EDA_Affinity_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science in Action Project \n",
    "\n",
    "#### Leonardo Crescenzi - Simone Lu - Enrico Romano "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df0 =pd.read_excel('Invoices_Payment condition_v.1.0.xlsx')\n",
    "df1 = pd.read_excel('Invoices_v2.0.xlsx')\n",
    "df2 = pd.read_excel('Planned_Good_Receipts_22_v.1.0.xlsx')\n",
    "df3 =pd.read_excel('Goods_Receipts_v2.0.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID Goods receipts       0\n",
      "Planned Month           0\n",
      "ID (Material Group)    24\n",
      "Type                    0\n",
      "Amount                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoice Payments\n",
    "We started our analysis with the 'Invoices' dataset. So, we have plotted some of the most important features, in order to have a better understanding of the variables. \n",
    "Before plots, we have created a new variable, i.e. , quarter which clusters 'Invoice date' into different quarters for each year. Moreover, we have decided to remove the variable 'Contract' because it has more then 6000 missing values that are not possible to estimate. Then, we drop the other missing values from the other columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df11 = df1.copy()\n",
    "df11=df11.drop('Contract', axis=1, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID Invoice              0\n",
      "Invoice date            0\n",
      "Posting date            0\n",
      "Amount                  0\n",
      "ID Company Group        0\n",
      "Invoice Due Date        0\n",
      "Invoice Status          0\n",
      "ID Purchase Order    1273\n",
      "Payment Condition     148\n",
      "Industry Group          2\n",
      "User Code               0\n",
      "ID Supplier             0\n",
      "pos/neg                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df11.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df11=df11.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df11['quarter'] = pd.PeriodIndex(df11['Invoice date'], freq = 'Q')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain the figures below, which refer to Amount per quarters, we filtered the dataset in order to divide the amounts that refer to an Invoice Status = Free, from those that refer to an Invoice status = Blocked.\n",
    "From the first figure, which refers to Invoice status=Free, negative values can be seen in some quarters; this is not present, however, in the graph with Invoice Status = Blocked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2 = plt.figure(figsize=(30,20),constrained_layout=False)\n",
    "gs = f2.add_gridspec(2, 2)\n",
    "f2.suptitle(\"Amount per Quarters with Free/Blocked Invoice\",fontsize=50)\n",
    "\n",
    "f2_ax1 = f2.add_subplot(gs[0,0])\n",
    "df1filter=df11[df11['Invoice Status']== 'Free']\n",
    "\n",
    "ax=sns.barplot(y='Amount',x='quarter', data=df1filter,ci=None, palette='Blues_d' )\n",
    "_=plt.xticks(rotation=90,size=20)\n",
    "_=plt.yticks(size=20)\n",
    "_=plt.xlabel('Quarters', fontsize=30)\n",
    "_=plt.ylabel('Amount', fontsize=30)\n",
    "plt.title('Amount per Quarters with Free Invoice',fontsize=25)\n",
    "\n",
    "f2_ax1 = f2.add_subplot(gs[0,1])\n",
    "df1filter2=df11[df11['Invoice Status']== 'Blocked']\n",
    "\n",
    "ax=sns.barplot(y='Amount',x='quarter', data=df1filter2,ci=None, palette='Blues_d' )\n",
    "_=plt.xticks(rotation=90,size=20)\n",
    "_=plt.yticks(size=20)\n",
    "_=plt.xlabel('Quarters', fontsize=30)\n",
    "_=plt.ylabel('Amount', fontsize=30)\n",
    "_=plt.title('Amount per Quarters with Blocked Invoice',fontsize=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df11=df11[df11['Amount']>0]\n",
    "f2 = plt.figure(figsize=(30,20),constrained_layout=False)\n",
    "gs = f2.add_gridspec(2, 2)\n",
    "f2.suptitle(\"Amount per Quarters with Free/Blocked Invoice w/ positive amount\",fontsize=50)\n",
    "\n",
    "f2_ax1 = f2.add_subplot(gs[0,0])\n",
    "df1filter=df11[df11['Invoice Status']== 'Free']\n",
    "\n",
    "ax=sns.barplot(y='Amount',x='quarter', data=df1filter,ci=None, palette='Blues_d' )\n",
    "_=plt.xticks(rotation=90,size=20)\n",
    "_=plt.yticks(size=20)\n",
    "_=plt.xlabel('Quarters', fontsize=30)\n",
    "_=plt.ylabel('Amount', fontsize=30)\n",
    "plt.title('Amount per Quarters with Free Invoice',fontsize=25)\n",
    "\n",
    "f2_ax1 = f2.add_subplot(gs[0,1])\n",
    "df1filter2=df11[df11['Invoice Status']== 'Blocked']\n",
    "\n",
    "ax=sns.barplot(y='Amount',x='quarter', data=df1filter2,ci=None, palette='Blues_d' )\n",
    "_=plt.xticks(rotation=90,size=20)\n",
    "_=plt.yticks(size=20)\n",
    "_=plt.xlabel('Quarters', fontsize=30)\n",
    "_=plt.ylabel('Amount', fontsize=30)\n",
    "_=plt.title('Amount per Quarters with Blocked Invoice',fontsize=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we count the number of Blocked and Free Invoices with the countplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.countplot(data = df1, x = 'Invoice Status', palette=\"Set1\")\n",
    "_=plt.title('Number of Free/Blocked Invoice',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we created a new variable 'pos/neg', which refers to the number of positive and negative values in Amount. In this way, we know, thanks to the countplot, how many Blocked and Free Invoices have positive values and how many negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "posneg = []\n",
    "\n",
    "for i in df1['Amount']:\n",
    "    if i<=0 : \n",
    "        posneg.append('Neg. Inv.')\n",
    "    else: \n",
    "        posneg.append('Pos. Inv.')\n",
    "        \n",
    "df1['pos/neg'] = posneg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "_=sns.countplot(data = df1, x = \"pos/neg\", hue=\"Invoice Status\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last plot is useful to understand which are the payment condition for each invoice status. So, for Blocked invoice we have a 70.88% of J000, while for Free Invoice, we have 16.23% of F060. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "ax = sns.countplot(data = df11, x = \"Invoice Status\", hue=\"Payment Condition\", palette=\"Set2\")\n",
    "plt.legend(fontsize=8,loc='upper right')\n",
    "n_records = float(len(df11))\n",
    "# Looping over entire dataset to add perentage values as labels\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2., height + 1, '{:1.2%}'.format(height/n_records), ha=\"center\", fontsize =7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Good receipts\n",
    "The same analysis has been done also for the second dataset. So, also there we have removed the missing values and also the vairable 'Cost center' because there are more than 20'000 missing values out of 27'000 observations. \n",
    "Then, we add the variable quarter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID Purchase order               18\n",
      "ID Goods Receipts                0\n",
      "ID Company Group                 0\n",
      "Document code                    0\n",
      "Document code description        0\n",
      "Date                             0\n",
      "Amount                           0\n",
      "GR internal Code              2859\n",
      "GR Type                       2859\n",
      "Cost Center                  20609\n",
      "ID Supplier                    244\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df3.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>ID Purchase order</th>\n",
       "      <th>ID Goods Receipts</th>\n",
       "      <th>ID Company Group</th>\n",
       "      <th>Document code</th>\n",
       "      <th>Document code description</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "      <th>GR internal Code</th>\n",
       "      <th>GR Type</th>\n",
       "      <th>Cost Center</th>\n",
       "      <th>ID Supplier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.000058e+09</td>\n",
       "      <td>1811000086</td>\n",
       "      <td>GX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>-3000.00</td>\n",
       "      <td>2GV1A0LIN</td>\n",
       "      <td>Investment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>401988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.000063e+09</td>\n",
       "      <td>1811000130</td>\n",
       "      <td>TR26</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>-7727.24</td>\n",
       "      <td>2CS1A0LIN</td>\n",
       "      <td>Investment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-90196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000060e+09</td>\n",
       "      <td>1811000395</td>\n",
       "      <td>EX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>-40757.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000061e+09</td>\n",
       "      <td>1811000468</td>\n",
       "      <td>FX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>-432.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.000061e+09</td>\n",
       "      <td>1811000469</td>\n",
       "      <td>FX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>-348.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123514.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Purchase order  ID Goods Receipts ID Company Group Document code  \\\n",
       "0       3.000058e+09         1811000086             GX01            TF   \n",
       "1       4.000063e+09         1811000130             TR26            TF   \n",
       "2       3.000060e+09         1811000395             EX01            TF   \n",
       "3       3.000061e+09         1811000468             FX01            TF   \n",
       "4       3.000061e+09         1811000469             FX01            TF   \n",
       "\n",
       "  Document code description       Date    Amount GR internal Code     GR Type  \\\n",
       "0      Other (paid Invoice) 2018-01-04  -3000.00        2GV1A0LIN  Investment   \n",
       "1      Other (paid Invoice) 2018-01-02  -7727.24        2CS1A0LIN  Investment   \n",
       "2      Other (paid Invoice) 2018-01-12 -40757.00              NaN         NaN   \n",
       "3      Other (paid Invoice) 2018-01-10   -432.45              NaN         NaN   \n",
       "4      Other (paid Invoice) 2018-01-10   -348.75              NaN         NaN   \n",
       "\n",
       "  Cost Center  ID Supplier  \n",
       "0         NaN     401988.0  \n",
       "1         NaN     -90196.0  \n",
       "2         NaN     108186.0  \n",
       "3         NaN     123514.0  \n",
       "4         NaN     123514.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df33=df3.copy()\n",
    "df33['quarter'] = pd.PeriodIndex(df33['Date'], freq = 'Q')\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df33=df33.drop('Cost Center', axis=1, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df33=df33.dropna(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#amount per quarter\n",
    "plt.figure(figsize=(10,5))\n",
    "px=sns.barplot(y='Amount',x='quarter', data=df33,ci=None, palette='Blues_d' )\n",
    "_=px.set(ylim=[-3400,35000])\n",
    "_=plt.xticks(rotation=45,size=10)\n",
    "_=plt.yticks(size=10)\n",
    "_=plt.xlabel('Quarters', fontsize=15)\n",
    "_=plt.ylabel('Amount', fontsize=15)\n",
    "_=plt.title('Amount per Quarters',fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df33=df33[df33['Amount']>0]\n",
    "plt.figure(figsize=(10,5))\n",
    "px=sns.barplot(y='Amount',x='quarter', data=df33,ci=None, palette='Blues_d' )\n",
    "_=plt.xticks(rotation=45,size=10)\n",
    "_=plt.yticks(size=10)\n",
    "_=plt.xlabel('Quarters', fontsize=15)\n",
    "_=plt.ylabel('Amount', fontsize=15)\n",
    "_=plt.title('Amount per Quarters',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also for this dataset we created the variable 'pos/neg', in order to understand how many positive and negative value are in Investment or Other GR Type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "posneg = []\n",
    "\n",
    "for i in df3['Amount']:\n",
    "    if i<=0 : \n",
    "        posneg.append('Neg. Inv.')\n",
    "    else: \n",
    "        posneg.append('Pos. Inv.')\n",
    "        \n",
    "df3['pos/neg'] = posneg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "_=sns.countplot(data = df3, x = \"pos/neg\", hue=\"GR Type\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The countplot below, count the number of investment or other encoded by the Document code description. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAE9CAYAAABk/zSyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnCklEQVR4nO3dfZhWdb3v8fd3owgkliiaSQm5SeVhQBkG3bITNYHUhEoDtUJ3HrdmllaGHbfV7kS6TY9PmWVlyM4UQ0WyrBRE0lAYdFIeDClJ2RKinhJJEKbv+WMW0wADDApzz4L367rmutf6rvVbv9+aa7ivD+sxMhNJkiSVyz9VegCSJEnaeoY4SZKkEjLESZIklZAhTpIkqYQMcZIkSSVkiJMkSSqhXSo9gNa29957Z/fu3Ss9DEmSpC2aM2fOS5nZtbllO12I6969O7W1tZUehiRJ0hZFxJ82tczTqZIkSSVkiJMkSSohQ5wkSVIJ7XTXxDVnzZo1LFmyhFWrVlV6KGpjOnToQLdu3dh1110rPRRJktZjiAOWLFlC586d6d69OxFR6eGojchMXn75ZZYsWUKPHj0qPRxJktbj6VRg1apV7LXXXgY4rSci2GuvvTxCK0lqkwxxBQOcmuPfhSSprTLEtRFLlixhxIgR9OzZkwMPPJDPfe5zvPHGGwDU1dXxi1/8onHdr33ta1x55ZXbtP8LLriAGTNmvKm2U6ZM4fLLL2922e67775V9bfi+OOP5y9/+ctWt7v33nv56le/us3HI0nS9uQ1cc34wn0Ttun2rvrgJze7PDP5yEc+wrnnnss999xDfX09Z599Npdccgnf+ta3qKuro7a2luOPP36bjKe+vp527do1zr/yyis8+uijXHPNNW9qeyeddBInnXTSNhnbW9E06G6NE044gUsvvZSxY8fSqVOnbTwqSZK2D4/EtQHTpk2jQ4cOnHnmmQC0a9eOq6++mptvvplXX32Vr3zlK0ycOJH+/fszceJEAObPn8+QIUN473vfy3XXXde4rR//+MfU1NTQv39//v3f/536+nqg4cjXV77yFQYNGsTMmTPX63/SpEkMHz68cb579+6MHTuWmpoaampqWLRoEQA/+9nPGDRoEIceeigf+MAHWLZsGQDjx4/nM5/5DADPPvssRxxxBAMHDuTSSy/d4r5Pnz6dIUOGcPLJJ3PwwQdz+umnk5ncd999fOxjH1tvvQ996EMA3HbbbfTt25c+ffowduzY9cb90ksvATBhwgSqqqro168fn/jEJwBYvnw5H/3oRxk4cCADBw7kkUceARpOmQ4ZMoR77713i+OVJKmtMMS1AfPmzWPAgAHr1fbYYw/e8573sHjxYr7+9a8zatQo6urqGDVqFABPP/00v/rVr5g1axb/+Z//yZo1a1iwYAETJ07kkUceoa6ujnbt2nHrrbcCsHLlSvr06cNjjz3G4MGD1+vrkUceabb/WbNm8ZnPfIYLLrgAgMGDB/Poo4/yxBNPMHr0aK644oqN9uVzn/sc5557LrNnz+ad73xni/b/iSee4JprrmH+/Pn88Y9/5JFHHuG4447j0UcfZeXKlQBMnDiRUaNG8cILLzB27FimTZtGXV0ds2fPZvLkyRv9PseNG8e0adP43e9+x7XXXts4tgsvvJDZs2dz5513ctZZZzW2qa6u5je/+U2LxitJUlvg6dQ2IDObvYB+U3VoOAW42267sdtuu7HPPvuwbNkypk6dypw5cxg4cCAAr7/+Ovvssw/QcHTvox/9aLPbWrp0KV27rv9u3VNPPbXx88ILLwQartsbNWoUS5cu5Y033mj2sRuPPPIId955JwCf+MQn1jtStik1NTV069YNgP79+7N48WIGDx7M8OHD+dnPfsbJJ5/Mz3/+c6644gqmTZvGkCFDGsd7+umnM2PGDEaOHNm4vWnTpnHyySez9957A9ClSxcAHnjgAebPn9+43quvvsqKFSvo3Lkz++yzDy+88MIWxypJO6rv/PjhSg+hFD798cFbXqmVGOLagN69ezcGn3VeffVVnn/+eQ488EDmzJmzUZvddtutcbpdu3asXbuWzGTMmDFcdtllG63foUOH9a6Da6pjx44bPUajaXhcN33++efz+c9/npNOOonp06fzta99rdntbe0dnc3tC8CoUaO44YYb6NKlCwMHDqRz585k5ha3t6nw+/e//52ZM2fSsWPHjZatWrWq2bokSW2Vp1PbgGOPPZa//e1vTJjQcENFfX09X/jCFzjjjDPo1KkTnTt3ZsWKFS3azqRJk3jxxReBhhsW/vSnP22x3SGHHNJ43ds66669mzhxIkcccQQAf/3rX9l///0BuOWWW5rd1pFHHsntt98O0Hgq980aMmQIjz/+ON///vcbTyMPGjSIhx56iJdeeon6+npuu+02jjrqqPXaHXvssdxxxx28/PLLQMPvAWDo0KF8+9vfblyvrq6ucXrhwoX06dPnLY1XkqTWZIhrAyKCu+++m5/+9Kf07NmT973vfXTo0IFvfvObABx99NHMnz9/vRsbmtOrVy++8Y1vMHToUKqqqjjuuONYunTpFvs/4YQTmD59+nq11atXM2jQIK699lquvvpqoOHRJqeccgr/+q//2niqckPXXnstN9xwAwMHDuSvf/1rC38DzWvXrh0nnngi9913HyeeeCIA++23H5dddhlHH300/fr147DDDmPEiBHrtevduzeXXHIJRx11FP369ePzn/88ANdddx21tbVUVVXRq1cvvvvd7za2efDBBznhhBPe0nglSWpN0ZLTUzuS6urqrK2tXa+2YMECDjnkkAqNqG0YPHgw9957L+94xzvo3r07tbW1mwxqO5ply5Zx2mmnMXXq1GaX+/chaWfgNXEt09rXxEXEnMysbm7ZdjsSFxE3R8SLETG3Sa1LRNwfEc8Un3s2WfbliFgUEb+PiGFN6gMi4qli2XVRXOwUEbtFxMSi/lhEdN9e+7IzuOqqq3juuecqPYyKeO6557jqqqsqPQxJkrbK9jydOh4YvkHtYmBqZvYEphbzREQvYDTQu2jznYhYdxX+jcDZQM/iZ902PwX8v8z8Z+Bq4L+2257sBAYNGkRVVRUAixcv3mmOwgEMHDiQ/v37V3oYkiRtle0W4jJzBvDKBuURwLor4m8BRjap356ZqzPzWWARUBMR+wF7ZObMbDjvO2GDNuu2NQk4Nrb2tkhJkqSSau0bG/bNzKUAxec+RX1/4Pkm6y0pavsX0xvW12uTmWuBvwJ7bbeRS5IktSFt5e7U5o6g5Wbqm2uz8cYjzo6I2oioXb58+ZscoiRJUtvR2iFuWXGKlOLzxaK+BHh3k/W6AS8U9W7N1NdrExG7AG9n49O3AGTmTZlZnZnVG76ZQJIkqYxaO8RNAcYU02OAe5rURxd3nPag4QaGWcUp1xURcXhxvdsnN2izblsnA9Oy5M9L+fOf/8zo0aM58MAD6dWrF8cffzwLFy5slb7Hjx+/3munzjrrrPVeUSVJktqW7fbarYi4DRgC7B0RS4CvApcDd0TEp4DngFMAMnNeRNwBzAfWAudlZn2xqXNpuNO1I3Bf8QPwQ+C/I2IRDUfgRm+rsb9445e21aYA2OfcjV8Uv6HM5MMf/jBjxoxpfONBXV0dy5Yt433ve99m29bX16/3Sq0N51ti/Pjx9OnTh3e9610A/OAHP9iq9pIkqXVtz7tTT83M/TJz18zslpk/zMyXM/PYzOxZfL7SZP1xmXlgZh6Umfc1qddmZp9i2WfWHW3LzFWZeUpm/nNm1mTmH7fXvrSGBx98kF133ZVzzjmnsda/f38GDx7MRRddRJ8+fejbt2/jGxumT5/O0UcfzWmnnUbfvn03mq+vr+eiiy5i4MCBVFVV8b3vfa9xu1dccQV9+/alX79+XHzxxUyaNIna2lpOP/10+vfvz+uvv86QIUNY91Dk2267jb59+9KnT5/1Xmi/++67c8kll9CvXz8OP/xwli1b1kq/LUmStN2OxGnrzJ07lwEDBmxUv+uuu6irq+N3v/sdL730EgMHDuT9738/ALNmzWLu3Ln06NGD6dOnrzd/00038fa3v53Zs2ezevVqjjzySIYOHcrTTz/N5MmTeeyxx+jUqROvvPIKXbp04dvf/jZXXnkl1dXrPxT6hRdeYOzYscyZM4c999yToUOHMnnyZEaOHMnKlSs5/PDDGTduHF/60pf4/ve/z3/8x3+0yu9LkqSdXVu5O1Wb8PDDD3PqqafSrl079t13X4466ihmz54NQE1NDT169Ghct+n8r3/9ayZMmED//v0ZNGgQL7/8Ms888wwPPPAAZ555Jp06dQKgS5cum+1/9uzZDBkyhK5du7LLLrtw+umnM2PGDADat2/f+E7TAQMGsHjx4m29+5IkaRM8EtdG9O7dm0mTJm1U39y9Gm9729s2OZ+ZXH/99QwbNmy9dX75y1+yNc9E3lz/u+66a+O22rVrx9q1a1u8XUmS9NZ4JK6NOOaYY1i9ejXf//73G2uzZ89mzz33ZOLEidTX17N8+XJmzJhBTU3NFrc3bNgwbrzxRtasWQPAwoULWblyJUOHDuXmm2/mb3/7GwCvvNJwWWLnzp1ZsWLFRtsZNGgQDz30EC+99BL19fXcdtttHHXUUdtilyVJ0lvgkbg2IiK4++67ueCCC7j88svp0KED3bt355prruG1116jX79+RARXXHEF73znO3n66ac3u72zzjqLxYsXc9hhh5GZdO3alcmTJzN8+HDq6uqorq6mffv2HH/88Xzzm9/kjDPO4JxzzqFjx47MnDmzcTv77bcfl112GUcffTSZyfHHH8+IESO2969DkiRtQZT80Wpbrbq6OtfddbnOggULOOSQQyo0IrV1/n1I2hl858cPV3oIpfDpjw9u1f4iYk5mVje3zNOpkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsS1EcuWLeO0007jve99LwMGDOCII47g7rvv3ibbbvoy+w3rBx10EP369WPgwIHU1dVtk/7W+cpXvsIDDzyw2XUmT57M/Pnzt2m/kiTtDHzYbzO29bNytvRMmcxk5MiRjBkzhp/85CcA/OlPf2LKlCnbdBzNufXWW6muruZHP/oRF110Effff/822/bXv/71La4zefJkTjzxRHr16rXN+pUkaWfgkbg2YNq0abRv355zzjmnsXbAAQdw/vnnA7Bq1SrOPPNM+vbty6GHHsqDDz642frrr7/O6NGjqaqqYtSoUbz++utbHMMRRxzB//zP/wCwcuVK/u3f/o2BAwdy6KGHcs899wBQX1/PF7/4Rfr27UtVVRXXX389AHPmzOGoo45iwIABDBs2jKVLlwJwxhlnNL4Ptnv37owdO5aamhpqampYtGgRv/3tb5kyZQoXXXQR/fv35w9/+MO2+HVKkrRT8EhcGzBv3jwOO+ywTS6/4YYbAHjqqad4+umnGTp0KAsXLtxk/cYbb6RTp048+eSTPPnkk5vd9jq//OUvGTlyJADjxo3jmGOO4eabb+Yvf/kLNTU1fOADH2DChAk8++yzPPHEE+yyyy688sorrFmzhvPPP5977rmHrl27MnHiRC655BJuvvnmjfrYY489mDVrFhMmTOCCCy7g3nvv5aSTTuLEE0/k5JNPfhO/OUmSdl6GuDbovPPO4+GHH6Z9+/bMnj2bhx9+uPGo3MEHH8wBBxzAwoULN1mfMWMGn/3sZwGoqqqiqqpqk32dfvrprFy5kvr6eh5//HEAfv3rXzNlyhSuvPJKoOGI33PPPccDDzzAOeecwy67NPzZdOnShblz5zJ37lyOO+44oOFo3X777ddsX6eeemrj54UXXvhWf02SJO3UDHFtQO/evbnzzjsb52+44QZeeuklqqsbXpW2qffbbu69txHRor5vvfVW+vXrx8UXX8x5553HXXfdRWZy5513ctBBB23U34bbzUx69+7NzJkzt9hX07YtHZ8kSWqe18S1AccccwyrVq3ixhtvbKz97W9/a5x+//vfz6233grAwoULee655zjooINaVJ87dy5PPvnkZvvfdddd+cY3vsGjjz7KggULGDZsGNdff31jSHziiScAGDp0KN/97ndZu3YtAK+88goHHXQQy5cvbwxxa9asYd68ec32M3HixMbPI444AoDOnTuzYsWKrfhtSZIkMMS1CRHB5MmTeeihh+jRowc1NTWMGTOG//qv/wLg05/+NPX19fTt25dRo0Yxfvx4dtttt03Wzz33XF577TWqqqq44oorqKmp2eIYOnbsyBe+8AWuvPJKLr30UtasWUNVVRV9+vTh0ksvBeCss87iPe95D1VVVfTr14+f/OQntG/fnkmTJjF27Fj69etH//79+e1vf9tsH6tXr2bQoEFce+21XH311QCMHj2ab33rWxx66KHe2CBJ0laIzZ2S2xFVV1fnhs9MW7BgAYccckiFRrRz6N69O7W1tey9996VHspW8+9D0s5gWz9ea0e1pceGbWsRMSczq5tb5pE4SZKkEvLGBrWKxYsXV3oIkiTtUDwSJ0mSVEKGuMLOdm2gWsa/C0lSW+XpVKBDhw68/PLL7LXXXj6/TI0yk5dffpkOHTpUeijr8eLjlmnti48lqbUZ4oBu3bqxZMkSli9fXumhqI3p0KED3bp1q/QwJEnaiCGOhofd9ujRo9LDkCRJajGviZMkSSohQ5wkSVIJGeIkSZJKyBAnSZJUQoY4SZKkEjLESZIklZAhTpIkqYQMcZIkSSVkiJMkSSohQ5wkSVIJGeIkSZJKyBAnSZJUQoY4SZKkEqpIiIuICyNiXkTMjYjbIqJDRHSJiPsj4pnic88m6385IhZFxO8jYliT+oCIeKpYdl1ERCX2R5IkqbW1eoiLiP2BzwLVmdkHaAeMBi4GpmZmT2BqMU9E9CqW9waGA9+JiHbF5m4EzgZ6Fj/DW3FXJEmSKqZSp1N3ATpGxC5AJ+AFYARwS7H8FmBkMT0CuD0zV2fms8AioCYi9gP2yMyZmZnAhCZtJEmSdmitHuIy83+AK4HngKXAXzPz18C+mbm0WGcpsE/RZH/g+SabWFLU9i+mN6xLkiTt8CpxOnVPGo6u9QDeBbwtIj6+uSbN1HIz9eb6PDsiaiOidvny5Vs7ZEmSpDanEqdTPwA8m5nLM3MNcBfwL8Cy4hQpxeeLxfpLgHc3ad+NhtOvS4rpDesbycybMrM6M6u7du26TXdGkiSpEioR4p4DDo+ITsXdpMcCC4ApwJhinTHAPcX0FGB0ROwWET1ouIFhVnHKdUVEHF5s55NN2kiSJO3QdmntDjPzsYiYBDwOrAWeAG4CdgfuiIhP0RD0TinWnxcRdwDzi/XPy8z6YnPnAuOBjsB9xY8kSdIOr9VDHEBmfhX46gbl1TQclWtu/XHAuGbqtUCfbT5ASZKkNs43NkiSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJVQRUJcRLwjIiZFxNMRsSAijoiILhFxf0Q8U3zu2WT9L0fEooj4fUQMa1IfEBFPFcuui4ioxP5IkiS1tkodibsW+GVmHgz0AxYAFwNTM7MnMLWYJyJ6AaOB3sBw4DsR0a7Yzo3A2UDP4md4a+6EJElSpbR6iIuIPYD3Az8EyMw3MvMvwAjglmK1W4CRxfQI4PbMXJ2ZzwKLgJqI2A/YIzNnZmYCE5q0kSRJ2qFV4kjce4HlwI8i4omI+EFEvA3YNzOXAhSf+xTr7w8836T9kqK2fzG9YV2SJGmHV4kQtwtwGHBjZh4KrKQ4dboJzV3nlpupb7yBiLMjojYiapcvX76145UkSWpzKhHilgBLMvOxYn4SDaFuWXGKlOLzxSbrv7tJ+27AC0W9WzP1jWTmTZlZnZnVXbt23WY7IkmSVCktCnERMbUltZbIzD8Dz0fEQUXpWGA+MAUYU9TGAPcU01OA0RGxW0T0oOEGhlnFKdcVEXF4cVfqJ5u0kSRJ2qHtsrmFEdEB6ATsXTzyY90pzD2Ad72Ffs8Hbo2I9sAfgTNpCJR3RMSngOeAUwAyc15E3EFD0FsLnJeZ9cV2zgXGAx2B+4ofSZKkHd5mQxzw78AFNAS2OfwjxL0K3PBmO83MOqC6mUXHbmL9ccC4Zuq1QJ83Ow5JkqSy2myIy8xrgWsj4vzMvL6VxiRJkqQt2NKROAAy8/qI+Bege9M2mTlhO41LkiRJm9GiEBcR/w0cCNQB665HW/eAXUmSJLWyFoU4Gq5f61W8GUGSJEkV1tLnxM0F3rk9ByJJkqSWa+mRuL2B+RExC1i9rpiZJ22XUUmSJGmzWhrivrY9ByFJkqSt09K7Ux/a3gORJElSy7X07tQV/OPl8u2BXYGVmbnH9hqYJEmSNq2lR+I6N52PiJFAzfYYkCRJkraspXenriczJwPHbNuhSJIkqaVaejr1I01m/4mG58b5zDhJkqQKaendqR9qMr0WWAyM2OajkSRJUou09Jq4M7f3QCRJktRyLbomLiK6RcTdEfFiRCyLiDsjotv2HpwkSZKa19IbG34ETAHeBewP/KyoSZIkqQJaGuK6ZuaPMnNt8TMe6LodxyVJkqTNaGmIeykiPh4R7YqfjwMvb8+BSZIkadNaGuL+DfgY8GdgKXAy4M0OkiRJFdLSR4z8H2BMZv4/gIjoAlxJQ7iTJElSK2vpkbiqdQEOIDNfAQ7dPkOSJEnSlrQ0xP1TROy5bqY4EtfSo3iSJEnaxloaxK4CfhsRk2h43dbHgHHbbVSSJEnarJa+sWFCRNTS8NL7AD6SmfO368gkSZK0SS0+JVqENoObJElSG9DSa+IkSZLUhhjiJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmEDHGSJEklZIiTJEkqoYqFuIhoFxFPRMS9xXyXiLg/Ip4pPvdssu6XI2JRRPw+IoY1qQ+IiKeKZddFRFRiXyRJklpbJY/EfQ5Y0GT+YmBqZvYEphbzREQvYDTQGxgOfCci2hVtbgTOBnoWP8NbZ+iSJEmVVZEQFxHdgBOAHzQpjwBuKaZvAUY2qd+emasz81lgEVATEfsBe2TmzMxMYEKTNpIkSTu0Sh2Juwb4EvD3JrV9M3MpQPG5T1HfH3i+yXpLitr+xfSGdUmSpB1eq4e4iDgReDEz57S0STO13Ey9uT7PjojaiKhdvnx5C7uVJElquypxJO5I4KSIWAzcDhwTET8GlhWnSCk+XyzWXwK8u0n7bsALRb1bM/WNZOZNmVmdmdVdu3bdlvsiSZJUEa0e4jLzy5nZLTO703DDwrTM/DgwBRhTrDYGuKeYngKMjojdIqIHDTcwzCpOua6IiMOLu1I/2aSNJEnSDm2XSg+gicuBOyLiU8BzwCkAmTkvIu4A5gNrgfMys75ocy4wHugI3Ff8SJIk7fAqGuIyczowvZh+GTh2E+uNA8Y1U68F+my/EUqSJLVNvrFBkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphFo9xEXEuyPiwYhYEBHzIuJzRb1LRNwfEc8Un3s2afPliFgUEb+PiGFN6gMi4qli2XUREa29P5IkSZVQiSNxa4EvZOYhwOHAeRHRC7gYmJqZPYGpxTzFstFAb2A48J2IaFds60bgbKBn8TO8NXdEkiSpUlo9xGXm0sx8vJheASwA9gdGALcUq90CjCymRwC3Z+bqzHwWWATURMR+wB6ZOTMzE5jQpI0kSdIOraLXxEVEd+BQ4DFg38xcCg1BD9inWG1/4PkmzZYUtf2L6Q3rkiRJO7yKhbiI2B24E7ggM1/d3KrN1HIz9eb6OjsiaiOidvny5Vs/WEmSpDamIiEuInalIcDdmpl3FeVlxSlSis8Xi/oS4N1NmncDXijq3ZqpbyQzb8rM6sys7tq167bbEUmSpAqpxN2pAfwQWJCZ/7fJoinAmGJ6DHBPk/roiNgtInrQcAPDrOKU64qIOLzY5iebtJEkSdqh7VKBPo8EPgE8FRF1Re1/A5cDd0TEp4DngFMAMnNeRNwBzKfhztbzMrO+aHcuMB7oCNxX/EiSJO3wWj3EZebDNH89G8Cxm2gzDhjXTL0W6LPtRidJklQOvrFBkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphAxxkiRJJWSIkyRJKiFDnCRJUgkZ4iRJkkrIECdJklRChjhJkqQSMsRJkiSVkCFOkiSphEof4iJieET8PiIWRcTFlR6PJElSayh1iIuIdsANwAeBXsCpEdGrsqOSJEna/nap9ADeohpgUWb+ESAibgdGAPMrOqomvnDfhEoPoRSu+uAnKz0EqVT8bmkZv1u0Iyv1kThgf+D5JvNLipokSdIOLTKz0mN40yLiFGBYZp5VzH8CqMnM8zdY72zg7GL2IOD3rTpQldnewEuVHoSkHY7fLWqpAzKza3MLyn46dQnw7ibz3YAXNlwpM28CbmqtQWnHERG1mVld6XFI2rH43aJtoeynU2cDPSOiR0S0B0YDUyo8JkmSpO2u1EfiMnNtRHwG+BXQDrg5M+dVeFiSJEnbXalDHEBm/gL4RaXHoR2Wp+ElbQ9+t+gtK/WNDZIkSTursl8TJ0mStFMyxKlUIuK1VupnZGu8/aO1+pG0/UREt4i4JyKeiYg/RMS1EdE+IvpHxPFN1vtaRHyxkmPVjsUQJzVvJA2vcttR+pG0HUREAHcBkzOzJ/A+YHdgHNAfOH7Trbe6r3bbalvaMRjiVEoRMSQipkfEpIh4OiJujQYfjIg7NljvZ8X00IiYGRGPR8RPI2L3on55RMyPiCcj4sqI+BfgJOBbEVEXEQcWfV0dETMiYkFEDIyIu4r/eX+jSX8fj4hZRbvvrfvSjYjXImJcRPwuIh6NiH2b66c1f4eStoljgFWZ+SOAzKwHLgTOAq4ARhX/vkcV6/cqvk/+GBGfXbeRLXx3fD0iHgOOaNU9U5tniFOZHQpcQMORrPcCRwL3A4dHxNuKdUYBEyNib+A/gA9k5mFALfD5iOgCfBjonZlVwDcy87c0PG/woszsn5l/KLb1Rma+H/gucA9wHtAHOCMi9oqIQ4r+jszM/kA9cHrR9m3Ao5nZD5gB/K/N9COpPHoDc5oWMvNVYDHwDWBi8e97YrH4YGAYDe/+/mpE7NqC7465mTkoMx/e3jujcin9I0a0U5uVmUsAIqIO6J6ZD0fEL4EPRcQk4ATgS8BRNIS9RxrOftAemAm8CqwCfhARPwfu3Ux/6x4k/RQwLzOXFn3/kYY3hwwGBgCziz46Ai8Wbd5osu05wHFvac8ltRUBNPeYh03Vf56Zq4HVEfEisC9wLJv+7qgH7tzWg9aOwRCnMlvdZLqef/w9T6ThKNkrwOzMXFFct3J/Zp664UYiooaGL9HRwGdoOD2yuf7+vkHffy/6DuCWzPxyM23X5D+e59N0rJLKbR7w0aaFiNiDhv/Y1TezfnPfW5v77lhVnKKVNuLpVO2IpgOHAf+LhkAH8ChwZET8M0BEdIqI9xXXxb29eGj0BTRciAywAui8lf1OBU6OiH2KPrpExAFbaPNm+pHUdkwFOkXEJ6Hx5oOrgPHAMlr27/vNfHdIhjjteIr/td4LfLD4JDOXA2cAt0XEkzSEuoNp+IK9t6g9RMMFyQC3AxdFxBMtveEgM+fTcN3dr4vt3Q/st4VmW92PpLajOML+YeCUiHgGWEjDJRr/G3iQhhsZmt7Y0Nw23sx3h+QbGyRJksrII3GSJEklZIiTJEkqIUOcJElSCRniJEmSSsgQJ0mSVEKGOEk7reIdtj8p3mM5p3i37oeLZUMi4q/F41+ejogrm2k/rHh8RF3xjsvfF9MTWn9vJO1sfGq8pJ1S8RaPyTQ8Kf+0onYAcFKT1X6TmSdGREfgiYi4OzMfWbcwM38F/KpoOx34YmbWttIuSNrJeSRO0s7qGOCNzPzuukJm/ikzr99wxcx8HagD9t/SRiPi2Ii4u8n8cRFxVzH9WkRcFRGPR8TUiOha1A+MiF8WRwN/ExEHv/Xdk7SjM8RJ2ln1Bh5vyYoRsSfQE5jRgtWnAYesC2jAmcCPium3AY9n5mE0vCHkq0X9JuD8zBwAfBH4Tov2QNJOzRAnSUBE3BARv4uI2U3K/1q8BunPwL2Z+ectbad4DdN/Ax+PiHcARwD3FYv/zj/e5/tjYHDx/t5/AX4aEXXA9/CVS5JawGviJO2s5gEfXTeTmedFxN5A02va1l0T9z7g4eKauLoWbPtHwM9oeIfmTzNz7SbWSxr+M/2XzOz/JvZB0k7MI3GSdlbTgA4RcW6TWqfmVszMhcBlwNiWbDgzXwBeoOGl5uObLPon4ORi+jTg4cx8FXg2Ik6BhhsuIqLfVuyHpJ2UIU7STqk47TkSOCoino2IWcAtbDqofRd4f0T0aGEXtwLPZ+b8JrWVQO+ImEPDjRVfL+qnA5+KiN/RcIRwxFbtjKSdUjR8j0mStqWI+DbwRGb+sEnttczcvYLDkrQDMcRJ0jZWHGlbCRyXmaub1A1xkrYZQ5wkSVIJeU2cJElSCRniJEmSSsgQJ0mSVEKGOEmSpBIyxEmSJJWQIU6SJKmE/j/BJ33pzOqo8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data = df33, x = \"GR Type\", hue=\"Document code description\", palette=\"Set2\")\n",
    "_=plt.legend(fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp = sns.countplot(data = df33, x = 'GR Type', palette=\"Set1\")\n",
    "_=plt.title('number of Investment/Other',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, there is a scatterplot. In order to avoid flattening of the scatterplot, we have decided to implement a x and y limits. In this way the graph is more 'zoomed in'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.figure()\n",
    "fig, axes = plt.subplots(2,1, figsize = (10,10))\n",
    "_=fig.suptitle('Comparison of data spread in the two datasets, invoices and goods receipts',fontsize=20)\n",
    "#scatterplot with amount and date in Goods receipt dataset\n",
    "_=plt.figure(figsize=(10,5))\n",
    "sp1=sns.scatterplot(x='Date',y='Amount',hue='GR Type',data=df33, ax = axes [0])\n",
    "_=sp1.set(ylim=[0,3000000])\n",
    "_=axes[0].set_title('Goods receipt')\n",
    "\n",
    "#scatterplot with amount and date in Invoices dataset\n",
    "_=plt.figure(figsize=(10,5))\n",
    "sp2=sns.scatterplot(x='Invoice date',y='Amount',hue='Payment Condition',data=df11,ax = axes[1])\n",
    "_=sp2.set(ylim=[0,900000])\n",
    "_=axes[1].set_title('Invoices')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Affinity analysis\n",
    "It has been realized that inside the dataset \"Goods_receipt\" there are the transactions that the company has made in the years x - x , and moreover it is the only dataset that shows us the type of good or service (GR Internal Code) that is purchased. Then according to the theory of Affinity Analysis, also known as Market Basket Analysis, it extracts \"accidental\" patterns of purchases that may influence the purchase of another product or service. In fact, in order to extract this pattern, we have observed that more transactions under the same ['ID purchase order'] were distinguished on the basis of the type of good or service purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ID Purchase order</th>\n",
       "      <th>ID Goods Receipts</th>\n",
       "      <th>ID Company Group</th>\n",
       "      <th>Document code</th>\n",
       "      <th>Document code description</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "      <th>GR internal Code</th>\n",
       "      <th>GR Type</th>\n",
       "      <th>ID Supplier</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.000066e+09</td>\n",
       "      <td>1811001234</td>\n",
       "      <td>TR26</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>190.00</td>\n",
       "      <td>2SR1A0LIN</td>\n",
       "      <td>Investment</td>\n",
       "      <td>129083.0</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.000066e+09</td>\n",
       "      <td>1811001235</td>\n",
       "      <td>TR26</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>140.00</td>\n",
       "      <td>2SR1A0LIN</td>\n",
       "      <td>Investment</td>\n",
       "      <td>129083.0</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3.000061e+09</td>\n",
       "      <td>1811002545</td>\n",
       "      <td>GX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2GV1A0SOI</td>\n",
       "      <td>Other</td>\n",
       "      <td>131768.0</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3.000061e+09</td>\n",
       "      <td>1811004265</td>\n",
       "      <td>EX01</td>\n",
       "      <td>TF</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>507.45</td>\n",
       "      <td>2EV1A0DST</td>\n",
       "      <td>Other</td>\n",
       "      <td>108224.0</td>\n",
       "      <td>2018Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>4.000068e+09</td>\n",
       "      <td>1811023933</td>\n",
       "      <td>BX01</td>\n",
       "      <td>TS</td>\n",
       "      <td>Other (paid Invoice)</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>59237.50</td>\n",
       "      <td>2NO1A0LIN</td>\n",
       "      <td>Investment</td>\n",
       "      <td>-90375.0</td>\n",
       "      <td>2018Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID Purchase order  ID Goods Receipts ID Company Group Document code  \\\n",
       "25        4.000066e+09         1811001234             TR26            TF   \n",
       "26        4.000066e+09         1811001235             TR26            TF   \n",
       "44        3.000061e+09         1811002545             GX01            TF   \n",
       "80        3.000061e+09         1811004265             EX01            TF   \n",
       "549       4.000068e+09         1811023933             BX01            TS   \n",
       "\n",
       "    Document code description       Date    Amount GR internal Code  \\\n",
       "25       Other (paid Invoice) 2018-01-31    190.00        2SR1A0LIN   \n",
       "26       Other (paid Invoice) 2018-01-31    140.00        2SR1A0LIN   \n",
       "44       Other (paid Invoice) 2018-02-26      0.11        2GV1A0SOI   \n",
       "80       Other (paid Invoice) 2018-03-30    507.45        2EV1A0DST   \n",
       "549      Other (paid Invoice) 2018-11-19  59237.50        2NO1A0LIN   \n",
       "\n",
       "        GR Type  ID Supplier quarter  \n",
       "25   Investment     129083.0  2018Q1  \n",
       "26   Investment     129083.0  2018Q1  \n",
       "44        Other     131768.0  2018Q1  \n",
       "80        Other     108224.0  2018Q1  \n",
       "549  Investment     -90375.0  2018Q4  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df33.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop all null values\n",
    "Before performing the affinity analysis, it is strongly advised to handle with missing values, either drop them, or replace them according to a predefined methology. We decided to drop them because some of them are variables for identification purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID Purchase order            0\n",
       "ID Goods Receipts            0\n",
       "ID Company Group             0\n",
       "Document code                0\n",
       "Document code description    0\n",
       "Date                         0\n",
       "Amount                       0\n",
       "GR internal Code             0\n",
       "GR Type                      0\n",
       "ID Supplier                  0\n",
       "quarter                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for the missing values \n",
    "df33.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WE    19146\n",
       "TN       50\n",
       "TF       27\n",
       "TS       27\n",
       "3S       11\n",
       "2N       10\n",
       "SA        8\n",
       "NS        8\n",
       "1F        6\n",
       "B3        3\n",
       "SS        1\n",
       "UN        1\n",
       "3P        1\n",
       "TO        1\n",
       "Name: Document code, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df33['Document code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation for Affinity Analysis \n",
    "In our understanding of the datasets provided, we decided to consider only positive values of column Amount, because in EDA, we found out that most of the Amount were positive, which intuively we associate them as cost/liability since our main goal is to estimate cash flows related to the account payable in the 1st and 2nd quarters of 2022.\n",
    "\n",
    "We observed in the EDA, that most of invoices' amount are positive, so it is reasonable to deduce that the negative transactions are refunds, accounting changes or corrections and returned goods/services. When there is a cancellation on a particular transaction, it will be datificated in Amount column as a negative value. Since we’re doing market basket analysis, we basically would like to analyze what’s inside the basket that the company bought. This negative value is not one of them. That is why we’re not going to use them. Also we do not consider in the column ['Document code description'], the corrections transactions, as they are not related to puchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.000068e+09    309\n",
       "3.000070e+09    189\n",
       "3.000069e+09    169\n",
       "3.000078e+09    158\n",
       "3.000068e+09    137\n",
       "               ... \n",
       "4.000085e+09      1\n",
       "7.000032e+09      1\n",
       "3.000071e+09      1\n",
       "3.000082e+09      1\n",
       "7.000033e+09      1\n",
       "Name: ID Purchase order, Length: 5552, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df33['ID Purchase order'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df33=df33.loc[(df33['Document code description'] == 'Good Receipt') | \n",
    "             (df33['Document code description'] == 'Other (paid Invoice)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GR internal Code</th>\n",
       "      <th>2004A0SAM</th>\n",
       "      <th>200500AUS</th>\n",
       "      <th>2AA4A0PRO</th>\n",
       "      <th>2AG1A0SIT</th>\n",
       "      <th>2AG400FCY</th>\n",
       "      <th>2AM4A0AFI</th>\n",
       "      <th>2AS1A0LIN</th>\n",
       "      <th>2AV1A0DLI</th>\n",
       "      <th>2AV1A0DST</th>\n",
       "      <th>2AV1A0LIN</th>\n",
       "      <th>...</th>\n",
       "      <th>2SP1A0LIN</th>\n",
       "      <th>2SR1A0ITR</th>\n",
       "      <th>2SR1A0LIN</th>\n",
       "      <th>2SR1A0SVI</th>\n",
       "      <th>2SR5Y1EPC</th>\n",
       "      <th>2SR5Y9EPC</th>\n",
       "      <th>2SZ4A0LIN</th>\n",
       "      <th>2SZ500AST</th>\n",
       "      <th>2VE4A0STC</th>\n",
       "      <th>2VR1A0LIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID Purchase order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5.000100e+04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000200e+04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000300e+04</th>\n",
       "      <td>5595.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000400e+04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000500e+04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>7.000034e+09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5544 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GR internal Code   2004A0SAM  200500AUS  2AA4A0PRO  2AG1A0SIT  2AG400FCY  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04            0.00        0.0        0.0        0.0        0.0   \n",
       "5.000200e+04            0.00        0.0        0.0        0.0        0.0   \n",
       "5.000300e+04         5595.71        0.0        0.0        0.0        0.0   \n",
       "5.000400e+04            0.00        0.0        0.0        0.0        0.0   \n",
       "5.000500e+04            0.00        0.0        0.0        0.0        0.0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "7.000034e+09            0.00        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09            0.00        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09            0.00        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09            0.00        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09            0.00        0.0        0.0        0.0        0.0   \n",
       "\n",
       "GR internal Code   2AM4A0AFI  2AS1A0LIN  2AV1A0DLI  2AV1A0DST  2AV1A0LIN  ...  \\\n",
       "ID Purchase order                                                         ...   \n",
       "5.000100e+04             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "5.000200e+04             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "5.000300e+04             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "5.000400e+04             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "5.000500e+04             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "...                      ...        ...        ...        ...        ...  ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  ...   \n",
       "\n",
       "GR internal Code   2SP1A0LIN  2SR1A0ITR  2SR1A0LIN  2SR1A0SVI  2SR5Y1EPC  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04             0.0        0.0        0.0        0.0        0.0   \n",
       "5.000200e+04             0.0        0.0        0.0        0.0        0.0   \n",
       "5.000300e+04             0.0        0.0        0.0        0.0        0.0   \n",
       "5.000400e+04             0.0        0.0        0.0        0.0        0.0   \n",
       "5.000500e+04             0.0        0.0        0.0        0.0        0.0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0   \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "GR internal Code   2SR5Y9EPC  2SZ4A0LIN  2SZ500AST  2VE4A0STC  2VR1A0LIN  \n",
       "ID Purchase order                                                         \n",
       "5.000100e+04             0.0        0.0        0.0        0.0        0.0  \n",
       "5.000200e+04             0.0        0.0        0.0        0.0        0.0  \n",
       "5.000300e+04             0.0        0.0        0.0        0.0        0.0  \n",
       "5.000400e+04             0.0        0.0        0.0        0.0        0.0  \n",
       "5.000500e+04             0.0        0.0        0.0        0.0        0.0  \n",
       "...                      ...        ...        ...        ...        ...  \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  \n",
       "7.000034e+09             0.0        0.0        0.0        0.0        0.0  \n",
       "\n",
       "[5544 rows x 165 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we grouped the data by the transactions (ID purchase order) and type of items (GR internal code)\n",
    "basket_plus=(df33.groupby(['ID Purchase order','GR internal Code'])['Amount']\n",
    "             .sum().unstack().fillna(0).reset_index().set_index('ID Purchase order'))\n",
    "basket_plus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In affinity analysis, the quantity of each item bought is not strictly important. What is important is if the firm has bought or not. So we encoded the table above into binary data that shows whether an item is bought (1) or not (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>GR internal Code</th>\n",
       "      <th>2004A0SAM</th>\n",
       "      <th>200500AUS</th>\n",
       "      <th>2AA4A0PRO</th>\n",
       "      <th>2AG1A0SIT</th>\n",
       "      <th>2AG400FCY</th>\n",
       "      <th>2AM4A0AFI</th>\n",
       "      <th>2AS1A0LIN</th>\n",
       "      <th>2AV1A0DLI</th>\n",
       "      <th>2AV1A0DST</th>\n",
       "      <th>2AV1A0LIN</th>\n",
       "      <th>...</th>\n",
       "      <th>2SP1A0LIN</th>\n",
       "      <th>2SR1A0ITR</th>\n",
       "      <th>2SR1A0LIN</th>\n",
       "      <th>2SR1A0SVI</th>\n",
       "      <th>2SR5Y1EPC</th>\n",
       "      <th>2SR5Y9EPC</th>\n",
       "      <th>2SZ4A0LIN</th>\n",
       "      <th>2SZ500AST</th>\n",
       "      <th>2VE4A0STC</th>\n",
       "      <th>2VR1A0LIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID Purchase order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5.000100e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000200e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000300e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000400e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000500e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7.000034e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000034e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>5544 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GR internal Code   2004A0SAM  200500AUS  2AA4A0PRO  2AG1A0SIT  2AG400FCY  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04               0          0          0          0          0   \n",
       "5.000200e+04               0          0          0          0          0   \n",
       "5.000300e+04               1          0          0          0          0   \n",
       "5.000400e+04               0          0          0          0          0   \n",
       "5.000500e+04               0          0          0          0          0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "7.000034e+09               0          0          0          0          0   \n",
       "7.000034e+09               0          0          0          0          0   \n",
       "7.000034e+09               0          0          0          0          0   \n",
       "7.000034e+09               0          0          0          0          0   \n",
       "7.000034e+09               0          0          0          0          0   \n",
       "\n",
       "GR internal Code   2AM4A0AFI  2AS1A0LIN  2AV1A0DLI  2AV1A0DST  2AV1A0LIN  ...  \\\n",
       "ID Purchase order                                                         ...   \n",
       "5.000100e+04               0          0          0          0        0.0  ...   \n",
       "5.000200e+04               0          0          0          0        0.0  ...   \n",
       "5.000300e+04               0          0          0          0        0.0  ...   \n",
       "5.000400e+04               0          0          0          0        0.0  ...   \n",
       "5.000500e+04               0          0          0          0        0.0  ...   \n",
       "...                      ...        ...        ...        ...        ...  ...   \n",
       "7.000034e+09               0          0          0          0        0.0  ...   \n",
       "7.000034e+09               0          0          0          0        0.0  ...   \n",
       "7.000034e+09               0          0          0          0        0.0  ...   \n",
       "7.000034e+09               0          0          0          0        0.0  ...   \n",
       "7.000034e+09               0          0          0          0        0.0  ...   \n",
       "\n",
       "GR internal Code   2SP1A0LIN  2SR1A0ITR  2SR1A0LIN  2SR1A0SVI  2SR5Y1EPC  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04               0          0        0.0          0          0   \n",
       "5.000200e+04               0          0        0.0          0          0   \n",
       "5.000300e+04               0          0        0.0          0          0   \n",
       "5.000400e+04               0          0        0.0          0          0   \n",
       "5.000500e+04               0          0        0.0          0          0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "7.000034e+09               0          0        0.0          0          0   \n",
       "7.000034e+09               0          0        0.0          0          0   \n",
       "7.000034e+09               0          0        0.0          0          0   \n",
       "7.000034e+09               0          0        0.0          0          0   \n",
       "7.000034e+09               0          0        0.0          0          0   \n",
       "\n",
       "GR internal Code   2SR5Y9EPC  2SZ4A0LIN  2SZ500AST  2VE4A0STC  2VR1A0LIN  \n",
       "ID Purchase order                                                         \n",
       "5.000100e+04             0.0          0          0          0          0  \n",
       "5.000200e+04             0.0          0          0          0          0  \n",
       "5.000300e+04             0.0          0          0          0          0  \n",
       "5.000400e+04             0.0          0          0          0          0  \n",
       "5.000500e+04             0.0          0          0          0          0  \n",
       "...                      ...        ...        ...        ...        ...  \n",
       "7.000034e+09             0.0          0          0          0          0  \n",
       "7.000034e+09             0.0          0          0          0          0  \n",
       "7.000034e+09             0.0          0          0          0          0  \n",
       "7.000034e+09             0.0          0          0          0          0  \n",
       "7.000034e+09             0.0          0          0          0          0  \n",
       "\n",
       "[5544 rows x 165 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def encode_units(x):\n",
    "    if x<=0:\n",
    "        return 0\n",
    "    if x>=1:\n",
    "        return 1\n",
    "\n",
    "basket_encode_plus= basket_plus.applymap(encode_units)\n",
    "basket_encode_plus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the following step, we are uncovering the association between 2 or more items that is bought according to historical data. We are filtering out when the association in purchasing patterns even though the company buy only one item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GR internal Code</th>\n",
       "      <th>2004A0SAM</th>\n",
       "      <th>200500AUS</th>\n",
       "      <th>2AA4A0PRO</th>\n",
       "      <th>2AG1A0SIT</th>\n",
       "      <th>2AG400FCY</th>\n",
       "      <th>2AM4A0AFI</th>\n",
       "      <th>2AS1A0LIN</th>\n",
       "      <th>2AV1A0DLI</th>\n",
       "      <th>2AV1A0DST</th>\n",
       "      <th>2AV1A0LIN</th>\n",
       "      <th>...</th>\n",
       "      <th>2SP1A0LIN</th>\n",
       "      <th>2SR1A0ITR</th>\n",
       "      <th>2SR1A0LIN</th>\n",
       "      <th>2SR1A0SVI</th>\n",
       "      <th>2SR5Y1EPC</th>\n",
       "      <th>2SR5Y9EPC</th>\n",
       "      <th>2SZ4A0LIN</th>\n",
       "      <th>2SZ500AST</th>\n",
       "      <th>2VE4A0STC</th>\n",
       "      <th>2VR1A0LIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID Purchase order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5.000100e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000200e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000300e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000400e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.000500e+04</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>4.000089e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000089e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000089e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000089e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.000033e+09</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>189 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GR internal Code   2004A0SAM  200500AUS  2AA4A0PRO  2AG1A0SIT  2AG400FCY  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04               0          0          0          0          0   \n",
       "5.000200e+04               0          0          0          0          0   \n",
       "5.000300e+04               1          0          0          0          0   \n",
       "5.000400e+04               0          0          0          0          0   \n",
       "5.000500e+04               0          0          0          0          0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "4.000089e+09               1          0          0          0          0   \n",
       "4.000089e+09               0          0          0          0          0   \n",
       "4.000089e+09               0          0          0          0          0   \n",
       "4.000089e+09               0          0          0          0          0   \n",
       "7.000033e+09               0          0          0          0          0   \n",
       "\n",
       "GR internal Code   2AM4A0AFI  2AS1A0LIN  2AV1A0DLI  2AV1A0DST  2AV1A0LIN  ...  \\\n",
       "ID Purchase order                                                         ...   \n",
       "5.000100e+04               0          0          0          0        0.0  ...   \n",
       "5.000200e+04               0          0          0          0        0.0  ...   \n",
       "5.000300e+04               0          0          0          0        0.0  ...   \n",
       "5.000400e+04               0          0          0          0        0.0  ...   \n",
       "5.000500e+04               0          0          0          0        0.0  ...   \n",
       "...                      ...        ...        ...        ...        ...  ...   \n",
       "4.000089e+09               0          0          0          0        0.0  ...   \n",
       "4.000089e+09               0          0          0          0        0.0  ...   \n",
       "4.000089e+09               0          0          0          0        0.0  ...   \n",
       "4.000089e+09               0          0          0          0        0.0  ...   \n",
       "7.000033e+09               0          0          0          0        0.0  ...   \n",
       "\n",
       "GR internal Code   2SP1A0LIN  2SR1A0ITR  2SR1A0LIN  2SR1A0SVI  2SR5Y1EPC  \\\n",
       "ID Purchase order                                                          \n",
       "5.000100e+04               0          0        0.0          0          0   \n",
       "5.000200e+04               0          0        0.0          0          0   \n",
       "5.000300e+04               0          0        0.0          0          0   \n",
       "5.000400e+04               0          0        0.0          0          0   \n",
       "5.000500e+04               0          0        0.0          0          0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "4.000089e+09               0          0        0.0          0          0   \n",
       "4.000089e+09               0          0        0.0          0          0   \n",
       "4.000089e+09               0          0        0.0          0          0   \n",
       "4.000089e+09               0          0        0.0          0          0   \n",
       "7.000033e+09               0          0        0.0          0          0   \n",
       "\n",
       "GR internal Code   2SR5Y9EPC  2SZ4A0LIN  2SZ500AST  2VE4A0STC  2VR1A0LIN  \n",
       "ID Purchase order                                                         \n",
       "5.000100e+04             0.0          0          0          0          0  \n",
       "5.000200e+04             0.0          0          0          0          0  \n",
       "5.000300e+04             0.0          0          0          0          0  \n",
       "5.000400e+04             0.0          0          0          0          0  \n",
       "5.000500e+04             0.0          0          0          0          0  \n",
       "...                      ...        ...        ...        ...        ...  \n",
       "4.000089e+09             0.0          0          0          0          0  \n",
       "4.000089e+09             0.0          0          0          0          0  \n",
       "4.000089e+09             0.0          0          0          0          0  \n",
       "4.000089e+09             0.0          0          0          0          0  \n",
       "7.000033e+09             0.0          0          0          0          0  \n",
       "\n",
       "[189 rows x 165 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket_filter_plus=basket_encode_plus[(basket_encode_plus>0).sum(axis=1) >=2]\n",
    "\n",
    "basket_filter_plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 189 entries, 50001.0 to 7000033433.0\n",
      "Columns: 165 entries, 2004A0SAM to 2VR1A0LIN\n",
      "dtypes: float64(12), int64(153)\n",
      "memory usage: 245.1 KB\n"
     ]
    }
   ],
   "source": [
    "basket_filter_plus.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apriori Algorithm\n",
    "Apriori algorithm is a classic algorithm that is useful to mine frequent itemsets and relevant association rules. Usually this algorithm works well on database containing a large number of transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>0.111111</td>\n",
       "      <td>(2CV1A0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.095238</td>\n",
       "      <td>(2BV1A0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.095238</td>\n",
       "      <td>(2CV1A0DST)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.079365</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>(2BV1A0DLI)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>(2CV1A0DST, 2CV1A0LIN)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.068783</td>\n",
       "      <td>(2EV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.058201</td>\n",
       "      <td>(2FV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.058201</td>\n",
       "      <td>(2CV500EPC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.058201</td>\n",
       "      <td>(2CS2A0TRF)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.058201</td>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.052910</td>\n",
       "      <td>(2BV1A0SOI)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.052910</td>\n",
       "      <td>(2BV1A0DLI, 2BV1A0LIN)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.047619</td>\n",
       "      <td>(2DV500EPC, 2CV500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.047619</td>\n",
       "      <td>(2ITFA0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.047619</td>\n",
       "      <td>(2DV500EPC, 2CS500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2SR1A0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2CV1A0SOI)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2GV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2CV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2CS1A0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2DV500EPC, 2AV500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2BV1A0SIT, 2AV1A0SIT)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2HV1A0SIT, 2AV1A0SIT)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.042328</td>\n",
       "      <td>(2BV500EPC, 2AV500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.037037</td>\n",
       "      <td>(2AV500EPC, 2CS500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.037037</td>\n",
       "      <td>(2CV1A0DLI)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.037037</td>\n",
       "      <td>(2DV1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.037037</td>\n",
       "      <td>(2NO2A0TRF)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.037037</td>\n",
       "      <td>(2NE1A0LIN)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2DV500EPC, 2BV500EPC, 2AV500EPC)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2HV1A0SIT, 2BV1A0SIT, 2AV1A0SIT)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2CS2A0TRF, 2FV1A0SIT)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2004A0SAM)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2DV500EPC, 2BV500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2HV1A0SIT, 2BV1A0SIT)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2CV500EPC, 2AV500EPC)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2AG1A0SIT)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2DS2A0IDC)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>(2DV500EPC, 2AV500EPC, 2CS500EPC)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support                           itemsets  length\n",
       "0   0.111111                        (2CV1A0LIN)       1\n",
       "1   0.095238                        (2BV1A0LIN)       1\n",
       "2   0.095238                        (2CV1A0DST)       1\n",
       "3   0.079365                        (2DV500EPC)       1\n",
       "4   0.074074                        (2CS500EPC)       1\n",
       "5   0.074074                        (2AV1A0SIT)       1\n",
       "6   0.074074                        (2AV500EPC)       1\n",
       "7   0.074074                        (2BV1A0DLI)       1\n",
       "8   0.074074             (2CV1A0DST, 2CV1A0LIN)       2\n",
       "9   0.068783                        (2EV1A0SIT)       1\n",
       "10  0.058201                        (2FV1A0SIT)       1\n",
       "11  0.058201                        (2CV500EPC)       1\n",
       "12  0.058201                        (2CS2A0TRF)       1\n",
       "13  0.058201                        (2BV1A0SIT)       1\n",
       "14  0.052910                        (2BV1A0SOI)       1\n",
       "15  0.052910             (2BV1A0DLI, 2BV1A0LIN)       2\n",
       "16  0.047619             (2DV500EPC, 2CV500EPC)       2\n",
       "17  0.047619                        (2ITFA0LIN)       1\n",
       "18  0.047619             (2DV500EPC, 2CS500EPC)       2\n",
       "19  0.042328                        (2SR1A0LIN)       1\n",
       "20  0.042328                        (2CV1A0SOI)       1\n",
       "21  0.042328                        (2BV500EPC)       1\n",
       "22  0.042328                        (2GV1A0SIT)       1\n",
       "23  0.042328                        (2HV1A0SIT)       1\n",
       "24  0.042328                        (2CV1A0SIT)       1\n",
       "25  0.042328                        (2CS1A0LIN)       1\n",
       "26  0.042328             (2DV500EPC, 2AV500EPC)       2\n",
       "27  0.042328             (2BV1A0SIT, 2AV1A0SIT)       2\n",
       "28  0.042328             (2HV1A0SIT, 2AV1A0SIT)       2\n",
       "29  0.042328             (2BV500EPC, 2AV500EPC)       2\n",
       "30  0.037037             (2AV500EPC, 2CS500EPC)       2\n",
       "31  0.037037                        (2CV1A0DLI)       1\n",
       "32  0.037037                        (2DV1A0SIT)       1\n",
       "33  0.037037                        (2NO2A0TRF)       1\n",
       "34  0.037037                        (2NE1A0LIN)       1\n",
       "35  0.031746  (2DV500EPC, 2BV500EPC, 2AV500EPC)       3\n",
       "36  0.031746  (2HV1A0SIT, 2BV1A0SIT, 2AV1A0SIT)       3\n",
       "37  0.031746             (2CS2A0TRF, 2FV1A0SIT)       2\n",
       "38  0.031746                        (2004A0SAM)       1\n",
       "39  0.031746             (2DV500EPC, 2BV500EPC)       2\n",
       "40  0.031746             (2HV1A0SIT, 2BV1A0SIT)       2\n",
       "41  0.031746             (2CV500EPC, 2AV500EPC)       2\n",
       "42  0.031746                        (2AG1A0SIT)       1\n",
       "43  0.031746                        (2DS2A0IDC)       1\n",
       "44  0.031746  (2DV500EPC, 2AV500EPC, 2CS500EPC)       3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets_plus=apriori(basket_filter_plus,min_support=.03,\n",
    "                              use_colnames=True).sort_values('support',ascending=False).reset_index(drop=True)\n",
    "\n",
    "#we applied the apriori algorithm to our filtered dataframe by finding the items that is bought as many mas 3% out of the whole transactions. \n",
    "\n",
    "frequent_itemsets_plus['length']=frequent_itemsets_plus['itemsets'].apply(lambda x: len(x))\n",
    "\n",
    "#lenghth is the column that contains the number of item that is bought \n",
    "\n",
    "frequent_itemsets_plus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have 44 entries in the table basket filter plus, the table that we get from the apriori is telling us that the Item \"(2CV1A0LIN) \"is bought it 19 times (0.11111*189) out of the whole transaction. The support simply emphasizes how populat an itemset is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Association rules\n",
    "Association rules has been carried out in order to extract information and discover knowledge about which items that more effective sold together and analyse numerically metrics associated with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>(2BV500EPC)</td>\n",
       "      <td>(2DV500EPC, 2AV500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>17.718750</td>\n",
       "      <td>0.029954</td>\n",
       "      <td>3.830688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(2DV500EPC, 2AV500EPC)</td>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>17.718750</td>\n",
       "      <td>0.029954</td>\n",
       "      <td>3.830688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(2AV1A0SIT, 2BV1A0SIT)</td>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>17.718750</td>\n",
       "      <td>0.029954</td>\n",
       "      <td>3.830688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>(2AV1A0SIT, 2BV1A0SIT)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>17.718750</td>\n",
       "      <td>0.029954</td>\n",
       "      <td>3.830688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2DV500EPC, 2BV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.029394</td>\n",
       "      <td>1.694444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(2DV500EPC, 2BV500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.029394</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>2.234568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>2.234568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(2HV1A0SIT, 2BV1A0SIT)</td>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.029394</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>(2HV1A0SIT, 2BV1A0SIT)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.029394</td>\n",
       "      <td>1.694444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12.886364</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>3.767196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>(2HV1A0SIT)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>12.886364</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>2.106878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(2HV1A0SIT, 2AV1A0SIT)</td>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12.886364</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>3.767196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>(2HV1A0SIT, 2AV1A0SIT)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>12.886364</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>2.106878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(2AV500EPC, 2CS500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.028807</td>\n",
       "      <td>6.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2AV500EPC, 2CS500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.028807</td>\n",
       "      <td>1.604938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(2CV500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>10.309091</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>5.063492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2CV500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>10.309091</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>2.354497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(2DV500EPC, 2AV500EPC)</td>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>10.125000</td>\n",
       "      <td>0.028611</td>\n",
       "      <td>3.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>(2DV500EPC, 2AV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>10.125000</td>\n",
       "      <td>0.028611</td>\n",
       "      <td>1.675926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>9.818182</td>\n",
       "      <td>0.038017</td>\n",
       "      <td>2.197531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(2BV1A0SIT)</td>\n",
       "      <td>(2AV1A0SIT)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>9.818182</td>\n",
       "      <td>0.038017</td>\n",
       "      <td>3.395062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>0.028387</td>\n",
       "      <td>3.682540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(2BV500EPC, 2AV500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>0.028387</td>\n",
       "      <td>3.682540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2BV500EPC, 2AV500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>0.028387</td>\n",
       "      <td>1.596120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2BV500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>0.028387</td>\n",
       "      <td>1.596120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(2CS2A0TRF)</td>\n",
       "      <td>(2FV1A0SIT)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>9.371901</td>\n",
       "      <td>0.028359</td>\n",
       "      <td>2.071958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(2FV1A0SIT)</td>\n",
       "      <td>(2CS2A0TRF)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>9.371901</td>\n",
       "      <td>0.028359</td>\n",
       "      <td>2.071958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2DV500EPC, 2CS500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.028219</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(2DV500EPC, 2CS500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.028219</td>\n",
       "      <td>2.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.041740</td>\n",
       "      <td>2.577778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.041740</td>\n",
       "      <td>2.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(2BV1A0LIN)</td>\n",
       "      <td>(2BV1A0DLI)</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.052910</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.045855</td>\n",
       "      <td>2.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(2BV1A0DLI)</td>\n",
       "      <td>(2BV1A0LIN)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.052910</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.045855</td>\n",
       "      <td>3.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(2CV500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>7.363636</td>\n",
       "      <td>0.027435</td>\n",
       "      <td>2.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2CV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>7.363636</td>\n",
       "      <td>0.027435</td>\n",
       "      <td>1.648148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.036449</td>\n",
       "      <td>2.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(2DV500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.036449</td>\n",
       "      <td>1.984127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(2CV1A0DST)</td>\n",
       "      <td>(2CV1A0LIN)</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(2CV1A0LIN)</td>\n",
       "      <td>(2CV1A0DST)</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>2.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.031550</td>\n",
       "      <td>1.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(2CS500EPC)</td>\n",
       "      <td>(2AV500EPC)</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.031550</td>\n",
       "      <td>1.851852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               antecedents             consequents  antecedent support  \\\n",
       "0              (2BV500EPC)  (2DV500EPC, 2AV500EPC)            0.042328   \n",
       "1   (2DV500EPC, 2AV500EPC)             (2BV500EPC)            0.042328   \n",
       "2   (2AV1A0SIT, 2BV1A0SIT)             (2HV1A0SIT)            0.042328   \n",
       "3              (2HV1A0SIT)  (2AV1A0SIT, 2BV1A0SIT)            0.042328   \n",
       "4              (2AV500EPC)  (2DV500EPC, 2BV500EPC)            0.074074   \n",
       "5   (2DV500EPC, 2BV500EPC)             (2AV500EPC)            0.031746   \n",
       "6              (2AV500EPC)             (2BV500EPC)            0.074074   \n",
       "7              (2BV500EPC)             (2AV500EPC)            0.042328   \n",
       "8              (2AV1A0SIT)             (2HV1A0SIT)            0.074074   \n",
       "9              (2HV1A0SIT)             (2AV1A0SIT)            0.042328   \n",
       "10  (2HV1A0SIT, 2BV1A0SIT)             (2AV1A0SIT)            0.031746   \n",
       "11             (2AV1A0SIT)  (2HV1A0SIT, 2BV1A0SIT)            0.074074   \n",
       "12             (2HV1A0SIT)             (2BV1A0SIT)            0.042328   \n",
       "13             (2BV1A0SIT)             (2HV1A0SIT)            0.058201   \n",
       "14  (2HV1A0SIT, 2AV1A0SIT)             (2BV1A0SIT)            0.042328   \n",
       "15             (2BV1A0SIT)  (2HV1A0SIT, 2AV1A0SIT)            0.058201   \n",
       "16  (2AV500EPC, 2CS500EPC)             (2DV500EPC)            0.037037   \n",
       "17             (2DV500EPC)  (2AV500EPC, 2CS500EPC)            0.079365   \n",
       "18             (2CV500EPC)             (2DV500EPC)            0.058201   \n",
       "19             (2DV500EPC)             (2CV500EPC)            0.079365   \n",
       "20  (2DV500EPC, 2AV500EPC)             (2CS500EPC)            0.042328   \n",
       "21             (2CS500EPC)  (2DV500EPC, 2AV500EPC)            0.074074   \n",
       "22             (2AV1A0SIT)             (2BV1A0SIT)            0.074074   \n",
       "23             (2BV1A0SIT)             (2AV1A0SIT)            0.058201   \n",
       "24             (2BV500EPC)             (2DV500EPC)            0.042328   \n",
       "25  (2BV500EPC, 2AV500EPC)             (2DV500EPC)            0.042328   \n",
       "26             (2DV500EPC)  (2BV500EPC, 2AV500EPC)            0.079365   \n",
       "27             (2DV500EPC)             (2BV500EPC)            0.079365   \n",
       "28             (2CS2A0TRF)             (2FV1A0SIT)            0.058201   \n",
       "29             (2FV1A0SIT)             (2CS2A0TRF)            0.058201   \n",
       "30             (2AV500EPC)  (2DV500EPC, 2CS500EPC)            0.074074   \n",
       "31  (2DV500EPC, 2CS500EPC)             (2AV500EPC)            0.047619   \n",
       "32             (2CS500EPC)             (2DV500EPC)            0.074074   \n",
       "33             (2DV500EPC)             (2CS500EPC)            0.079365   \n",
       "34             (2BV1A0LIN)             (2BV1A0DLI)            0.095238   \n",
       "35             (2BV1A0DLI)             (2BV1A0LIN)            0.074074   \n",
       "36             (2CV500EPC)             (2AV500EPC)            0.058201   \n",
       "37             (2AV500EPC)             (2CV500EPC)            0.074074   \n",
       "38             (2AV500EPC)             (2DV500EPC)            0.074074   \n",
       "39             (2DV500EPC)             (2AV500EPC)            0.079365   \n",
       "40             (2CV1A0DST)             (2CV1A0LIN)            0.095238   \n",
       "41             (2CV1A0LIN)             (2CV1A0DST)            0.111111   \n",
       "42             (2AV500EPC)             (2CS500EPC)            0.074074   \n",
       "43             (2CS500EPC)             (2AV500EPC)            0.074074   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \n",
       "0             0.042328  0.031746    0.750000  17.718750  0.029954    3.830688  \n",
       "1             0.042328  0.031746    0.750000  17.718750  0.029954    3.830688  \n",
       "2             0.042328  0.031746    0.750000  17.718750  0.029954    3.830688  \n",
       "3             0.042328  0.031746    0.750000  17.718750  0.029954    3.830688  \n",
       "4             0.031746  0.031746    0.428571  13.500000  0.029394    1.694444  \n",
       "5             0.074074  0.031746    1.000000  13.500000  0.029394         inf  \n",
       "6             0.042328  0.042328    0.571429  13.500000  0.039193    2.234568  \n",
       "7             0.074074  0.042328    1.000000  13.500000  0.039193         inf  \n",
       "8             0.042328  0.042328    0.571429  13.500000  0.039193    2.234568  \n",
       "9             0.074074  0.042328    1.000000  13.500000  0.039193         inf  \n",
       "10            0.074074  0.031746    1.000000  13.500000  0.029394         inf  \n",
       "11            0.031746  0.031746    0.428571  13.500000  0.029394    1.694444  \n",
       "12            0.058201  0.031746    0.750000  12.886364  0.029282    3.767196  \n",
       "13            0.042328  0.031746    0.545455  12.886364  0.029282    2.106878  \n",
       "14            0.058201  0.031746    0.750000  12.886364  0.029282    3.767196  \n",
       "15            0.042328  0.031746    0.545455  12.886364  0.029282    2.106878  \n",
       "16            0.079365  0.031746    0.857143  10.800000  0.028807    6.444444  \n",
       "17            0.037037  0.031746    0.400000  10.800000  0.028807    1.604938  \n",
       "18            0.079365  0.047619    0.818182  10.309091  0.043000    5.063492  \n",
       "19            0.058201  0.047619    0.600000  10.309091  0.043000    2.354497  \n",
       "20            0.074074  0.031746    0.750000  10.125000  0.028611    3.703704  \n",
       "21            0.042328  0.031746    0.428571  10.125000  0.028611    1.675926  \n",
       "22            0.058201  0.042328    0.571429   9.818182  0.038017    2.197531  \n",
       "23            0.074074  0.042328    0.727273   9.818182  0.038017    3.395062  \n",
       "24            0.079365  0.031746    0.750000   9.450000  0.028387    3.682540  \n",
       "25            0.079365  0.031746    0.750000   9.450000  0.028387    3.682540  \n",
       "26            0.042328  0.031746    0.400000   9.450000  0.028387    1.596120  \n",
       "27            0.042328  0.031746    0.400000   9.450000  0.028387    1.596120  \n",
       "28            0.058201  0.031746    0.545455   9.371901  0.028359    2.071958  \n",
       "29            0.058201  0.031746    0.545455   9.371901  0.028359    2.071958  \n",
       "30            0.047619  0.031746    0.428571   9.000000  0.028219    1.666667  \n",
       "31            0.074074  0.031746    0.666667   9.000000  0.028219    2.777778  \n",
       "32            0.079365  0.047619    0.642857   8.100000  0.041740    2.577778  \n",
       "33            0.074074  0.047619    0.600000   8.100000  0.041740    2.314815  \n",
       "34            0.074074  0.052910    0.555556   7.500000  0.045855    2.083333  \n",
       "35            0.095238  0.052910    0.714286   7.500000  0.045855    3.166667  \n",
       "36            0.074074  0.031746    0.545455   7.363636  0.027435    2.037037  \n",
       "37            0.058201  0.031746    0.428571   7.363636  0.027435    1.648148  \n",
       "38            0.079365  0.042328    0.571429   7.200000  0.036449    2.148148  \n",
       "39            0.074074  0.042328    0.533333   7.200000  0.036449    1.984127  \n",
       "40            0.111111  0.074074    0.777778   7.000000  0.063492    4.000000  \n",
       "41            0.095238  0.074074    0.666667   7.000000  0.063492    2.714286  \n",
       "42            0.074074  0.037037    0.500000   6.750000  0.031550    1.851852  \n",
       "43            0.074074  0.037037    0.500000   6.750000  0.031550    1.851852  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules(frequent_itemsets_plus, metric='lift',\n",
    "                 min_threshold=1).sort_values('lift',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above is sorted by its lift. The most important metrics in the affinity analysis are:\n",
    "\n",
    "- Support, emphasizes how popular an itemset is, it is the join proability between 2 items, it is used to determine the strength of association between items.\n",
    "- Confidence: it denotes the likelihood of certain items are purchased together. It is basically the Conditional Probability formula of purchasing event of item A and B.\n",
    "- Lift is equal to the confidence factor divided by the expected confidence. The higher the lift value, the higher the association between the items will. If the lift value is more than 1, it is enough for us to say that those two items are associated each other.\n",
    "\n",
    "From the above association rules results, we could observe that almost all the itemsets that we individuated are bought together either randomly or by choice. Because the lift values are all greater than 1.\n",
    "\n",
    "Instead, we could also observe from Support values that highest values are given by (2BV500EPC) and (2DV500EPC, 2AV500EPC), which accounts 0.03% out of 44 transactions where the company bought more than 2 items.\n",
    "\n",
    "Finally, the confidence is influenced by the antecedent and consequent as conditionality is present here. If the antecedent is higher than the consequent, then the rule that will be applied is rule number 1. \n",
    "\n",
    "https://medium.com/@jihargifari/how-to-perform-market-basket-analysis-in-python-bd00b745b106"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}