Items').sum().sort_values('incident_count',ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Items</th>\n",
       "      <th>incident_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'SET'</td>\n",
       "      <td>54430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'OF'</td>\n",
       "      <td>53351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'BAG'</td>\n",
       "      <td>51911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'RED'</td>\n",
       "      <td>42902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'HEART'</td>\n",
       "      <td>38713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>'PARTITION'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>'POM-POM'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>'POSSIBLE'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>'PURPLE/TURQ'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>'zero'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2603 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Items  incident_count\n",
       "0             'SET'           54430\n",
       "1              'OF'           53351\n",
       "2             'BAG'           51911\n",
       "3             'RED'           42902\n",
       "4           'HEART'           38713\n",
       "...             ...             ...\n",
       "2598    'PARTITION'               1\n",
       "2599      'POM-POM'               1\n",
       "2600     'POSSIBLE'               1\n",
       "2601  'PURPLE/TURQ'               1\n",
       "2602         'zero'               1\n",
       "\n",
       "[2603 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5c19e_row0_col1 {\n",
       "  background-color: #00441b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5c19e_row1_col1 {\n",
       "  background-color: #005020;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5c19e_row2_col1 {\n",
       "  background-color: #006027;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5c19e_row3_col1 {\n",
       "  background-color: #48ae60;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5c19e_row4_col1 {\n",
       "  background-color: #81ca81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5c19e_row5_col1 {\n",
       "  background-color: #abdda5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5c19e_row6_col1 {\n",
       "  background-color: #bae3b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5c19e_row7_col1 {\n",
       "  background-color: #dbf1d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5c19e_row8_col1 {\n",
       "  background-color: #dff3da;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5c19e_row9_col1 {\n",
       "  background-color: #f7fcf5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5c19e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Items</th>\n",
       "      <th class=\"col_heading level0 col1\" >incident_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5c19e_row0_col0\" class=\"data row0 col0\" >'SET'</td>\n",
       "      <td id=\"T_5c19e_row0_col1\" class=\"data row0 col1\" >54430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5c19e_row1_col0\" class=\"data row1 col0\" >'OF'</td>\n",
       "      <td id=\"T_5c19e_row1_col1\" class=\"data row1 col1\" >53351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5c19e_row2_col0\" class=\"data row2 col0\" >'BAG'</td>\n",
       "      <td id=\"T_5c19e_row2_col1\" class=\"data row2 col1\" >51911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5c19e_row3_col0\" class=\"data row3 col0\" >'RED'</td>\n",
       "      <td id=\"T_5c19e_row3_col1\" class=\"data row3 col1\" >42902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5c19e_row4_col0\" class=\"data row4 col0\" >'HEART'</td>\n",
       "      <td id=\"T_5c19e_row4_col1\" class=\"data row4 col1\" >38713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5c19e_row5_col0\" class=\"data row5 col0\" >'RETROSPOT'</td>\n",
       "      <td id=\"T_5c19e_row5_col1\" class=\"data row5 col1\" >35126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_5c19e_row6_col0\" class=\"data row6 col0\" >'VINTAGE'</td>\n",
       "      <td id=\"T_5c19e_row6_col1\" class=\"data row6 col1\" >33748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_5c19e_row7_col0\" class=\"data row7 col0\" >'DESIGN'</td>\n",
       "      <td id=\"T_5c19e_row7_col1\" class=\"data row7 col1\" >30066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_5c19e_row8_col0\" class=\"data row8 col0\" >'PINK'</td>\n",
       "      <td id=\"T_5c19e_row8_col1\" class=\"data row8 col1\" >29526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c19e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_5c19e_row9_col0\" class=\"data row9 col0\" >'CHRISTMAS'</td>\n",
       "      <td id=\"T_5c19e_row9_col1\" class=\"data row9 col1\" >25131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19d5611dc10>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Initial Visualizations\n",
    "df_table.head(10).style.background_gradient(cmap='Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The most demanded items in dataset / Top30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_table['all']='all' #to have same origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Items</th>\n",
       "      <th>incident_count</th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'SET'</td>\n",
       "      <td>54430</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'OF'</td>\n",
       "      <td>53351</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'BAG'</td>\n",
       "      <td>51911</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'RED'</td>\n",
       "      <td>42902</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'HEART'</td>\n",
       "      <td>38713</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>'PARTITION'</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>'POM-POM'</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>'POSSIBLE'</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>'PURPLE/TURQ'</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>'zero'</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2603 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Items  incident_count  all\n",
       "0             'SET'           54430  all\n",
       "1              'OF'           53351  all\n",
       "2             'BAG'           51911  all\n",
       "3             'RED'           42902  all\n",
       "4           'HEART'           38713  all\n",
       "...             ...             ...  ...\n",
       "2598    'PARTITION'               1  all\n",
       "2599      'POM-POM'               1  all\n",
       "2600     'POSSIBLE'               1  all\n",
       "2601  'PURPLE/TURQ'               1  all\n",
       "2602         'zero'               1  all\n",
       "\n",
       "[2603 rows x 3 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "branchvalues": "total",
         "customdata": [
          [
           "'3'",
           18695
          ],
          [
           "'6'",
           13146
          ],
          [
           "'BAG'",
           51911
          ],
          [
           "'BLUE'",
           19316
          ],
          [
           "'BOX'",
           24403
          ],
          [
           "'CAKE'",
           21461
          ],
          [
           "'CARD'",
           13280
          ],
          [
           "'CHRISTMAS'",
           25131
          ],
          [
           "'DECORATION'",
           12981
          ],
          [
           "'DESIGN'",
           30066
          ],
          [
           "'HANGING'",
           17036
          ],
          [
           "'HEART'",
           38713
          ],
          [
           "'HOLDER'",
           16260
          ],
          [
           "'JUMBO'",
           21139
          ],
          [
           "'LUNCH'",
           18525
          ],
          [
           "'METAL'",
           20842
          ],
          [
           "'OF'",
           53351
          ],
          [
           "'PACK'",
           15637
          ],
          [
           "'PAPER'",
           14142
          ],
          [
           "'PINK'",
           29526
          ],
          [
           "'POLKADOT'",
           12472
          ],
          [
           "'RED'",
           42902
          ],
          [
           "'RETROSPOT'",
           35126
          ],
          [
           "'SET'",
           54430
          ],
          [
           "'SIGN'",
           16998
          ],
          [
           "'SMALL'",
           14333
          ],
          [
           "'T-LIGHT'",
           14558
          ],
          [
           "'VINTAGE'",
           33748
          ],
          [
           "'WHITE'",
           20812
          ],
          [
           "'WOODEN'",
           12708
          ],
          [
           "(?)",
           30804.24105838222
          ]
         ],
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "labels=%{label}<br>incident_count=%{value}<br>parent=%{parent}<br>id=%{id}<br>Items=%{customdata[0]}<br>color=%{color}<extra></extra>",
         "ids": [
          "all/'3'",
          "all/'6'",
          "all/'BAG'",
          "all/'BLUE'",
          "all/'BOX'",
          "all/'CAKE'",
          "all/'CARD'",
          "all/'CHRISTMAS'",
          "all/'DECORATION'",
          "all/'DESIGN'",
          "all/'HANGING'",
          "all/'HEART'",
          "all/'HOLDER'",
          "all/'JUMBO'",
          "all/'LUNCH'",
          "all/'METAL'",
          "all/'OF'",
          "all/'PACK'",
          "all/'PAPER'",
          "all/'PINK'",
          "all/'POLKADOT'",
          "all/'RED'",
          "all/'RETROSPOT'",
          "all/'SET'",
          "all/'SIGN'",
          "all/'SMALL'",
          "all/'T-LIGHT'",
          "all/'VINTAGE'",
          "all/'WHITE'",
          "all/'WOODEN'",
          "all"
         ],
         "labels": [
          "'3'",
          "'6'",
          "'BAG'",
          "'BLUE'",
          "'BOX'",
          "'CAKE'",
          "'CARD'",
          "'CHRISTMAS'",
          "'DECORATION'",
          "'DESIGN'",
          "'HANGING'",
          "'HEART'",
          "'HOLDER'",
          "'JUMBO'",
          "'LUNCH'",
          "'METAL'",
          "'OF'",
          "'PACK'",
          "'PAPER'",
          "'PINK'",
          "'POLKADOT'",
          "'RED'",
          "'RETROSPOT'",
          "'SET'",
          "'SIGN'",
          "'SMALL'",
          "'T-LIGHT'",
          "'VINTAGE'",
          "'WHITE'",
          "'WOODEN'",
          "all"
         ],
         "marker": {
          "coloraxis": "coloraxis",
          "colors": [
           18695,
           13146,
           51911,
           19316,
           24403,
           21461,
           13280,
           25131,
           12981,
           30066,
           17036,
           38713,
           16260,
           21139,
           18525,
           20842,
           53351,
           15637,
           14142,
           29526,
           12472,
           42902,
           35126,
           54430,
           16998,
           14333,
           14558,
           33748,
           20812,
           12708,
           30804.24105838222
          ]
         },
         "name": "",
         "parents": [
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          "all",
          ""
         ],
         "type": "treemap",
         "values": [
          18695,
          13146,
          51911,
          19316,
          24403,
          21461,
          13280,
          25131,
          12981,
          30066,
          17036,
          38713,
          16260,
          21139,
          18525,
          20842,
          53351,
          15637,
          14142,
          29526,
          12472,
          42902,
          35126,
          54430,
          16998,
          14333,
          14558,
          33748,
          20812,
          12708,
          733648
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "color"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(247,251,255)"
          ],
          [
           0.125,
           "rgb(222,235,247)"
          ],
          [
           0.25,
           "rgb(198,219,239)"
          ],
          [
           0.375,
           "rgb(158,202,225)"
          ],
          [
           0.5,
           "rgb(107,174,214)"
          ],
          [
           0.625,
           "rgb(66,146,198)"
          ],
          [
           0.75,
           "rgb(33,113,181)"
          ],
          [
           0.875,
           "rgb(8,81,156)"
          ],
          [
           1,
           "rgb(8,48,107)"
          ]
         ]
        },
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"9a4889fe-ce0a-4c33-aa1b-7e1aec98b710\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9a4889fe-ce0a-4c33-aa1b-7e1aec98b710\")) {                    Plotly.newPlot(                        \"9a4889fe-ce0a-4c33-aa1b-7e1aec98b710\",                        [{\"branchvalues\":\"total\",\"customdata\":[[\"'3'\",18695.0],[\"'6'\",13146.0],[\"'BAG'\",51911.0],[\"'BLUE'\",19316.0],[\"'BOX'\",24403.0],[\"'CAKE'\",21461.0],[\"'CARD'\",13280.0],[\"'CHRISTMAS'\",25131.0],[\"'DECORATION'\",12981.0],[\"'DESIGN'\",30066.0],[\"'HANGING'\",17036.0],[\"'HEART'\",38713.0],[\"'HOLDER'\",16260.0],[\"'JUMBO'\",21139.0],[\"'LUNCH'\",18525.0],[\"'METAL'\",20842.0],[\"'OF'\",53351.0],[\"'PACK'\",15637.0],[\"'PAPER'\",14142.0],[\"'PINK'\",29526.0],[\"'POLKADOT'\",12472.0],[\"'RED'\",42902.0],[\"'RETROSPOT'\",35126.0],[\"'SET'\",54430.0],[\"'SIGN'\",16998.0],[\"'SMALL'\",14333.0],[\"'T-LIGHT'\",14558.0],[\"'VINTAGE'\",33748.0],[\"'WHITE'\",20812.0],[\"'WOODEN'\",12708.0],[\"(?)\",30804.24105838222]],\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"labels=%{label}<br>incident_count=%{value}<br>parent=%{parent}<br>id=%{id}<br>Items=%{customdata[0]}<br>color=%{color}<extra></extra>\",\"ids\":[\"all/'3'\",\"all/'6'\",\"all/'BAG'\",\"all/'BLUE'\",\"all/'BOX'\",\"all/'CAKE'\",\"all/'CARD'\",\"all/'CHRISTMAS'\",\"all/'DECORATION'\",\"all/'DESIGN'\",\"all/'HANGING'\",\"all/'HEART'\",\"all/'HOLDER'\",\"all/'JUMBO'\",\"all/'LUNCH'\",\"all/'METAL'\",\"all/'OF'\",\"all/'PACK'\",\"all/'PAPER'\",\"all/'PINK'\",\"all/'POLKADOT'\",\"all/'RED'\",\"all/'RETROSPOT'\",\"all/'SET'\",\"all/'SIGN'\",\"all/'SMALL'\",\"all/'T-LIGHT'\",\"all/'VINTAGE'\",\"all/'WHITE'\",\"all/'WOODEN'\",\"all\"],\"labels\":[\"'3'\",\"'6'\",\"'BAG'\",\"'BLUE'\",\"'BOX'\",\"'CAKE'\",\"'CARD'\",\"'CHRISTMAS'\",\"'DECORATION'\",\"'DESIGN'\",\"'HANGING'\",\"'HEART'\",\"'HOLDER'\",\"'JUMBO'\",\"'LUNCH'\",\"'METAL'\",\"'OF'\",\"'PACK'\",\"'PAPER'\",\"'PINK'\",\"'POLKADOT'\",\"'RED'\",\"'RETROSPOT'\",\"'SET'\",\"'SIGN'\",\"'SMALL'\",\"'T-LIGHT'\",\"'VINTAGE'\",\"'WHITE'\",\"'WOODEN'\",\"all\"],\"marker\":{\"coloraxis\":\"coloraxis\",\"colors\":[18695.0,13146.0,51911.0,19316.0,24403.0,21461.0,13280.0,25131.0,12981.0,30066.0,17036.0,38713.0,16260.0,21139.0,18525.0,20842.0,53351.0,15637.0,14142.0,29526.0,12472.0,42902.0,35126.0,54430.0,16998.0,14333.0,14558.0,33748.0,20812.0,12708.0,30804.24105838222]},\"name\":\"\",\"parents\":[\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"all\",\"\"],\"values\":[18695,13146,51911,19316,24403,21461,13280,25131,12981,30066,17036,38713,16260,21139,18525,20842,53351,15637,14142,29526,12472,42902,35126,54430,16998,14333,14558,33748,20812,12708,733648],\"type\":\"treemap\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"color\"}},\"colorscale\":[[0.0,\"rgb(247,251,255)\"],[0.125,\"rgb(222,235,247)\"],[0.25,\"rgb(198,219,239)\"],[0.375,\"rgb(158,202,225)\"],[0.5,\"rgb(107,174,214)\"],[0.625,\"rgb(66,146,198)\"],[0.75,\"rgb(33,113,181)\"],[0.875,\"rgb(8,81,156)\"],[1.0,\"rgb(8,48,107)\"]]},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('9a4889fe-ce0a-4c33-aa1b-7e1aec98b710');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.treemap(df_table.head(30), path=['all', \"Items\"], values='incident_count',\n",
    "                  color=df_table[\"incident_count\"].head(30), hover_data=['Items'],\n",
    "                  color_continuous_scale='Blues',\n",
    "                  )\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lets check whether the items have multiple records in a transaction or not\n",
    "- If the answer is \"Yes\", we need to handle them since they might mislead the apriori algorithm in further steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction1 = []\n",
    "for i in range(0,df.shape[0]):\n",
    "    transaction1.append([str(df.values[i,j]) for j in range(0,df.shape[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'FROSTED'\", \"'GLASS'\", \"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DOT'\", \"'HAND'\", \"'POLKA'\", \"'RED'\", \"'WARMER'\", 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'BEDROOM'\", \"'PLAYHOUSE'\", \"'POPPY''S'\", 'nan', 'nan', 'nan'],\n",
       " [\"'KITCHEN'\", \"'PLAYHOUSE'\", \"'POPPY''S'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHARLOTTE'\", \"'DOLL'\", \"'FELTCRAFT'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'IVORY'\", \"'KNITTED'\", \"'MUG'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'ASSORTED'\", \"'BOX'\", \"'COLOUR'\", \"'OF'\", \"'TEASPOONS'\"],\n",
       " [\"'BLOCKS'\", \"'BOX'\", \"'JIGSAW'\", \"'OF'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'ALPHABET'\", \"'BLOCKS'\", \"'BOX'\", \"'OF'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'HOME'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'LOVE'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'HEART'\", \"'METAL'\", \"'RECIPE'\", \"'WITH'\", 'nan'],\n",
       " [\"'DOORMAT'\", \"'ENGLAND'\", \"'NEW'\", 'nan', 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'COAT'\", \"'FASHION'\", \"'PARIS'\", \"'RACK'\", \"'RED'\", 'nan'],\n",
       " [\"'COAT'\", \"'FASHION'\", \"'PARIS'\", \"'RACK'\", \"'YELLOW'\", 'nan'],\n",
       " [\"'BLUE'\", \"'COAT'\", \"'FASHION'\", \"'PARIS'\", \"'RACK'\", 'nan'],\n",
       " [\"'BATH'\", \"'BLOCK'\", \"'BUILDING'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'GREEN'\", 'nan', 'nan'],\n",
       " [\"'AND'\", \"'BUNNIES'\", \"'PANDA'\", \"'SHEET'\", \"'STICKER'\", 'nan'],\n",
       " [\"'GIFT'\", \"'STARS'\", \"'TAPE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'GLOBE'\", \"'INFLATABLE'\", \"'POLITICAL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'AND'\", \"'CARD'\", \"'GAME'\", \"'HEADS'\", \"'TAILS'\", \"'VINTAGE'\"],\n",
       " [\"'RED'\", \"'RETROSPOT'\", \"'SET/2'\", \"'TEA'\", \"'TOWELS'\", 'nan'],\n",
       " [\"'BOXES'\", \"'OF4'\", \"'ROUND'\", \"'SET'\", \"'SNACK'\", \"'WOODLAND'\"],\n",
       " [\"'BOX'\", \"'LUNCH'\", \"'SPACEBOY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'LUNCH'\", 'nan'],\n",
       " [\"'BOX'\", \"'CIRCUS'\", \"'LUNCH'\", \"'PARADE'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'CHARLOTTE'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", 'nan'],\n",
       " [\"'LED'\", \"'LIGHT'\", \"'NIGHT'\", \"'RED'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'JIGSAW'\", \"'PUZZLES'\", \"'SEASIDE'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'CIRCUS'\", \"'JIGSAW'\", \"'MINI'\", \"'PARADE'\", 'nan', 'nan'],\n",
       " [\"'JIGSAW'\", \"'MINI'\", \"'SPACEBOY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'MINI'\", \"'PAINT'\", \"'SET'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'POSTAGE'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'DOT'\", \"'HAND'\", \"'POLKA'\", \"'RED'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       " [\"'EDWARDIAN'\", \"'PARASOL'\", \"'RED'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'COFFEE'\", \"'MUGS'\", \"'RETRO'\", 'nan', 'nan'],\n",
       " [\"'MUG'\", \"'PLANET'\", \"'SAVE'\", \"'THE'\", 'nan', 'nan'],\n",
       " [\"'BILLBOARD'\", \"'DRINK'\", \"'ME'\", \"'MUG'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BILLBOARD'\", \"'LOVE/HATE'\", \"'MUG'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CABINET'\", \"'DRAWER'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'ANT'\", \"'CABINET'\", \"'FINISH'\", \"'S/3'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'FINISH'\", \"'FRAME'\", \"'PICTURE'\", \"'WHITE'\", \"'WOODEN'\", 'nan'],\n",
       " [\"'ANTIQUE'\", \"'FRAME'\", \"'WHITE'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'FROSTED'\", \"'GLASS'\", \"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'BOX'\", \"'LARGE'\", \"'SEWING'\", \"'VICTORIAN'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       " [\"'EDWARDIAN'\", \"'PARASOL'\", \"'RED'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'COFFEE'\", \"'MUGS'\", \"'RETRO'\", 'nan', 'nan'],\n",
       " [\"'MUG'\", \"'PLANET'\", \"'SAVE'\", \"'THE'\", 'nan', 'nan'],\n",
       " [\"'BILLBOARD'\", \"'DRINK'\", \"'ME'\", \"'MUG'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BILLBOARD'\", \"'LOVE/HATE'\", \"'MUG'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CABINET'\", \"'DRAWER'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'ANT'\", \"'CABINET'\", \"'FINISH'\", \"'S/3'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'FINISH'\", \"'FRAME'\", \"'PICTURE'\", \"'WHITE'\", \"'WOODEN'\", 'nan'],\n",
       " [\"'ANTIQUE'\", \"'FRAME'\", \"'WHITE'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'FROSTED'\", \"'GLASS'\", \"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'AND'\", \"'BOTTLE'\", \"'HOT'\", \"'SYMPATHY'\", \"'TEA'\", \"'WATER'\"],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'RED'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'DOT'\", \"'HAND'\", \"'POLKA'\", \"'RED'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'PINK'\", \"'POLKADOT'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BAROQUE'\", \"'BLACK'\", \"'JUMBO'\", \"'WHITE'\", 'nan'],\n",
       " [\"'AND'\", \"'BAG'\", \"'CHARLIE'\", \"'JUMBO'\", \"'LOLA'\", \"'TOYS'\"],\n",
       " [\"'BAG'\", \"'CHARLOTTE'\", \"'STRAWBERRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'CUTLERY'\", \"'PIECE'\", \"'RED'\", \"'RETROSPOT'\", \"'SET'\"],\n",
       " [\"'3'\", \"'BLUE'\", \"'CUTLERY'\", \"'PIECE'\", \"'POLKADOT'\", \"'SET'\"],\n",
       " [\"'PAPER'\", \"'PLATES'\", \"'RED'\", \"'SET/6'\", \"'SPOTTY'\", 'nan'],\n",
       " [\"'BAG'\", \"'LUNCH'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CUTLERY'\", \"'LUNCH'\", \"'STRAWBERRY'\", \"'WITH'\", 'nan'],\n",
       " [\"'BOX'\", \"'CUTLERY'\", \"'LUNCH'\", \"'RETROSPOT'\", \"'WITH'\", 'nan'],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'DINOSAUR'\", \"'OF'\", \"'PACK'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\"],\n",
       " [\"'PINK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'FAIRY'\", \"'TEATIME'\", 'nan'],\n",
       " [\"'CHARLIE+LOLA'\", \"'COASTER'\", \"'SET'\", \"'TOMATO'\", 'nan', 'nan'],\n",
       " [\"'&'\", \"'BIN'\", \"'CHARLIE'\", \"'FLORA'\", \"'LOLA'\", \"'WASTEPAPER'\"],\n",
       " [\"'CHARLIE+LOLA'\", \"'DOORSIGN'\", \"'PERSONAL'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'STORAGE'\", \"'SUKI'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'PAISLEY'\", \"'PINK'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'11'\", \"'CERAMIC'\", \"'PC'\", \"'RETROSPOT'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'GIRLY'\", \"'PINK'\", \"'SET'\", \"'TOOL'\", 'nan', 'nan'],\n",
       " [\"'JUMBO'\", \"'PAISLEY'\", \"'RED'\", \"'SHOPPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'AIRLINE'\", \"'LOUNGE,METAL'\", \"'SIGN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CERAMIC'\", \"'DRAWER'\", \"'KNOB'\", \"'RED'\", \"'SPOT'\", \"'WHITE'\"],\n",
       " [\"'ACRYLIC'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'KNOB'\", \"'RED'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'CLEAR'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'KNOB'\", 'nan'],\n",
       " [\"'CLIP'\", \"'LINE'\", \"'PHOTO'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHICKEN'\", \"'COSY'\", \"'EGG'\", \"'FELT'\", 'nan', 'nan'],\n",
       " [\"'BANK'\", \"'PIGGY'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'SHOULDER'\", \"'SKULL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CONFUSING'\", \"'ME'\", \"'METAL'\", \"'SIGN'\", \"'YOU''RE'\", 'nan'],\n",
       " [\"'COOK'\", \"'METAL'\", \"'SIGN'\", \"'WINE'\", \"'WITH'\", 'nan'],\n",
       " [\"'+'\", \"'DIET'\", \"'GIN'\", \"'METAL'\", \"'SIGN'\", \"'TONIC'\"],\n",
       " [\"'AND'\", \"'BREAKFAST'\", \"'CUP'\", \"'SAUCER'\", \"'YELLOW'\", 'nan'],\n",
       " [\"'AND'\", \"'BREAKFAST'\", \"'CUP'\", \"'PINK'\", \"'SAUCER'\", 'nan'],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'CHAIN'\", \"'KIT'\", \"'PAPER'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'FLOWERS'\", \"'HEART'\", \"'HOOK'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'CLIP'\", \"'LINE'\", \"'PHOTO'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'DES'\", \"'TEA'\", \"'TIME'\", 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'EGG'\", \"'FELT'\", \"'RABBIT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'CANDLE'\", \"'STICK'\", \"'WILLIE'\", \"'WINKIE'\", \"'ZINC'\", 'nan'],\n",
       " [\"'BANK'\", \"'CAKE'\", \"'CERAMIC'\", \"'CHERRY'\", \"'MONEY'\", 'nan'],\n",
       " [\"'JUG'\", \"'LARGE'\", \"'MILK'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'BEAKERS'\", \"'FUNKY'\", \"'OF'\", \"'SET'\", 'nan'],\n",
       " [\"'BLACK'\", \"'EDWARDIAN'\", \"'PARASOL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'EDWARDIAN'\", \"'NATURAL'\", \"'PARASOL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BANK'\", \"'CAKE'\", \"'CERAMIC'\", \"'MONEY'\", \"'STRAWBERRY'\", 'nan'],\n",
       " [\"'BLUE'\", \"'OWL'\", \"'SOFT'\", \"'TOY'\", 'nan', 'nan'],\n",
       " [\"'ART'\", \"'BALLOON'\", \"'FLOWERS'\", \"'MAKE'\", \"'OWN'\", \"'YOUR'\"],\n",
       " [\"'LED'\", \"'LIGHT'\", \"'NIGHT'\", \"'RED'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'CLOCHE'\", \"'GLASS'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'GUMBALL'\", \"'MONOCHROME'\", \"'RACK'\", 'nan', 'nan'],\n",
       " [\"'DOORMAT'\", \"'FANCY'\", \"'FONT'\", \"'HOME'\", \"'SWEET'\", 'nan'],\n",
       " [\"'Discount'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'GLOBE'\", \"'INFLATABLE'\", \"'POLITICAL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'&'\", \"'LADDERS'\", \"'SNAKES'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'CALCULATOR'\", \"'CHOCOLATE'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'JUMBO'\", \"'PAISLEY'\", \"'RED'\", \"'SHOPPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BAG'\", \"'RECYCLING'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'PINK'\", \"'POLKADOT'\", \"'TIDY'\", \"'TOY'\", 'nan', 'nan'],\n",
       " [\"'ANTIQUE'\", \"'DRESSING'\", \"'GLASS'\", \"'POT'\", \"'TABLE'\", 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'GREEN'\", 'nan', 'nan'],\n",
       " [\"'GARDEN'\", \"'GIANT'\", \"'IVORY'\", \"'THERMOMETER'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'AND'\", \"'CAKE'\", \"'CREAM'\", \"'GREEN'\", \"'TIER'\"],\n",
       " [\"'TIN'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'AND'\", \"'CAKE'\", \"'CREAM'\", \"'RED'\", \"'TIER'\"],\n",
       " [\"'TIN'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'BASKETS'\", \"'LIDS'\", \"'OVAL'\", \"'SET'\", \"'W'\"],\n",
       " [\"'WICKER'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'COLOURED'\", \"'DUCKS'\", \"'FLYING'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'ANT'\", \"'BLACK'\", \"'BOARD'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'COLOUR'\", \"'GLASS'\", \"'HANGING'\", \"'HOLDER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'LANTERN'\", \"'METAL'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'LANTERN'\", \"'MEDINA'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'CHALKBOARD'\", \"'HEART'\", \"'NATURAL'\", \"'SLATE'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'OF'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'LARGE'\", \"'OF'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'LANTERN'\", \"'LOVEBIRD'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BIRDCAGE'\", \"'CLASSIC'\", \"'HOLDER'\", \"'METAL'\", \"'PLANT'\", 'nan'],\n",
       " [\"'CARD'\", \"'CREAM'\", \"'HEART'\", \"'HOLDER'\", 'nan', 'nan'],\n",
       " [\"'CREAM'\", \"'ENAMEL'\", \"'FLOWER'\", \"'JUG'\", 'nan', 'nan'],\n",
       " [\"'BUCKET'\", \"'CREAM'\", \"'ENAMEL'\", \"'FIRE'\", 'nan', 'nan'],\n",
       " [\"'BIN'\", \"'BREAD'\", \"'CREAM'\", \"'ENAMEL'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'BASKETS'\", \"'LIDS'\", \"'OVAL'\", \"'SET'\", \"'W'\"],\n",
       " [\"'WICKER'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'JUMBO'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'RIBBONS'\", \"'TRADITIONAL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ANTIQUE'\", \"'ORGANISER'\", \"'WHITE'\", \"'WOOD'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'LUNCH'\", 'nan'],\n",
       " [\"'EGG'\", \"'HOLDER'\", \"'WHITE'\", \"'WIRE'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BAROQUE'\", \"'BLACK'\", \"'JUMBO'\", \"'WHITE'\", 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'CHILLI'\", \"'LIGHTS'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BUTTERFILES'\", \"'GARLAND'\", \"'LIGHT'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'GARLAND'\", \"'LIGHT'\", \"'OWLS'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'COTTAGE'\", \"'FAIRY'\", \"'NIGHTLIGHT'\", \"'TALE'\", 'nan', 'nan'],\n",
       " [\"'LED'\", \"'LIGHT'\", \"'NIGHT'\", \"'RED'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'HOME'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'LOVE'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'DOORMAT'\", \"'FANCY'\", \"'FONT'\", \"'HOME'\", \"'SWEET'\", 'nan'],\n",
       " [\"'HOME'\", \"'LETTERS'\", \"'SMALL'\", \"'WOOD'\", 'nan', 'nan'],\n",
       " [\"'DOORSTOP'\", \"'GINGHAM'\", \"'HEART'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'DECORATION'\", \"'FIVE'\", \"'HANGING'\", \"'HEART'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'LANTERN'\", \"'METAL'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BOTTLE'\", \"'MAGNETS'\", \"'TOP'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'DINER'\", \"'FRIDGE'\", \"'MAGNETS'\", \"'US'\", 'nan'],\n",
       " [\"'CANDLES'\", \"'HOMEMADE'\", \"'JAM'\", \"'SCENTED'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'ENFANTS'\", \"'FRIDGE'\", \"'LES'\", \"'MAGNETS'\", 'nan'],\n",
       " [\"'CARAVAN'\", \"'DOORSTOP'\", \"'ROSE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'OF'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'5'\", \"'HANGER'\", \"'HOOK'\", \"'MAGIC'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'10'\", \"'CHRISTMAS'\", \"'LIGHTS'\", \"'REINDEER'\", 'nan', 'nan'],\n",
       " [\"'COVER'\", \"'CUSHION'\", \"'JACK'\", \"'UNION'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'AND'\", \"'CARD'\", \"'GAME'\", \"'HEADS'\", \"'TAILS'\", \"'VINTAGE'\"],\n",
       " [\"'3'\", \"'COLOURED'\", \"'DUCKS'\", \"'FLYING'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'3'\", \"'DUCKS'\", \"'FLYING'\", \"'GOLD'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'RED'\", \"'RETROSPOT'\", \"'UMBRELLA'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK/BLUE'\", \"'POLKADOT'\", \"'UMBRELLA'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CLOCK'\", \"'DINER'\", \"'RED'\", \"'WALL'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'GREEN'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'CLOCK'\", \"'DINER'\", \"'WALL'\", 'nan', 'nan'],\n",
       " [\"'CLOCK'\", \"'DINER'\", \"'IVORY'\", \"'WALL'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'LARGE'\", \"'MEASURING'\", \"'SPOONS'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'MEASURING'\", \"'SMALL'\", \"'SPOONS'\", 'nan', 'nan'],\n",
       " [\"'10'\", \"'CHRISTMAS'\", \"'LIGHTS'\", \"'REINDEER'\", 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'JAR'\", \"'LID'\", \"'PINK'\", \"'WITH'\", 'nan'],\n",
       " [\"'GREEN'\", \"'JAM'\", \"'JAR'\", \"'LID'\", \"'WITH'\", 'nan'],\n",
       " [\"'BOX'\", \"'COTTAGE'\", \"'KEEPSAKE'\", \"'ROSE'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'ZINC'\", 'nan'],\n",
       " [\"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BALL'\", \"'CHRISTMAS'\", \"'DECORATION'\", \"'DISCO'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HOLDER'\", \"'LARGE'\", \"'POPCORN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'JUG'\", \"'LARGE'\", \"'MILK'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'NAPKINS'\", \"'PAPER'\", \"'RED'\", \"'RETROSPOT'\", \"'SET/20'\", 'nan'],\n",
       " [\"'PAPER'\", \"'PLATES'\", \"'RED'\", \"'SET/6'\", \"'SPOTTY'\", 'nan'],\n",
       " [\"'CUPS'\", \"'PAPER'\", \"'RED'\", \"'SET/6'\", \"'SPOTTY'\", 'nan'],\n",
       " [\"'HAT'\", \"'POLKADOT'\", \"'RAIN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DELUXE'\", \"'KIT'\", \"'SEWING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'HEART'\", \"'HOT'\", \"'RETROSPOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'BOTTLE'\", \"'ENGLISH'\", \"'HOT'\", \"'ROSE'\", \"'WATER'\", 'nan'],\n",
       " [\"'CUBE'\", \"'PHOTO'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CANDLES'\", \"'HOMEMADE'\", \"'JAM'\", \"'SCENTED'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'CIRCUS'\", \"'IN'\", \"'PARADE'\", \"'PLASTERS'\", \"'TIN'\", 'nan'],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\", \"'PINK'\", \"'TISSUES'\"],\n",
       " [\"'12'\", \"'BLUE'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\", \"'TISSUES'\"],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'RED'\", \"'RETROSPOT'\", \"'TISSUES'\"],\n",
       " [\"'BOTTLE'\", \"'CHICK'\", \"'GREY'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'IN'\", \"'PAISLEY'\", \"'PLASTERS'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'IN'\", \"'PLASTERS'\", \"'SKULLS'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'FELTCRAFT'\", \"'MICE'\", \"'STRIPEY'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'ROPE'\", \"'SKIPPING'\", \"'TRADITIONAL'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DOMINOES'\", \"'OF'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'DRAWER'\", \"'RUSTIC'\", \"'SEVENTEEN'\", \"'SIDEBOARD'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'CARNIVAL'\", \"'CONES'\", \"'PARTY'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'CANDY'\", \"'CONES'\", \"'PARTY'\", 'nan', 'nan'],\n",
       " [\"'BASKET'\", \"'PICNIC'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'DECORATION'\", \"'PAINTED'\", \"'STAR'\", \"'ZINC'\", 'nan', 'nan'],\n",
       " [\"'LAMP'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BIRTHDAY'\", \"'CARD,'\", \"'FANCY'\", \"'FONT'\", 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'HAND'\", \"'OWL'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'HEART'\", \"'HOT'\", \"'RETROSPOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BALL'\", \"'BOWL'\", \"'CHASING'\", \"'DESIGN'\", \"'DOG'\", 'nan'],\n",
       " [\"'24'\", \"'CLOTHES'\", \"'PACK'\", \"'PEGS'\", \"'RETROSPOT'\", 'nan'],\n",
       " [\"'CHOCOLATE'\", \"'HAND'\", \"'OVER'\", \"'SIGN'\", \"'THE'\", 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'KIT'\", \"'SEWING'\", \"'TRAVEL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'CARD'\", \"'HEART'\", \"'HOLDER'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\"],\n",
       " [\"'PINK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'FAIRY'\", \"'TEATIME'\", 'nan'],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'BOTTLE'\", \"'CHICK'\", \"'GREY'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'GLASS'\", \"'HEART'\", \"'POT'\", \"'SMALL'\", \"'TRINKET'\", 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'IVORY'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'ORANGE'\", 'nan', 'nan'],\n",
       " [\"'ALARM'\", \"'BAKELIKE'\", \"'CLOCK'\", \"'GREEN'\", 'nan', 'nan'],\n",
       " [\"'AND'\", \"'BOTTLE'\", \"'HOT'\", \"'SYMPATHY'\", \"'TEA'\", \"'WATER'\"],\n",
       " [\"'BIRD'\", \"'DESIGN'\", \"'HAND'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       " [\"'BLACK'\", \"'EDWARDIAN'\", \"'PARASOL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'EDWARDIAN'\", \"'PARASOL'\", \"'RED'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'COFFEE'\", \"'MUGS'\", \"'RETRO'\", 'nan', 'nan'],\n",
       " [\"'MUG'\", \"'PLANET'\", \"'SAVE'\", \"'THE'\", 'nan', 'nan'],\n",
       " [\"'BILLBOARD'\", \"'DRINK'\", \"'ME'\", \"'MUG'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BILLBOARD'\", \"'LOVE/HATE'\", \"'MUG'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CABINET'\", \"'DRAWER'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'ANT'\", \"'CABINET'\", \"'FINISH'\", \"'S/3'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'FINISH'\", \"'FRAME'\", \"'PICTURE'\", \"'WHITE'\", \"'WOODEN'\", 'nan'],\n",
       " [\"'ANTIQUE'\", \"'FRAME'\", \"'WHITE'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'EMBROIDERED'\", \"'IVORY'\", \"'QUILT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'FROSTED'\", \"'GLASS'\", \"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'3'\", \"'BLACK'\", \"'DUCKS'\", \"'FLYING'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'3'\", \"'COLOURED'\", \"'DUCKS'\", \"'FLYING'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'RED'\", \"'RETROSPOT'\", \"'TISSUES'\"],\n",
       " [\"'MUG'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'10'\", \"'BABUSHKA'\", \"'LIGHTS'\", \"'OF'\", \"'STRING'\", 'nan'],\n",
       " [\"'BANK'\", \"'PIGGY'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'CAKE'\", \"'DOORMAT'\", \"'FAIRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'HAND'\", \"'OWL'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CERAMIC'\", \"'STRAWBERRY'\", \"'TRINKET'\", 'nan', 'nan'],\n",
       " [\"'DOUGHNUT'\", \"'PINK'\", \"'POT'\", \"'TRINKET'\", 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'PINK'\", \"'PURSE'\", \"'SILK'\", 'nan', 'nan'],\n",
       " [\"'AND'\", \"'BOTTLE'\", \"'HOT'\", \"'SYMPATHY'\", \"'TEA'\", \"'WATER'\"],\n",
       " [\"'AM'\", \"'BOTTLE'\", \"'HOT'\", \"'I'\", \"'POORLY'\", \"'SO'\"],\n",
       " [\"'WATER'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'CHOCOLATE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'HOT'\", \"'SKULL'\", \"'WATER'\", \"'WHITE'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'DOG'\", \"'HOT'\", \"'SCOTTIE'\", \"'WATER'\", 'nan'],\n",
       " [\"'DOT'\", \"'HAND'\", \"'POLKA'\", \"'RED'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'CANDLES'\", \"'HOMEMADE'\", \"'JAM'\", \"'SCENTED'\", 'nan', 'nan'],\n",
       " [\"'BIRD'\", \"'BOTTLE'\", \"'HOT'\", \"'HOUSE'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOUDOIR'\", \"'BOX'\", \"'SQUARE'\", \"'TISSUE'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'SKULLS'\", \"'SQUARE'\", \"'TISSUE'\", 'nan', 'nan'],\n",
       " [\"'CORNICE'\", \"'FRAME'\", \"'PHOTO'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'PURSE'\", \"'RED'\", \"'SILK'\", 'nan', 'nan'],\n",
       " [\"'DOMINOES'\", \"'PICTURE'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CROCHET'\", \"'FLOWERS'\", \"'ON'\", \"'S/6'\", \"'SEW'\", 'nan'],\n",
       " [\"'REDS'\", \"'RIBBONS'\", \"'SCANDINAVIAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BALLOONS'\", \"'SET'\", \"'WRITING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'IN'\", \"'INCENSE'\", \"'LAVENDER'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'DINNER'\", \"'PAISLEY'\", \"'TRAY'\", \"'TV'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'4'\", \"'ENGLISH'\", \"'OF'\", \"'PLACEMATS'\", \"'ROSE'\", \"'SET'\"],\n",
       " [\"'4'\", \"'COASTERS'\", \"'ENGLISH'\", \"'OF'\", \"'ROSE'\", \"'SET'\"],\n",
       " [\"'CORNICE'\", \"'FRAME'\", \"'PHOTO'\", \"'TRIPLE'\", 'nan', 'nan'],\n",
       " [\"'CORNICE'\", \"'FAMILY'\", \"'FRAME'\", \"'PHOTO'\", 'nan', 'nan'],\n",
       " [\"'BALL'\", \"'DISCO'\", \"'MIRRORED'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BALL'\", \"'BATTERY'\", \"'DISCO'\", \"'OPERATED'\", \"'ROTATOR'\", 'nan'],\n",
       " [\"'LOOKING'\", \"'MIRROR'\", \"'SILVER'\", 'nan', 'nan', 'nan'],\n",
       " [\"'&'\", \"'GENTLEMEN'\", \"'LADIES'\", \"'METAL'\", \"'SIGN'\", 'nan'],\n",
       " [\"'DINNER'\", \"'HER'\", \"'IS'\", \"'METAL'\", \"'SERVED'\", \"'SIGN'\"],\n",
       " [\"'CONFUSING'\", \"'ME'\", \"'METAL'\", \"'SIGN'\", \"'YOU''RE'\", 'nan'],\n",
       " [\"'DOORMAT'\", \"'TOPIARY'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BATHROOM'\", \"'METAL'\", \"'SIGN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'KITCHEN'\", \"'METAL'\", \"'SIGN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'METAL'\", \"'SIGN'\", \"'TOILET'\", 'nan', 'nan', 'nan'],\n",
       " [\"'IT'\", \"'LEAVE'\", \"'METAL'\", \"'OR'\", \"'SIGN'\", \"'TAKE'\"],\n",
       " [\"'HOLIDAY'\", \"'I''M'\", \"'METAL'\", \"'ON'\", \"'SIGN'\", 'nan'],\n",
       " [\"'BASIL'\", \"'ENAMEL'\", \"'GROW'\", \"'IN'\", \"'MUG'\", \"'OWN'\"],\n",
       " [\"'YOUR'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CANDLES'\", \"'PARTY'\", \"'PINK'\", \"'POLKADOT'\", \"'SET/10'\", 'nan'],\n",
       " [\"'20'\", \"'CAKES'\", \"'DESIGN'\", \"'FAIRY'\", \"'NAPKINS'\", \"'SET'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'FAIRY'\", \"'TEATIME'\", 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SNOWMEN'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SANTA'\", \"'SET'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'9'\", \"'BALLOONS'\", \"'HEART'\", \"'OF'\", \"'SET'\", \"'SHAPED'\"],\n",
       " [\"'BATH'\", \"'SANDWICH'\", \"'SPONGE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'APPLE'\", \"'BATH'\", \"'SPONGE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BATH'\", \"'SPONGE'\", \"'STRAWBERRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'CHEST'\", \"'PIRATE'\", \"'TREASURE'\", 'nan', 'nan'],\n",
       " [\"'LIGHT'\", \"'PORTABLE'\", \"'STAR'\", \"'TABLE'\", 'nan', 'nan'],\n",
       " [\"'LIGHT'\", \"'PORTABLE'\", \"'SNOWFLAKE'\", \"'TABLE'\", 'nan', 'nan'],\n",
       " [\"'JEWELLED'\", \"'MIRROR'\", \"'OVAL'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CIGAR'\", \"'MATCHES'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'GIANT'\", \"'HOUR'\", \"'MATCHES'\", \"'TUBE'\", 'nan'],\n",
       " [\"'HEARTS'\", \"'JAZZ'\", \"'NOTEBOOK'\", \"'PURSE'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'LUNCH'\", 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'CANDLEHOLDER'\", \"'HANGING'\", \"'HEART'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CABINET'\", \"'DRAWER'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'ASSORTED'\", \"'CASES'\", \"'COLOUR'\", \"'MINI'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'LUNCH'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'LUNCH'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'LUNCH'\", \"'WOODLAND'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'LUNCH'\", \"'PINK'\", \"'POLKADOT'\", 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'GUMBALL'\", \"'RACK'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAROQUE'\", \"'BLUE'\", \"'CANDLE'\", \"'CANDLESTICK'\", \"'NEW'\", 'nan'],\n",
       " [\"'BAROQUECANDLESTICK'\", \"'CANDLE'\", \"'NEW'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'CAKE'\", \"'COLOUR'\", \"'FAIRY'\", \"'FLANNEL'\", 'nan'],\n",
       " [\"'BIN'\", \"'BREAD'\", \"'DINER'\", \"'PINK'\", \"'STYLE'\", 'nan'],\n",
       " [\"'AND'\", \"'BAG'\", \"'CHARLIE'\", \"'JUMBO'\", \"'LOLA'\", \"'TOYS'\"],\n",
       " [\"'CLOTH'\", \"'TABLE'\", \"'TEA'\", \"'TIME'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'ENGLISH'\", \"'HOT'\", \"'ROSE'\", \"'WATER'\", 'nan'],\n",
       " [\"'BUCKET'\", \"'CREAM'\", \"'ENAMEL'\", \"'FIRE'\", 'nan', 'nan'],\n",
       " [\"'BASKET'\", \"'PICNIC'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BABUSHKA'\", \"'BOTTLE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'BIRD'\", \"'DESIGN'\", \"'HAND'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'POSTAGE'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'IVORY'\", \"'SMALL'\", \"'TRELLIS'\", 'nan', 'nan'],\n",
       " [\"'ACRYLIC'\", \"'CLEAR'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'KNOB'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'KNOB'\", \"'PINK'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'GREEN'\", \"'KNOB'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'BLUE'\", \"'DRAWER'\", \"'EDWARDIAN'\", \"'KNOB'\", 'nan'],\n",
       " [\"'HEART'\", \"'OF'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'6'\", \"'CANDLES'\", \"'CELEBRATION'\", \"'FOOTBALL'\", \"'SET'\", 'nan'],\n",
       " [\"'6'\", \"'CANDLES'\", \"'CELEBRATION'\", \"'GIRLS'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'PINKS'\", \"'RIBBONS'\", \"'ROMANTIC'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLUES'\", \"'BRIGHT'\", \"'RIBBONS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'REDS'\", \"'RIBBONS'\", \"'SCANDINAVIAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CHOCOLATE'\", \"'RIBBONS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'FOOTBALL'\", \"'INVITES'\", \"'PARTY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEARTS'\", \"'INVITES'\", \"'JAZZ'\", \"'PARTY'\", 'nan', 'nan'],\n",
       " [\"'INVITES'\", \"'PARTY'\", \"'SPACEMAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'BUTTERFLY'\", \"'COOKIE'\", \"'CUTTERS'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'3'\", \"'COOKIE'\", \"'CUTTERS'\", \"'HEART'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'3'\", \"'COOKIE'\", \"'CUTTER'\", \"'PIECE'\", \"'SET'\", \"'SPACEBOY'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'SKULL'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'SPACEBOY'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'FAIRY'\", \"'SWEETHEART'\", 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'DINOSAUR'\", \"'OF'\", \"'PACK'\"],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'LUNCH'\", \"'SUKI'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BLUE'\", \"'CARS'\", \"'LUNCH'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BLACK'\", \"'LUNCH'\", \"'SKULL.'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'IVORY'\", \"'LARGE'\", \"'TRELLIS'\", 'nan', 'nan'],\n",
       " [\"'BOWLS'\", \"'GLASS'\", \"'LID'\", \"'RED'\", \"'RETROSPOT'\", \"'SET/5'\"],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       " [\"'EDWARDIAN'\", \"'PARASOL'\", \"'RED'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'COFFEE'\", \"'MUGS'\", \"'RETRO'\", 'nan', 'nan'],\n",
       " [\"'MUG'\", \"'PLANET'\", \"'SAVE'\", \"'THE'\", 'nan', 'nan'],\n",
       " [\"'BILLBOARD'\", \"'DRINK'\", \"'ME'\", \"'MUG'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BILLBOARD'\", \"'LOVE/HATE'\", \"'MUG'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CABINET'\", \"'DRAWER'\", \"'FINISH'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'ANT'\", \"'CABINET'\", \"'FINISH'\", \"'S/3'\", \"'WHITE'\", \"'WOOD'\"],\n",
       " [\"'FINISH'\", \"'FRAME'\", \"'PICTURE'\", \"'WHITE'\", \"'WOODEN'\", 'nan'],\n",
       " [\"'ANTIQUE'\", \"'FRAME'\", \"'WHITE'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'7'\", \"'BABUSHKA'\", \"'BOXES'\", \"'NESTING'\", \"'SET'\", 'nan'],\n",
       " [\"'EMBROIDERED'\", \"'IVORY'\", \"'QUILT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'FROSTED'\", \"'GLASS'\", \"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'DOT'\", \"'HAND'\", \"'POLKA'\", \"'RED'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DINOSAUR'\", \"'DRAWING'\", \"'MAGIC'\", \"'SLATE'\", 'nan', 'nan'],\n",
       " [\"'A'\", \"'BAKE'\", \"'CAKE'\", \"'DRAWING'\", \"'MAGIC'\", \"'SLATE'\"],\n",
       " [\"'12'\", \"'PENCILS'\", \"'SKULLS'\", \"'TALL'\", \"'TUBE'\", 'nan'],\n",
       " [\"'CALCULATOR'\", \"'CHOCOLATE'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'HARMONICA'\", \"'IN'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'BOX'\", \"'HARMONICA'\", \"'IN'\", 'nan', 'nan'],\n",
       " [\"'SKULLS'\", \"'TATTOOS'\", \"'TRANSFER'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'BIRD'\", \"'BOXES'\", \"'PACK'\", \"'PANNETONE'\", 'nan'],\n",
       " [\"'AND'\", \"'BOTTLE'\", \"'HOT'\", \"'SYMPATHY'\", \"'TEA'\", \"'WATER'\"],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'HOT'\", \"'SKULL'\", \"'WATER'\", \"'WHITE'\", 'nan'],\n",
       " [\"'3'\", \"'COOKIE'\", \"'CUTTER'\", \"'PIECE'\", \"'SET'\", \"'SPACEBOY'\"],\n",
       " [\"'20'\", \"'COOKIE'\", \"'CUTTERS'\", \"'KIDS'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'10'\", \"'COLOUR'\", \"'PEN'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'GLASS'\", \"'HANGING'\", \"'T-LIGHT'\", \"'VICTORIAN'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'HOLDER'\", \"'SINGLE'\", \"'T-LIGHT'\", \"'ZINC'\", 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'RED'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'HANGING'\", \"'LANTERN'\", \"'METAL'\", \"'STAR'\", 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HOLDER'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'DOORMAT'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DOORMAT'\", \"'HEARTS'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CHALKBOARD'\", \"'NATURAL'\", \"'RECTANGLE'\", \"'SLATE'\", 'nan', 'nan'],\n",
       " [\"'DECORATION'\", \"'HANGING'\", \"'LOVEBIRD'\", \"'WHITE'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'HOME'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'LOVE'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'CORNICE'\", \"'FRAME'\", \"'PHOTO'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'FACE'\", \"'FUNNY'\", \"'PENS'\", 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'CIRCUS'\", \"'PARADE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COWBOYS'\", \"'WRAP'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'78'\", \"'AIRLINE'\", \"'BAG'\", \"'TOKYO'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'RED'\", \"'RETROSPOT'\", \"'UMBRELLA'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK/BLUE'\", \"'POLKADOT'\", \"'UMBRELLA'\", 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'CUTLERY'\", \"'PIECE'\", \"'RED'\", \"'RETROSPOT'\", \"'SET'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'72'\", \"'DOILIES'\", \"'OF'\", \"'PAPER'\", \"'RETROSPOT'\", \"'SET'\"],\n",
       " [\"'BLUE'\", \"'COAT'\", \"'FASHION'\", \"'PARIS'\", \"'RACK'\", 'nan'],\n",
       " [\"'IN'\", \"'PLASTERS'\", \"'SKULLS'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'CAT'\", \"'ERASERS'\", \"'SLEEPING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BIRTHDAY'\", \"'CARD'\", \"'COWBOY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'BOXES'\", \"'CHRISTMAS'\", \"'PACK'\", \"'PANNETONE'\", 'nan'],\n",
       " [\"'ANGELS'\", \"'HLDR'\", \"'ROTATING'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'COSY'\", \"'GIANT'\", \"'HOUR'\", \"'MATCHES'\", \"'TUBE'\", 'nan'],\n",
       " [\"'MAKING'\", \"'REEL'\", \"'RIBBON'\", \"'SNOWMEN'\", 'nan', 'nan'],\n",
       " [\"'MINI'\", \"'PAINT'\", \"'SET'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'4'\", \"'SPINNING'\", \"'TOPS'\", \"'TRADITIONAL'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'500g'\", \"'BAG'\", \"'MARBLES'\", \"'SWIRLY'\", 'nan', 'nan'],\n",
       " [\"'5'\", \"'CRYSTAL'\", \"'GLASS'\", \"'NECKLACE'\", \"'STRAND'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'HOT'\", \"'SKULL'\", \"'WATER'\", \"'WHITE'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'DOG'\", \"'HOT'\", \"'SCOTTIE'\", \"'WATER'\", 'nan'],\n",
       " [\"'COVER'\", \"'FLAG'\", \"'PINK'\", \"'SQUARECUSHION'\", \"'UNION'\", 'nan'],\n",
       " [\"'CHILDRENS'\", \"'CUP'\", \"'EGG'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'CHILDREN''S'\", \"'MUG'\", \"'SPACEBOY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'CUSHION'\", \"'FELTCRAFT'\", \"'OWL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'CANDELABRA'\", \"'HOLDER'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'TIDY'\", \"'TOY'\", 'nan'],\n",
       " [\"'12'\", \"'BOTANICAL'\", \"'LAVENDER'\", \"'SET'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'FLAG'\", \"'JACK'\", \"'LUGGAGE'\", \"'TAG'\", \"'UNION'\", 'nan'],\n",
       " [\"'HEART'\", \"'LUGGAGE'\", \"'RED'\", \"'TAG'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'CHARM'\", \"'GLASS'\", \"'RED'\", \"'TASSLE'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'BANGLE'\", \"'CLEAR'\", \"'FACETED'\", 'nan', 'nan'],\n",
       " [\"'5'\", \"'CRYSTAL'\", \"'GLASS'\", \"'NECKLACE'\", \"'STRAND'\", 'nan'],\n",
       " [\"'AND'\", \"'DOORMAT'\", \"'GUNS'\", \"'JACK'\", \"'ROSES'\", \"'UNION'\"],\n",
       " [\"'BOX'\", \"'CANDLE'\", \"'JEWELLED'\", \"'SCENT'\", \"'VANILLA'\", 'nan'],\n",
       " [\"'BREAKFAST'\", \"'ENGLISH'\", \"'FULL'\", \"'PLATE'\", 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'CANDELABRA'\", \"'HOLDER'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'CIRCUS'\", \"'DRAWING'\", \"'MAGIC'\", \"'PARADE'\", \"'SLATE'\", 'nan'],\n",
       " [\"'BELL'\", \"'CHRISTMAS'\", \"'HANGING'\", \"'HEART'\", \"'WITH'\", 'nan'],\n",
       " [\"'CAKE'\", \"'FILIGREE'\", \"'MED'\", \"'STAND'\", \"'VICTORIAN'\", 'nan'],\n",
       " [\"'PAISLEY'\", \"'PATTERN'\", \"'STICKERS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'FLOWERS'\", \"'STICKERS'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BAROQUECANDLESTICK'\", \"'CANDLE'\", \"'NEW'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'BAROQUE'\", \"'BLUE'\", \"'CANDLE'\", \"'CANDLESTICK'\", \"'NEW'\", 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'STORAGE'\", \"'SUKI'\", 'nan', 'nan'],\n",
       " [\"'BIRD'\", \"'BLACK'\", \"'CANDLE'\", \"'LOVE'\", 'nan', 'nan'],\n",
       " [\"'ERASERS'\", \"'PARTY'\", \"'PENCIL'\", \"'TIME'\", 'nan', 'nan'],\n",
       " [\"'BALL'\", \"'CHRISTMAS'\", \"'DECORATION'\", \"'DISCO'\", 'nan', 'nan'],\n",
       " [\"'B''FLY'\", \"'BOBBLES'\", \"'C/COVER'\", \"'PINK'\", \"'W'\", 'nan'],\n",
       " [\"'JACK'\", \"'LUGGAGE'\", \"'PINK'\", \"'TAG'\", \"'UNION'\", 'nan'],\n",
       " [\"'6'\", \"'CHRISTMAS'\", \"'ELEGANT'\", \"'RIBBONS'\", 'nan', 'nan'],\n",
       " [\"'FLAG'\", \"'JACK'\", \"'LUGGAGE'\", \"'TAG'\", \"'UNION'\", 'nan'],\n",
       " [\"'AGED'\", \"'GLASS'\", \"'HOLDER'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'BOOM'\", \"'BOX'\", \"'GIRLS'\", \"'SPEAKER'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'PINKS'\", \"'RIBBONS'\", \"'SHIMMERING'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'OWLS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BATH'\", \"'SANDWICH'\", \"'SPONGE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DOORSTOP'\", \"'OWL'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'5'\", \"'CRYSTAL'\", \"'GLASS'\", \"'NECKLACE'\", \"'STRAND'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'LARGE'\", \"'POPCORN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BELLS'\", \"'HOLLY'\", \"'METAL'\", \"'PAINTED'\", \"'STAR'\", \"'WITH'\"],\n",
       " [\"'BELL'\", \"'HEART'\", \"'HOLLY'\", \"'METAL'\", \"'PAINTED'\", \"'WITH'\"],\n",
       " [\"'ANGEL'\", \"'DECORATION'\", \"'DRESS'\", \"'ON'\", \"'STARS'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'STRAWBERRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'SHOPPER'\", \"'STRAWBERRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'CHARLOTTE'\", \"'STRAWBERRY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'RED'\", \"'RETROSPOT'\", \"'TISSUES'\"],\n",
       " [\"'BOTTLE'\", \"'DOG'\", \"'HOT'\", \"'SCOTTIE'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'CHOCOLATE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BLUE'\", \"'CARS'\", \"'LUNCH'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'LUNCH'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'BOXES'\", \"'OF4'\", \"'ROUND'\", \"'SET'\", \"'SNACK'\", \"'WOODLAND'\"],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'LUNCH'\", 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'LUNCH'\", \"'SUKI'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BLACK'\", \"'LUNCH'\", \"'SKULL.'\", 'nan', 'nan'],\n",
       " [\"'4'\", \"'BOXES'\", \"'FRUITS'\", \"'OF'\", \"'ROUND'\", \"'SET'\"],\n",
       " [\"'SNACK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'4'\", \"'BOXES'\", \"'OF'\", \"'ROUND'\", \"'SET'\", \"'SKULLS'\"],\n",
       " [\"'SNACK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DOLLY'\", \"'GIRL'\", \"'LUNCH'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'LUNCH'\", \"'SPACEBOY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'GREEN'\", \"'PLATE'\", \"'POLKADOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'PLATE'\", \"'POLKADOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'PLATE'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'PINK'\", \"'PLATE'\", \"'POLKADOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'4'\", \"'BOXES'\", \"'OF'\", \"'ROUND'\", \"'SET'\", \"'SKULLS'\"],\n",
       " [\"'SNACK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'MOLLY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'FAIRY'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'KIT'\", \"'SEWING'\", \"'TRAVEL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'3'\", \"'IN'\", \"'NOTEBOOKS'\", \"'OF'\", \"'PARCEL'\", \"'SET'\"],\n",
       " [\"'RED'\", \"'RETROSPOT'\", \"'TAPE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'RED'\", \"'SHOES'\", \"'SLIPPER'\", \"'SMALL'\", 'nan'],\n",
       " [\"'6'\", \"'CHARM'\", \"'RIBBONS'\", \"'RUSTIC'\", 'nan', 'nan'],\n",
       " [\"'REDS'\", \"'RIBBONS'\", \"'SCANDINAVIAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'12'\", \"'BOX'\", \"'DAISY'\", \"'IN'\", \"'PEGS'\", \"'WOOD'\"],\n",
       " [\"'BABUSHKA'\", \"'BOTTLE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'CHARLOTTE'\", \"'DOLL'\", \"'FELTCRAFT'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'FAIRY'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHARLOTTE'\", \"'DOLL'\", \"'FELTCRAFT'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'CHARM'\", \"'RIBBONS'\", \"'RUSTIC'\", 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'RED'\", \"'SHOES'\", \"'SLIPPER'\", \"'SMALL'\", 'nan'],\n",
       " [\"'REDS'\", \"'RIBBONS'\", \"'SCANDINAVIAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'5'\", \"'HANGER'\", \"'HOOK'\", \"'MAGIC'\", \"'RED'\", \"'TOADSTOOL'\"],\n",
       " [\"'ANGEL'\", \"'CHRISTMAS'\", \"'CRAFT'\", \"'TOP'\", \"'TREE'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FRIENDS'\", \"'LITTLE'\", 'nan', 'nan'],\n",
       " [\"'5'\", \"'HANGER'\", \"'HOOK'\", \"'MAGIC'\", \"'RED'\", \"'TOADSTOOL'\"],\n",
       " [\"'6'\", \"'CHARM'\", \"'RIBBONS'\", \"'RUSTIC'\", 'nan', 'nan'],\n",
       " [\"'12'\", \"'BOX'\", \"'DAISY'\", \"'IN'\", \"'PEGS'\", \"'WOOD'\"],\n",
       " [\"'3'\", \"'IN'\", \"'NOTEBOOKS'\", \"'OF'\", \"'PARCEL'\", \"'SET'\"],\n",
       " [\"'5'\", \"'HANGER'\", \"'HOOK'\", \"'MAGIC'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'REDS'\", \"'RIBBONS'\", \"'SCANDINAVIAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'LED'\", \"'LIGHT'\", \"'NIGHT'\", \"'RED'\", \"'TOADSTOOL'\", 'nan'],\n",
       " [\"'5'\", \"'HANGER'\", \"'HOOK'\", \"'MAGIC'\", \"'RED'\", \"'TOADSTOOL'\"],\n",
       " [\"'COSY'\", \"'GREEN'\", \"'SHOES'\", \"'SLIPPER'\", \"'SMALL'\", 'nan'],\n",
       " [\"'COSY'\", \"'RED'\", \"'SHOES'\", \"'SLIPPER'\", \"'SMALL'\", 'nan'],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'ROSIE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'MOLLY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DOLL'\", \"'EMILY'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHARLOTTE'\", \"'DOLL'\", \"'FELTCRAFT'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'OLIVIA'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'CASH+CARRY'\", \"'JUMBO'\", \"'REX'\", \"'SHOPPER'\", 'nan', 'nan'],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'MOLLY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CUSHION'\", \"'FELTCRAFT'\", \"'OWL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ANGEL'\", \"'CHRISTMAS'\", \"'CRAFT'\", \"'TOP'\", \"'TREE'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FRIENDS'\", \"'LITTLE'\", 'nan', 'nan'],\n",
       " [\"'CUSHION'\", \"'FELTCRAFT'\", \"'RABBIT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BUTTERFLY'\", \"'CUSHION'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'12'\", \"'BOX'\", \"'DAISY'\", \"'IN'\", \"'PEGS'\", \"'WOOD'\"],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'BUTTERFLY'\", \"'CUSHION'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'12'\", \"'BOX'\", \"'DAISY'\", \"'IN'\", \"'PEGS'\", \"'WOOD'\"],\n",
       " [\"'BOX'\", \"'COTTAGE'\", \"'KEEPSAKE'\", \"'ROSE'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'TOTE'\", 'nan'],\n",
       " [\"'12'\", \"'BOX'\", \"'DAISY'\", \"'IN'\", \"'PEGS'\", \"'WOOD'\"],\n",
       " [\"'BAG'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'TOTE'\", 'nan'],\n",
       " [\"'FOLDING'\", \"'PINKWHITE'\", \"'POLKADOT'\", \"'UMBRELLA'\", 'nan', 'nan'],\n",
       " [\"'CREAM'\", \"'FOLDING'\", \"'POLKADOT'\", \"'UMBRELLA'\", 'nan', 'nan'],\n",
       " [\"'FOLDING'\", \"'POLKADOT'\", \"'UMBRELLA'\", \"'WHITE/RED'\", 'nan', 'nan'],\n",
       " [\"'10'\", \"'LIGHTS'\", \"'NIGHT'\", \"'OWL'\", \"'SET'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'FOLDING'\", \"'POLKADOT'\", \"'RED/WHITE'\", \"'UMBRELLA'\", 'nan', 'nan'],\n",
       " [\"'10'\", \"'BABUSHKA'\", \"'LIGHTS'\", \"'OF'\", \"'STRING'\", 'nan'],\n",
       " [\"'BAG'\", \"'LUNCH'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DECOUPAGE'\", \"'HEART'\", \"'S/4'\", \"'VALENTINE'\", 'nan'],\n",
       " [\"'CREAM'\", \"'FOLDING'\", \"'POLKADOT'\", \"'UMBRELLA'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'FOLDING'\", \"'POLKADOT'\", \"'RED/WHITE'\", \"'UMBRELLA'\", 'nan', 'nan'],\n",
       " [\"'CUP'\", \"'EGG'\", \"'SOLDIERS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DECOUPAGE'\", \"'HEART'\", \"'S/4'\", \"'VALENTINE'\", 'nan'],\n",
       " [\"'CASES'\", \"'MINI'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'CHRISTMAS'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BAUBLE'\", \"'CHRISTMAS'\", \"'REEL'\", \"'RIBBON'\", \"'SOCK'\", 'nan'],\n",
       " [\"'REEL'\", \"'RIBBON'\", \"'SNOWY'\", \"'VILLAGE'\", 'nan', 'nan'],\n",
       " [\"'MAKING'\", \"'REEL'\", \"'RIBBON'\", \"'SNOWMEN'\", 'nan', 'nan'],\n",
       " [\"'20'\", \"'CHRISTMAS'\", \"'NAPKINS'\", \"'OF'\", \"'SET'\", \"'VINTAGE'\"],\n",
       " [\"'CHRISTMAS'\", \"'TREE'\", \"'TURQUOISE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'HOLDER'\", \"'RED'\", \"'STAR'\", 'nan', 'nan'],\n",
       " [\"'SMALL'\", \"'WICKER'\", \"'WREATH'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'LARGE'\", \"'OF'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'ADVENT'\", \"'CALENDAR'\", \"'GINGHAM'\", \"'SACK'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'OF'\", \"'SMALL'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'BUTTERFLY'\", \"'FELTCRAFT'\", \"'HEARTS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CONE'\", \"'DECORATION'\", \"'PARTY'\", 'nan', 'nan'],\n",
       " [\"'AND'\", \"'CARD'\", \"'GAME'\", \"'HEADS'\", \"'TAILS'\", \"'VINTAGE'\"],\n",
       " [\"'STAR'\", \"'WICKER'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CARDS'\", \"'SNAP'\", \"'VINTAGE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'6'\", \"'FELTCRAFT'\", \"'FLOWER'\", \"'FRIENDS'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'BIRD'\", \"'DECORATION'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'GINGHAM'\", \"'TREE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'GINGHAM'\", \"'STAR'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'GINGHAM'\", \"'HEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'PINK'\", \"'POLKADOT'\", \"'TISSUES'\"],\n",
       " [\"'DOLL'\", \"'FELTCRAFT'\", \"'LOLA'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'12'\", \"'LONDON'\", \"'OF'\", \"'PACK'\", \"'TISSUES'\", 'nan'],\n",
       " [\"'ACRYLIC'\", \"'DRAWERKNOB'\", \"'EDWARDIAN'\", \"'PURPLE'\", 'nan', 'nan'],\n",
       " [\"'APRON'\", \"'CHILDREN''S'\", \"'DOLLY'\", \"'GIRL'\", 'nan', 'nan'],\n",
       " [\"'APRON'\", \"'CHILDRENS'\", \"'DESIGN'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'10'\", \"'CHRISTMAS'\", \"'LIGHTS'\", \"'SANTAS'\", 'nan', 'nan'],\n",
       " [\"'10'\", \"'CHRISTMAS'\", \"'LIGHTS'\", \"'REINDEER'\", 'nan', 'nan'],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'FAIRY'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'MUSHROOM'\", \"'OF'\", \"'PACK'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\"],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\"],\n",
       " [\"'PINK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'DESIGN'\", \"'HAND'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BIRD'\", \"'DESIGN'\", \"'HAND'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'HAND'\", \"'OWL'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'12'\", \"'CHALK'\", \"'RETRO'\", \"'SET'\", \"'STICKS'\", \"'WHITE'\"],\n",
       " [\"'AND'\", \"'BLUE'\", \"'FELTCRAFT'\", \"'HAIRBAND'\", \"'RED'\", 'nan'],\n",
       " [\"'AND'\", \"'FELTCRAFT'\", \"'HAIRBAND'\", \"'PINK'\", \"'PURPLE'\", 'nan'],\n",
       " [\"'AND'\", \"'FELTCRAFT'\", \"'HAIRBANDS'\", \"'PINK'\", \"'WHITE'\", 'nan'],\n",
       " [\"'DINNER'\", \"'DOLLY'\", \"'GIRL'\", \"'TRAY'\", \"'TV'\", 'nan'],\n",
       " [\"'IN'\", \"'PAISLEY'\", \"'PLASTERS'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'IN'\", \"'PLASTERS'\", \"'SPACEBOY'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'ANIMALS'\", \"'IN'\", \"'PLASTERS'\", \"'TIN'\", \"'WOODLAND'\", 'nan'],\n",
       " [\"'DRAWING'\", \"'MAGIC'\", \"'SLATE'\", \"'SPACEBOY'\", 'nan', 'nan'],\n",
       " [\"'CIRCUS'\", \"'DRAWING'\", \"'MAGIC'\", \"'PARADE'\", \"'SLATE'\", 'nan'],\n",
       " [\"'DRAWING'\", \"'FAIR'\", \"'GO'\", \"'MAGIC'\", \"'SLATE'\", \"'THE'\"],\n",
       " [\"'TO'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'DOLLY'\", \"'DRAWING'\", \"'GIRL'\", \"'MAGIC'\", \"'SLATE'\", 'nan'],\n",
       " [\"'+'\", \"'BOTTLE'\", \"'CHARLIE'\", \"'HOT'\", \"'LOLA'\", \"'RED'\"],\n",
       " [\"'WATER'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'BOTTLE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'RIBBONS'\", \"'URBAN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHARLOTTE'\", \"'DOLL'\", \"'FELTCRAFT'\", \"'PRINCESS'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'T-LIGHTS'\", \"'TOADSTOOLS'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'LEAVES'\", \"'ROTATING'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'HARMONICA'\", \"'IN'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'CUPCAKE'\", \"'LACE'\", \"'PAPER'\", \"'SET'\", 'nan'],\n",
       " [\"'CORNICE'\", \"'FAMILY'\", \"'FRAME'\", \"'PHOTO'\", 'nan', 'nan'],\n",
       " [\"'CORNICE'\", \"'FRAME'\", \"'PHOTO'\", \"'TRIPLE'\", 'nan', 'nan'],\n",
       " [\"'ANTIQUE'\", \"'FRAME'\", \"'WHITE'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DOMINOES'\", \"'OF'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'CATCH'\", \"'CUP'\", \"'GAME'\", \"'TRADITIONAL'\", \"'WOODEN'\", 'nan'],\n",
       " [\"'ROPE'\", \"'SKIPPING'\", \"'TRADITIONAL'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'COTTON'\", \"'IN'\", \"'S/6'\", \"'SKITTLES'\", \"'WOODEN'\"],\n",
       " [\"'BEACH'\", \"'METAL'\", \"'RED'\", \"'SPADE'\", 'nan', 'nan'],\n",
       " [\"'BOYS'\", \"'BUCKET'\", \"'SEASIDE'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BUCKET'\", \"'GIRLS'\", \"'SEASIDE'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'GREY'\", \"'HEART'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'DOG'\", \"'HOT'\", \"'SCOTTIE'\", \"'WATER'\", 'nan'],\n",
       " [\"'BIRD'\", \"'BOTTLE'\", \"'HOT'\", \"'HOUSE'\", \"'WATER'\", 'nan'],\n",
       " [\"'CADDY'\", \"'CHOICE'\", \"'KINGS'\", \"'TEA'\", 'nan', 'nan'],\n",
       " [\"'BISCUIT'\", \"'CHOICE'\", \"'KINGS'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'EMPIRE'\", \"'METAL'\", \"'SIGN'\", \"'TEA'\", 'nan', 'nan'],\n",
       " [\"'CANDLES'\", \"'HOMEMADE'\", \"'JAM'\", \"'SCENTED'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'BLUE'\", \"'BOTTLE'\", \"'FAWN'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'BOTTLE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'HOT'\", \"'SKULL'\", \"'WATER'\", \"'WHITE'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'HEART'\", \"'HOT'\", \"'RETROSPOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'DOG'\", \"'HOT'\", \"'SCOTTIE'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'GREY'\", \"'HEART'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BABUSHKA'\", \"'BOTTLE'\", \"'HOT'\", \"'WATER'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'BIRDY'\", \"'GIFT'\", \"'OF'\", \"'PACK'\", \"'TAGS'\"],\n",
       " [\"'CHILLI'\", \"'LIGHTS'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BANK'\", \"'PIGGY'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CERAMIC'\", \"'MONEY'\", \"'STRAWBERRY'\", 'nan', 'nan'],\n",
       " [\"'BANK'\", \"'CAKE'\", \"'CERAMIC'\", \"'MONEY'\", \"'STRAWBERRY'\", 'nan'],\n",
       " [\"'BANK'\", \"'CAKE'\", \"'CERAMIC'\", \"'CHERRY'\", \"'MONEY'\", 'nan'],\n",
       " [\"'BANK'\", \"'CAKE'\", \"'CERAMIC'\", \"'FAIRY'\", \"'HEART'\", \"'MONEY'\"],\n",
       " [\"'BANK'\", \"'CERAMIC'\", \"'CHEST'\", \"'MONEY'\", \"'PIRATE'\", 'nan'],\n",
       " [\"'DOORSTOP'\", \"'HEART'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DOORSTOP'\", \"'GINGHAM'\", \"'HEART'\", \"'RED'\", 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'DOORSTOP'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'PURSE'\", \"'RED'\", \"'SILK'\", 'nan', 'nan'],\n",
       " [\"'BABUSHKA'\", \"'PINK'\", \"'PURSE'\", \"'SILK'\", 'nan', 'nan'],\n",
       " [\"'CORNICE'\", \"'FAMILY'\", \"'FRAME'\", \"'PHOTO'\", 'nan', 'nan'],\n",
       " [\"'SWEETHEART'\", \"'TIDY'\", \"'WALL'\", \"'WIRE'\", 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'GLOVE'\", \"'OVEN'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'CREAM'\", \"'HEART'\", \"'HOLDER'\", 'nan', 'nan'],\n",
       " [\"'INCENSE'\", \"'NAMASTE'\", \"'SWAGAT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CERAMIC'\", \"'DESIGN'\", \"'MUG'\", \"'STRAWBERRY'\", 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'CARD'\", \"'HEART'\", \"'HOLDER'\", 'nan', 'nan'],\n",
       " [\"'CLASSICAL'\", \"'ROSE'\", \"'SMALL'\", \"'VASE'\", 'nan', 'nan'],\n",
       " [\"'BULB'\", \"'CANDLES'\", \"'HYACINTH'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FAIRY'\", \"'WHITE'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FRIENDS'\", \"'LITTLE'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'CIGAR'\", \"'COSY'\", \"'HOUR'\", \"'MATCHES'\", 'nan'],\n",
       " [\"'CLASSIC'\", \"'DESIGN'\", \"'RED'\", \"'TEA'\", \"'TOWEL'\", 'nan'],\n",
       " [\"'DOVE'\", \"'FILIGREE'\", \"'HEART'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'APRON'\", \"'CHILDRENS'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BROWN'\", \"'COLOURING'\", \"'PENCILS'\", \"'TUBE'\", 'nan', 'nan'],\n",
       " [\"'4'\", \"'CANDLES'\", \"'DINNER'\", \"'FLOCK'\", \"'IVORY'\", \"'SILVER'\"],\n",
       " [\"'DOVE'\", \"'FILIGREE'\", \"'HEART'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'DECORATION'\", \"'HEART'\", \"'METAL'\", \"'ZINC'\", 'nan', 'nan'],\n",
       " [\"'FERN'\", \"'GREEN'\", \"'NOTEBOOK'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'NOTEBOOK'\", \"'PAISLEY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRYSANTHEMUM'\", \"'NOTEBOOK'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CHINESE'\", \"'LARGE'\", \"'SCISSOR'\", \"'STYLE'\", 'nan', 'nan'],\n",
       " [\"'CHINESE'\", \"'SCISSOR'\", \"'SMALL'\", \"'STYLE'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'COTTON'\", \"'IN'\", \"'S/6'\", \"'SKITTLES'\", \"'WOODEN'\"],\n",
       " [\"'ROPE'\", \"'SKIPPING'\", \"'TRADITIONAL'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'4'\", \"'SPINNING'\", \"'TOPS'\", \"'TRADITIONAL'\", 'nan', 'nan'],\n",
       " [\"'ANGELS'\", \"'HLDR'\", \"'ROTATING'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'BIRD'\", \"'BLACK'\", \"'CANDLE'\", \"'LOVE'\", 'nan', 'nan'],\n",
       " [\"'APRON'\", \"'CHILDRENS'\", \"'RETROSPOT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ETCHED'\", \"'GLASS'\", \"'HANGING'\", \"'TEALIGHT'\", 'nan', 'nan'],\n",
       " [\"'ETCHED'\", \"'GLASS'\", \"'HANGING'\", \"'TEALIGHT'\", 'nan', 'nan'],\n",
       " [\"'BAROQUE'\", \"'BLUE'\", \"'CANDLE'\", \"'CANDLESTICK'\", \"'NEW'\", 'nan'],\n",
       " [\"'COOKING'\", \"'RETROSPOT'\", \"'SET'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHOICE'\", \"'GIANT'\", \"'KINGS'\", \"'MATCHES'\", \"'TUBE'\", 'nan'],\n",
       " [\"'COSY'\", \"'GIANT'\", \"'HOUR'\", \"'MATCHES'\", \"'TUBE'\", 'nan'],\n",
       " [\"'PLACE'\", \"'SETTING'\", \"'STAR'\", \"'WHITE'\", 'nan', 'nan'],\n",
       " [\"'BULB'\", \"'CANDLES'\", \"'HYACINTH'\", \"'T-LIGHT'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BAUBLES'\", \"'GLASS'\", \"'IN'\", \"'S/15'\", \"'SILVER'\"],\n",
       " [\"'BLUE'\", \"'FRENCH'\", \"'METAL'\", \"'SIGN'\", \"'WC'\", 'nan'],\n",
       " [\"'CHOICE'\", \"'GIANT'\", \"'KINGS'\", \"'MATCHES'\", \"'TUBE'\", 'nan'],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'FAIRY'\", \"'SWEETHEART'\", 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'FAIRY'\", \"'HOLDER'\", \"'SOAP'\", 'nan', 'nan', 'nan'],\n",
       " [\"'GARDEN'\", \"'GIANT'\", \"'GREEN'\", \"'THERMOMETER'\", 'nan', 'nan'],\n",
       " [\"'GARDEN'\", \"'GIANT'\", \"'IVORY'\", \"'THERMOMETER'\", 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'GARDEN'\", \"'GIANT'\", \"'THERMOMETER'\", 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\"],\n",
       " [\"'PINK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'HEART'\", \"'HOT'\", \"'RETROSPOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'CHICK'\", \"'GREY'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'BOX'\", \"'SEWING'\", \"'SMALL'\", \"'VICTORIAN'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'MEDIUM'\", \"'SEWING'\", \"'VICTORIAN'\", 'nan', 'nan'],\n",
       " [\"'CANDLES'\", \"'PARTY'\", \"'POLKADOT'\", \"'RED'\", \"'SET/10'\", 'nan'],\n",
       " [\"'METAL'\", \"'SIGN'\", \"'WASHROOM'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'BRACELET'\", \"'SWEETHEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BLACK'\", \"'DIA'\", \"'DIAMANTE'\", \"'GRIP'\", \"'HAIR'\", \"'PACK/2'\"],\n",
       " [\"'BLACK'\", \"'DIAMANTE'\", \"'EXPANDABLE'\", \"'RING'\", 'nan', 'nan'],\n",
       " [\"'DIAMANTE'\", \"'GRIP'\", \"'HAIR'\", \"'PACK/2'\", \"'RUBY'\", 'nan'],\n",
       " [\"'DIAMANTE'\", \"'GRIP'\", \"'HAIR'\", \"'MONTANA'\", \"'PACK/2'\", 'nan'],\n",
       " [\"'BLUE'\", \"'BRACELET'\", \"'SWEETHEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BRACELET'\", \"'PINK'\", \"'SWEETHEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BRACELET'\", \"'PURPLE'\", \"'SWEETHEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'PINK'\", \"'POLKADOT'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'JUMBO'\", \"'RED'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'50''S'\", \"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'PRESENT'\", \"'REEL'\", \"'RIBBON'\", 'nan', 'nan'],\n",
       " [\"'AID'\", \"'FIRST'\", \"'TIN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'KIT'\", \"'MAKE'\", \"'MONSOON'\", \"'OWN'\", \"'YOUR'\"],\n",
       " [\"'12'\", \"'DESIGN'\", \"'HEARTS'\", \"'OF'\", \"'PACK'\", \"'TISSUES'\"],\n",
       " [\"'12'\", \"'OF'\", \"'PACK'\", \"'RED'\", \"'RETROSPOT'\", \"'TISSUES'\"],\n",
       " [\"'CUP'\", \"'EGG'\", \"'SOLDIERS'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'IVORY'\", \"'SMALL'\", \"'TRELLIS'\", 'nan', 'nan'],\n",
       " [\"'DOVE'\", \"'FILIGREE'\", \"'HEART'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'DOVE'\", \"'FILIGREE'\", \"'HEART'\", \"'LARGE'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'BIRD'\", \"'BOXES'\", \"'PACK'\", \"'PANNETONE'\", 'nan'],\n",
       " [\"'3'\", \"'BOXES'\", \"'CHRISTMAS'\", \"'PACK'\", \"'PANNETONE'\", 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'PAISLEY'\"],\n",
       " [\"'PINK'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'BAG'\", \"'CIRCULAR'\", \"'IN'\", \"'RUBBERS'\", \"'SWIRLY'\", 'nan'],\n",
       " [\"'PAISLEY'\", \"'SET'\", \"'STATIONERY'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'BLUE'\", \"'CANDLES'\", \"'PARTY'\", \"'POLKADOT'\", \"'SET/10'\", 'nan'],\n",
       " [\"'AND'\", \"'MITTENS'\", \"'REEL'\", \"'RIBBON'\", \"'SOCKS'\", 'nan'],\n",
       " [\"'60'\", \"'CAKE'\", \"'CASES'\", \"'CHRISTMAS'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'ASSORTED'\", \"'CHRISTMAS'\", \"'METAL'\", \"'TAGS'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'TREE'\", 'nan', 'nan'],\n",
       " [\"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'FUNKY'\", \"'MINI'\", \"'TAPES'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SNOWMEN'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'DECORATION'\", \"'STAR'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'HEART'\", \"'SCANDINAVIAN'\", \"'WOODEN'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'RETROSPOT'\", \"'TREE'\", \"'WOOD'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SANTA'\", \"'SET'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'SCENTED'\", \"'SET/9'\", \"'T-LIGHTS'\", 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'FABRIC'\", \"'MIRROR'\", \"'ROSE'\", 'nan', 'nan'],\n",
       " [\"'CANVAS'\", \"'LUGGAGE'\", \"'TAGS'\", \"'THREE'\", 'nan', 'nan'],\n",
       " [\"'+'\", \"'BAG'\", \"'PARTY'\", \"'RETROSPOT'\", \"'SET'\", \"'STICKER'\"],\n",
       " [\"'IN'\", \"'PAISLEY'\", \"'PLASTERS'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'MATCHES'\", \"'RETROSPOT'\", \"'SMALL'\", \"'TUBE'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'3D'\", \"'CHRISTMAS'\", \"'STAMPS'\", \"'STICKERS'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'24'\", \"'CLOTHES'\", \"'PACK'\", \"'PEGS'\", \"'RETROSPOT'\", 'nan'],\n",
       " [\"'36'\", \"'CAKE'\", \"'CASES'\", \"'FOIL'\", \"'HEART'\", 'nan'],\n",
       " [\"'ASSORTED'\", \"'CHRISTMAS'\", \"'METAL'\", \"'TAGS'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'BOXES'\", \"'GIFT'\", \"'OF'\", \"'PACK'\", \"'SWEETIE'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'72'\", \"'CAKE'\", \"'CASES'\", \"'OF'\", \"'PACK'\", \"'RETROSPOT'\"],\n",
       " [\"'6'\", \"'BIRDY'\", \"'GIFT'\", \"'OF'\", \"'PACK'\", \"'TAGS'\"],\n",
       " [\"'CANDLES'\", \"'PARTY'\", \"'POLKADOT'\", \"'RED'\", \"'SET/10'\", 'nan'],\n",
       " [\"'CANDLES'\", \"'PARTY'\", \"'PINK'\", \"'POLKADOT'\", \"'SET/10'\", 'nan'],\n",
       " [\"'3'\", \"'IN'\", \"'NOTEBOOKS'\", \"'OF'\", \"'PARCEL'\", \"'SET'\"],\n",
       " [\"'HOLDER'\", \"'STAR'\", \"'T-LIGHT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'9'\", \"'CINAMMON'\", \"'OF'\", \"'SET'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'ORANGE'\", \"'SCENTED'\", \"'SET/9'\", \"'T-LIGHTS'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'REEL'\", \"'RIBBON'\", \"'STRIPES'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'DE'\", \"'JARDIN'\", \"'OF'\", \"'PROVENCE'\", \"'SET'\"],\n",
       " [\"'TINS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'KIT'\", \"'MAKE'\", \"'OWN'\", \"'PLAYTIME'\", \"'YOUR'\"],\n",
       " [\"'DINOSAURS'\", \"'SET'\", \"'WRITING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BALLOONS'\", \"'SET'\", \"'WRITING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'SET'\", \"'SKULLS'\", \"'WRITING'\", 'nan', 'nan', 'nan'],\n",
       " [\"'PAISLEY'\", \"'SET'\", \"'STATIONERY'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'COSY'\", \"'RED'\", \"'SHOES'\", \"'SLIPPER'\", \"'SMALL'\", 'nan'],\n",
       " [\"'BLACK'\", \"'DOORMAT'\", \"'FLOCK'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'BAROQUE'\", \"'BLACK'\", \"'JUMBO'\", \"'WHITE'\", 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'ORANGE'\", \"'SCENTED'\", \"'SET/9'\", \"'T-LIGHTS'\", 'nan', 'nan'],\n",
       " [\"'9'\", \"'CINAMMON'\", \"'OF'\", \"'SET'\", \"'T-LIGHTS'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'CANDLE'\", \"'CHRISTMAS'\", \"'DECOUPAGE'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BASIL'\", \"'ENAMEL'\", \"'GROW'\", \"'IN'\", \"'MUG'\", \"'OWN'\"],\n",
       " [\"'YOUR'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'MUG'\", \"'OFFICE'\", \"'POLKADOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HOLDER'\", \"'POPCORN'\", \"'SMALL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'DESIGN'\", \"'DOLLY'\", \"'GIRL'\", \"'JUMBO'\", 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'GLASS'\", \"'HEART'\", \"'POT'\", \"'SMALL'\", \"'TRINKET'\", 'nan'],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'BLACK'\", \"'COVER'\", \"'FRAME'\", \"'RECORD'\", 'nan', 'nan'],\n",
       " [\"'7'\", \"'FRAME'\", \"'RECORD'\", \"'SINGLE'\", \"'SIZE'\", 'nan'],\n",
       " [\"'36'\", \"'PENCILS'\", \"'RED'\", \"'RETROSPOT'\", \"'TUBE'\", 'nan'],\n",
       " [\"'36'\", \"'PENCILS'\", \"'TUBE'\", \"'WOODLAND'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'SKITTLES'\", \"'SOLDIER'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'HORSE'\", \"'RED'\", \"'ROCKING'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'GINGHAM'\", \"'HEART'\", 'nan', 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'GINGHAM'\", \"'STAR'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'3'\", \"'CAKESTAND'\", \"'REGENCY'\", \"'TIER'\", 'nan', 'nan'],\n",
       " [\"'JUMBO'\", \"'PAISLEY'\", \"'RED'\", \"'SHOPPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'REEL'\", \"'RIBBON'\", \"'SNOWY'\", \"'VILLAGE'\", 'nan', 'nan'],\n",
       " [\"'BUNTING'\", \"'JACK'\", \"'UNION'\", \"'VINTAGE'\", 'nan', 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'REEL'\", \"'RIBBON'\", \"'STRIPES'\", 'nan', 'nan'],\n",
       " [\"'BOARD'\", \"'COVER'\", \"'IRONING'\", \"'LONGBOARD'\", \"'RETRO'\", 'nan'],\n",
       " [\"'3D'\", \"'CARDS'\", \"'DOG'\", \"'PICTURE'\", \"'PLAYING'\", 'nan'],\n",
       " [\"'BOX'\", \"'CIGAR'\", \"'MATCHES'\", \"'RETROSPOT'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'SCENTED'\", \"'SET/9'\", \"'T-LIGHTS'\", 'nan', 'nan'],\n",
       " [\"'ANGELS'\", \"'HLDR'\", \"'ROTATING'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'ANGELS'\", \"'HLDR'\", \"'ROTATING'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'BAG'\", \"'COTTON'\", \"'PLANET'\", \"'SAVE'\", \"'THE'\", \"'TOTE'\"],\n",
       " [\"'20'\", \"'CHRISTMAS'\", \"'NAPKINS'\", \"'OF'\", \"'SET'\", \"'VINTAGE'\"],\n",
       " [\"'BUNTING'\", \"'LACE'\", \"'PAPER'\", \"'WHITE'\", 'nan', 'nan'],\n",
       " [\"'GENTLEMAN'\", \"'KIT'\", \"'REPAIR'\", \"'SHIRT'\", 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", 'nan', 'nan'],\n",
       " [\"'CHAIN'\", \"'CHRISTMAS'\", \"'KIT'\", \"'PAPER'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'ART'\", \"'FOXY'\", \"'MIRRORED'\", \"'WALL'\", 'nan', 'nan'],\n",
       " [\"'ART'\", \"'GENTS'\", \"'MIRRORED'\", \"'WALL'\", 'nan', 'nan'],\n",
       " [\"'AIRLINE'\", \"'BAG'\", \"'JET'\", \"'SET'\", \"'VINTAGE'\", \"'WHITE'\"],\n",
       " [\"'JAM'\", \"'MAKING'\", \"'PRINTED'\", \"'SET'\", 'nan', 'nan'],\n",
       " [\"'''N'''\", \"'GROW'\", \"'POTTING'\", \"'SET'\", \"'SHED'\", \"'SOW'\"],\n",
       " [\"'10'\", \"'BABUSHKA'\", \"'LIGHTS'\", \"'OF'\", \"'STRING'\", 'nan'],\n",
       " [\"'CASH+CARRY'\", \"'JUMBO'\", \"'REX'\", \"'SHOPPER'\", 'nan', 'nan'],\n",
       " [\"'AIRLINE'\", \"'BAG'\", \"'CHAMPION'\", \"'VINTAGE'\", \"'WORLD'\", 'nan'],\n",
       " [\"'BIRD'\", \"'DESIGN'\", \"'HAND'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FRIENDS'\", \"'LITTLE'\", 'nan', 'nan'],\n",
       " [\"'GARLAND'\", \"'HEARTS'\", \"'PAPER'\", \"'PINK'\", 'nan', 'nan'],\n",
       " [\"'ENVELOPES'\", \"'POTTING'\", \"'SEED'\", \"'SHED'\", 'nan', 'nan'],\n",
       " [\"'ALPINE'\", \"'HAND'\", \"'HI'\", \"'TEC'\", \"'WARMER'\", 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'FAIRY'\", \"'FELTCRAFT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'ALPINE'\", \"'HAND'\", \"'HI'\", \"'TEC'\", \"'WARMER'\", 'nan'],\n",
       " [\"'ANGELS'\", \"'HLDR'\", \"'ROTATING'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'3'\", \"'FELTCRAFT'\", \"'MICE'\", \"'STRIPEY'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'FELTCRAFT'\", \"'FLOWER'\", \"'FRIENDS'\", 'nan', 'nan'],\n",
       " [\"'CHRISTMAS'\", \"'CRAFT'\", \"'FRIENDS'\", \"'LITTLE'\", 'nan', 'nan'],\n",
       " [\"'COVER'\", \"'CUSHION'\", \"'JACK'\", \"'UNION'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'45x45cm'\", \"'FILLER'\", \"'PAD'\", \"'POLYESTER'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'DOLLY'\", \"'GIRL'\", \"'LUNCH'\", 'nan', 'nan'],\n",
       " [\"'12'\", \"'LONDON'\", \"'OF'\", \"'PACK'\", \"'TISSUES'\", 'nan'],\n",
       " [\"'CARD'\", \"'DOLLY'\", \"'GIRL'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BILLBOARD'\", \"'CARD'\", \"'FONT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BIRTHDAY'\", \"'CARD'\", \"'LADIES'\", \"'RAINY'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'GREY'\", \"'HEART'\", \"'HOT'\", \"'WATER'\", 'nan'],\n",
       " [\"'HEART.'\", \"'HOTTIE'\", \"'RED'\", \"'WHITE'\", \"'WOOLLY'\", 'nan'],\n",
       " [\"'BOTTLE'\", \"'HOT'\", \"'RED'\", \"'SCARF'\", \"'WATER'\", \"'WHITE'\"],\n",
       " [\"'2'\", \"'I'\", \"'LONDON'\", \"'LOVE'\", \"'SET'\", \"'TEA'\"],\n",
       " [\"'TOWELS'\", 'nan', 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'2'\", \"'AND'\", \"'APPLE'\", \"'OF'\", \"'PEARS'\", \"'SET'\"],\n",
       " [\"'TEA'\", \"'TOWELS'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'RED'\", \"'RETROSPOT'\", \"'SET/2'\", \"'TEA'\", \"'TOWELS'\", 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'LOVE'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'BLOCK'\", \"'BUILDING'\", \"'HOME'\", \"'WORD'\", 'nan', 'nan'],\n",
       " [\"'ASSORTED'\", \"'BIRD'\", \"'COLOUR'\", \"'ORNAMENT'\", 'nan', 'nan'],\n",
       " [\"'CREAM'\", \"'GAZEBO'\", \"'LANTERN'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BIRDHOUSE'\", \"'BLUE'\", \"'DECORATION'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HANGING'\", \"'HEART'\", \"'HOLDER'\", \"'T-LIGHT'\", \"'WHITE'\", 'nan'],\n",
       " [\"'3'\", \"'CAKESTAND'\", \"'REGENCY'\", \"'TIER'\", 'nan', 'nan'],\n",
       " [\"'ANTIQUE'\", \"'ENGRAVED'\", \"'GLASS'\", \"'SILVER'\", \"'TEA'\", 'nan'],\n",
       " [\"'AGED'\", \"'GLASS'\", \"'HOLDER'\", \"'SILVER'\", \"'T-LIGHT'\", 'nan'],\n",
       " [\"'JAM'\", \"'JARS'\", \"'MAKING'\", \"'SET'\", \"'WITH'\", 'nan'],\n",
       " [\"'DESIGN'\", \"'HAND'\", \"'OWL'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'2'\", \"'CRATES'\", \"'MARKET'\", \"'OF'\", \"'SET'\", \"'WOODEN'\"],\n",
       " [\"'6'\", \"'OF'\", \"'SET'\", \"'T-LIGHTS'\", \"'TOADSTOOLS'\", 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'DESIGN'\", \"'DOG'\", \"'HAND'\", \"'SCOTTY'\", \"'WARMER'\", 'nan'],\n",
       " [\"'BASKET'\", \"'CLASSIC'\", \"'FRENCH'\", \"'NATURAL'\", \"'STYLE'\", 'nan'],\n",
       " [\"'CRAZY'\", \"'DAISY'\", \"'DECORATION'\", \"'HEART'\", 'nan', 'nan'],\n",
       " [\"'+'\", \"'DIET'\", \"'GIN'\", \"'METAL'\", \"'SIGN'\", \"'TONIC'\"],\n",
       " [\"'GARDEN'\", \"'GIANT'\", \"'IVORY'\", \"'THERMOMETER'\", 'nan', 'nan'],\n",
       " [\"'CARD'\", \"'GINGHAM'\", \"'HEART'\", \"'HOLDER'\", 'nan', 'nan'],\n",
       " [\"'BAG'\", \"'CHOCOLATE'\", \"'GIFT'\", \"'SMALL'\", \"'STRIPES'\", 'nan'],\n",
       " [\"'BAG'\", \"'COTTON'\", \"'IN'\", \"'S/6'\", \"'SKITTLES'\", \"'WOODEN'\"],\n",
       " [\"'HAND'\", \"'RED'\", \"'RETROSPOT'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'HAND'\", \"'JACK'\", \"'UNION'\", \"'WARMER'\", 'nan', 'nan'],\n",
       " [\"'BLUE'\", \"'FRENCH'\", \"'METAL'\", \"'SIGN'\", \"'WC'\", 'nan'],\n",
       " [\"'JACK'\", \"'LUGGAGE'\", \"'PINK'\", \"'TAG'\", \"'UNION'\", 'nan'],\n",
       " [\"'BED'\", \"'BREAKFAST'\", \"'IN'\", \"'TRAY,'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'IN'\", \"'PIZZA'\", \"'PLATE'\", 'nan', 'nan'],\n",
       " [\"'HEART'\", \"'LARGE'\", \"'OF'\", \"'WICKER'\", 'nan', 'nan'],\n",
       " [\"'BOTTLE'\", \"'DOTS'\", \"'HEART'\", \"'HOT'\", \"'PINK'\", \"'WATER'\"],\n",
       " [\"'HEART'\", \"'PLACE'\", \"'SETTING'\", \"'WHITE'\", 'nan', 'nan'],\n",
       " [\"'4'\", \"'CHARMS'\", \"'LEAVES'\", \"'NAPKIN'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'4'\", \"'CHARMS'\", \"'NAPKIN'\", \"'OF'\", \"'SET'\", \"'STARS'\"],\n",
       " [\"'4'\", \"'CHARMS'\", \"'CUTLERY'\", \"'NAPKIN'\", \"'OF'\", \"'SET'\"],\n",
       " [\"'BOX'\", \"'CARAVAN'\", \"'SQUARE'\", \"'TISSUE'\", 'nan', 'nan'],\n",
       " [\"'IN'\", \"'PLASTERS'\", \"'SKULLS'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'IN'\", \"'PLASTERS'\", \"'SPACEBOY'\", \"'TIN'\", 'nan', 'nan'],\n",
       " [\"'BOX'\", \"'BUTTON'\", 'nan', 'nan', 'nan', 'nan'],\n",
       " [\"'IN'\", \"'PAISLEY'\", \"'PLASTERS'\", \"'TIN'\", \"'VINTAGE'\", 'nan'],\n",
       " [\"'12'\", \"'CARDS'\", \"'ENVELOPES'\", \"'MESSAGE'\", \"'WITH'\", 'nan'],\n",
       " [\"'BABUSHKA'\", \"'NOTEBOOK'\", \"'RED'\", \"'SMALL'\", 'nan', 'nan'],\n",
       " [\"'6'\", \"'CUPCAKE'\", \"'LACE'\", \"'PAPER'\", \"'SET'\", 'nan'],\n",
       " [\"'3'\", \"'BOXES'\", \"'CHRISTMAS'\", \"'PACK'\", \"'PANNETONE'\", 'nan'],\n",
       " [\"'HERB'\", \"'MARKER'\", \"'MINT'\", 'nan', 'nan', 'nan'],\n",
       " [\"'HERB'\", \"'MARKER'\", \"'ROSEMARY'\", 'nan', 'nan', 'nan'],\n",
       " [\"'BASIL'\", \"'HERB'\", \"'MARKER'\", 'nan', 'nan', 'nan'],\n",
       " ...]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction1 = np.array(transaction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[\"'LANTERN'\", \"'METAL'\", \"'WHITE'\", 'nan', 'nan', 'nan'],\n",
       "       [\"'COAT'\", \"'CREAM'\", \"'CUPID'\", \"'HANGER'\", \"'HEARTS'\", 'nan'],\n",
       "       [\"'BOTTLE'\", \"'FLAG'\", \"'HOT'\", \"'KNITTED'\", \"'UNION'\", \"'WATER'\"],\n",
       "       ...,\n",
       "       [\"'CHILDRENS'\", \"'CUTLERY'\", \"'DOLLY'\", \"'GIRL'\", 'nan', 'nan'],\n",
       "       [\"'CHILDRENS'\", \"'CIRCUS'\", \"'CUTLERY'\", \"'PARADE'\", 'nan', 'nan'],\n",
       "       [\"'9'\", \"'BAKING'\", \"'PIECE'\", \"'RETROSPOT'\", \"'SET'\", 'nan']],\n",
       "      dtype='<U23')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "top20 = df_table['Items'].head(20).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"'SET'\", \"'OF'\", \"'BAG'\", \"'RED'\", \"'HEART'\", \"'RETROSPOT'\",\n",
       "       \"'VINTAGE'\", \"'DESIGN'\", \"'PINK'\", \"'CHRISTMAS'\", \"'BOX'\",\n",
       "       \"'CAKE'\", \"'JUMBO'\", \"'METAL'\", \"'WHITE'\", \"'BLUE'\", \"'3'\",\n",
       "       \"'LUNCH'\", \"'HANGING'\", \"'SIGN'\"], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = []\n",
    "df_top20_multiple_record_check =pd.DataFrame(columns=top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>'SET'</th>\n",
       "      <th>'OF'</th>\n",
       "      <th>'BAG'</th>\n",
       "      <th>'RED'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'RETROSPOT'</th>\n",
       "      <th>'VINTAGE'</th>\n",
       "      <th>'DESIGN'</th>\n",
       "      <th>'PINK'</th>\n",
       "      <th>'CHRISTMAS'</th>\n",
       "      <th>'BOX'</th>\n",
       "      <th>'CAKE'</th>\n",
       "      <th>'JUMBO'</th>\n",
       "      <th>'METAL'</th>\n",
       "      <th>'WHITE'</th>\n",
       "      <th>'BLUE'</th>\n",
       "      <th>'3'</th>\n",
       "      <th>'LUNCH'</th>\n",
       "      <th>'HANGING'</th>\n",
       "      <th>'SIGN'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: ['SET', 'OF', 'BAG', 'RED', 'HEART', 'RETROSPOT', 'VINTAGE', 'DESIGN', 'PINK', 'CHRISTMAS', 'BOX', 'CAKE', 'JUMBO', 'METAL', 'WHITE', 'BLUE', '3', 'LUNCH', 'HANGING', 'SIGN']\n",
       "Index: []"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top20_multiple_record_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(top20)):\n",
    "    array=[]\n",
    "    for j in range(0,transaction1.shape[0]):\n",
    "        array.append(np.count_nonzero(transaction1[j]==top20[i]))\n",
    "        if len(array) == len(df):\n",
    "            df_top20_multiple_record_check[top20[i]] = array\n",
    "        else:\n",
    "            continue\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>'SET'</th>\n",
       "      <th>'OF'</th>\n",
       "      <th>'BAG'</th>\n",
       "      <th>'RED'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'RETROSPOT'</th>\n",
       "      <th>'VINTAGE'</th>\n",
       "      <th>'DESIGN'</th>\n",
       "      <th>'PINK'</th>\n",
       "      <th>'CHRISTMAS'</th>\n",
       "      <th>'BOX'</th>\n",
       "      <th>'CAKE'</th>\n",
       "      <th>'JUMBO'</th>\n",
       "      <th>'METAL'</th>\n",
       "      <th>'WHITE'</th>\n",
       "      <th>'BLUE'</th>\n",
       "      <th>'3'</th>\n",
       "      <th>'LUNCH'</th>\n",
       "      <th>'HANGING'</th>\n",
       "      <th>'SIGN'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   'SET'  'OF'  'BAG'  'RED'  'HEART'  'RETROSPOT'  'VINTAGE'  'DESIGN'  \\\n",
       "0      0     0      0      0        0            0          0         0   \n",
       "1      0     0      0      0        0            0          0         0   \n",
       "2      0     0      0      0        0            0          0         0   \n",
       "3      0     0      0      1        0            0          0         0   \n",
       "4      1     0      0      0        0            0          0         0   \n",
       "5      0     0      0      0        0            0          0         0   \n",
       "6      0     0      0      0        0            0          0         0   \n",
       "7      0     0      0      1        0            0          0         0   \n",
       "8      0     0      0      0        0            0          0         0   \n",
       "9      0     0      0      0        0            0          0         0   \n",
       "\n",
       "   'PINK'  'CHRISTMAS'  'BOX'  'CAKE'  'JUMBO'  'METAL'  'WHITE'  'BLUE'  '3'  \\\n",
       "0       0            0      0       0        0        1        1       0    0   \n",
       "1       0            0      0       0        0        0        0       0    0   \n",
       "2       0            0      0       0        0        0        0       0    0   \n",
       "3       0            0      0       0        0        0        1       0    0   \n",
       "4       0            0      0       0        0        0        0       0    0   \n",
       "5       0            0      0       0        0        0        0       0    0   \n",
       "6       0            0      0       0        0        0        0       0    0   \n",
       "7       0            0      0       0        0        0        0       0    0   \n",
       "8       0            0      0       0        0        0        0       0    0   \n",
       "9       0            0      0       0        0        0        0       0    0   \n",
       "\n",
       "   'LUNCH'  'HANGING'  'SIGN'  \n",
       "0        0          0       0  \n",
       "1        0          0       0  \n",
       "2        0          0       0  \n",
       "3        0          0       0  \n",
       "4        0          0       0  \n",
       "5        0          0       0  \n",
       "6        0          0       0  \n",
       "7        0          0       0  \n",
       "8        0          0       0  \n",
       "9        0          0       0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top20_multiple_record_check.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>'SET'</th>\n",
       "      <th>'OF'</th>\n",
       "      <th>'BAG'</th>\n",
       "      <th>'RED'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'RETROSPOT'</th>\n",
       "      <th>'VINTAGE'</th>\n",
       "      <th>'DESIGN'</th>\n",
       "      <th>'PINK'</th>\n",
       "      <th>'CHRISTMAS'</th>\n",
       "      <th>'BOX'</th>\n",
       "      <th>'CAKE'</th>\n",
       "      <th>'JUMBO'</th>\n",
       "      <th>'METAL'</th>\n",
       "      <th>'WHITE'</th>\n",
       "      <th>'BLUE'</th>\n",
       "      <th>'3'</th>\n",
       "      <th>'LUNCH'</th>\n",
       "      <th>'HANGING'</th>\n",
       "      <th>'SIGN'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "      <td>557040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.077018</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.063058</td>\n",
       "      <td>0.060585</td>\n",
       "      <td>0.053975</td>\n",
       "      <td>0.053005</td>\n",
       "      <td>0.045115</td>\n",
       "      <td>0.043808</td>\n",
       "      <td>0.038527</td>\n",
       "      <td>0.037949</td>\n",
       "      <td>0.037416</td>\n",
       "      <td>0.037362</td>\n",
       "      <td>0.034676</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.033256</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.030515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.296926</td>\n",
       "      <td>0.294284</td>\n",
       "      <td>0.290700</td>\n",
       "      <td>0.266620</td>\n",
       "      <td>0.254299</td>\n",
       "      <td>0.243068</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>0.225968</td>\n",
       "      <td>0.224044</td>\n",
       "      <td>0.207557</td>\n",
       "      <td>0.204669</td>\n",
       "      <td>0.192465</td>\n",
       "      <td>0.191073</td>\n",
       "      <td>0.189778</td>\n",
       "      <td>0.189647</td>\n",
       "      <td>0.182958</td>\n",
       "      <td>0.180097</td>\n",
       "      <td>0.179305</td>\n",
       "      <td>0.172185</td>\n",
       "      <td>0.171999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               'SET'           'OF'          'BAG'          'RED'  \\\n",
       "count  557040.000000  557040.000000  557040.000000  557040.000000   \n",
       "mean        0.097713       0.095776       0.093191       0.077018   \n",
       "std         0.296926       0.294284       0.290700       0.266620   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "             'HEART'    'RETROSPOT'      'VINTAGE'       'DESIGN'  \\\n",
       "count  557040.000000  557040.000000  557040.000000  557040.000000   \n",
       "mean        0.069498       0.063058       0.060585       0.053975   \n",
       "std         0.254299       0.243068       0.238567       0.225968   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "              'PINK'    'CHRISTMAS'          'BOX'         'CAKE'  \\\n",
       "count  557040.000000  557040.000000  557040.000000  557040.000000   \n",
       "mean        0.053005       0.045115       0.043808       0.038527   \n",
       "std         0.224044       0.207557       0.204669       0.192465   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "             'JUMBO'        'METAL'        'WHITE'         'BLUE'  \\\n",
       "count  557040.000000  557040.000000  557040.000000  557040.000000   \n",
       "mean        0.037949       0.037416       0.037362       0.034676   \n",
       "std         0.191073       0.189778       0.189647       0.182958   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 '3'        'LUNCH'      'HANGING'         'SIGN'  \n",
       "count  557040.000000  557040.000000  557040.000000  557040.000000  \n",
       "mean        0.033561       0.033256       0.030583       0.030515  \n",
       "std         0.180097       0.179305       0.172185       0.171999  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000       0.000000  \n",
       "max         1.000000       1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top20_multiple_record_check.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The others have max value of 1. From that reason we can say that the data is homogenous, we can proceed without any inference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Choice Analysis / Customers' First Choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gather Only First Choice of Each Transactions into Numpy Array\n",
    "# Similar Pattern to Above, Only Change is the Column Number \"0\" in Append Function\n",
    "transaction2 = []\n",
    "for i in range(0,df.shape[0]):\n",
    "    transaction2.append(df.values[i,0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction2 = np.array(transaction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Transform Them a Pandas DataFrame\n",
    "df_first = pd.DataFrame(transaction, columns=[\"items\"])\n",
    "df_first[\"incident_count\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>items</th>\n",
       "      <th>incident_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'LANTERN'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'METAL'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'WHITE'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342235</th>\n",
       "      <td>'BAKING'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342236</th>\n",
       "      <td>'PIECE'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342237</th>\n",
       "      <td>'RETROSPOT'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342238</th>\n",
       "      <td>'SET'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342239</th>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3342240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               items  incident_count\n",
       "0          'LANTERN'               1\n",
       "1            'METAL'               1\n",
       "2            'WHITE'               1\n",
       "3                nan               1\n",
       "4                nan               1\n",
       "...              ...             ...\n",
       "3342235     'BAKING'               1\n",
       "3342236      'PIECE'               1\n",
       "3342237  'RETROSPOT'               1\n",
       "3342238        'SET'               1\n",
       "3342239          nan               1\n",
       "\n",
       "[3342240 rows x 2 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete nan values\n",
    "nan_index = df_first[df_first['items']=='nan'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_first.drop(nan_index,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Final Step: Make a New Appropriate Pandas DataFrame for Visualizations  \n",
    "df_table_first = df_first.groupby(\"items\").sum().sort_values(\"incident_count\", ascending=False).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_table_first[\"goods\"] = \"goods\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_table_first = df_table_first.truncate(before=-1, after=21) # Fist 20 Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>items</th>\n",
       "      <th>incident_count</th>\n",
       "      <th>goods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'SET'</td>\n",
       "      <td>54430</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'OF'</td>\n",
       "      <td>53351</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'BAG'</td>\n",
       "      <td>51911</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'RED'</td>\n",
       "      <td>42902</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'HEART'</td>\n",
       "      <td>38713</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>'RETROSPOT'</td>\n",
       "      <td>35126</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>'VINTAGE'</td>\n",
       "      <td>33748</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>'DESIGN'</td>\n",
       "      <td>30066</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>'PINK'</td>\n",
       "      <td>29526</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>'CHRISTMAS'</td>\n",
       "      <td>25131</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>'BOX'</td>\n",
       "      <td>24403</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>'CAKE'</td>\n",
       "      <td>21461</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>'JUMBO'</td>\n",
       "      <td>21139</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>'METAL'</td>\n",
       "      <td>20842</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>'WHITE'</td>\n",
       "      <td>20812</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>'BLUE'</td>\n",
       "      <td>19316</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>'3'</td>\n",
       "      <td>18695</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>'LUNCH'</td>\n",
       "      <td>18525</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>'HANGING'</td>\n",
       "      <td>17036</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>'SIGN'</td>\n",
       "      <td>16998</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>'HOLDER'</td>\n",
       "      <td>16260</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>'PACK'</td>\n",
       "      <td>15637</td>\n",
       "      <td>goods</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          items  incident_count  goods\n",
       "0         'SET'           54430  goods\n",
       "1          'OF'           53351  goods\n",
       "2         'BAG'           51911  goods\n",
       "3         'RED'           42902  goods\n",
       "4       'HEART'           38713  goods\n",
       "5   'RETROSPOT'           35126  goods\n",
       "6     'VINTAGE'           33748  goods\n",
       "7      'DESIGN'           30066  goods\n",
       "8        'PINK'           29526  goods\n",
       "9   'CHRISTMAS'           25131  goods\n",
       "10        'BOX'           24403  goods\n",
       "11       'CAKE'           21461  goods\n",
       "12      'JUMBO'           21139  goods\n",
       "13      'METAL'           20842  goods\n",
       "14      'WHITE'           20812  goods\n",
       "15       'BLUE'           19316  goods\n",
       "16          '3'           18695  goods\n",
       "17      'LUNCH'           18525  goods\n",
       "18    'HANGING'           17036  goods\n",
       "19       'SIGN'           16998  goods\n",
       "20     'HOLDER'           16260  goods\n",
       "21       'PACK'           15637  goods"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_table_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 15)\n",
    "first_choice = nx.from_pandas_edgelist(df_table_first,\n",
    "                                       source='goods',\n",
    "                                       target='items',\n",
    "                                       edge_attr=True)\n",
    "pos = nx.spring_layout(first_choice)\n",
    "nx.draw_networkx_nodes(first_choice,\n",
    "                       pos,\n",
    "                       node_size=12500,\n",
    "                       node_color=\"#01fe9c\")\n",
    "nx.draw_networkx_edges(first_choice,\n",
    "                       pos,\n",
    "                       width=3,\n",
    "                       alpha=0.6,\n",
    "                       edge_color='#0015ff')\n",
    "nx.draw_networkx_labels(first_choice,\n",
    "                        pos,\n",
    "                        font_size=18,\n",
    "                        font_family='sans-serif')\n",
    "plt.grid()\n",
    "plt.title('Top 21 First Choices', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gather Only Second Choice of Each Transaction into Numpy Array\n",
    "\n",
    "transaction3 = []\n",
    "for i in range(0, df.shape[0]):\n",
    "    transaction3.append(df.values[i, 1])\n",
    "\n",
    "transaction3 = np.array(transaction3)\n",
    "\n",
    "# 2. Transform Them a Pandas DataFrame\n",
    "df_second = pd.DataFrame(transaction3, columns=[\"items\"])\n",
    "df_second[\"incident_count\"] = 1\n",
    "\n",
    "# 3. Delete NaN Items from Dataset\n",
    "nan_items = df_second[df_second['items'] == \"nan\"].index\n",
    "df_second.drop(nan_items, inplace=True)\n",
    "\n",
    "# 4. Final Step: Make a New Appropriate Pandas DataFrame for Visualizations\n",
    "df_table_second = df_second.groupby(\"items\").sum().sort_values(\n",
    "    \"incident_count\", ascending=False).reset_index()\n",
    "df_table_second[\"goods\"] = \"goods\"\n",
    "df_table_second = df_table_second.truncate(before=-1,\n",
    "                                           after=10)  # 2nd 10 Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "second_choice = nx.from_pandas_edgelist(df_table_second,\n",
    "                                        source='goods',\n",
    "                                        target=\"items\",\n",
    "                                        edge_attr=True)\n",
    "pos = nx.spring_layout(second_choice)\n",
    "nx.draw_networkx_nodes(second_choice,\n",
    "                       pos,\n",
    "                       node_size=12500,\n",
    "                       node_color=\"#0080ff\")\n",
    "nx.draw_networkx_edges(second_choice,\n",
    "                       pos,\n",
    "                       width=3,\n",
    "                       alpha=0.6,\n",
    "                       edge_color='black')\n",
    "nx.draw_networkx_labels(second_choice,\n",
    "                        pos,\n",
    "                        font_size=18,\n",
    "                        font_family='sans-serif')\n",
    "plt.rcParams['figure.figsize'] = (15, 15)\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.title('Top 10 Second Choices', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " order to be able to use apriori algorithm and get most frequent itemsets, we have to transform our dataset into a 1 – 0 matrix where rows are transactions and columns are products. In that matrix, “1” should be encoded if the product has been bought on that transaction and “0” should be encoded if the product has not been bought on that transaction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform Every Transaction to Seperate List & Gather Them into Numpy Array\n",
    "transaction4 = []\n",
    "for i in range(df.shape[0]):\n",
    "    transaction4.append([str(df.values[i,j]) for j in range(df.shape[1])])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction4 = np.array(transaction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "te = TransactionEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_array = te.fit(transaction4).transform(transaction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'!'\",\n",
       " \"'&'\",\n",
       " \"'''N'''\",\n",
       " \"'(22467)'\",\n",
       " \"'(22719)'\",\n",
       " \"'*Boombox'\",\n",
       " \"'*USB'\",\n",
       " \"'+'\",\n",
       " \"','\",\n",
       " \"',MAGIC'\",\n",
       " \"',PUDDINGS'\",\n",
       " \"'-'\",\n",
       " \"'-PAPER'\",\n",
       " \"'/'\",\n",
       " \"'0'\",\n",
       " \"'1'\",\n",
       " \"'1.8M'\",\n",
       " \"'10'\",\n",
       " \"'10/11/2010'\",\n",
       " \"'11'\",\n",
       " \"'12'\",\n",
       " \"'120CM'\",\n",
       " \"'125g'\",\n",
       " \"'15'\",\n",
       " \"'15C'\",\n",
       " \"'15CM'\",\n",
       " \"'16'\",\n",
       " \"'16954'\",\n",
       " \"'16955'\",\n",
       " \"'16956'\",\n",
       " \"'16957'\",\n",
       " \"'16960'\",\n",
       " \"'16961'\",\n",
       " \"'16963'\",\n",
       " \"'16965'\",\n",
       " \"'16X16CM'\",\n",
       " \"'17058'\",\n",
       " \"'17059'\",\n",
       " \"'17065'\",\n",
       " \"'17067'\",\n",
       " \"'17068'\",\n",
       " \"'17070'\",\n",
       " \"'17071'\",\n",
       " \"'17074'\",\n",
       " \"'17076'\",\n",
       " \"'17077'\",\n",
       " \"'17080'\",\n",
       " \"'17081'\",\n",
       " \"'17084'\",\n",
       " \"'17086'\",\n",
       " \"'17087'\",\n",
       " \"'17089'\",\n",
       " \"'17090'\",\n",
       " \"'17093'\",\n",
       " \"'17095'\",\n",
       " \"'17096'\",\n",
       " \"'17097'\",\n",
       " \"'17099'\",\n",
       " \"'17100'\",\n",
       " \"'17104'\",\n",
       " \"'17107'\",\n",
       " \"'17112'\",\n",
       " \"'17116'\",\n",
       " \"'17211'\",\n",
       " \"'17214'\",\n",
       " \"'17216'\",\n",
       " \"'17217'\",\n",
       " \"'17218'\",\n",
       " \"'17219'\",\n",
       " \"'17220'\",\n",
       " \"'17259'\",\n",
       " \"'17262'\",\n",
       " \"'1800'\",\n",
       " \"'18PC'\",\n",
       " \"'2'\",\n",
       " \"'20'\",\n",
       " \"'200'\",\n",
       " \"'20713'\",\n",
       " \"'20LIGHT'\",\n",
       " \"'21'\",\n",
       " \"'21/5/10'\",\n",
       " \"'22467'\",\n",
       " \"'22804'\",\n",
       " \"'23'\",\n",
       " \"'23343'\",\n",
       " \"'24'\",\n",
       " \"'250g'\",\n",
       " \"'25W,'\",\n",
       " \"'25x24x12cm'\",\n",
       " \"'3'\",\n",
       " \"'3.5g'\",\n",
       " \"'30'\",\n",
       " \"'30CM'\",\n",
       " \"'30CMx30CM'\",\n",
       " \"'34X20CM'\",\n",
       " \"'35'\",\n",
       " \"'36'\",\n",
       " \"'3D'\",\n",
       " \"'4'\",\n",
       " \"'40'\",\n",
       " \"'40CM'\",\n",
       " \"'40x40cm'\",\n",
       " \"'42'\",\n",
       " \"'42NECKL.'\",\n",
       " \"'45CM'\",\n",
       " \"'45x30cm'\",\n",
       " \"'45x45cm'\",\n",
       " \"'47'\",\n",
       " \"'5'\",\n",
       " \"'50'\",\n",
       " \"'50''S'\",\n",
       " \"'500g'\",\n",
       " \"'50CM'\",\n",
       " \"'6'\",\n",
       " \"'6''s'\",\n",
       " \"'60'\",\n",
       " \"'60CM'\",\n",
       " \"'60x40cm'\",\n",
       " \"'65CMx65CM'\",\n",
       " \"'6PC'\",\n",
       " \"'7'\",\n",
       " \"'70''S'\",\n",
       " \"'72'\",\n",
       " \"'75'\",\n",
       " \"'78'\",\n",
       " \"'8'\",\n",
       " \"'84930'\",\n",
       " \"'85123a'\",\n",
       " \"'9'\",\n",
       " \"'?'\",\n",
       " \"'?10.00'\",\n",
       " \"'?100.00'\",\n",
       " \"'?20.00'\",\n",
       " \"'?30.00'\",\n",
       " \"'?40.00'\",\n",
       " \"'?50.00'\",\n",
       " \"'??'\",\n",
       " \"'???'\",\n",
       " \"'????damages????'\",\n",
       " \"'????missing'\",\n",
       " \"'???lost'\",\n",
       " \"'???missing'\",\n",
       " \"'?display?'\",\n",
       " \"'?lost'\",\n",
       " \"'?missing'\",\n",
       " \"'?sold'\",\n",
       " \"'A'\",\n",
       " \"'A,B,C'\",\n",
       " \"'A4'\",\n",
       " \"'A5'\",\n",
       " \"'A6'\",\n",
       " \"'A7'\",\n",
       " \"'ABC'\",\n",
       " \"'ABSTRACT'\",\n",
       " \"'ACAPULCO'\",\n",
       " \"'ACCOUNT'\",\n",
       " \"'ACRYLIC'\",\n",
       " \"'ADDRESS'\",\n",
       " \"'ADE'\",\n",
       " \"'ADULT'\",\n",
       " \"'ADVENT'\",\n",
       " \"'AFGHAN'\",\n",
       " \"'AGED'\",\n",
       " \"'AHOY'\",\n",
       " \"'AID'\",\n",
       " \"'AIR'\",\n",
       " \"'AIRLINE'\",\n",
       " \"'AIRMAIL'\",\n",
       " \"'ALARM'\",\n",
       " \"'ALBUM'\",\n",
       " \"'ALL'\",\n",
       " \"'ALLIUM'\",\n",
       " \"'ALPHABET'\",\n",
       " \"'ALPINE'\",\n",
       " \"'ALUMINIUM'\",\n",
       " \"'AM'\",\n",
       " \"'AMAZON'\",\n",
       " \"'AMBER'\",\n",
       " \"'AMELIE'\",\n",
       " \"'AMETHYST'\",\n",
       " \"'AND'\",\n",
       " \"'ANEMONE'\",\n",
       " \"'ANGEL'\",\n",
       " \"'ANGEL,PINK'\",\n",
       " \"'ANGELS'\",\n",
       " \"'ANIMAL'\",\n",
       " \"'ANIMALS'\",\n",
       " \"'ANT'\",\n",
       " \"'ANTIQUE'\",\n",
       " \"'ANTOIENETT'\",\n",
       " \"'ANTOINETTE'\",\n",
       " \"'APERITIF'\",\n",
       " \"'APOTHECARY'\",\n",
       " \"'APPLE'\",\n",
       " \"'APPLES'\",\n",
       " \"'APRON'\",\n",
       " \"'AQUA'\",\n",
       " \"'AREA'\",\n",
       " \"'ARMY'\",\n",
       " \"'ART'\",\n",
       " \"'ART,ONLY'\",\n",
       " \"'ARTICULATED'\",\n",
       " \"'ARTIFICIAL'\",\n",
       " \"'ARTIIFCIAL'\",\n",
       " \"'ASHTRAY'\",\n",
       " \"'ASHTRAY,REVOLUTIONARY'\",\n",
       " \"'ASOS.'\",\n",
       " \"'ASS'\",\n",
       " \"'ASSORTED'\",\n",
       " \"'ASST'\",\n",
       " \"'ASSTD'\",\n",
       " \"'Adjust'\",\n",
       " \"'Adjustment'\",\n",
       " \"'Amazon'\",\n",
       " \"'Arouzer'\",\n",
       " \"'B'\",\n",
       " \"'B''DRAW'\",\n",
       " \"'B''FLY'\",\n",
       " \"'BABUSHKA'\",\n",
       " \"'BABY'\",\n",
       " \"'BACK'\",\n",
       " \"'BACKPACK'\",\n",
       " \"'BAD'\",\n",
       " \"'BADGES'\",\n",
       " \"'BAG'\",\n",
       " \"'BAGS'\",\n",
       " \"'BAIN'\",\n",
       " \"'BAKE'\",\n",
       " \"'BAKELIKE'\",\n",
       " \"'BAKER'\",\n",
       " \"'BAKING'\",\n",
       " \"'BALL'\",\n",
       " \"'BALLOON'\",\n",
       " \"'BALLOONS'\",\n",
       " \"'BALLS'\",\n",
       " \"'BALM'\",\n",
       " \"'BAMBOO'\",\n",
       " \"'BAND'\",\n",
       " \"'BANGLE'\",\n",
       " \"'BANK'\",\n",
       " \"'BANNER'\",\n",
       " \"'BANQUET'\",\n",
       " \"'BAR'\",\n",
       " \"'BARN'\",\n",
       " \"'BAROQUE'\",\n",
       " \"'BAROQUECANDLESTICK'\",\n",
       " \"'BARREL'\",\n",
       " \"'BARRIER'\",\n",
       " \"'BASE'\",\n",
       " \"'BASEBALL'\",\n",
       " \"'BASIL'\",\n",
       " \"'BASIN'\",\n",
       " \"'BASKET'\",\n",
       " \"'BASKETS'\",\n",
       " \"'BATH'\",\n",
       " \"'BATHROOM'\",\n",
       " \"'BATHS'\",\n",
       " \"'BATTERY'\",\n",
       " \"'BAUBLE'\",\n",
       " \"'BAUBLES'\",\n",
       " \"'BAZAAR'\",\n",
       " \"'BEA'\",\n",
       " \"'BEACH'\",\n",
       " \"'BEAD'\",\n",
       " \"'BEADED'\",\n",
       " \"'BEADS'\",\n",
       " \"'BEADS+HAND'\",\n",
       " \"'BEAK'\",\n",
       " \"'BEAKER'\",\n",
       " \"'BEAKERS'\",\n",
       " \"'BEAR'\",\n",
       " \"'BEATEN'\",\n",
       " \"'BEAUTIFUL'\",\n",
       " \"'BEAUTY'\",\n",
       " \"'BED'\",\n",
       " \"'BEDROOM'\",\n",
       " \"'BEDSIDE'\",\n",
       " \"'BEE'\",\n",
       " \"'BEETLES'\",\n",
       " \"'BELL'\",\n",
       " \"'BELLE'\",\n",
       " \"'BELLS'\",\n",
       " \"'BENDY'\",\n",
       " \"'BERRIES'\",\n",
       " \"'BERTIE'\",\n",
       " \"'BEST'\",\n",
       " \"'BEURRE'\",\n",
       " \"'BEWARE'\",\n",
       " \"'BIB'\",\n",
       " \"'BICYCLE'\",\n",
       " \"'BIG'\",\n",
       " \"'BILI'\",\n",
       " \"'BILL'\",\n",
       " \"'BILLBOARD'\",\n",
       " \"'BIN'\",\n",
       " \"'BIN,'\",\n",
       " \"'BINGO'\",\n",
       " \"'BIRD'\",\n",
       " \"'BIRDCAGE'\",\n",
       " \"'BIRDHOUSE'\",\n",
       " \"'BIRDIE'\",\n",
       " \"'BIRDS'\",\n",
       " \"'BIRDY'\",\n",
       " \"'BIRTHDAY'\",\n",
       " \"'BISCUIT'\",\n",
       " \"'BISCUITS'\",\n",
       " \"'BITTY'\",\n",
       " \"'BLACK'\",\n",
       " \"'BLACK+SILVER'\",\n",
       " \"'BLACK+WHITE'\",\n",
       " \"'BLACK/BLUE'\",\n",
       " \"'BLACK/WHITE'\",\n",
       " \"'BLACKBLUE'\",\n",
       " \"'BLACKBOARD'\",\n",
       " \"'BLACKCHRISTMAS'\",\n",
       " \"'BLANK'\",\n",
       " \"'BLING'\",\n",
       " \"'BLK/WHIT'\",\n",
       " \"'BLOCK'\",\n",
       " \"'BLOCKS'\",\n",
       " \"'BLOND'\",\n",
       " \"'BLOOD'\",\n",
       " \"'BLOSSOM'\",\n",
       " \"'BLUE'\",\n",
       " \"'BLUE/CREAM'\",\n",
       " \"'BLUE/GREEN'\",\n",
       " \"'BLUE/NAT'\",\n",
       " \"'BLUE/YELLOW'\",\n",
       " \"'BLUEBERRY'\",\n",
       " \"'BLUES'\",\n",
       " \"'BOARD'\",\n",
       " \"'BOBBLES'\",\n",
       " \"'BOHEMIAN'\",\n",
       " \"'BOMB'\",\n",
       " \"'BON'\",\n",
       " \"'BONBON'\",\n",
       " \"'BONBONS'\",\n",
       " \"'BONE'\",\n",
       " \"'BONHEUR'\",\n",
       " \"'BONNE'\",\n",
       " \"'BOOK'\",\n",
       " \"'BOOKCOVER'\",\n",
       " \"'BOOKS'\",\n",
       " \"'BOOM'\",\n",
       " \"'BOOT'\",\n",
       " \"'BOOZE'\",\n",
       " \"'BOTANICAL'\",\n",
       " \"'BOTANIQUE'\",\n",
       " \"'BOTTLE'\",\n",
       " \"'BOTTLES'\",\n",
       " \"'BOUDICCA'\",\n",
       " \"'BOUDOIR'\",\n",
       " \"'BOUQUET'\",\n",
       " \"'BOW'\",\n",
       " \"'BOWL'\",\n",
       " \"'BOWLS'\",\n",
       " \"'BOWS'\",\n",
       " \"'BOX'\",\n",
       " \"'BOX,FUNKY'\",\n",
       " \"'BOX.'\",\n",
       " \"'BOX/12'\",\n",
       " \"'BOXED'\",\n",
       " \"'BOXES'\",\n",
       " \"'BOY'\",\n",
       " \"'BOYS'\",\n",
       " \"'BRACELET'\",\n",
       " \"'BRACLET'\",\n",
       " \"'BRANCH'\",\n",
       " \"'BRASS'\",\n",
       " \"'BREAD'\",\n",
       " \"'BREAKFAST'\",\n",
       " \"'BRIAR'\",\n",
       " \"'BRIDGE'\",\n",
       " \"'BRIGHT'\",\n",
       " \"'BROCADE'\",\n",
       " \"'BROCANTE'\",\n",
       " \"'BRONZE'\",\n",
       " \"'BROOCH'\",\n",
       " \"'BROWN'\",\n",
       " \"'BRUSH'\",\n",
       " \"'BUBBLE'\",\n",
       " \"'BUBBLEGUM'\",\n",
       " \"'BUBBLES'\",\n",
       " \"'BUCKET'\",\n",
       " \"'BUDAH'\",\n",
       " \"'BUDDHA'\",\n",
       " \"'BUFFALO'\",\n",
       " \"'BUILDING'\",\n",
       " \"'BULB'\",\n",
       " \"'BULL'\",\n",
       " \"'BULLET'\",\n",
       " \"'BUM'\",\n",
       " \"'BUNDLE'\",\n",
       " \"'BUNNIES'\",\n",
       " \"'BUNNY'\",\n",
       " \"'BUNTING'\",\n",
       " \"'BURGUNDY'\",\n",
       " \"'BURNER'\",\n",
       " \"'BURST'\",\n",
       " \"'BUS'\",\n",
       " \"'BUSY'\",\n",
       " \"'BUTTER'\",\n",
       " \"'BUTTERFILES'\",\n",
       " \"'BUTTERFL'\",\n",
       " \"'BUTTERFLIES'\",\n",
       " \"'BUTTERFLIES,PINK'\",\n",
       " \"'BUTTERFLY'\",\n",
       " \"'BUTTERFLY/CRYSTAL'\",\n",
       " \"'BUTTON'\",\n",
       " \"'BUTTONS'\",\n",
       " \"'Ball'\",\n",
       " \"'Bank'\",\n",
       " \"'Breakages'\",\n",
       " \"'C'\",\n",
       " \"'C/COVER'\",\n",
       " \"'C550456'\",\n",
       " \"'CABIN'\",\n",
       " \"'CABINET'\",\n",
       " \"'CABINET,'\",\n",
       " \"'CACTI'\",\n",
       " \"'CACTUS'\",\n",
       " \"'CADDY'\",\n",
       " \"'CADET'\",\n",
       " \"'CAFE'\",\n",
       " \"'CAGE'\",\n",
       " \"'CAKE'\",\n",
       " \"'CAKES'\",\n",
       " \"'CAKESTAND'\",\n",
       " \"'CAKESTAND,'\",\n",
       " \"'CALCULATOR'\",\n",
       " \"'CALENDAR'\",\n",
       " \"'CALM'\",\n",
       " \"'CAMDEN'\",\n",
       " \"'CAMEMBERT'\",\n",
       " \"'CAMO'\",\n",
       " \"'CAMOUFLAGE'\",\n",
       " \"'CAMPAGNE'\",\n",
       " \"'CAMPER'\",\n",
       " \"'CAMPHOR'\",\n",
       " \"'CAMPING'\",\n",
       " \"'CAN'\",\n",
       " \"'CAN''T'\",\n",
       " \"'CANARY'\",\n",
       " \"'CANDELABRA'\",\n",
       " \"'CANDLE'\",\n",
       " \"'CANDLE+INCENSE'\",\n",
       " \"'CANDLEHOLDER'\",\n",
       " \"'CANDLEPOT'\",\n",
       " \"'CANDLES'\",\n",
       " \"'CANDLESTAND'\",\n",
       " \"'CANDLESTICK'\",\n",
       " \"'CANDY'\",\n",
       " \"'CANDYSTUFT'\",\n",
       " \"'CANNABIS'\",\n",
       " \"'CANNISTER'\",\n",
       " \"'CANVAS'\",\n",
       " \"'CAP'\",\n",
       " \"'CAPIZ'\",\n",
       " \"'CAR'\",\n",
       " \"'CARAVAN'\",\n",
       " \"'CARD'\",\n",
       " \"'CARD,'\",\n",
       " \"'CARD,SQUARE,'\",\n",
       " \"'CARD,pack/12'\",\n",
       " \"'CARDHOLDER'\",\n",
       " \"'CARDS'\",\n",
       " \"'CARLO'\",\n",
       " \"'CARNIVAL'\",\n",
       " \"'CAROUSEL'\",\n",
       " \"'CARPET'\",\n",
       " \"'CARRIAGE'\",\n",
       " \"'CARRIER'\",\n",
       " \"'CARROT'\",\n",
       " \"'CARRY'\",\n",
       " \"'CARRY-ALL'\",\n",
       " \"'CARS'\",\n",
       " \"'CARTOON'\",\n",
       " \"'CASE'\",\n",
       " \"'CASES'\",\n",
       " \"'CASH+CARRY'\",\n",
       " \"'CAST'\",\n",
       " \"'CAT'\",\n",
       " \"'CATCH'\",\n",
       " \"'CATS'\",\n",
       " \"'CD'\",\n",
       " \"'CEATURE'\",\n",
       " \"'CELEBRATION'\",\n",
       " \"'CENTURY'\",\n",
       " \"'CERAMIC'\",\n",
       " \"'CHAIN'\",\n",
       " \"'CHAIN,THE'\",\n",
       " \"'CHAIR'\",\n",
       " \"'CHALET'\",\n",
       " \"'CHALICE'\",\n",
       " \"'CHALK'\",\n",
       " \"'CHALKBOARD'\",\n",
       " \"'CHAMBRE'\",\n",
       " \"'CHAMPAGNE'\",\n",
       " \"'CHAMPION'\",\n",
       " \"'CHANDELIER'\",\n",
       " \"'CHANDLER'\",\n",
       " \"'CHAR'\",\n",
       " \"'CHARGE'\",\n",
       " \"'CHARGER'\",\n",
       " \"'CHARLIE'\",\n",
       " \"'CHARLIE+LOLA'\",\n",
       " \"'CHARLIE+LOLAEXTREMELY'\",\n",
       " \"'CHARLOTTE'\",\n",
       " \"'CHARM'\",\n",
       " \"'CHARMS'\",\n",
       " \"'CHART'\",\n",
       " \"'CHASING'\",\n",
       " \"'CHATEAU'\",\n",
       " \"'CHECK'\",\n",
       " \"'CHEERFUL'\",\n",
       " \"'CHEESE'\",\n",
       " \"'CHENILLE'\",\n",
       " \"'CHERRY'\",\n",
       " \"'CHERUB'\",\n",
       " \"'CHEST'\",\n",
       " \"'CHESTS'\",\n",
       " \"'CHIC'\",\n",
       " \"'CHICK'\",\n",
       " \"'CHICKEN'\",\n",
       " \"'CHICKEN,'\",\n",
       " \"'CHICKS'\",\n",
       " \"'CHILDHOOD'\",\n",
       " \"'CHILDREN'\",\n",
       " \"'CHILDREN''S'\",\n",
       " \"'CHILDRENS'\",\n",
       " \"'CHILDS'\",\n",
       " \"'CHILLI'\",\n",
       " \"'CHIME'\",\n",
       " \"'CHINA'\",\n",
       " \"'CHINESE'\",\n",
       " \"'CHIVES'\",\n",
       " \"'CHOC'\",\n",
       " \"'CHOC+BLUE'\",\n",
       " \"'CHOCOLATE'\",\n",
       " \"'CHOCOLATECANDLE'\",\n",
       " \"'CHOCOLATES'\",\n",
       " \"'CHOICE'\",\n",
       " \"'CHOIR'\",\n",
       " \"'CHOKER'\",\n",
       " \"'CHOPPING'\",\n",
       " \"'CHOPSTICKS'\",\n",
       " \"'CHRISTMAS'\",\n",
       " \"'CHROME'\",\n",
       " \"'CHRYSANTHEMUM'\",\n",
       " \"'CHRYSANTHEMUMS'\",\n",
       " \"'CHUNKY'\",\n",
       " \"'CIGAR'\",\n",
       " \"'CINAMMON'\",\n",
       " \"'CINDERELLA'\",\n",
       " \"'CINNAMON'\",\n",
       " \"'CIRCLE'\",\n",
       " \"'CIRCLES'\",\n",
       " \"'CIRCULAR'\",\n",
       " \"'CIRCUS'\",\n",
       " \"'CITRONELLA'\",\n",
       " \"'CITRUS'\",\n",
       " \"'CLAIRE'\",\n",
       " \"'CLAM'\",\n",
       " \"'CLASS'\",\n",
       " \"'CLASSIC'\",\n",
       " \"'CLASSICAL'\",\n",
       " \"'CLAY'\",\n",
       " \"'CLEAN'\",\n",
       " \"'CLEAR'\",\n",
       " \"'CLIMBING'\",\n",
       " \"'CLIP'\",\n",
       " \"'CLIPS'\",\n",
       " \"'CLOCHE'\",\n",
       " \"'CLOCK'\",\n",
       " \"'CLOSED'\",\n",
       " \"'CLOTH'\",\n",
       " \"'CLOTHES'\",\n",
       " \"'CLUSTER'\",\n",
       " \"'COASTER'\",\n",
       " \"'COASTER,ELVIS'\",\n",
       " \"'COASTERS'\",\n",
       " \"'COAT'\",\n",
       " \"'COATHANGER'\",\n",
       " \"'COCKEREL'\",\n",
       " \"'COCKLE'\",\n",
       " \"'COCKTAIL'\",\n",
       " \"'COCONUT'\",\n",
       " \"'COFFEE'\",\n",
       " \"'COL'\",\n",
       " \"'COLANDER'\",\n",
       " \"'COLLAGE'\",\n",
       " \"'COLLAR'\",\n",
       " \"'COLOUR'\",\n",
       " \"'COLOURED'\",\n",
       " \"'COLOURFUL'\",\n",
       " \"'COLOURING'\",\n",
       " \"'COLOURS'\",\n",
       " \"'COLUMBIAN'\",\n",
       " \"'COMB'\",\n",
       " \"'COMBO'\",\n",
       " \"'COME'\",\n",
       " \"'COMPACT'\",\n",
       " \"'CONDIMENT'\",\n",
       " \"'CONE'\",\n",
       " \"'CONES'\",\n",
       " \"'CONEY'\",\n",
       " \"'CONFECTIONERY'\",\n",
       " \"'CONFETTI'\",\n",
       " \"'CONFUSING'\",\n",
       " \"'CONGRATULATIONS'\",\n",
       " \"'CONTAINER'\",\n",
       " \"'COOK'\",\n",
       " \"'COOKIE'\",\n",
       " \"'COOKING'\",\n",
       " \"'COPPER'\",\n",
       " \"'COPPER/OLIVE'\",\n",
       " \"'CORDIAL'\",\n",
       " \"'CORDON'\",\n",
       " \"'CORER'\",\n",
       " \"'CORNICE'\",\n",
       " \"'CORONA'\",\n",
       " \"'COSMETIC'\",\n",
       " \"'COSMETICS'\",\n",
       " \"'COSY'\",\n",
       " \"'COTE'\",\n",
       " \"'COTTAGE'\",\n",
       " \"'COTTON'\",\n",
       " \"'COUNTRY'\",\n",
       " \"'COVENT'\",\n",
       " \"'COVER'\",\n",
       " \"'COWBOY'\",\n",
       " \"'COWBOYS'\",\n",
       " \"'CRACKED'\",\n",
       " \"'CRACKER'\",\n",
       " \"'CRACKERS'\",\n",
       " \"'CRACKLE'\",\n",
       " \"'CRAFT'\",\n",
       " \"'CRATE'\",\n",
       " \"'CRATES'\",\n",
       " \"'CRAWLIE'\",\n",
       " \"'CRAWLIES'\",\n",
       " \"'CRAYONS'\",\n",
       " \"'CRAZY'\",\n",
       " \"'CREAM'\",\n",
       " \"'CREATURES'\",\n",
       " \"'CREDIT'\",\n",
       " \"'CREEPY'\",\n",
       " \"'CRESTED'\",\n",
       " \"'CROCHET'\",\n",
       " \"'CROME'\",\n",
       " \"'CROQUET'\",\n",
       " \"'CROSS'\",\n",
       " \"'CROSSBONES'\",\n",
       " \"'CROSSES'\",\n",
       " \"'CROWNS'\",\n",
       " \"'CRUK'\",\n",
       " \"'CRUSOE'\",\n",
       " \"'CRYSTAL'\",\n",
       " \"'CRYSTAL+GLASS'\",\n",
       " \"'CRYSTALS'\",\n",
       " \"'CUBE'\",\n",
       " \"'CUBES'\",\n",
       " \"'CUBIC'\",\n",
       " \"'CUP'\",\n",
       " \"'CUPBOARD'\",\n",
       " \"'CUPCAKE'\",\n",
       " \"'CUPCAKES'\",\n",
       " \"'CUPID'\",\n",
       " \"'CUPS'\",\n",
       " \"'CURIO'\",\n",
       " \"'CURIOUS'\",\n",
       " \"'CURTAIN'\",\n",
       " \"'CUSHION'\",\n",
       " \"'CUSHIONS'\",\n",
       " \"'CUT'\",\n",
       " \"'CUTE'\",\n",
       " \"'CUTGLASS'\",\n",
       " \"'CUTLERY'\",\n",
       " \"'CUTTER'\",\n",
       " \"'CUTTERS'\",\n",
       " \"'CZECH'\",\n",
       " \"'Cab'\",\n",
       " \"'Carriage'\",\n",
       " \"'Charges'\",\n",
       " \"'Classic'\",\n",
       " \"'Commission'\",\n",
       " \"'Crushed'\",\n",
       " \"'D'\",\n",
       " \"'D''AZURE'\",\n",
       " \"'D.O.F.'\",\n",
       " \"'DAD'\",\n",
       " \"'DADS'\",\n",
       " \"'DAIRY'\",\n",
       " \"'DAISIES'\",\n",
       " \"'DAISY'\",\n",
       " \"'DAMAGED'\",\n",
       " \"'DAMAGES'\",\n",
       " \"'DANISH'\",\n",
       " \"'DARK'\",\n",
       " \"'DAVID''S'\",\n",
       " \"'DAY'\",\n",
       " \"'DAYS'\",\n",
       " \"'DE'\",\n",
       " \"'DEC'\",\n",
       " \"'DECK'\",\n",
       " \"'DECO'\",\n",
       " \"'DECO.WHITE'\",\n",
       " \"'DECORATION'\",\n",
       " \"'DECORATIONS'\",\n",
       " \"'DECORATIVE'\",\n",
       " \"'DECOUPAGE'\",\n",
       " \"'DECOUPAGE,GREETING'\",\n",
       " \"'DECROTIVEVINTAGE'\",\n",
       " \"'DELIGHT'\",\n",
       " \"'DELPHINIUM'\",\n",
       " \"'DELUXE'\",\n",
       " \"'DEMI'\",\n",
       " \"'DENIM'\",\n",
       " \"'DES'\",\n",
       " \"'DESIGN'\",\n",
       " \"'DESIGNS'\",\n",
       " \"'DESK'\",\n",
       " \"'DIA'\",\n",
       " \"'DIAMANTE'\",\n",
       " \"'DIAMON'\",\n",
       " \"'DIAMOND'\",\n",
       " \"'DIET'\",\n",
       " \"'DIGESTIVE'\",\n",
       " \"'DIGITALIS'\",\n",
       " \"'DINER'\",\n",
       " \"'DINKY'\",\n",
       " \"'DINNER'\",\n",
       " \"'DINOSAUR'\",\n",
       " \"'DINOSAURS'\",\n",
       " \"'DISC'\",\n",
       " \"'DISC/MIRROR'\",\n",
       " \"'DISCO'\",\n",
       " \"'DISH'\",\n",
       " \"'DISPENSER'\",\n",
       " \"'DISPLAY'\",\n",
       " \"'DISPOSABLE'\",\n",
       " \"'DIVA'\",\n",
       " \"'DO'\",\n",
       " \"'DOC'\",\n",
       " \"'DOCTOR''S'\",\n",
       " \"'DOG'\",\n",
       " \"'DOGGY'\",\n",
       " \"'DOGS'\",\n",
       " \"'DOILEY'\",\n",
       " \"'DOILIES'\",\n",
       " \"'DOILY'\",\n",
       " \"'DOLL'\",\n",
       " \"'DOLLCRAFT'\",\n",
       " \"'DOLLY'\",\n",
       " \"'DOLPHIN'\",\n",
       " \"'DOLPHINS'\",\n",
       " \"'DOMINOES'\",\n",
       " \"'DONKEY'\",\n",
       " \"'DOOR'\",\n",
       " \"'DOORBELL'\",\n",
       " \"'DOORKNOB'\",\n",
       " \"'DOORMAT'\",\n",
       " \"'DOORS'\",\n",
       " \"'DOORSIGN'\",\n",
       " \"'DOORSTOP'\",\n",
       " \"'DOT'\",\n",
       " \"'DOTCOM'\",\n",
       " \"'DOTCOMGIFTSHOP'\",\n",
       " \"'DOTCOMGIFTSHOP.COM'\",\n",
       " \"'DOTS'\",\n",
       " \"'DOUBLE'\",\n",
       " \"'DOUGHNUT'\",\n",
       " \"'DOUGHNUTS'\",\n",
       " \"'DOVE'\",\n",
       " \"'DOVECOTE'\",\n",
       " \"'DOWN'\",\n",
       " \"'DRAGON'\",\n",
       " \"'DRAGONFLY'\",\n",
       " \"'DRAGONS'\",\n",
       " \"'DRAW'\",\n",
       " \"'DRAWER'\",\n",
       " \"'DRAWERKNOB'\",\n",
       " \"'DRAWERS'\",\n",
       " \"'DRAWING'\",\n",
       " \"'DRAWSTRING'\",\n",
       " \"'DREAM'\",\n",
       " \"'DREAMS'\",\n",
       " \"'DRESS'\",\n",
       " \"'DRESSING'\",\n",
       " \"'DRINK'\",\n",
       " \"'DROP'\",\n",
       " \"'DROPLET'\",\n",
       " \"'DU'\",\n",
       " \"'DUCK'\",\n",
       " \"'DUCKS'\",\n",
       " \"'DUSTY'\",\n",
       " \"'DUTCH'\",\n",
       " \"'Dad''s'\",\n",
       " \"'Dagamed'\",\n",
       " \"'Damaged'\",\n",
       " \"'Damages'\",\n",
       " \"'Damages/samples'\",\n",
       " \"'Day'\",\n",
       " \"'Discount'\",\n",
       " \"'Display'\",\n",
       " \"'Dotcom'\",\n",
       " \"'Dotcomgiftshop'\",\n",
       " \"'Dr.'\",\n",
       " \"'E'\",\n",
       " \"'EAR'\",\n",
       " \"'EARRING'\",\n",
       " \"'EARRINGS'\",\n",
       " \"'EARTH'\",\n",
       " \"'EASTER'\",\n",
       " \"'EAU'\",\n",
       " \"'ECONOMY'\",\n",
       " \"'EDGE)'\",\n",
       " \"'EDWARDIAN'\",\n",
       " \"'EGG'\",\n",
       " \"'EGGS'\",\n",
       " \"'EIGHT'\",\n",
       " \"'ELEGANT'\",\n",
       " \"'ELEPHANT'\",\n",
       " \"'ELEPHANT,'\",\n",
       " \"'ELIXIR'\",\n",
       " \"'ELVIS'\",\n",
       " \"'EMBOSSED'\",\n",
       " \"'EMBROIDERED'\",\n",
       " \"'EMBROIDERY'\",\n",
       " \"'EMERGENCY'\",\n",
       " \"'EMILY'\",\n",
       " \"'EMPIRE'\",\n",
       " \"'EN'\",\n",
       " \"'ENAMEL'\",\n",
       " \"'ENAMEL+GLASS'\",\n",
       " \"'ENCHANTED'\",\n",
       " \"'ENFANTS'\",\n",
       " \"'ENGINE/CAR'\",\n",
       " \"'ENGLAND'\",\n",
       " \"'ENGLISH'\",\n",
       " \"'ENGRAVED'\",\n",
       " \"'ENVELOPE'\",\n",
       " \"'ENVELOPES'\",\n",
       " \"'ERASER'\",\n",
       " \"'ERASERS'\",\n",
       " \"'ERROR,'\",\n",
       " \"'ESSENTIAL'\",\n",
       " \"'ETCHED'\",\n",
       " \"'EUCALYPTUS'\",\n",
       " \"'EVENING'\",\n",
       " \"'EXERCISE'\",\n",
       " \"'EXPANDABLE'\",\n",
       " \"'EYE'\",\n",
       " \"'Electronic'\",\n",
       " \"'F'\",\n",
       " \"'FABRIC'\",\n",
       " \"'FACE'\",\n",
       " \"'FACETED'\",\n",
       " \"'FAIR'\",\n",
       " \"'FAIRY'\",\n",
       " \"'FAIRY,5'\",\n",
       " \"'FAMILY'\",\n",
       " \"'FAN'\",\n",
       " \"'FANCY'\",\n",
       " \"'FANNY''S'\",\n",
       " \"'FARM'\",\n",
       " \"'FARMYARD'\",\n",
       " \"'FARTHING'\",\n",
       " \"'FASHION'\",\n",
       " \"'FAUNA'\",\n",
       " \"'FAUX'\",\n",
       " \"'FAVOURITES'\",\n",
       " \"'FAWN'\",\n",
       " \"'FBA'\",\n",
       " \"'FEATHER'\",\n",
       " \"'FEATHERS'\",\n",
       " \"'FEE'\",\n",
       " \"'FEEDER'\",\n",
       " \"'FEEDING'\",\n",
       " \"'FELT'\",\n",
       " \"'FELTCRAFT'\",\n",
       " \"'FENG'\",\n",
       " \"'FERN'\",\n",
       " \"'FIELD'\",\n",
       " \"'FIELDS'\",\n",
       " \"'FIGURES'\",\n",
       " \"'FILIGREE'\",\n",
       " \"'FILIGRIS'\",\n",
       " \"'FILLER'\",\n",
       " \"'FINE'\",\n",
       " \"'FINGER'\",\n",
       " \"'FINISH'\",\n",
       " \"'FIRE'\",\n",
       " \"'FIRST'\",\n",
       " \"'FISHING'\",\n",
       " \"'FIVE'\",\n",
       " \"'FLAG'\",\n",
       " \"'FLAGS'\",\n",
       " \"'FLAME'\",\n",
       " \"'FLAMES'\",\n",
       " \"'FLAMINGO'\",\n",
       " \"'FLANNEL'\",\n",
       " \"'FLASK'\",\n",
       " \"'FLOCK'\",\n",
       " \"'FLOOR'\",\n",
       " \"'FLORA'\",\n",
       " \"'FLORAL'\",\n",
       " \"'FLORE'\",\n",
       " \"'FLOWE'\",\n",
       " \"'FLOWER'\",\n",
       " \"'FLOWER/BUTTERFLY'\",\n",
       " \"'FLOWERPOT'\",\n",
       " \"'FLOWERPOWER'\",\n",
       " \"'FLOWERS'\",\n",
       " \"'FLOWR'\",\n",
       " \"'FLU'\",\n",
       " \"'FLUFFY'\",\n",
       " \"'FLUTED'\",\n",
       " \"'FLY'\",\n",
       " \"'FLYING'\",\n",
       " \"'FLYTRAP'\",\n",
       " \"'FOB'\",\n",
       " \"'FOIL'\",\n",
       " \"'FOLDING'\",\n",
       " \"'FOLK'\",\n",
       " \"'FOLKART'\",\n",
       " \"'FONDANT'\",\n",
       " \"'FONT'\",\n",
       " \"'FONTS'\",\n",
       " \"'FOOD'\",\n",
       " \"'FOOD/DRINK'\",\n",
       " \"'FOOT'\",\n",
       " \"'FOOTBALL'\",\n",
       " \"'FOOTPRINTS'\",\n",
       " \"'FOR'\",\n",
       " \"'FOREST'\",\n",
       " \"'FORK'\",\n",
       " \"'FORKED'\",\n",
       " \"'FORTIES'\",\n",
       " \"'FOUND'\",\n",
       " \"'FOUR'\",\n",
       " \"'FOXGLOVE'\",\n",
       " \"'FOXY'\",\n",
       " \"'FRAGRANCE'\",\n",
       " \"'FRAME'\",\n",
       " \"'FRAMES'\",\n",
       " \"'FRANCE'\",\n",
       " \"'FRANGIPANI'\",\n",
       " \"'FRAPPUCINO'\",\n",
       " \"'FREESTYLE'\",\n",
       " \"'FRENCH'\",\n",
       " \"'FRESH'\",\n",
       " \"'FRESHWATER'\",\n",
       " \"'FRIDGE'\",\n",
       " \"'FRIENDS'\",\n",
       " \"'FRIENDSHIP'\",\n",
       " \"'FRIEZE'\",\n",
       " \"'FRILL'\",\n",
       " \"'FRINGE'\",\n",
       " \"'FROG'\",\n",
       " \"'FROM'\",\n",
       " \"'FRONT'\",\n",
       " \"'FROST'\",\n",
       " \"'FROSTED'\",\n",
       " \"'FRUIT'\",\n",
       " \"'FRUIT+FLOWERS'\",\n",
       " \"'FRUITBOWL'\",\n",
       " \"'FRUITS'\",\n",
       " \"'FRUTTI'\",\n",
       " \"'FRYING'\",\n",
       " \"'FULL'\",\n",
       " \"'FUN'\",\n",
       " \"'FUNKY'\",\n",
       " \"'FUNNY'\",\n",
       " \"'FUR'\",\n",
       " \"'FUSCHIA'\",\n",
       " \"'Found'\",\n",
       " \"'G'\",\n",
       " \"'GAME'\",\n",
       " \"'GAMES'\",\n",
       " \"'GAOLERS'\",\n",
       " \"'GARAGE'\",\n",
       " \"'GARDEN'\",\n",
       " \"'GARDENERS'\",\n",
       " \"'GARDENIA'\",\n",
       " \"'GARDENS'\",\n",
       " \"'GARLAND'\",\n",
       " \"'GARLAND,'\",\n",
       " \"'GARLD'\",\n",
       " \"'GAUZE'\",\n",
       " \"'GAZEBO'\",\n",
       " \"'GEBRA'\",\n",
       " \"'GECKO'\",\n",
       " \"'GEISHA'\",\n",
       " \"'GEL'\",\n",
       " \"'GEMS'\",\n",
       " \"'GEMSTONE'\",\n",
       " \"'GENTLEMAN'\",\n",
       " \"'GENTLEMEN'\",\n",
       " \"'GENTS'\",\n",
       " ...]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.columns_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame(tr_array,columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(557040, 2604)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 2604 columns/features at the moment. Extracting the most frequent itemsets from 121 feature would be compelling for a start\n",
    "From that reason, we will start with Top 50 items which are already illustrated in Section-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>'SET'</th>\n",
       "      <th>'OF'</th>\n",
       "      <th>'BAG'</th>\n",
       "      <th>'RED'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'RETROSPOT'</th>\n",
       "      <th>'VINTAGE'</th>\n",
       "      <th>'DESIGN'</th>\n",
       "      <th>'PINK'</th>\n",
       "      <th>'CHRISTMAS'</th>\n",
       "      <th>...</th>\n",
       "      <th>'BLUE'</th>\n",
       "      <th>'3'</th>\n",
       "      <th>'LUNCH'</th>\n",
       "      <th>'HANGING'</th>\n",
       "      <th>'SIGN'</th>\n",
       "      <th>'HOLDER'</th>\n",
       "      <th>'PACK'</th>\n",
       "      <th>'T-LIGHT'</th>\n",
       "      <th>'SMALL'</th>\n",
       "      <th>'PAPER'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557035</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557036</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557037</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557038</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557039</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>557040 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        'SET'   'OF'  'BAG'  'RED'  'HEART'  'RETROSPOT'  'VINTAGE'  'DESIGN'  \\\n",
       "0       False  False  False  False    False        False      False     False   \n",
       "1       False  False  False  False    False        False      False     False   \n",
       "2       False  False  False  False    False        False      False     False   \n",
       "3       False  False  False   True    False        False      False     False   \n",
       "4        True  False  False  False    False        False      False     False   \n",
       "...       ...    ...    ...    ...      ...          ...        ...       ...   \n",
       "557035  False   True  False  False    False        False      False     False   \n",
       "557036  False  False  False  False    False        False      False     False   \n",
       "557037  False  False  False  False    False        False      False     False   \n",
       "557038  False  False  False  False    False        False      False     False   \n",
       "557039   True  False  False  False    False         True      False     False   \n",
       "\n",
       "        'PINK'  'CHRISTMAS'  ...  'BLUE'    '3'  'LUNCH'  'HANGING'  'SIGN'  \\\n",
       "0        False        False  ...   False  False    False      False   False   \n",
       "1        False        False  ...   False  False    False      False   False   \n",
       "2        False        False  ...   False  False    False      False   False   \n",
       "3        False        False  ...   False  False    False      False   False   \n",
       "4        False        False  ...   False  False    False      False   False   \n",
       "...        ...          ...  ...     ...    ...      ...        ...     ...   \n",
       "557035   False        False  ...   False  False    False      False   False   \n",
       "557036   False        False  ...   False  False    False      False   False   \n",
       "557037   False        False  ...   False  False    False      False   False   \n",
       "557038   False        False  ...   False  False    False      False   False   \n",
       "557039   False        False  ...   False  False    False      False   False   \n",
       "\n",
       "        'HOLDER'  'PACK'  'T-LIGHT'  'SMALL'  'PAPER'  \n",
       "0          False   False      False    False    False  \n",
       "1          False   False      False    False    False  \n",
       "2          False   False      False    False    False  \n",
       "3          False   False      False    False    False  \n",
       "4          False   False      False    False    False  \n",
       "...          ...     ...        ...      ...      ...  \n",
       "557035     False    True      False    False    False  \n",
       "557036     False   False      False    False    False  \n",
       "557037     False   False      False    False    False  \n",
       "557038     False   False      False    False    False  \n",
       "557039     False   False      False    False    False  \n",
       "\n",
       "[557040 rows x 25 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first25 = df_table[\"Items\"].head(25).values # Select Top25\n",
    "dataset = dataset.loc[:,first25] # Extract Top25\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''SET'', ''OF'', ''BAG'', ''RED'', ''HEART'', ''RETROSPOT'',\n",
       "       ''VINTAGE'', ''DESIGN'', ''PINK'', ''CHRISTMAS'', ''BOX'', ''CAKE'',\n",
       "       ''JUMBO'', ''METAL'', ''WHITE'', ''BLUE'', ''3'', ''LUNCH'',\n",
       "       ''HANGING'', ''SIGN'', ''HOLDER'', ''PACK'', ''T-LIGHT'', ''SMALL'',\n",
       "       ''PAPER''],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dataset into 1-0 encoding\n",
    "\n",
    "def encode_units(x):\n",
    "    if x == False:\n",
    "        return 0 \n",
    "    if x == True:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>'SET'</th>\n",
       "      <th>'OF'</th>\n",
       "      <th>'BAG'</th>\n",
       "      <th>'RED'</th>\n",
       "      <th>'HEART'</th>\n",
       "      <th>'RETROSPOT'</th>\n",
       "      <th>'VINTAGE'</th>\n",
       "      <th>'DESIGN'</th>\n",
       "      <th>'PINK'</th>\n",
       "      <th>'CHRISTMAS'</th>\n",
       "      <th>...</th>\n",
       "      <th>'BLUE'</th>\n",
       "      <th>'3'</th>\n",
       "      <th>'LUNCH'</th>\n",
       "      <th>'HANGING'</th>\n",
       "      <th>'SIGN'</th>\n",
       "      <th>'HOLDER'</th>\n",
       "      <th>'PACK'</th>\n",
       "      <th>'T-LIGHT'</th>\n",
       "      <th>'SMALL'</th>\n",
       "      <th>'PAPER'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   'SET'  'OF'  'BAG'  'RED'  'HEART'  'RETROSPOT'  'VINTAGE'  'DESIGN'  \\\n",
       "0      0     0      0      0        0            0          0         0   \n",
       "1      0     0      0      0        0            0          0         0   \n",
       "2      0     0      0      0        0            0          0         0   \n",
       "3      0     0      0      1        0            0          0         0   \n",
       "4      1     0      0      0        0            0          0         0   \n",
       "5      0     0      0      0        0            0          0         0   \n",
       "6      0     0      0      0        0            0          0         0   \n",
       "7      0     0      0      1        0            0          0         0   \n",
       "8      0     0      0      0        0            0          0         0   \n",
       "9      0     0      0      0        0            0          0         0   \n",
       "\n",
       "   'PINK'  'CHRISTMAS'  ...  'BLUE'  '3'  'LUNCH'  'HANGING'  'SIGN'  \\\n",
       "0       0            0  ...       0    0        0          0       0   \n",
       "1       0            0  ...       0    0        0          0       0   \n",
       "2       0            0  ...       0    0        0          0       0   \n",
       "3       0            0  ...       0    0        0          0       0   \n",
       "4       0            0  ...       0    0        0          0       0   \n",
       "5       0            0  ...       0    0        0          0       0   \n",
       "6       0            0  ...       0    0        0          0       0   \n",
       "7       0            0  ...       0    0        0          0       0   \n",
       "8       0            0  ...       0    0        0          0       0   \n",
       "9       0            0  ...       0    0        0          0       0   \n",
       "\n",
       "   'HOLDER'  'PACK'  'T-LIGHT'  'SMALL'  'PAPER'  \n",
       "0         0       0          0        0        0  \n",
       "1         0       0          0        0        0  \n",
       "2         0       0          0        0        0  \n",
       "3         0       0          0        0        0  \n",
       "4         0       0          0        0        0  \n",
       "5         1       0          1        0        0  \n",
       "6         0       0          0        0        0  \n",
       "7         0       0          0        0        0  \n",
       "8         0       0          0        0        0  \n",
       "9         0       0          0        0        0  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.applymap(encode_units)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.097713</td>\n",
       "      <td>('SET')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.095776</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.093191</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.077018</td>\n",
       "      <td>('RED')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.069498</td>\n",
       "      <td>('HEART')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.063058</td>\n",
       "      <td>('RETROSPOT')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.060585</td>\n",
       "      <td>('VINTAGE')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.053975</td>\n",
       "      <td>('DESIGN')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.053005</td>\n",
       "      <td>('PINK')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.045115</td>\n",
       "      <td>('CHRISTMAS')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.043808</td>\n",
       "      <td>('BOX')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.038527</td>\n",
       "      <td>('CAKE')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.037949</td>\n",
       "      <td>('JUMBO')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.037416</td>\n",
       "      <td>('METAL')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.037362</td>\n",
       "      <td>('WHITE')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.034676</td>\n",
       "      <td>('BLUE')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.033561</td>\n",
       "      <td>('3')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.033256</td>\n",
       "      <td>('LUNCH')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.030583</td>\n",
       "      <td>('HANGING')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.030515</td>\n",
       "      <td>('SIGN')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.029190</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.028072</td>\n",
       "      <td>('PACK')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.026135</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.025731</td>\n",
       "      <td>('SMALL')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.025388</td>\n",
       "      <td>('PAPER')</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.047189</td>\n",
       "      <td>('OF', 'SET')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.016055</td>\n",
       "      <td>('SET', '3')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.010258</td>\n",
       "      <td>('OF', 'HEART')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.016920</td>\n",
       "      <td>('OF', 'CAKE')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.014636</td>\n",
       "      <td>('OF', '3')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.025377</td>\n",
       "      <td>('OF', 'PACK')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.014891</td>\n",
       "      <td>('BAG', 'RED')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.014961</td>\n",
       "      <td>('BAG', 'RETROSPOT')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.013660</td>\n",
       "      <td>('BAG', 'VINTAGE')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.017112</td>\n",
       "      <td>('DESIGN', 'BAG')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.034055</td>\n",
       "      <td>('BAG', 'JUMBO')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.024761</td>\n",
       "      <td>('BAG', 'LUNCH')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.036380</td>\n",
       "      <td>('RETROSPOT', 'RED')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.013627</td>\n",
       "      <td>('HANGING', 'HEART')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.012295</td>\n",
       "      <td>('HOLDER', 'HEART')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.010439</td>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.011473</td>\n",
       "      <td>('CHRISTMAS', 'VINTAGE')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.010416</td>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.027169</td>\n",
       "      <td>('METAL', 'SIGN')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.011798</td>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.013572</td>\n",
       "      <td>('HANGING', 'T-LIGHT')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.019912</td>\n",
       "      <td>('HOLDER', 'T-LIGHT')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.011933</td>\n",
       "      <td>('OF', 'SET', '3')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.012493</td>\n",
       "      <td>('BAG', 'RETROSPOT', 'RED')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.010416</td>\n",
       "      <td>('BAG', 'LUNCH', 'DESIGN')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.010439</td>\n",
       "      <td>('HOLDER', 'HEART', 'T-LIGHT')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.011480</td>\n",
       "      <td>('HANGING', 'HOLDER', 'T-LIGHT')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support                          itemsets  length\n",
       "0   0.097713                           ('SET')       1\n",
       "1   0.095776                            ('OF')       1\n",
       "2   0.093191                           ('BAG')       1\n",
       "3   0.077018                           ('RED')       1\n",
       "4   0.069498                         ('HEART')       1\n",
       "5   0.063058                     ('RETROSPOT')       1\n",
       "6   0.060585                       ('VINTAGE')       1\n",
       "7   0.053975                        ('DESIGN')       1\n",
       "8   0.053005                          ('PINK')       1\n",
       "9   0.045115                     ('CHRISTMAS')       1\n",
       "10  0.043808                           ('BOX')       1\n",
       "11  0.038527                          ('CAKE')       1\n",
       "12  0.037949                         ('JUMBO')       1\n",
       "13  0.037416                         ('METAL')       1\n",
       "14  0.037362                         ('WHITE')       1\n",
       "15  0.034676                          ('BLUE')       1\n",
       "16  0.033561                             ('3')       1\n",
       "17  0.033256                         ('LUNCH')       1\n",
       "18  0.030583                       ('HANGING')       1\n",
       "19  0.030515                          ('SIGN')       1\n",
       "20  0.029190                        ('HOLDER')       1\n",
       "21  0.028072                          ('PACK')       1\n",
       "22  0.026135                       ('T-LIGHT')       1\n",
       "23  0.025731                         ('SMALL')       1\n",
       "24  0.025388                         ('PAPER')       1\n",
       "25  0.047189                     ('OF', 'SET')       2\n",
       "26  0.016055                      ('SET', '3')       2\n",
       "27  0.010258                   ('OF', 'HEART')       2\n",
       "28  0.016920                    ('OF', 'CAKE')       2\n",
       "29  0.014636                       ('OF', '3')       2\n",
       "30  0.025377                    ('OF', 'PACK')       2\n",
       "31  0.014891                    ('BAG', 'RED')       2\n",
       "32  0.014961              ('BAG', 'RETROSPOT')       2\n",
       "33  0.013660                ('BAG', 'VINTAGE')       2\n",
       "34  0.017112                 ('DESIGN', 'BAG')       2\n",
       "35  0.034055                  ('BAG', 'JUMBO')       2\n",
       "36  0.024761                  ('BAG', 'LUNCH')       2\n",
       "37  0.036380              ('RETROSPOT', 'RED')       2\n",
       "38  0.013627              ('HANGING', 'HEART')       2\n",
       "39  0.012295               ('HOLDER', 'HEART')       2\n",
       "40  0.010439              ('HEART', 'T-LIGHT')       2\n",
       "41  0.011473          ('CHRISTMAS', 'VINTAGE')       2\n",
       "42  0.010416               ('DESIGN', 'LUNCH')       2\n",
       "43  0.027169                 ('METAL', 'SIGN')       2\n",
       "44  0.011798             ('HANGING', 'HOLDER')       2\n",
       "45  0.013572            ('HANGING', 'T-LIGHT')       2\n",
       "46  0.019912             ('HOLDER', 'T-LIGHT')       2\n",
       "47  0.011933                ('OF', 'SET', '3')       3\n",
       "48  0.012493       ('BAG', 'RETROSPOT', 'RED')       3\n",
       "49  0.010416        ('BAG', 'LUNCH', 'DESIGN')       3\n",
       "50  0.010439    ('HOLDER', 'HEART', 'T-LIGHT')       3\n",
       "51  0.011480  ('HANGING', 'HOLDER', 'T-LIGHT')       3"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(dataset, min_support=0.01, use_colnames=True)\n",
    "frequent_itemsets['length'] = frequent_itemsets['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.047189</td>\n",
       "      <td>('OF', 'SET')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.025377</td>\n",
       "      <td>('OF', 'PACK')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.034055</td>\n",
       "      <td>('BAG', 'JUMBO')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.024761</td>\n",
       "      <td>('BAG', 'LUNCH')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.036380</td>\n",
       "      <td>('RETROSPOT', 'RED')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.027169</td>\n",
       "      <td>('METAL', 'SIGN')</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support              itemsets  length\n",
       "25  0.047189         ('OF', 'SET')       2\n",
       "30  0.025377        ('OF', 'PACK')       2\n",
       "35  0.034055      ('BAG', 'JUMBO')       2\n",
       "36  0.024761      ('BAG', 'LUNCH')       2\n",
       "37  0.036380  ('RETROSPOT', 'RED')       2\n",
       "43  0.027169     ('METAL', 'SIGN')       2"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[ (frequent_itemsets['length'] == 2) &\n",
    "                   (frequent_itemsets['support'] >= 0.02) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.011933</td>\n",
       "      <td>('OF', 'SET', '3')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.012493</td>\n",
       "      <td>('BAG', 'RETROSPOT', 'RED')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.010416</td>\n",
       "      <td>('BAG', 'LUNCH', 'DESIGN')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.010439</td>\n",
       "      <td>('HOLDER', 'HEART', 'T-LIGHT')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.011480</td>\n",
       "      <td>('HANGING', 'HOLDER', 'T-LIGHT')</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support                          itemsets  length\n",
       "47  0.011933                ('OF', 'SET', '3')       3\n",
       "48  0.012493       ('BAG', 'RETROSPOT', 'RED')       3\n",
       "49  0.010416        ('BAG', 'LUNCH', 'DESIGN')       3\n",
       "50  0.010439    ('HOLDER', 'HEART', 'T-LIGHT')       3\n",
       "51  0.011480  ('HANGING', 'HOLDER', 'T-LIGHT')       3"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[ (frequent_itemsets['length'] == 3) ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can create our rules by defining metric and its threshold.\n",
    "\n",
    "# For a start, \n",
    "#      We set our metric as \"Lift\" to define whether antecedents & consequents are dependent our not.\n",
    "#      Treshold is selected as \"1.2\" since it is required to have lift scores above than 1 if there is dependency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "rules = association_rules(frequent_itemsets,metric='lift',min_threshold=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('SET')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.047189</td>\n",
       "      <td>0.492699</td>\n",
       "      <td>5.042315</td>\n",
       "      <td>0.037830</td>\n",
       "      <td>1.778604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>('SET')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.047189</td>\n",
       "      <td>0.482932</td>\n",
       "      <td>5.042315</td>\n",
       "      <td>0.037830</td>\n",
       "      <td>1.748753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>('SET')</td>\n",
       "      <td>('3')</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.164303</td>\n",
       "      <td>4.895599</td>\n",
       "      <td>0.012775</td>\n",
       "      <td>1.156446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>('3')</td>\n",
       "      <td>('SET')</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.478363</td>\n",
       "      <td>4.895599</td>\n",
       "      <td>0.012775</td>\n",
       "      <td>1.729723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('HEART')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.107102</td>\n",
       "      <td>1.541087</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>1.042115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>('HANGING', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.013572</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.845899</td>\n",
       "      <td>28.979080</td>\n",
       "      <td>0.011084</td>\n",
       "      <td>6.299849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>('HOLDER', 'T-LIGHT')</td>\n",
       "      <td>('HANGING')</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.576542</td>\n",
       "      <td>18.851653</td>\n",
       "      <td>0.010871</td>\n",
       "      <td>2.289285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>('HANGING')</td>\n",
       "      <td>('HOLDER', 'T-LIGHT')</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.375382</td>\n",
       "      <td>18.851653</td>\n",
       "      <td>0.010871</td>\n",
       "      <td>1.569098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>('HANGING', 'T-LIGHT')</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.013572</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.393296</td>\n",
       "      <td>28.979080</td>\n",
       "      <td>0.011084</td>\n",
       "      <td>1.625882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.439277</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>1.762372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               antecedents             consequents  antecedent support  \\\n",
       "0                   ('OF')                 ('SET')            0.095776   \n",
       "1                  ('SET')                  ('OF')            0.097713   \n",
       "2                  ('SET')                   ('3')            0.097713   \n",
       "3                    ('3')                 ('SET')            0.033561   \n",
       "4                   ('OF')               ('HEART')            0.095776   \n",
       "..                     ...                     ...                 ...   \n",
       "69  ('HANGING', 'T-LIGHT')              ('HOLDER')            0.013572   \n",
       "70   ('HOLDER', 'T-LIGHT')             ('HANGING')            0.019912   \n",
       "71             ('HANGING')   ('HOLDER', 'T-LIGHT')            0.030583   \n",
       "72              ('HOLDER')  ('HANGING', 'T-LIGHT')            0.029190   \n",
       "73             ('T-LIGHT')   ('HANGING', 'HOLDER')            0.026135   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \n",
       "0             0.097713  0.047189    0.492699   5.042315  0.037830    1.778604  \n",
       "1             0.095776  0.047189    0.482932   5.042315  0.037830    1.748753  \n",
       "2             0.033561  0.016055    0.164303   4.895599  0.012775    1.156446  \n",
       "3             0.097713  0.016055    0.478363   4.895599  0.012775    1.729723  \n",
       "4             0.069498  0.010258    0.107102   1.541087  0.003602    1.042115  \n",
       "..                 ...       ...         ...        ...       ...         ...  \n",
       "69            0.029190  0.011480    0.845899  28.979080  0.011084    6.299849  \n",
       "70            0.030583  0.011480    0.576542  18.851653  0.010871    2.289285  \n",
       "71            0.019912  0.011480    0.375382  18.851653  0.010871    1.569098  \n",
       "72            0.013572  0.011480    0.393296  28.979080  0.011084    1.625882  \n",
       "73            0.011798  0.011480    0.439277  37.232968  0.011172    1.762372  \n",
       "\n",
       "[74 rows x 9 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "rules['antecedents_len'] = rules['antecedents'].apply(lambda x: len(x))\n",
    "rules['consequents_len'] = rules['consequents'].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>antecedents_len</th>\n",
       "      <th>consequents_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.439277</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>1.762372</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.973068</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>36.159568</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.357626</td>\n",
       "      <td>34.258303</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>1.540475</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.258303</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>('HOLDER', 'HEART')</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.012295</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.399437</td>\n",
       "      <td>32.486821</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>1.644630</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>('VINTAGE')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.060585</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.013660</td>\n",
       "      <td>0.225465</td>\n",
       "      <td>2.419394</td>\n",
       "      <td>0.008014</td>\n",
       "      <td>1.170779</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>('BAG')</td>\n",
       "      <td>('RED')</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.077018</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.159793</td>\n",
       "      <td>2.074750</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>1.098517</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>('RED')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.077018</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.193348</td>\n",
       "      <td>2.074750</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>1.124164</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>('HEART')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.147599</td>\n",
       "      <td>1.541087</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>1.060797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('HEART')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.107102</td>\n",
       "      <td>1.541087</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>1.042115</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents            consequents  antecedent support  \\\n",
       "73            ('T-LIGHT')  ('HANGING', 'HOLDER')            0.026135   \n",
       "68  ('HANGING', 'HOLDER')            ('T-LIGHT')            0.011798   \n",
       "65             ('HOLDER')   ('HEART', 'T-LIGHT')            0.029190   \n",
       "64   ('HEART', 'T-LIGHT')             ('HOLDER')            0.010439   \n",
       "67            ('T-LIGHT')    ('HOLDER', 'HEART')            0.026135   \n",
       "..                    ...                    ...                 ...   \n",
       "17            ('VINTAGE')                ('BAG')            0.060585   \n",
       "12                ('BAG')                ('RED')            0.093191   \n",
       "13                ('RED')                ('BAG')            0.077018   \n",
       "5               ('HEART')                 ('OF')            0.069498   \n",
       "4                  ('OF')              ('HEART')            0.095776   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "73            0.011798  0.011480    0.439277  37.232968  0.011172    1.762372   \n",
       "68            0.026135  0.011480    0.973068  37.232968  0.011172   36.159568   \n",
       "65            0.010439  0.010439    0.357626  34.258303  0.010134    1.540475   \n",
       "64            0.029190  0.010439    1.000000  34.258303  0.010134         inf   \n",
       "67            0.012295  0.010439    0.399437  32.486821  0.010118    1.644630   \n",
       "..                 ...       ...         ...        ...       ...         ...   \n",
       "17            0.093191  0.013660    0.225465   2.419394  0.008014    1.170779   \n",
       "12            0.077018  0.014891    0.159793   2.074750  0.007714    1.098517   \n",
       "13            0.093191  0.014891    0.193348   2.074750  0.007714    1.124164   \n",
       "5             0.095776  0.010258    0.147599   1.541087  0.003602    1.060797   \n",
       "4             0.069498  0.010258    0.107102   1.541087  0.003602    1.042115   \n",
       "\n",
       "    antecedents_len  consequents_len  \n",
       "73                1                2  \n",
       "68                2                1  \n",
       "65                1                2  \n",
       "64                2                1  \n",
       "67                1                2  \n",
       "..              ...              ...  \n",
       "17                1                1  \n",
       "12                1                1  \n",
       "13                1                1  \n",
       "5                 1                1  \n",
       "4                 1                1  \n",
       "\n",
       "[74 rows x 11 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.sort_values(\"lift\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>antecedents_len</th>\n",
       "      <th>consequents_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.730674</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.258303</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.973068</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>36.159568</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>('PACK')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.028072</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.025377</td>\n",
       "      <td>0.904010</td>\n",
       "      <td>9.438803</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>9.419955</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>('JUMBO')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.037949</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.034055</td>\n",
       "      <td>0.897393</td>\n",
       "      <td>9.629636</td>\n",
       "      <td>0.030519</td>\n",
       "      <td>8.837732</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>('BAG')</td>\n",
       "      <td>('RETROSPOT', 'RED')</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>0.134056</td>\n",
       "      <td>3.684913</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>1.112798</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('SET', '3')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.124590</td>\n",
       "      <td>7.760439</td>\n",
       "      <td>0.010395</td>\n",
       "      <td>1.123982</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>('SET')</td>\n",
       "      <td>('OF', '3')</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.014636</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.122120</td>\n",
       "      <td>8.343654</td>\n",
       "      <td>0.010503</td>\n",
       "      <td>1.122436</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>('BAG')</td>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.111768</td>\n",
       "      <td>10.730674</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>1.114106</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('HEART')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.107102</td>\n",
       "      <td>1.541087</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>1.042115</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents           consequents  antecedent support  \\\n",
       "58    ('DESIGN', 'LUNCH')               ('BAG')            0.010416   \n",
       "64   ('HEART', 'T-LIGHT')            ('HOLDER')            0.010439   \n",
       "68  ('HANGING', 'HOLDER')           ('T-LIGHT')            0.011798   \n",
       "11               ('PACK')                ('OF')            0.028072   \n",
       "21              ('JUMBO')               ('BAG')            0.037949   \n",
       "..                    ...                   ...                 ...   \n",
       "53                ('BAG')  ('RETROSPOT', 'RED')            0.093191   \n",
       "47                 ('OF')          ('SET', '3')            0.095776   \n",
       "48                ('SET')           ('OF', '3')            0.097713   \n",
       "59                ('BAG')   ('DESIGN', 'LUNCH')            0.093191   \n",
       "4                  ('OF')             ('HEART')            0.095776   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "58            0.093191  0.010416    1.000000  10.730674  0.009445         inf   \n",
       "64            0.029190  0.010439    1.000000  34.258303  0.010134         inf   \n",
       "68            0.026135  0.011480    0.973068  37.232968  0.011172   36.159568   \n",
       "11            0.095776  0.025377    0.904010   9.438803  0.022688    9.419955   \n",
       "21            0.093191  0.034055    0.897393   9.629636  0.030519    8.837732   \n",
       "..                 ...       ...         ...        ...       ...         ...   \n",
       "53            0.036380  0.012493    0.134056   3.684913  0.009103    1.112798   \n",
       "47            0.016055  0.011933    0.124590   7.760439  0.010395    1.123982   \n",
       "48            0.014636  0.011933    0.122120   8.343654  0.010503    1.122436   \n",
       "59            0.010416  0.010416    0.111768  10.730674  0.009445    1.114106   \n",
       "4             0.069498  0.010258    0.107102   1.541087  0.003602    1.042115   \n",
       "\n",
       "    antecedents_len  consequents_len  \n",
       "58                2                1  \n",
       "64                2                1  \n",
       "68                2                1  \n",
       "11                1                1  \n",
       "21                1                1  \n",
       "..              ...              ...  \n",
       "53                1                2  \n",
       "47                1                2  \n",
       "48                1                2  \n",
       "59                1                2  \n",
       "4                 1                1  \n",
       "\n",
       "[74 rows x 11 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.sort_values(\"confidence\",ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the most demanded product is SET in the dataset, the association results are mainly dominated by it. From that reason, to get more insights, it’s better to create a confidence table excluding the SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>antecedents_len</th>\n",
       "      <th>consequents_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.730674</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.258303</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.973068</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>36.159568</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>('PACK')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.028072</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.025377</td>\n",
       "      <td>0.904010</td>\n",
       "      <td>9.438803</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>9.419955</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>('JUMBO')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.037949</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.034055</td>\n",
       "      <td>0.897393</td>\n",
       "      <td>9.629636</td>\n",
       "      <td>0.030519</td>\n",
       "      <td>8.837732</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>('BAG')</td>\n",
       "      <td>('RETROSPOT', 'RED')</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>0.134056</td>\n",
       "      <td>3.684913</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>1.112798</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('SET', '3')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.124590</td>\n",
       "      <td>7.760439</td>\n",
       "      <td>0.010395</td>\n",
       "      <td>1.123982</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>('SET')</td>\n",
       "      <td>('OF', '3')</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.014636</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.122120</td>\n",
       "      <td>8.343654</td>\n",
       "      <td>0.010503</td>\n",
       "      <td>1.122436</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>('BAG')</td>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.111768</td>\n",
       "      <td>10.730674</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>1.114106</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>('OF')</td>\n",
       "      <td>('HEART')</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.069498</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.107102</td>\n",
       "      <td>1.541087</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>1.042115</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents           consequents  antecedent support  \\\n",
       "58    ('DESIGN', 'LUNCH')               ('BAG')            0.010416   \n",
       "64   ('HEART', 'T-LIGHT')            ('HOLDER')            0.010439   \n",
       "68  ('HANGING', 'HOLDER')           ('T-LIGHT')            0.011798   \n",
       "11               ('PACK')                ('OF')            0.028072   \n",
       "21              ('JUMBO')               ('BAG')            0.037949   \n",
       "..                    ...                   ...                 ...   \n",
       "53                ('BAG')  ('RETROSPOT', 'RED')            0.093191   \n",
       "47                 ('OF')          ('SET', '3')            0.095776   \n",
       "48                ('SET')           ('OF', '3')            0.097713   \n",
       "59                ('BAG')   ('DESIGN', 'LUNCH')            0.093191   \n",
       "4                  ('OF')             ('HEART')            0.095776   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "58            0.093191  0.010416    1.000000  10.730674  0.009445         inf   \n",
       "64            0.029190  0.010439    1.000000  34.258303  0.010134         inf   \n",
       "68            0.026135  0.011480    0.973068  37.232968  0.011172   36.159568   \n",
       "11            0.095776  0.025377    0.904010   9.438803  0.022688    9.419955   \n",
       "21            0.093191  0.034055    0.897393   9.629636  0.030519    8.837732   \n",
       "..                 ...       ...         ...        ...       ...         ...   \n",
       "53            0.036380  0.012493    0.134056   3.684913  0.009103    1.112798   \n",
       "47            0.016055  0.011933    0.124590   7.760439  0.010395    1.123982   \n",
       "48            0.014636  0.011933    0.122120   8.343654  0.010503    1.122436   \n",
       "59            0.010416  0.010416    0.111768  10.730674  0.009445    1.114106   \n",
       "4             0.069498  0.010258    0.107102   1.541087  0.003602    1.042115   \n",
       "\n",
       "    antecedents_len  consequents_len  \n",
       "58                2                1  \n",
       "64                2                1  \n",
       "68                2                1  \n",
       "11                1                1  \n",
       "21                1                1  \n",
       "..              ...              ...  \n",
       "53                1                2  \n",
       "47                1                2  \n",
       "48                1                2  \n",
       "59                1                2  \n",
       "4                 1                1  \n",
       "\n",
       "[74 rows x 11 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[~rules['consequents'].str.contains('SET', regex=False) &\n",
    "      ~rules['antecedents'].str.contains('SET', regex=False)].sort_values('confidence',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "confidence = rules[~rules['consequents'].str.contains('OF', regex=False) &\n",
    "      ~rules['antecedents'].str.contains('OF', regex=False)].sort_values('confidence',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>antecedents_len</th>\n",
       "      <th>consequents_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>('DESIGN', 'LUNCH')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.730674</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>('HEART', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.258303</td>\n",
       "      <td>0.010134</td>\n",
       "      <td>inf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>('HANGING', 'HOLDER')</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.973068</td>\n",
       "      <td>37.232968</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>36.159568</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>('PACK')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.028072</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.025377</td>\n",
       "      <td>0.904010</td>\n",
       "      <td>9.438803</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>9.419955</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>('JUMBO')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.037949</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.034055</td>\n",
       "      <td>0.897393</td>\n",
       "      <td>9.629636</td>\n",
       "      <td>0.030519</td>\n",
       "      <td>8.837732</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>('SIGN')</td>\n",
       "      <td>('METAL')</td>\n",
       "      <td>0.030515</td>\n",
       "      <td>0.037416</td>\n",
       "      <td>0.027169</td>\n",
       "      <td>0.890340</td>\n",
       "      <td>23.795942</td>\n",
       "      <td>0.026027</td>\n",
       "      <td>8.777902</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>('HOLDER', 'HEART')</td>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>0.012295</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.849029</td>\n",
       "      <td>32.486821</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>6.450681</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>('HANGING', 'T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.013572</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.845899</td>\n",
       "      <td>28.979080</td>\n",
       "      <td>0.011084</td>\n",
       "      <td>6.299849</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>('BAG', 'RED')</td>\n",
       "      <td>('RETROSPOT')</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.063058</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>0.838939</td>\n",
       "      <td>13.304181</td>\n",
       "      <td>0.011554</td>\n",
       "      <td>5.817314</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>('BAG', 'RETROSPOT')</td>\n",
       "      <td>('RED')</td>\n",
       "      <td>0.014961</td>\n",
       "      <td>0.077018</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>0.835013</td>\n",
       "      <td>10.841820</td>\n",
       "      <td>0.011341</td>\n",
       "      <td>5.594279</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>('OF', '3')</td>\n",
       "      <td>('SET')</td>\n",
       "      <td>0.014636</td>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.815283</td>\n",
       "      <td>8.343654</td>\n",
       "      <td>0.010503</td>\n",
       "      <td>4.884692</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>('T-LIGHT')</td>\n",
       "      <td>('HOLDER')</td>\n",
       "      <td>0.026135</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.761918</td>\n",
       "      <td>26.102012</td>\n",
       "      <td>0.019150</td>\n",
       "      <td>4.077626</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>('LUNCH')</td>\n",
       "      <td>('BAG')</td>\n",
       "      <td>0.033256</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.024761</td>\n",
       "      <td>0.744561</td>\n",
       "      <td>7.989645</td>\n",
       "      <td>0.021662</td>\n",
       "      <td>3.550009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>('SET', '3')</td>\n",
       "      <td>('OF')</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>0.095776</td>\n",
       "      <td>0.011933</td>\n",
       "      <td>0.743263</td>\n",
       "      <td>7.760439</td>\n",
       "      <td>0.010395</td>\n",
       "      <td>3.521984</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>('METAL')</td>\n",
       "      <td>('SIGN')</td>\n",
       "      <td>0.037416</td>\n",
       "      <td>0.030515</td>\n",
       "      <td>0.027169</td>\n",
       "      <td>0.726130</td>\n",
       "      <td>23.795942</td>\n",
       "      <td>0.026027</td>\n",
       "      <td>3.539946</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               antecedents    consequents  antecedent support  \\\n",
       "58     ('DESIGN', 'LUNCH')        ('BAG')            0.010416   \n",
       "64    ('HEART', 'T-LIGHT')     ('HOLDER')            0.010439   \n",
       "68   ('HANGING', 'HOLDER')    ('T-LIGHT')            0.011798   \n",
       "11                ('PACK')         ('OF')            0.028072   \n",
       "21               ('JUMBO')        ('BAG')            0.037949   \n",
       "37                ('SIGN')      ('METAL')            0.030515   \n",
       "62     ('HOLDER', 'HEART')    ('T-LIGHT')            0.012295   \n",
       "69  ('HANGING', 'T-LIGHT')     ('HOLDER')            0.013572   \n",
       "51          ('BAG', 'RED')  ('RETROSPOT')            0.014891   \n",
       "50    ('BAG', 'RETROSPOT')        ('RED')            0.014961   \n",
       "45             ('OF', '3')        ('SET')            0.014636   \n",
       "43             ('T-LIGHT')     ('HOLDER')            0.026135   \n",
       "23               ('LUNCH')        ('BAG')            0.033256   \n",
       "46            ('SET', '3')         ('OF')            0.016055   \n",
       "36               ('METAL')       ('SIGN')            0.037416   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "58            0.093191  0.010416    1.000000  10.730674  0.009445         inf   \n",
       "64            0.029190  0.010439    1.000000  34.258303  0.010134         inf   \n",
       "68            0.026135  0.011480    0.973068  37.232968  0.011172   36.159568   \n",
       "11            0.095776  0.025377    0.904010   9.438803  0.022688    9.419955   \n",
       "21            0.093191  0.034055    0.897393   9.629636  0.030519    8.837732   \n",
       "37            0.037416  0.027169    0.890340  23.795942  0.026027    8.777902   \n",
       "62            0.026135  0.010439    0.849029  32.486821  0.010118    6.450681   \n",
       "69            0.029190  0.011480    0.845899  28.979080  0.011084    6.299849   \n",
       "51            0.063058  0.012493    0.838939  13.304181  0.011554    5.817314   \n",
       "50            0.077018  0.012493    0.835013  10.841820  0.011341    5.594279   \n",
       "45            0.097713  0.011933    0.815283   8.343654  0.010503    4.884692   \n",
       "43            0.029190  0.019912    0.761918  26.102012  0.019150    4.077626   \n",
       "23            0.093191  0.024761    0.744561   7.989645  0.021662    3.550009   \n",
       "46            0.095776  0.011933    0.743263   7.760439  0.010395    3.521984   \n",
       "36            0.030515  0.027169    0.726130  23.795942  0.026027    3.539946   \n",
       "\n",
       "    antecedents_len  consequents_len  \n",
       "58                2                1  \n",
       "64                2                1  \n",
       "68                2                1  \n",
       "11                1                1  \n",
       "21                1                1  \n",
       "37                1                1  \n",
       "62                2                1  \n",
       "69                2                1  \n",
       "51                2                1  \n",
       "50                2                1  \n",
       "45                2                1  \n",
       "43                1                1  \n",
       "23                1                1  \n",
       "46                2                1  \n",
       "36                1                1  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence[confidence['confidence']>=0.7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many associations with high confidence and lift score. We are on the right way!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "545.844px",
    "left": "1029px",
    "right": "20px",
    "top": "120px",
    "width": "317px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}