1-cleaning_exploring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Importations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Open data and look into it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset\n"
     ]
    },
    {
     "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting current path\n",
    "path = os.getcwd()\n",
    "try :\n",
    "    data_raw = pd.read_excel(path + \"/data/Online_Retail.xlsx\")\n",
    "    print(\"Loading dataset\")\n",
    "    display(data_raw.head())\n",
    "except ImportError:\n",
    "    print(\"ERROR : check if the 'Online_Retail.xlsx' file is in the 'data' folder\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1) Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial shape :  (541909, 8)\n",
      "duplicates found :  5268\n",
      "Shape without duplicates:  (536641, 8)\n"
     ]
    }
   ],
   "source": [
    "# Getting rid of the duplicates\n",
    "print(\"initial shape : \", data_raw.shape)\n",
    "dup = data_raw[data_raw.duplicated()].shape[0]\n",
    "if dup > 0 :\n",
    "    print(\"duplicates found : \", dup)\n",
    "    data_raw = data_raw.drop_duplicates(keep='first')\n",
    "    print(\"Shape without duplicates: \", data_raw.shape)\n",
    "else :\n",
    "    print(\"No duplicate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2) Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>feature</th>\n",
       "      <th>missing values</th>\n",
       "      <th>missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CustomerID</td>\n",
       "      <td>135037</td>\n",
       "      <td>25.163377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Description</td>\n",
       "      <td>1454</td>\n",
       "      <td>0.270945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>InvoiceNo</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StockCode</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quantity</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>InvoiceDate</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UnitPrice</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Country</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature  missing values  missing values (%)\n",
       "6   CustomerID          135037           25.163377\n",
       "2  Description            1454            0.270945\n",
       "0    InvoiceNo               0            0.000000\n",
       "1    StockCode               0            0.000000\n",
       "3     Quantity               0            0.000000\n",
       "4  InvoiceDate               0            0.000000\n",
       "5    UnitPrice               0            0.000000\n",
       "7      Country               0            0.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for missing values\n",
    "missing_val_df = data_raw.isnull().sum(axis=0).reset_index()\n",
    "missing_val_df.columns = ['feature', 'missing values']\n",
    "missing_val_df['missing values (%)']=100 - ((data_raw.shape[0]-missing_val_df['missing values'])/data_raw.shape[0]*100)\n",
    "missing_val_df = missing_val_df.sort_values('missing values (%)', ascending=False)\n",
    "missing_val_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As *'CustomerID'* will be our main concern to build customers behaviours, we delete those missing customerID values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape :  (401604, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = data_raw.dropna()\n",
    "print(\"New shape : \", data_raw.shape)\n",
    "data_raw.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3) Looking for errors in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 401604 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      401604 non-null object\n",
      "StockCode      401604 non-null object\n",
      "Description    401604 non-null object\n",
      "Quantity       401604 non-null int64\n",
      "InvoiceDate    401604 non-null datetime64[ns]\n",
      "UnitPrice      401604 non-null float64\n",
      "CustomerID     401604 non-null float64\n",
      "Country        401604 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data_raw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'InvoiceNo' is object type because some have been canceled and are therefore preceded by a 'C'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw_explo = data_raw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number and origin of unique customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We count 4372 unique customers coming from 37 countries\n"
     ]
    }
   ],
   "source": [
    "customers_counts = data_raw_explo.loc[:, 'CustomerID'].value_counts()\n",
    "countries_counts = pd.DataFrame(data_raw_explo.loc[:, 'Country'].value_counts(), columns=['Country'])\n",
    "print(\"We count %i unique customers coming from %i countries\" % (customers_counts.shape[0], countries_counts.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group countries that account for less than 0.5% of orders in \"other\" category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting percentage of country representation over customers\n",
    "countries_counts.loc[:, 'perc'] = countries_counts.loc[:, 'Country'].apply(lambda x : x * 100 / countries_counts.loc[:, 'Country'].sum())\n",
    "# Attributing 'Other' origin for small amount of orders\n",
    "countries_counts.loc[:, 'Origin'] = countries_counts.apply(lambda x : x.name if x['perc'] >= 0.5 else 'Other', axis=1)\n",
    "# Aggregating by Origin\n",
    "grouped = countries_counts.groupby('Origin')\n",
    "countries_counts_other = grouped.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "# Explode for small accounts\n",
    "explode = [0.5 if countries_counts_other['perc'][i] <= 4 else 0.0 for i in range(len(countries_counts_other.values))]\n",
    "ax.pie(countries_counts_other['Country'].values, labels=countries_counts_other.index.values, autopct='%.1f%%',\n",
    "       explode=explode, textprops={'fontsize': 13}, startangle=340, colors=sns.color_palette(\"PuOr\", 12))\n",
    "ax.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax.set_title('Sells repartition by country of customer', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As most of customers comes from UK, and that customers behaviours most likely differ from one country to another, we will limit the analysis to UK customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape :  (356728, 8)\n"
     ]
    }
   ],
   "source": [
    "data_raw_explo = data_raw_explo[data_raw_explo.Country == 'United Kingdom']\n",
    "print(\"New shape : \", data_raw_explo.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label encoding with Customers ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Customers ID\n",
    "le_id = preprocessing.LabelEncoder()\n",
    "le_id = le_id.fit(data_raw_explo.loc[:, 'CustomerID'].dropna().values)\n",
    "data_raw_explo.loc[:, 'CustomerID_enc'] = data_raw_explo.loc[:, 'CustomerID'].apply(lambda x : int(le_id.transform([x])) if not bool(np.isnan(x)) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New customers count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_counts = data_raw_explo.loc[:, 'CustomerID_enc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "ax = sns.distplot(data_raw_explo.loc[:, 'CustomerID'], kde=False, bins=1100)\n",
    "# Add axes labels\n",
    "ax.set_xlabel('CustomerID', fontsize=15)\n",
    "ax.set_ylabel('Number of orders', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe a few big customers but most of customers placed an amount of orders bellow 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3950.000000\n",
       "mean       90.310886\n",
       "std       216.804909\n",
       "min         1.000000\n",
       "25%        17.000000\n",
       "50%        41.000000\n",
       "75%        99.000000\n",
       "95%       314.000000\n",
       "99%       659.590000\n",
       "max      7812.000000\n",
       "Name: CustomerID_enc, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_counts_df = pd.DataFrame(customers_counts.values, columns=['counts'])\n",
    "customers_counts_df['cust_enc_id'] = customers_counts.index.values\n",
    "customers_counts.describe(percentiles=[0.25,0.5,0.75,0.95,0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In numbers 95% of the customers placed 314 orders or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to capitalise on the customers buying activity to infer a behaviour, we can't use the cancellations invoices neither can we use other kind of invoices (merchandise thrown away, damaged etc).\n",
    "\n",
    "Let's get rid of this cancellations. Notice that those may be interesting to study in order to look into the reasons of those cancellations. But this is not our purpose here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before removing cancellations :  (356728, 9)\n",
      "Shape after removing cancellations :  (349227, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape before removing cancellations : \", data_raw_explo.shape)\n",
    "data_raw_explo = data_raw_explo[data_raw_explo.Quantity > 0]\n",
    "print(\"Shape after removing cancellations : \", data_raw_explo.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the unit price evolves over time ? YES but no pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StockCode sample : 82582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.10    336\n",
       "1.85     22\n",
       "4.13      5\n",
       "1.69      3\n",
       "Name: UnitPrice, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stockcode_samp = data_raw_explo.StockCode.sample().values\n",
    "print(\"StockCode sample : %s\" % stockcode_samp[0])\n",
    "data_stockcode_samp = data_raw_explo.loc[:, ['InvoiceDate', 'Quantity', 'UnitPrice']][data_raw_explo.StockCode == stockcode_samp[0]]\n",
    "plt.scatter(data_stockcode_samp.InvoiceDate.values, data_stockcode_samp.UnitPrice.values)\n",
    "data_stockcode_samp.UnitPrice.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequency of orders over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "counts, bins, bars = plt.hist(data_raw_explo.InvoiceDate.values, bins=1000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe a rise in the orders close to the end of the year, which is consistent with the holidays period. Moreover the website seem closed from december the 24th to january the 4th."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an amount feature (quantity * unitprice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw_explo['Amount'] = data_raw_explo['Quantity'] * data_raw_explo['UnitPrice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for zeros in amounts : free order that might be gifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UnitPrice', 'CustomerID_enc', 'Amount'], dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Columns with zeros\n",
    "col_zeros = data_raw_explo.columns[(data_raw_explo == 0).any()]\n",
    "display(col_zeros)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CustomerID_enc has a zero, no chocking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>UnitPrice</th>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>454463</th>\n",
       "      <td>0.0</td>\n",
       "      <td>189</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436597</th>\n",
       "      <td>0.0</td>\n",
       "      <td>920</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47068</th>\n",
       "      <td>0.0</td>\n",
       "      <td>189</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47070</th>\n",
       "      <td>0.0</td>\n",
       "      <td>189</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446794</th>\n",
       "      <td>0.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UnitPrice  CustomerID_enc  Amount\n",
       "454463        0.0             189     0.0\n",
       "436597        0.0             920     0.0\n",
       "47068         0.0             189     0.0\n",
       "47070         0.0             189     0.0\n",
       "446794        0.0             829     0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw_explo.loc[:, col_zeros].sort_values(by=['Amount']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9302</th>\n",
       "      <td>537197</td>\n",
       "      <td>22841</td>\n",
       "      <td>ROUND CAKE TIN VINTAGE GREEN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-05 14:02:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12647.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33576</th>\n",
       "      <td>539263</td>\n",
       "      <td>22580</td>\n",
       "      <td>ADVENT CALENDAR GINGHAM SACK</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-12-16 14:36:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16560.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40089</th>\n",
       "      <td>539722</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-12-21 13:45:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>EIRE</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47068</th>\n",
       "      <td>540372</td>\n",
       "      <td>22090</td>\n",
       "      <td>PAPER BUNTING RETROSPOT</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-06 16:41:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47070</th>\n",
       "      <td>540372</td>\n",
       "      <td>22553</td>\n",
       "      <td>PLASTERS IN TIN SKULLS</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-06 16:41:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                   Description  Quantity  \\\n",
       "9302     537197     22841  ROUND CAKE TIN VINTAGE GREEN         1   \n",
       "33576    539263     22580  ADVENT CALENDAR GINGHAM SACK         4   \n",
       "40089    539722     22423      REGENCY CAKESTAND 3 TIER        10   \n",
       "47068    540372     22090       PAPER BUNTING RETROSPOT        24   \n",
       "47070    540372     22553        PLASTERS IN TIN SKULLS        24   \n",
       "\n",
       "              InvoiceDate  UnitPrice  CustomerID         Country  Amount  \n",
       "9302  2010-12-05 14:02:00        0.0     12647.0         Germany     0.0  \n",
       "33576 2010-12-16 14:36:00        0.0     16560.0  United Kingdom     0.0  \n",
       "40089 2010-12-21 13:45:00        0.0     14911.0            EIRE     0.0  \n",
       "47068 2011-01-06 16:41:00        0.0     13081.0  United Kingdom     0.0  \n",
       "47070 2011-01-06 16:41:00        0.0     13081.0  United Kingdom     0.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = data_raw.copy()\n",
    "data_test['Amount'] = data_test['Quantity'] * data_test['UnitPrice']\n",
    "data_test[data_test.Amount == 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 1, 20, 0)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime(2010,12,1,20,0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42451</th>\n",
       "      <td>539988</td>\n",
       "      <td>22854</td>\n",
       "      <td>CREAM SWEETHEART EGG HOLDER</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-23 16:06:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>18116.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42452</th>\n",
       "      <td>539988</td>\n",
       "      <td>47559B</td>\n",
       "      <td>TEA TIME OVEN GLOVE</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-23 16:06:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>18116.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42481</th>\n",
       "      <td>539993</td>\n",
       "      <td>22386</td>\n",
       "      <td>JUMBO BAG PINK POLKADOT</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-01-04 10:00:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>13313.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42482</th>\n",
       "      <td>539993</td>\n",
       "      <td>21499</td>\n",
       "      <td>BLUE POLKADOT WRAP</td>\n",
       "      <td>25</td>\n",
       "      <td>2011-01-04 10:00:00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>13313.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                  Description  Quantity  \\\n",
       "42451    539988     22854  CREAM SWEETHEART EGG HOLDER         2   \n",
       "42452    539988    47559B          TEA TIME OVEN GLOVE         2   \n",
       "42481    539993     22386      JUMBO BAG PINK POLKADOT        10   \n",
       "42482    539993     21499           BLUE POLKADOT WRAP        25   \n",
       "\n",
       "              InvoiceDate  UnitPrice  CustomerID         Country  Amount  \n",
       "42451 2010-12-23 16:06:00       4.95     18116.0  United Kingdom     9.9  \n",
       "42452 2010-12-23 16:06:00       1.25     18116.0  United Kingdom     2.5  \n",
       "42481 2011-01-04 10:00:00       1.95     13313.0  United Kingdom    19.5  \n",
       "42482 2011-01-04 10:00:00       0.42     13313.0  United Kingdom    10.5  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test[(data_test.InvoiceDate >= datetime.datetime(2010,12,23,16,5,0))].iloc[86:90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some zeros indeed, we won't be interested in those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before removing free orders :  (349227, 10)\n",
      "Shape after removing free orders :  (349203, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape before removing free orders : \", data_raw_explo.shape)\n",
    "data_raw_explo = data_raw_explo[data_raw_explo.Amount > 0]\n",
    "print(\"Shape after removing free orders : \", data_raw_explo.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Studying if customers are more likely to be bulk buyers or particular ones\n",
    "\n",
    "**_New feature : Quantity / (UnitPrice + 1) ratio_**, we add 1 to the denominator in order to avoid increasing the ratio by dividing by something smaller than 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw_explo['Bulk'] = data_raw_explo.loc[:, 'Quantity'] / data_raw_explo.loc[:, 'UnitPrice'].apply(lambda x : x + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set seaborn style\n",
    "sns.set_style(\"white\")\n",
    "# Seaborn scatter plot\n",
    "sns.lmplot('CustomerID_enc', 'Bulk', data=data_raw_explo, fit_reg=False, aspect=2.5)\n",
    "# Add axes labels\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel('CustomerID', fontsize=15)\n",
    "ax.set_ylabel('Tendency to buy in bulk', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We spot 2 outliers, let's verify if it bulk orders as it seems to be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>77183.6</td>\n",
       "      <td>36379.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540421</th>\n",
       "      <td>581483</td>\n",
       "      <td>23843</td>\n",
       "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
       "      <td>80995</td>\n",
       "      <td>2011-12-09 09:15:00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>16446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2619</td>\n",
       "      <td>168469.6</td>\n",
       "      <td>26297.077922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619     541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "540421    581483     23843     PAPER CRAFT , LITTLE BIRDIE     80995   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \\\n",
       "61619  2011-01-18 10:01:00       1.04     12346.0  United Kingdom   \n",
       "540421 2011-12-09 09:15:00       2.08     16446.0  United Kingdom   \n",
       "\n",
       "        CustomerID_enc    Amount          Bulk  \n",
       "61619                0   77183.6  36379.901961  \n",
       "540421            2619  168469.6  26297.077922  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw_explo.sort_values(by='Bulk', ascending=False).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well it is very true for those two fellows, what about the smaller ones ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>36379.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540421</th>\n",
       "      <td>581483</td>\n",
       "      <td>23843</td>\n",
       "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
       "      <td>80995</td>\n",
       "      <td>2011-12-09 09:15:00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>16446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2619</td>\n",
       "      <td>168469.60</td>\n",
       "      <td>26297.077922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421632</th>\n",
       "      <td>573008</td>\n",
       "      <td>84077</td>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>4800</td>\n",
       "      <td>2011-10-27 12:26:00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>12901.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>61</td>\n",
       "      <td>1008.00</td>\n",
       "      <td>3966.942149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270885</th>\n",
       "      <td>560599</td>\n",
       "      <td>18007</td>\n",
       "      <td>ESSENTIAL BALM 3.5g TIN IN ENVELOPE</td>\n",
       "      <td>3186</td>\n",
       "      <td>2011-07-19 17:04:00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>14609.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1279</td>\n",
       "      <td>191.16</td>\n",
       "      <td>3005.660377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206121</th>\n",
       "      <td>554868</td>\n",
       "      <td>22197</td>\n",
       "      <td>SMALL POPCORN HOLDER</td>\n",
       "      <td>4300</td>\n",
       "      <td>2011-05-27 10:52:00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>228</td>\n",
       "      <td>3096.00</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4945</th>\n",
       "      <td>536830</td>\n",
       "      <td>84077</td>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2880</td>\n",
       "      <td>2010-12-02 17:38:00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>16754.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2842</td>\n",
       "      <td>518.40</td>\n",
       "      <td>2440.677966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433788</th>\n",
       "      <td>573995</td>\n",
       "      <td>16014</td>\n",
       "      <td>SMALL CHINESE STYLE SCISSOR</td>\n",
       "      <td>3000</td>\n",
       "      <td>2011-11-02 11:24:00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>16308.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2514</td>\n",
       "      <td>960.00</td>\n",
       "      <td>2272.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91772</th>\n",
       "      <td>544152</td>\n",
       "      <td>18007</td>\n",
       "      <td>ESSENTIAL BALM 3.5g TIN IN ENVELOPE</td>\n",
       "      <td>2400</td>\n",
       "      <td>2011-02-16 12:10:00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>14609.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1279</td>\n",
       "      <td>144.00</td>\n",
       "      <td>2264.150943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97432</th>\n",
       "      <td>544612</td>\n",
       "      <td>22053</td>\n",
       "      <td>EMPIRE DESIGN ROSETTE</td>\n",
       "      <td>3906</td>\n",
       "      <td>2011-02-22 10:43:00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>18087.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3801</td>\n",
       "      <td>3202.92</td>\n",
       "      <td>2146.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80742</th>\n",
       "      <td>543057</td>\n",
       "      <td>84077</td>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2592</td>\n",
       "      <td>2011-02-03 10:50:00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>16333.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2531</td>\n",
       "      <td>544.32</td>\n",
       "      <td>2142.148760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "61619     541431     23166       MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "540421    581483     23843          PAPER CRAFT , LITTLE BIRDIE     80995   \n",
       "421632    573008     84077    WORLD WAR 2 GLIDERS ASSTD DESIGNS      4800   \n",
       "270885    560599     18007  ESSENTIAL BALM 3.5g TIN IN ENVELOPE      3186   \n",
       "206121    554868     22197                 SMALL POPCORN HOLDER      4300   \n",
       "4945      536830     84077    WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880   \n",
       "433788    573995     16014          SMALL CHINESE STYLE SCISSOR      3000   \n",
       "91772     544152     18007  ESSENTIAL BALM 3.5g TIN IN ENVELOPE      2400   \n",
       "97432     544612     22053                EMPIRE DESIGN ROSETTE      3906   \n",
       "80742     543057     84077    WORLD WAR 2 GLIDERS ASSTD DESIGNS      2592   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \\\n",
       "61619  2011-01-18 10:01:00       1.04     12346.0  United Kingdom   \n",
       "540421 2011-12-09 09:15:00       2.08     16446.0  United Kingdom   \n",
       "421632 2011-10-27 12:26:00       0.21     12901.0  United Kingdom   \n",
       "270885 2011-07-19 17:04:00       0.06     14609.0  United Kingdom   \n",
       "206121 2011-05-27 10:52:00       0.72     13135.0  United Kingdom   \n",
       "4945   2010-12-02 17:38:00       0.18     16754.0  United Kingdom   \n",
       "433788 2011-11-02 11:24:00       0.32     16308.0  United Kingdom   \n",
       "91772  2011-02-16 12:10:00       0.06     14609.0  United Kingdom   \n",
       "97432  2011-02-22 10:43:00       0.82     18087.0  United Kingdom   \n",
       "80742  2011-02-03 10:50:00       0.21     16333.0  United Kingdom   \n",
       "\n",
       "        CustomerID_enc     Amount          Bulk  \n",
       "61619                0   77183.60  36379.901961  \n",
       "540421            2619  168469.60  26297.077922  \n",
       "421632              61    1008.00   3966.942149  \n",
       "270885            1279     191.16   3005.660377  \n",
       "206121             228    3096.00   2500.000000  \n",
       "4945              2842     518.40   2440.677966  \n",
       "433788            2514     960.00   2272.727273  \n",
       "91772             1279     144.00   2264.150943  \n",
       "97432             3801    3202.92   2146.153846  \n",
       "80742             2531     544.32   2142.148760  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw_explo.sort_values(by='Bulk', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, those buy rather by large amounts of cheap items.\n",
    "\n",
    "What about those who buy a few expensive things ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173382</th>\n",
       "      <td>551697</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-05-03 13:46:00</td>\n",
       "      <td>8142.75</td>\n",
       "      <td>16029.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2310</td>\n",
       "      <td>8142.75</td>\n",
       "      <td>0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374542</th>\n",
       "      <td>569382</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-03 16:44:00</td>\n",
       "      <td>3155.95</td>\n",
       "      <td>15502.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1919</td>\n",
       "      <td>3155.95</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297438</th>\n",
       "      <td>562946</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-11 09:38:00</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>15581.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1982</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>0.000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343000</th>\n",
       "      <td>566927</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-15 15:20:00</td>\n",
       "      <td>2033.10</td>\n",
       "      <td>17846.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3623</td>\n",
       "      <td>2033.10</td>\n",
       "      <td>0.000492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150615</th>\n",
       "      <td>549468</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-04-08 14:27:00</td>\n",
       "      <td>1867.86</td>\n",
       "      <td>17940.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3697</td>\n",
       "      <td>1867.86</td>\n",
       "      <td>0.000535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528082</th>\n",
       "      <td>580727</td>\n",
       "      <td>DOT</td>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-05 17:17:00</td>\n",
       "      <td>1599.26</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>909</td>\n",
       "      <td>1599.26</td>\n",
       "      <td>0.000625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508459</th>\n",
       "      <td>579196</td>\n",
       "      <td>DOT</td>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-28 15:54:00</td>\n",
       "      <td>1526.76</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>909</td>\n",
       "      <td>1526.76</td>\n",
       "      <td>0.000655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467436</th>\n",
       "      <td>576339</td>\n",
       "      <td>DOT</td>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-14 15:27:00</td>\n",
       "      <td>1500.36</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>909</td>\n",
       "      <td>1500.36</td>\n",
       "      <td>0.000666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494728</th>\n",
       "      <td>578270</td>\n",
       "      <td>DOT</td>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-23 13:39:00</td>\n",
       "      <td>1270.06</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>909</td>\n",
       "      <td>1270.06</td>\n",
       "      <td>0.000787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455261</th>\n",
       "      <td>575607</td>\n",
       "      <td>DOT</td>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-10 12:37:00</td>\n",
       "      <td>908.16</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>909</td>\n",
       "      <td>908.16</td>\n",
       "      <td>0.001100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode     Description  Quantity         InvoiceDate  \\\n",
       "173382    551697      POST         POSTAGE         1 2011-05-03 13:46:00   \n",
       "374542    569382         M          Manual         1 2011-10-03 16:44:00   \n",
       "297438    562946         M          Manual         1 2011-08-11 09:38:00   \n",
       "343000    566927         M          Manual         1 2011-09-15 15:20:00   \n",
       "150615    549468         M          Manual         1 2011-04-08 14:27:00   \n",
       "528082    580727       DOT  DOTCOM POSTAGE         1 2011-12-05 17:17:00   \n",
       "508459    579196       DOT  DOTCOM POSTAGE         1 2011-11-28 15:54:00   \n",
       "467436    576339       DOT  DOTCOM POSTAGE         1 2011-11-14 15:27:00   \n",
       "494728    578270       DOT  DOTCOM POSTAGE         1 2011-11-23 13:39:00   \n",
       "455261    575607       DOT  DOTCOM POSTAGE         1 2011-11-10 12:37:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  CustomerID_enc   Amount  \\\n",
       "173382    8142.75     16029.0  United Kingdom            2310  8142.75   \n",
       "374542    3155.95     15502.0  United Kingdom            1919  3155.95   \n",
       "297438    2500.00     15581.0  United Kingdom            1982  2500.00   \n",
       "343000    2033.10     17846.0  United Kingdom            3623  2033.10   \n",
       "150615    1867.86     17940.0  United Kingdom            3697  1867.86   \n",
       "528082    1599.26     14096.0  United Kingdom             909  1599.26   \n",
       "508459    1526.76     14096.0  United Kingdom             909  1526.76   \n",
       "467436    1500.36     14096.0  United Kingdom             909  1500.36   \n",
       "494728    1270.06     14096.0  United Kingdom             909  1270.06   \n",
       "455261     908.16     14096.0  United Kingdom             909   908.16   \n",
       "\n",
       "            Bulk  \n",
       "173382  0.000123  \n",
       "374542  0.000317  \n",
       "297438  0.000400  \n",
       "343000  0.000492  \n",
       "150615  0.000535  \n",
       "528082  0.000625  \n",
       "508459  0.000655  \n",
       "467436  0.000666  \n",
       "494728  0.000787  \n",
       "455261  0.001100  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw_explo.sort_values(by='Bulk', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many 'POST' or 'Manual' or 'DOTCOM POSTAGE' ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "286 of those found in the dataset, that is to say 0.082 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "number = data_raw_explo[data_raw_explo.StockCode.isin(['POST', 'M', 'DOT'])].shape[0]\n",
    "print(\"%i of those found in the dataset, that is to say %.3f %% of the dataset\" % (number, number / data_raw_explo.shape[0] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well this is expensive things indeed, but those are not usual items for sale. It appears that manuals and shipment may be very expensive, we will not consider those items for our customers analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating those \"other codes\" :\n",
    "\n",
    "- Convert stock codes to strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw_explo.StockCode = data_raw_explo.StockCode.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List stock codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_codes = []\n",
    "other_codes = []\n",
    "for code in data_raw_explo.StockCode.unique() :\n",
    "    if code[0].isdigit() :\n",
    "        stock_codes.append(code)\n",
    "    else :\n",
    "        other_codes.append(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M', 'BANK CHARGES', 'C2', 'POST', 'PADS', 'DOT']\n"
     ]
    }
   ],
   "source": [
    "print(other_codes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- POST : Expedition cost\n",
    "-  C2  : Carriage cost\n",
    "- DOT  : DOTCOM Expedition cost\n",
    "-  M   : Manual order (is it really manuals ?)\n",
    "- PADS : Pad for cunshion, actual product ?\n",
    "- BANK CHARGES : Bank fees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the data about actual sold items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Only' 331 'other codes' that is to say 0.09 % of the data\n"
     ]
    }
   ],
   "source": [
    "data_stock_codes = data_raw_explo[data_raw_explo.StockCode.isin(stock_codes)]\n",
    "n_other_codes = data_raw_explo[data_raw_explo.StockCode.isin(other_codes)].shape[0]\n",
    "print(\"'Only' %i 'other codes' that is to say %.2f %% of the data\" % (n_other_codes, n_other_codes * 100 / data_raw_explo.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see again the 'selective' buyers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>222682</th>\n",
       "      <td>556446</td>\n",
       "      <td>22502</td>\n",
       "      <td>PICNIC BASKET WICKER 60 PIECES</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-06-10 15:33:00</td>\n",
       "      <td>649.5</td>\n",
       "      <td>15098.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1619</td>\n",
       "      <td>649.5</td>\n",
       "      <td>0.001537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171178</th>\n",
       "      <td>551393</td>\n",
       "      <td>22656</td>\n",
       "      <td>VINTAGE BLUE KITCHEN CABINET</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-04-28 12:22:00</td>\n",
       "      <td>295.0</td>\n",
       "      <td>14973.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1529</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.003378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51636</th>\n",
       "      <td>540647</td>\n",
       "      <td>22655</td>\n",
       "      <td>VINTAGE RED KITCHEN CABINET</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-10 14:57:00</td>\n",
       "      <td>295.0</td>\n",
       "      <td>17406.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3308</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.003378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82768</th>\n",
       "      <td>543253</td>\n",
       "      <td>22655</td>\n",
       "      <td>VINTAGE RED KITCHEN CABINET</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-04 15:32:00</td>\n",
       "      <td>295.0</td>\n",
       "      <td>14842.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1444</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.003378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205759</th>\n",
       "      <td>554836</td>\n",
       "      <td>22655</td>\n",
       "      <td>VINTAGE RED KITCHEN CABINET</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-05-26 16:25:00</td>\n",
       "      <td>295.0</td>\n",
       "      <td>13015.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>143</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.003378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                     Description  Quantity  \\\n",
       "222682    556446     22502  PICNIC BASKET WICKER 60 PIECES         1   \n",
       "171178    551393     22656    VINTAGE BLUE KITCHEN CABINET         1   \n",
       "51636     540647     22655     VINTAGE RED KITCHEN CABINET         1   \n",
       "82768     543253     22655     VINTAGE RED KITCHEN CABINET         1   \n",
       "205759    554836     22655     VINTAGE RED KITCHEN CABINET         1   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \\\n",
       "222682 2011-06-10 15:33:00      649.5     15098.0  United Kingdom   \n",
       "171178 2011-04-28 12:22:00      295.0     14973.0  United Kingdom   \n",
       "51636  2011-01-10 14:57:00      295.0     17406.0  United Kingdom   \n",
       "82768  2011-02-04 15:32:00      295.0     14842.0  United Kingdom   \n",
       "205759 2011-05-26 16:25:00      295.0     13015.0  United Kingdom   \n",
       "\n",
       "        CustomerID_enc  Amount      Bulk  \n",
       "222682            1619   649.5  0.001537  \n",
       "171178            1529   295.0  0.003378  \n",
       "51636             3308   295.0  0.003378  \n",
       "82768             1444   295.0  0.003378  \n",
       "205759             143   295.0  0.003378  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_stock_codes.sort_values(by='Bulk', ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set seaborn style\n",
    "sns.set_style(\"white\")\n",
    "# Seaborn scatter plot\n",
    "sns.lmplot('CustomerID_enc', 'Bulk', data=data_stock_codes, fit_reg=False, aspect=2.5)\n",
    "# Add axes labels\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel('CustomerID', fontsize=15)\n",
    "ax.set_ylabel('Tendency to buy in bulk', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same considerations but averaged by order (invoice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>CustomerID_enc</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>536365</th>\n",
       "      <td>3626</td>\n",
       "      <td>5.714286</td>\n",
       "      <td>3.910000</td>\n",
       "      <td>1.328253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536366</th>\n",
       "      <td>3626</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536367</th>\n",
       "      <td>165</td>\n",
       "      <td>6.916667</td>\n",
       "      <td>4.853333</td>\n",
       "      <td>1.941843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536368</th>\n",
       "      <td>165</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.775000</td>\n",
       "      <td>0.663866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536369</th>\n",
       "      <td>165</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>0.431655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CustomerID_enc  Quantity  UnitPrice      Bulk\n",
       "InvoiceNo                                               \n",
       "536365               3626  5.714286   3.910000  1.328253\n",
       "536366               3626  6.000000   1.850000  2.105263\n",
       "536367                165  6.916667   4.853333  1.941843\n",
       "536368                165  3.750000   4.775000  0.663866\n",
       "536369                165  3.000000   5.950000  0.431655"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoice_df = data_stock_codes.groupby('InvoiceNo').agg({'CustomerID_enc': lambda x: x.mean(),\n",
    "                                                        'Quantity': lambda x: x.mean(),\n",
    "                                                        'UnitPrice': lambda x: x.mean(),\n",
    "                                                        'Bulk': lambda x: x.mean()})\n",
    "invoice_df.head()"
   ]
  },
  {
   "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>CustomerID_enc</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>74215.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>36379.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12.796737</td>\n",
       "      <td>4.253949</td>\n",
       "      <td>3.543142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>8.900291</td>\n",
       "      <td>2.728614</td>\n",
       "      <td>5.151464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6.567653</td>\n",
       "      <td>4.812586</td>\n",
       "      <td>2.095416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12.361742</td>\n",
       "      <td>1.902633</td>\n",
       "      <td>6.277319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CustomerID_enc      Quantity  UnitPrice          Bulk\n",
       "CustomerID_enc                                                       \n",
       "0                            0  74215.000000   1.040000  36379.901961\n",
       "1                            1     12.796737   4.253949      3.543142\n",
       "2                            2      8.900291   2.728614      5.151464\n",
       "3                            3      6.567653   4.812586      2.095416\n",
       "4                            4     12.361742   1.902633      6.277319"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_invoice_df = invoice_df.groupby('CustomerID_enc').agg({'CustomerID_enc': lambda x: x.mean(),\n",
    "                                                                'Quantity': lambda x: x.mean(),\n",
    "                                                                'UnitPrice': lambda x: x.mean(),\n",
    "                                                                'Bulk': lambda x: x.mean()})\n",
    "customer_invoice_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o7ty5OnXqlAYMGKCxY8fqgQceUGJioqtfTk6Otm3bplWrVqmgoEDjx4/XmjVrNGvWLE2YMEGdO3dWWlqaHA6HmjRpUm1fAAAAAIB7lkJgcXGxHn/8cWVmZrra/Pz81K9fP02bNk1BQUHn3b5Xr15KSEhwPbbZbNq9e7dyc3PlcDgUERGhlJQUbd++XbGxsTIMQ02aNFFlZaVOnjypnJwcderUSZIUFxenzZs3KzIystq+P79MFQAAAABQlaWJYZ566inl5eXpjTfe0JdffqkvvvhCy5Yt0xdffKGZM2decPvg4GDZ7XYVFRXp0Ucf1YQJE9S+fXtNmjRJK1asUNOmTbV48WIVFRXJbrdX2a6wsFCmacowjCpt7voCAAAAANyzvFj8jBkzdPvtt6tevXqqX7++unTpovT0dK1fv97SjgoKCjRixAj1799fffv2VY8ePdSuXTtJUo8ePbRnzx7Z7XYVFxe7tikuLlZISIj8/PyqtIWGhrrtCwAAAABwz1IIDA4Olr//uVeOBgcHy2azXXD7EydOKDExURMnTtSgQYMkSSNHjtSuXbskSVu2bFHbtm0VHR2t7OxsOZ1O5efny+l0KiwsTG3atNHWrVslSVlZWYqJiXHbFwAAAADgnqV7Ah955BFNnTpV8+bNU2RkpCTp2LFjevrppzV69OgLbr906VKdPn1aS5Ys0ZIlSyRJSUlJmjlzpgICAtSwYUOlp6fLbrcrJiZGgwcPltPpVFpamiRp8uTJSk1N1fz58xUVFaWEhATZbLZq+wIAAAAA3DNM0zSre6Jt27au+/AkqaKiQoZhKDQ0VDabTd9//70kqVGjRlUmjLmS5OXlKT4+Xg6HQ+Hh4b4uBwAAAAB8zu1IYHp6epUQCAAAAACo/dyGwIEDB9ZkHQAAAACAGmBpYhgAAAAAwNWBEAgAAAAAdQghEAAAAADqEEsh8LPPPlNFRcU57WVlZfroo488XhQAAAAAwDsshcARI0bo9OnT57QfPXpUjz/+uMeLAgAAAAB4h9vZQd98800tX75ckmSapv7whz/Iz69qZjx9+rRr8XgAAAAAwJXvvEtEnD59Wk6nU88//7zuvvtu1a9f3/W8YRgKDg5Wz549a6RQAAAAAMDlcxsCg4KCNGbMGElS48aN1adPH11zzTU1VhgAAAAAwPPchsCfGzBggAoKCrRr1y6Vl5fLNM0qz/ft29crxQEAAAAAPMtSCFy5cqWmT5+uysrKc54zDIMQCAAAAAC1hKUQ+Oqrr2rgwIGaNGmSQkJCvF0TAAAAAMBLLC0RkZ+fr8TERAIgAAAAANRylkLgbbfdpv3793u7FgAAAACAl1meGGb69OnKyclRRESEAgMDqzzPPYEAAAAAUDsY5i+n+qxG69at3b+AYWjv3r0eLcpT8vLyFB8fL4fDofDwcF+XAwAAAAA+Z2kkcN++fd6uAwAAAABQAyzdE/iTEydOaOvWrSotLdV3333nrZoAAAAAAF5iKQSWlZUpOTlZsbGxeuCBB/Tf//5XaWlp+uMf/6jCwkJv1wgAAAAA8BBLIXDRokXavXu33nzzTV1zzTWSpAcffFDffvut5s6d69UCAQAAAACeYykErl+/XlOmTFF0dLSrrWPHjkpPT9emTZu8VhwAAAAAwLMshcDjx4+rSZMm57Q3bNiQy0EBAAAAoBaxFAJvvfVWORyOc9pXrlx53uUjAAAAAABXFktLRDzxxBN68MEHtWPHDlVUVOill17SwYMHtXPnTr344overhEAAAAA4CGWRgJjYmL01ltvKSAgQBEREfrqq6/UpEkTvfvuu7rzzju9XSMAAAAAwEMsjQRKP14SykygAAAAAFC7uQ2BqampSkpKUnBwsFJTU8/7Iunp6R4vDAAAAADgeW5D4DfffKPKykrX1+4YhuHxogAAAAAA3uE2BL7xxhvVfv2Ts2fPuhaOBwAAAADUDpYmhikpKdHEiRO1ZMkSV1uvXr2UnJys0tJSrxUHAAAAAPAsSyHw6aef1p49e6rMBPrUU09p165dmjdv3gW3Ly8v18SJE3Xfffdp0KBBcjgcOnz4sIYOHar77rtPU6dOldPplCQtWrRIgwYN0pAhQ7Rr1y5Juqi+AAAAAAD3LM0OumnTJi1dulTt27d3tXXp0kV2u13jx4/XlClTzrv9unXr1KBBA82dO1enTp3SgAED1Lp1a02YMEGdO3dWWlqaHA6HmjRpom3btmnVqlUqKCjQ+PHjtWbNGs2aNctyXwAAAACAe5ZC4NmzZxUUFHROu91uV3Fx8QW379WrlxISElyPbTabcnJy1KlTJ0lSXFycNm/erMjISMXGxsowDDVp0kSVlZU6efLkRfUNCwuz9MYBAAAAoC6ydDno7bffrueee05nzpxxtZWUlGjRokWKjo6+4PbBwcGy2+0qKirSo48+qgkTJsg0TdfMosHBwSosLFRRUZHsdnuV7QoLCy+qLwAAAADAPUsjgcnJybr//vsVFxenqKgoSVJubq6Cg4O1fPlySzsqKCjQ2LFjdd9996lv375VFp4vLi5WaGjoOSOLxcXFCgkJkZ+fn+W+AAAAAAD3LI0ERkRE6IMPPtATTzyh2267TR07dtTEiRO1fv16NW/e/ILbnzhxQomJiZo4caIGDRokSWrTpo22bt0qScrKylJMTIyio6OVnZ0tp9Op/Px8OZ1OhYWFXVRfAAAAAIB7hmmaprd3MmPGDK1fv941iihJTz75pGbMmKHy8nJFRUVpxowZstlsWrhwobKysuR0OpWcnKyYmBjl5uYqNTXVUt+fy8vLU3x8vBwOh8LDw739NgEAAADgiuc2BCYmJuq5555TSEiIHnjgAdc9edV55ZVXvFbg5SAEAgAAAEBVbu8JvOGGG1zB78Ybb6yxggAAAAAA3uM2BJaWlqqsrEySNHDgQHXo0EEBAQE1VhgAAAAAwPPcTgzjcDj0ww8/SJJGjBjB8gsAAAAAcBVwOxLYqlUrDR8+XJGRkTJNU2PHjnU7Evj66697rUAAAAAAgOe4DYHPP/+8/va3v6mwsFCfffaZbrrpJgUFBdVkbQAAAAAAD3MbAjdt2qSxY8cqODhY33zzjdLS0hQaGlqTtQEAAAAAPMztPYFz587V6dOnJUmff/65KioqaqwoAAAAAIB3uB0JbNiwoVJTU9WhQweZpqmXX35Z9evXr7bvuHHjvFYgAAAAAMBz3IbAadOm6bnnntP7778vwzC0YcMG2Wy2c/oZhkEIBAAAAIBawm0I7NKli7p06SJJat26tVauXKnrrruuxgoDAAAAAHie23sCf27fvn2uAJiXl6eKigqVl5d7tTAAAAAAgOdZCoGStHjxYnXo0EE9e/ZUQUGBJk2apJSUFMIgAAAAANQilkLgK6+8otWrVys9PV2BgYGSpD59+igzM1MLFizwaoEAAAAAAM+xFAJXr16ttLQ09e3bV4ZhSJJ69OihmTNnKiMjw6sFAgAAAAA8x1IIzMvLU4sWLc5pj4yM1MmTJz1eFAAAAADAOyyFwMjISH3++efntG/YsEGRkZEeLwoAAAAA4B1ul4j4ufHjx2vixIk6cOCAKisrtW7dOh0+fFgZGRl65plnvF0jAAAAAMBDLI0Edu/eXQsWLNCXX34pm82m1157TXl5eVq6dKl+//vfe7tGAAAAAICHWBoJlKSuXbuqa9eu3qwFAAAAAOBlltcJ/Oijj3TvvfeqQ4cOiomJ0ZAhQ7Rx40Zv1gYAAAAA8DBLIXD9+vV69NFHFR4erokTJ+rPf/6zbrjhBj322GMEQQAAAACoRSxdDrpkyRJNmDBBo0ePdrUNHz5cL774opYuXaqePXt6rUAAAAAAgOdYGgk8fPiwevXqdU57QkKCDh486PGiAAAAAADeYSkENm7cWPv37z+nfd++fbr22ms9XhQAAAAAwDssXQ46aNAgTZ06Vd9//72io6MlSdu3b9eCBQs0ePBgrxYIAAAAAPAcSyEwMTFRx44d0/Tp01VZWSnTNBUQEKAHHnhA48aN83aNAAAAAAAPMUzTNK12Lioq0qFDh3TNNdcoIiJCQUFB3qztsuXl5Sk+Pl4Oh0Ph4eG+LgcAAAAAfM7SPYFnzpzRxIkT9frrr6t9+/a65ZZb1Lt3byUnJ6u0tNTbNQIAAAAAPMRSCJw5c6b27NmjO++809X21FNPadeuXZo3b57XigMAAAAAeJalELhp0ybNmjVLHTp0cLV16dJFM2bM0Icffui14gAAAAAAnmUpBJ49e7ba+//sdruKi4s9XhQAAAAAwDsshcDbb79dzz33nM6cOeNqKykp0aJFi1xLRlixc+dODR8+XJKUk5OjLl26aPjw4Ro+fLg++OADSdKiRYs0aNAgDRkyRLt27ZL042L1Q4cO1X333aepU6fK6XS67QsAAAAAcM/SEhHJycm6//77FRcXp6ioKElSbm6ugoODtXz5cks7eumll7Ru3TrVq1dPkrRnzx498MADSkxMdPXJycnRtm3btGrVKhUUFGj8+PFas2aNZs2apQkTJqhz585KS0uTw+FQkyZNqu0LAAAAAHDPUgiMiIjQBx98oIyMDP373/+Wv7+/Bg0apL59+7pC3YU0a9ZMCxcu1KRJkyRJu3fvVm5urhwOhyIiIpSSkqLt27crNjZWhmGoSZMmqqys1MmTJ5WTk6NOnTpJkuLi4rR582ZFRkZW2zcsLOwSDwUAAAAAXP0shUBJCgkJ0ZAhQy55RwkJCcrLy3M9bt++ve699161a9dOL7zwghYvXqyQkBA1aNDA1Sc4OFiFhYUyTVOGYVRpKyoqqrYvIRAAAAAA3LN0T6A39OjRQ+3atXN9vWfPnnMmmikuLlZISIj8/PyqtIWGhrrtCwAAAABwz2chcOTIka7JXLZs2aK2bdsqOjpa2dnZcjqdys/Pl9PpVFhYmNq0aaOtW7dKkrKyshQTE+O2LwAAAADAPcuXg3ratGnTlJ6eroCAADVs2FDp6emy2+2KiYnR4MGD5XQ6lZaWJkmaPHmyUlNTNX/+fEVFRSkhIUE2m63avgAAAAAA9wzTNM0LdSopKXE7AUx2drZiY2M9Xpgn5OXlKT4+Xg6HQ+Hh4b4uBwAAAAB8ztLloA899JBKS0urtJ0+fVpJSUkaNWqUVwoDAAAAAHiepRBYVFSkUaNGuYLgRx99pD59+mjz5s3661//6tUCAQAAAACeY+mewNdee00PPvigRo0apbCwMG3cuFGDBw/WE088Ibvd7u0aAQAAAAAeYmkkMDQ0VK+++qqkH0cBly9frmnTphEAAQAAAKCWcTsSuHTp0nPaoqOjtXPnTj3//POu5R0kacyYMd6pDgAAAADgUW5D4MqVK6ttb9iwoY4fP+563jAMQiAAAAAA1BJuQ+CmTZtqsg4AAIBaJ3PfcS3LOqQjp86o6bX1NTouSne1buTrsgDgvNyGwGPHjll+kRtuuMEjxQAAANQWmfuOK21djgJshhrUC9DxwlKlrcvRUxJBEMAVzW0I7Nq1qwzDOO/GpmnKMAzt3bvX44UBAABcyZZlHVKAzVD9wB9/naof6K8zZRValnWIEAjgiuY2BL7++us1WQcAAECtcuTUGTWoF1ClrV6ATXmnzvioIgCwxm0I7NSpU03WAQAAUKs0vba+jheWukYCJamkvFLh19b3YVUAcGGWFotPTEw87/OvvPKKR4oBAACoLUbHRSltXY7OlFWoXoBNJeWVKq80NTouytelAcB5WQqBv5z4paKiQocPH9b+/fv1xz/+0SuFAQAAXMnuat1IT+nHewPzTp1ROLODAqglLIXAWbNmVdu+aNGii5pFFAAA4GpyV+tGhD4AtY7f5Wx8zz33aP369Z6qBQAAAADgZZcVAnft2iWbzeapWgAAAAAAXnbJE8MUFRVpz549Gjp0qMeLAgAAAAB4xyVNDCNJ4eHhGjJkiPr16+fxogAAAAAA3nFZE8MAAAAAAGoXSyFQko4dO6a3335b//73vxUYGKiWLVtq6NChatCggTfrAwAAAAB4kKWJYXbu3KmePXsqIyND/v7+qqio0KpVq5SQkKCcnBxv1wgAAAAA8BBLI4HTpk3ToEGDNGXKFBmGIenHBeOnTJmi9PR0vf32216vDEZsAAAe+0lEQVQtEgAAAADgGZZGAg8cOKD777/fFQAlyd/fX6NGjdKePXu8VhwAAAAAwLMshcBbb71VO3fuPKd93759ioqK8nhRAAAAAADvcHs56Pvvv+/6+je/+Y2mT5+u3NxcdejQQX5+ftq7d69efvllPfLIIzVSKAAAAADg8hmmaZrVPdG6dWtrL2AY2rt3r0eL8pS8vDzFx8fL4XAoPDzc1+UAAAAAgM+5HQnct29fTdYBAAAAAKgBlu4JBAAAAABcHQiBAAAAAFCHEAIBAAAAoA4hBAIAAABAHXLRITAvL08VFRUqLy+/6J3t3LlTw4cPlyQdPnxYQ4cO1X333aepU6fK6XRKkhYtWqRBgwZpyJAh2rVr10X3BQAAAAC4ZykEmqapxYsXq0OHDurZs6cKCgo0adIkpaSkWA6DL730kqZMmaKzZ89KkmbNmqUJEybozTfflGmacjgcysnJ0bZt27Rq1SrNnz9f06dPv+i+AAAAAAD3LIXAV199VatXr1Z6eroCAwMlSX369FFmZqYWLFhgaUfNmjXTwoULXY9zcnLUqVMnSVJcXJw++eQTbd++XbGxsTIMQ02aNFFlZaVOnjx5UX0BAAAAAO5ZCoGrV69WWlqa+vbtK8MwJEk9evTQzJkzlZGRYWlHCQkJ8vf//8sSmqbpeq3g4GAVFhaqqKhIdrvd1een9ovpCwAAAABwz1IIzMvLU4sWLc5pj4yMvOTRNz+//7/r4uJihYaGym63q7i4uEp7SEjIRfUFAAAAALhnKQRGRkbq888/P6d9w4YNioyMvKQdt2nTRlu3bpUkZWVlKSYmRtHR0crOzpbT6VR+fr6cTqfCwsIuqi8AAAAAwD3/C3eRxo8fr4kTJ+rAgQOqrKzUunXrdPjwYWVkZOiZZ565pB1PnjxZqampmj9/vqKiopSQkCCbzaaYmBgNHjxYTqdTaWlpF90XAAAAAOCeYZqmaaXjv/71Ly1btkx79+5VQECAWrRooYcfflhdunTxdo2XLC8vT/Hx8XI4HAoPD/d1OQAAAADgc5ZGAiWpa9eu6tq1qzdrAQAAAAB4maUQuGjRomrbDcNQQECAbrzxRsXFxalBgwYeLQ4AAAAA4FmWQuC2bdv0+eefKzAwUDfffLMk6fDhwyotLVXjxo31ww8/KCAgQP/7v/+r1q1be7NeAAAAAMBlsDQ7aLt27dSpUydt2rRJa9eu1dq1a/XPf/5TXbp0Ub9+/bR161Z1795dc+fO9Xa9AAAAAIDLYCkErlmzRklJSVWWYGjQoIEef/xxvfXWW/L399cDDzygHTt2eK1QAAAAAMDlsxQCJamoqOictsLCQlVWVkqSbDZblUXdAQAAAABXHkuprXv37kpNTdW2bdt09uxZlZaWauvWrZo6daq6deum0tJSvfTSS2rXrp236wUAAAAAXAZLE8M8+eSTmjx5skaMGCHDMCT9ODNojx49lJqaquzsbGVlZenFF1/0arEAAAAAgMtjebF46cfF1/fs2SObzaZWrVqpadOmkqSysjIFBgZ6rchLxWLxAAAAAFCV5cXiKyoqFBAQ4Lrk0zRN5ebm6quvvlK/fv28ViAAAAAAwHMshcD/+7//U1JSkk6ePHnOc/Xq1SMEAgAAAEAtYWlimGeffVbt27fXq6++qqCgIL3wwguaOnWqQkNDNXv2bG/XCAAAAADwEEsjgQcPHtQzzzyjVq1aqU2bNgoICNCQIUNUr149vfLKK+rZs6e36wQAAAAAeIClkUB/f38FBwdLkiIiIrR//35J0u23366DBw96rzoAAAAAgEdZCoHt2rXTmjVrJEmtWrXSli1bJEnffPMNC8QDAAAAQC1i6XLQcePG6aGHHlJISIj69++vJUuW6J577tHRo0fVvXt3b9cIAAAAAPAQSyHwf/7nf7RhwwaVl5crLCxMb775pt59912FhYVpxIgR3q4RAAAAAOAhlq7l7N+/v06dOuVaHL5FixaaNGmSHnzwwStykXgAAAAAQPUshcBTp04pKCjI27UAAAAAALzM0uWgf/zjH/XnP/9Zw4cPV3h4uK655poqz0dHR3ulOAAAAACAZxmmaZoX6tS6dWv3L2AY2rt3r0eL8pS8vDzFx8fL4XAoPDzc1+UAAAAAgM9ZGgl0OBzergMAAAAAUAMshcCbbrrJ9XVeXp5uvPFGmaapgIAArxUGAAAAAPA8SxPDmKapxYsXq0OHDurZs6cKCgo0adIkpaSkqLy83Ns1AgAAAAA8xFIIfPXVV7V69Wqlp6e7loTo06ePMjMztWDBAq8WCAAAAADwHEshcPXq1UpLS1Pfvn1lGIYkqUePHpo5c6YyMjK8WiAAAAAAwHMshcC8vDy1aNHinPbIyEidPHnS40UBAAAAALzDUgiMjIzU559/fk77hg0bFBkZ6fGiAAAAAADeYWl20PHjx2vixIk6cOCAKisrtW7dOh0+fFgZGRl65plnvF0jAAAAAMBDLI0Edu/eXQsWLNCXX34pm82m1157TXl5eVq6dKl+//vfe7tGAAAAAICHWBoJPHLkiLp27aquXbt6ux4AAAAAgBdZCoE9evRQdHS0BgwYoN69e8tut3usgHvuuUchISGSpPDwcA0ePFhPP/20bDabYmNjNW7cODmdTk2bNk1ff/21AgMDNWPGDEVERGjHjh3n9AUAAAAAuGcpBK5cuVLvv/++nnvuOc2YMUPdunXTPffcoy5dusjPz9IVpdU6e/asJOmNN95wtfXv318LFy5U06ZN9dBDDyknJ0dHjx5VWVmZ3nnnHe3YsUOzZ8/WCy+8oKlTp57Tt23btpdcDwAAAABc7SyFwPbt26t9+/ZKTk7WJ598ooyMDD3xxBO65ppr1LdvX02ePPmSdr5v3z6VlJQoMTFRFRUVGj9+vMrKytSsWTNJUmxsrLZs2aL//ve/6tKliySpQ4cO2r17t4qKiqrtSwgEAAAAAPcshcCf+Pn5KTY2Vs2aNVPTpk21fPlyvfXWW5ccAoOCgjRy5Ejde++9+uabbzRq1CiFhoa6ng8ODtaRI0dUVFRU5RJUm812TttPfQEAAAAA7lkOgcePH9cHH3ygjIwM7d69Wx06dNCkSZPUp0+fS955ZGSkIiIiZBiGIiMjFRISou+//971fHFxsUJDQ1VaWqri4mJXu9PplN1ur9L2U18AAAAAgHuWbugbPny4fve73+n111/Xb3/7W3344Yd66623NHjwYNekLpdi9erVmj17tiTp2LFjKikpUf369fWf//xHpmkqOztbMTExio6OVlZWliRpx44datWqlex2uwICAs7pCwAAAABwz9JI4E033aSxY8fqN7/5jautsLBQ7733nt555x29//77l7TzQYMGKTk5WUOHDpVhGJo5c6b8/Pz0xBNPqLKyUrGxsfr1r3+t2267TZs3b9aQIUNkmqZmzpwpSZo+ffo5fQEAAAAA7hmmaZoXs8EXX3yhlStX6sMPP1Rpaalat26ttWvXequ+y5KXl6f4+Hg5HA6Fh4f7uhwAAAAA8DlLI4GFhYVau3atVq5cqQMHDkiSfvvb3+rBBx+sMjoIAAAAALiynTcEbt++XStXrtSGDRtUWlqqNm3a6C9/+YsWLFigpKQktWjRoqbqBAAAAAB4gNsQePfdd+vgwYO69dZbNWbMGPXu3VsRERGSpAULFtRYgQAAAAAAz3E7O+ihQ4cUERGh3/3ud4qJiXEFQAAAAABA7eV2JDArK0vvvfee1q5dqyVLlui6665Tr169lJCQIMMwarJGAAAAAICHWJoddNeuXfr73/+uDz74QKdPn5YkDRs2TCNHjlTjxo29XuSlYnZQAAAAAKjqopaIKC8v18cff6y1a9cqOztbkvS73/1OixYt8lqBl4MQCAAAAABVWVoi4icBAQHq3bu3evfurRMnTmjt2rV67733vFUbAAAAAMDDLnqx+NqEkUAAAAAAqMrt7KAAAAAAgKsPIRAAAAAA6hBCIAAAAADUIRc1MQzqtsx9x7Us65COnDqjptfW1+i4KN3VupGvywIAAABwERgJhCWZ+44rbV2OjheWqkG9AB0vLFXauhxl7jvu69IAAAAAXARGAmHJsqxDCrAZqh/44ylTP9BfZ8oqtCzrEKOBAAAA8BmuVrt4jATCkiOnzqhegK1KW70Am/JOnfFRRQAAAKjruFrt0hACYUnTa+urpLyySltJeaXCr63vo4oAAABQ1/38ajXD+PH/ATZDy7IO+bq0KxohEJaMjotSeaWpM2UVMs0f/19eaWp0XJSvSwMAAEAdxdVql4Z7AmHJXa0b6Sn9+NeWvFNnFM711kAV3I8AAEDNa3ptfR0vLHXNWyFxtZoVhEBYdlfrRvxSC1Tjp/sRAmxGlfsRnpL4NwNcYeryH2zq8nvH1Wt0XJTS1uXoTFmF6gXYVFJeydVqFnA5KABcJu5HAGqHujyBRF1+77i63dW6kZ7q11aNQoL0Q0m5GoUE6al+bfkDxwUwEggAl+nIqTNqUC+gSltdvx+BEQdcieryckd1+b3j6sfVahePkUAAuEzMnlsVIw64UtXlCSTq8nsHcC5CIABcJmbPrYrLY3Glqst/sKnL7x3AuQiBV7jMfcc19MVPFTtnk4a++Cl/SQeuQNyPUBUjDrhS1eU/2NTl9w7gXNwTeAVjxkGg9uB+hP+P6bpxparLyx3V5feOuov7090jBF7BuIkbP+FDDLUJ03XjSlaX/2BTl9876h4GU86PEHgF88aMg4SJ2ocPMdQ2jDjUHD7TAaB6DKacHyHwClbdJVUnis7qTFmlYudsuugf+ISJ2okPMdRGFzPiQJC5NHymewbn38XjmJ0fx+fCauIYsXzT+RmmaZq+LsJb8vLyFB8fL4fDofDwcF+Xc17V/WOQ5PoBX1HpVP73JSp3SoE2Q41/FSR/m5/KK03LE1AMffHTc0LlmbIKNQoJ0lsP/cZr7w2XJ3bOJjWoFyDDMCRJp0vKdaLorEornOp0c5hPf7jwgw6X6+dB5ueXjlr9XKvL5yCf6ZfvfOefpDp7bp3P5f6bvdp5+vh48zPOV5+fPx2jsopKFZZW6GyFUzY/Q2Pvaq5Hu7e6pNer7n146jPyav05Qwi8Alzoh9Ds9Xv17/8WyemUfvpmGZJCg/x1ttIp05Sim117wZPyl2FCkkzT1A8l5fq/yd3OqelKPOHdheUrqdZLPXY/384eaJNhGCo8W6HTJeUKvsamhvYgnS4pV/4PJZKkAD9DjRvUu+APF09/L396vf3HTqvobKXCggN0XfA1dfoXgcx9xzV7/V7lfvfjXxejGgZrcq/WNfbD9Eo6/y/W5fyQvtRftq7EY3YpNf30mV5YWqETRWdVVulUoM1P9QL89Hlqzxqte/+x0yqvNBXo76eWjUJc9V+Jx/rn3J1/AX6GzpQ7vRJ0vHFMzveant7fhf7NXmh/V+I54e53i19+rvdud6O2HDp53n42P0O/quevhvYg1+tfTPD45e8C3xWXKbRegFfOQ0/9AeRiz79lWYeUe6JI3xWXyU+GDEOqNE35GYaW3f8/F/Xenv94vxZnHlSF06lrbH76Vf0ABdhsrvdxuYH8av6jR60PgU6nU9OmTdPXX3+twMBAzZgxQxEREZKu/BCYue+4HnztM1XU6u8AAAAAUDf5GdKE+JaXNIrpS7V+ncCPP/5YZWVleuedd/T4449r9uzZvi7Jksx9x/Wn/yUAAgAAALWV05Tmf/xvPf/xfl+XclFqfQjcvn27unTpIknq0KGDdu/e7eOKrFmWdcjXJQAAAADwgJezc31dwkWp9SGwqKhIdrvd9dhms6miosKHFVlzhJmJAAAAgKtCcVmlr0u4KLU+BNrtdhUXF7seO51O+ftf+StfNL22vq9LAAAAAOABwYE2X5dwUWp9CIyOjlZWVpYkaceOHWrVqnbclPnTjFIAAAAAarcHYyN9XcJFqfUhsEePHgoMDNSQIUM0a9YsJScn+7okS+5q3Uj/+6fb5W9cuC8AAACAK4+fIf2le+2bHbTWLxFxPlf6EhEAAAAAUNNq/UggAAAAAMA6QiAAAAAA1CGEQAAAAACoQwiBAAAAAFCHEAIBAAAAoA4hBAIAAABAHUIIBAAAAIA6hBAIAAAAAHWIv68L8KbKykpJ0rfffuvjSgAAAACg5t14443y968a+67qEPjf//5XkjRs2DAfVwIAAAAANc/hcCg8PLxKm2GapumjeryutLRUu3fv1vXXXy+bzebrcgAAAACgRlU3EnhVh0AAAAAAQFVMDAMAAAAAdQghEAAAAADqkKt6YpgrndPp1LRp0/T1118rMDBQM2bMUEREhK/Luirdc889CgkJkSSFh4dr8ODBevrpp2Wz2RQbG6tx48bx/fCwnTt3at68eXrjjTd0+PBhJSUlyTAMtWzZUlOnTpWfn58WLVqkzMxM+fv7KyUlRe3bt3fbF9b9/Njn5ORozJgxuvnmmyVJQ4cOVZ8+fTj2XlBeXq6UlBQdPXpUZWVlevjhh9WiRQvO/RpQ3bG/8cYbOfdrQGVlpaZMmaLc3FzZbDbNmjVLpmly3teQ6o5/YWEh534N+u677zRw4EC98sor8vf3rz3nvgmf2bBhgzl58mTTNE3zyy+/NMeMGePjiq5OpaWlZv/+/au09evXzzx8+LDpdDrNBx980Ny9ezffDw968cUXzbvvvtu89957TdM0zdGjR5uffvqpaZqmmZqaam7cuNHcvXu3OXz4cNPpdJpHjx41Bw4c6LYvrPvlsV+5cqW5fPnyKn049t6xevVqc8aMGaZpmubJkyfNrl27cu7XkOqOPed+zfjoo4/MpKQk0zRN89NPPzXHjBnDeV+Dqjv+nPs1p6yszHzkkUfMnj17mgcOHKhV5z5R34e2b9+uLl26SJI6dOig3bt3+7iiq9O+fftUUlKixMREjRgxQp999pnKysrUrFkzGYah2NhYbdmyhe+HBzVr1kwLFy50Pc7JyVGnTp0kSXFxcfrkk0+0fft2xcbGyjAMNWnSRJWVlTp58mS1fWHdL4/97t27lZmZqWHDhiklJUVFRUUcey/p1auX/vznP7se22w2zv0aUt2x59yvGd27d1d6erokKT8/Xw0bNuS8r0HVHX/O/ZozZ84cDRkyRI0aNZJUu37fIQT6UFFRkex2u+uxzWZTRUWFDyu6OgUFBWnkyJFavny5pk+fruTkZNWrV8/1fHBwsAoLC/l+eFBCQkKVqYhN05RhGJLcH++f2qvrC+t+eezbt2+vSZMmacWKFWratKkWL17MsfeS4OBg2e12FRUV6dFHH9WECRM492tIdceec7/m+Pv7a/LkyUpPT1dCQgLnfQ375fHn3K8Z7777rsLCwlwDCFLt+n2HEOhDdrtdxcXFrsdOp/OcNTxw+SIjI9WvXz8ZhqHIyEiFhITo+++/dz1fXFys0NBQvh9e9PNr3N0d7+LiYoWEhFTbF5euR48eateunevrPXv2cOy9qKCgQCNGjFD//v3Vt29fzv0a9Mtjz7lfs+bMmaMNGzYoNTVVZ8+edbVz3teMnx//2NhYzv0asGbNGn3yyScaPny49u7dq8mTJ+vkyZOu56/0c58Q6EPR0dHKysqSJO3YsUOtWrXycUVXp9WrV2v27NmSpGPHjqmkpET169fXf/7zH5mmqezsbMXExPD98KI2bdpo69atkqSsrCzX8c7OzpbT6VR+fr6cTqfCwsKq7YtLN3LkSO3atUuStGXLFrVt25Zj7yUnTpxQYmKiJk6cqEGDBkni3K8p1R17zv2asXbtWi1btkySVK9ePRmGoXbt2nHe15Dqjv+4ceM492vAihUr9Le//U1vvPGGbr31Vs2ZM0dxcXG15txnsXgf+mk2yv3798s0Tc2cOVPNmzf3dVlXnbKyMiUnJys/P1+GYeiJJ56Qn5+fZs6cqcrKSsXGxuqxxx7j++FheXl5+stf/qKVK1cqNzdXqampKi8vV1RUlGbMmCGbzaaFCxcqKytLTqdTycnJiomJcdsX1v382Ofk5Cg9PV0BAQFq2LCh0tPTZbfbOfZeMGPGDK1fv15RUVGutieffFIzZszg3Pey6o79hAkTNHfuXM59Lztz5oySk5N14sQJVVRUaNSoUWrevDmf+TWkuuPfuHFjPvdr2PDhwzVt2jT5+fnVmnOfEAgAAAAAdQiXgwIAAABAHUIIBAAAAIA6hBAIAAAAAHUIIRAAAAAA6hBCIAAAAADUIYRAAMBVoaysTMuXL9c999yjjh076s4779SYMWP01VdfeWwfmZmZOnDggMdez1MWLlyoHj16uB4nJSXplltucf3Xtm1b3XnnnXrsscd0+PBhH1YKALgSEAIBALVeSUmJhg0bprfeekuJiYlau3atXn75ZTVo0EDDhg3Tp59+etn7OHbsmEaPHq3vvvvOAxV7X0xMjLKzs5Wdna2NGzfqr3/9qwoKCjRkyBAdOXLE1+UBAHyIEAgAqPUWLFigb775RitWrFC/fv0UERGhNm3aaPbs2ercubPS09N1ucvi1rZldQMCAnT99dfr+uuv10033aTOnTvr5ZdfVmBgoObPn+/r8gAAPkQIBADUamVlZXr33Xc1aNAg3XDDDec8n5aWpmeffVZHjx7VLbfcos8//9z1XF5eXpW2HTt2aMiQIerQoYM6d+6siRMn6vvvv5ckde3aVZI0YsQIJSUlSZLy8/P12GOP6Y477lDHjh31yCOPVBll69atm1asWKHRo0erffv2io+P16ZNm7Rx40b17NlTHTt21KhRo3Ty5EnXNvv379fIkSP161//WnFxcUpLS9Pp06ervOacOXOUkJCg3/zmN8rJybF8rOx2uwYOHCiHw6GzZ89a3g4AcHUhBAIAarUjR47o9OnT+vWvf13t802bNlXr1q0v+DqVlZV6+OGHdccdd+gf//iHXnzxRX311VeaM2eOJOnvf/+7pB/vv3vyySdVVFSkoUOH6ocfftDLL7+sN954Q4WFhbr//vtVWFjoet158+apd+/e+sc//qFbbrlFTzzxhF5++WU9++yzeuGFF7Rz504tX75c0o+XnA4fPlytWrXS3//+dz3//PM6cOCAxo0bV6XWt956S+np6Vq2bJluvfXWizperVq10tmzZ/XNN99c1HYAgKuHv68LAADgcvw0ShYaGnpZr1NYWKhTp06pYcOGuummmxQeHq7FixervLxckhQWFiZJ+tWvfqWQkBCtWLFCp0+f1vz589WgQQNJ0nPPPadu3bpp3bp1+n/t3V8oc38cB/D31rP5MzxGU2Y2sSvlQsmFLeXKv7Uo7cIhlFKKdFyMUpOiSFGUmjQXUpQLUrtyseJCVktioYOsSP5FKC3zXDw9J6Jfv2e/q/32fl3tfL/nfPf9Xp3efc73HEEQAPyu3NXV1QEAHA4HNjY2IIoiioqKAABlZWU4Pj4GACwuLsJgMMDpdMrzmpiYQHl5OQKBAIqLi+UxS0tLo1rnz58/AQBPT09RXU9ERLGPIZCIiGKaVqsFAPmxzWilp6ejra0NQ0NDmJqagsViQUVFBaqrq789//j4GPn5+XIABH4HxYKCAhwdHcltJpNJ/p2UlAQAMBqNcltiYqI892AwiGAwKIe9jyRJkttzc3OjXuef8PdfQzMREcUuhkAiIoppRqMRmZmZ2N3dRU1NzZf+7e1teDwedHR0fOl7e3v7dOx0OiEIAnw+HzY3N9Hf34+1tTW43e4v1yYkJHw7n0gkApVKJR//+PH1VqtUfr8bQ6VSwWKxYGBg4Evfn0rkP/33v7G/v4/k5GTk5eVFPQYREcU27gkkIqKYplQqUV9fj5WVFVxdXX3qe39/h9vtxunpKfR6PQDg+flZ7v+4L+78/Bwulws6nQ6CIGBmZgajo6Pw+Xy4vb2FQqH4NLbZbMbJycmnCuTd3R1OT09RUFAQ1VrMZjMkSYJer4fJZILJZIJSqcTIyAguLy+jGvOjl5cXrK6uoqqq6lNQJSKi+MIQSEREMa+zsxMGgwGNjY1YX19HKBRCIBBAd3c3dnZ2MDw8jKysLOTk5GB+fh4nJyfw+/2YnJyUw51Wq4XX68Xg4CAkSYIkSfB6vTAajdBqtdBoNACAw8ND3N/fw263IyMjA6Io4uDgAPv7+xBFEWlpaaitrY1qHU1NTXh8fERfXx8ODw+xt7cHURRxdnb215W7cDiM6+trXF9f4+LiAltbW2htbUU4HEZPT09U8yMiov8HhkAiIop5Go0GCwsLsNlsmJ6ehs1mQ1dXFyKRCJaWllBSUgKFQoGxsTE8PDzAbrfD5XKht7dXfjQzNTUVs7OzCIVCcDgcaGhowOvrK9xuN5RKJVJSUtDc3Izx8XEMDAwgISEBc3NzUKvVEAQBLS0t8gtjot1vp9Pp4PF4cHNzA4fDgfb2dmRnZ8Pj8UCtVv/VWH6/H1arFVarFZWVlXC5XCgsLMTy8vK3n9IgIqL4oXiPta/fEhERERERUdRYCSQiIiIiIoojDIFERERERERxhCGQiIiIiIgojjAEEhERERERxRGGQCIiIiIiojjCEEhERERERBRHGAKJiIiIiIjiCEMgERERERFRHPkF39vNNe0cxaQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set seaborn style\n",
    "sns.set_style(\"white\")\n",
    "# Seaborn scatter plot\n",
    "sns.lmplot('CustomerID_enc', 'Bulk', data=customer_invoice_df, fit_reg=False, aspect=2.5)\n",
    "# Add axes labels\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel('CustomerID', fontsize=15)\n",
    "ax.set_ylabel('Average bulk coefficient by invoice', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) New Datasets by customer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1) New Dataset by customer according to 1st and main items ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "customers_df = pd.DataFrame(np.sort(data_stock_codes.CustomerID_enc.unique()), columns=['ID_enc'])\n",
    "customers_df['ID'] = le_id.inverse_transform(customers_df['ID_enc'].values.astype('int64'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First order specifics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_df['1st_order'] = customers_df.ID.apply(lambda x : data_stock_codes.StockCode[data_stock_codes.CustomerID == x].iloc[0])\n",
    "customers_df['1st_order_qty'] = customers_df.ID.apply(lambda x : data_stock_codes.Quantity[data_stock_codes.CustomerID == x].iloc[0])\n",
    "customers_df['1st_order_descr'] = customers_df.ID.apply(lambda x : data_stock_codes.Description[data_stock_codes.CustomerID == x].iloc[0])\n",
    "customers_df['1st_order_amount'] = customers_df.ID.apply(lambda x : data_stock_codes.Amount[data_stock_codes.CustomerID == x].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most frequent order specifics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product\n",
      "Quantity\n",
      "Description\n",
      "Amount\n"
     ]
    }
   ],
   "source": [
    "print(\"Product\")\n",
    "customers_df['mf_order'] = customers_df.ID.apply(lambda x : data_stock_codes.StockCode[data_stock_codes.CustomerID == x].value_counts().index.values[0])\n",
    "print(\"Quantity\")\n",
    "customers_df['mf_order_qty'] = customers_df.mf_order.apply(lambda x : data_stock_codes.Quantity[data_stock_codes.StockCode == x].value_counts().index.values[0])\n",
    "print(\"Description\")\n",
    "customers_df['mf_order_descr'] = customers_df.mf_order.apply(lambda x : data_stock_codes.Description[data_stock_codes.StockCode == x].iloc[0])\n",
    "print(\"Amount\")\n",
    "customers_df['mf_order_amount'] = customers_df.mf_order.apply(lambda x : data_stock_codes.Amount[data_stock_codes.StockCode == x].value_counts().index.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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_enc</th>\n",
       "      <th>ID</th>\n",
       "      <th>1st_order</th>\n",
       "      <th>1st_order_qty</th>\n",
       "      <th>1st_order_descr</th>\n",
       "      <th>1st_order_amount</th>\n",
       "      <th>mf_order</th>\n",
       "      <th>mf_order_qty</th>\n",
       "      <th>mf_order_descr</th>\n",
       "      <th>mf_order_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>23166</td>\n",
       "      <td>74215</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>23166</td>\n",
       "      <td>12</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12747.0</td>\n",
       "      <td>85124C</td>\n",
       "      <td>12</td>\n",
       "      <td>GREEN JUICY FRUIT PHOTO FRAME</td>\n",
       "      <td>30.60</td>\n",
       "      <td>82484</td>\n",
       "      <td>2</td>\n",
       "      <td>WOOD BLACK BOARD ANT WHITE FINISH</td>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12748.0</td>\n",
       "      <td>22854</td>\n",
       "      <td>1</td>\n",
       "      <td>CREAM SWEETHEART EGG HOLDER</td>\n",
       "      <td>4.95</td>\n",
       "      <td>21034</td>\n",
       "      <td>1</td>\n",
       "      <td>REX CASH+CARRY JUMBO SHOPPER</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12749.0</td>\n",
       "      <td>22768</td>\n",
       "      <td>2</td>\n",
       "      <td>FAMILY PHOTO FRAME CORNICE</td>\n",
       "      <td>19.90</td>\n",
       "      <td>22171</td>\n",
       "      <td>2</td>\n",
       "      <td>3 HOOK PHOTO SHELF ANTIQUE WHITE</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12820.0</td>\n",
       "      <td>21977</td>\n",
       "      <td>24</td>\n",
       "      <td>PACK OF 60 PINK PAISLEY CAKE CASES</td>\n",
       "      <td>13.20</td>\n",
       "      <td>84946</td>\n",
       "      <td>12</td>\n",
       "      <td>ANTIQUE SILVER TEA GLASS ETCHED</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_enc       ID 1st_order  1st_order_qty  \\\n",
       "0       0  12346.0     23166          74215   \n",
       "1       1  12747.0    85124C             12   \n",
       "2       2  12748.0     22854              1   \n",
       "3       3  12749.0     22768              2   \n",
       "4       4  12820.0     21977             24   \n",
       "\n",
       "                      1st_order_descr  1st_order_amount mf_order  \\\n",
       "0      MEDIUM CERAMIC TOP STORAGE JAR          77183.60    23166   \n",
       "1       GREEN JUICY FRUIT PHOTO FRAME             30.60    82484   \n",
       "2         CREAM SWEETHEART EGG HOLDER              4.95    21034   \n",
       "3          FAMILY PHOTO FRAME CORNICE             19.90    22171   \n",
       "4  PACK OF 60 PINK PAISLEY CAKE CASES             13.20    84946   \n",
       "\n",
       "   mf_order_qty                     mf_order_descr  mf_order_amount  \n",
       "0            12     MEDIUM CERAMIC TOP STORAGE JAR            15.00  \n",
       "1             2  WOOD BLACK BOARD ANT WHITE FINISH            15.90  \n",
       "2             1       REX CASH+CARRY JUMBO SHOPPER             0.95  \n",
       "3             2   3 HOOK PHOTO SHELF ANTIQUE WHITE            17.00  \n",
       "4            12    ANTIQUE SILVER TEA GLASS ETCHED            15.00  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save dataframe for clients segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_df.to_csv(\"data/customers_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2) New Dataset by customer : RFM matrix analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From : https://towardsdatascience.com/find-your-best-customers-with-customer-segmentation-in-python-61d602f9eee6\n",
    "When it comes to finding out who your best customers are, the old *RFM matrix* principle is the best. RFM stands for Recency, Frequency and Monetary. It is a customer segmentation technique that uses past purchase behavior to divide customers into groups.\n",
    "\n",
    "RFM Score Calculations :\n",
    "\n",
    "- **RECENCY (R)**: Days since last purchase\n",
    "- **FREQUENCY (F)**: Total number of purchases\n",
    "- **MONETARY VALUE (M)**: Total money this customer spent\n",
    "\n",
    "Steps :\n",
    "\n",
    "- **Step 1**: Calculate the RFM metrics for each customer.\n",
    "- **Step 2**: Add segment numbers to RFM table.\n",
    "- **Step 3**: Sort according to the RFM scores from the best customers (score 111).\n",
    "\n",
    "We won't be interested in step 3 as we don't want to sort but to classify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to set RFM score we need to set a point in time for calculation. Let's set it to the first day following the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOW_TIME = datetime.datetime(2011, 12, 10, 0, 0, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create RFM table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfmTable = data_stock_codes.groupby('CustomerID_enc').agg({'InvoiceDate': lambda x: (NOW_TIME - x.max()).days,\n",
    "                                                           'InvoiceNo': lambda x: len(x),\n",
    "                                                           'Amount': lambda x: x.sum()})\n",
    "rfmTable.rename(columns={'InvoiceDate': 'recency', \n",
    "                         'InvoiceNo': 'frequency', \n",
    "                         'Amount': 'monetary_value'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4397</td>\n",
       "      <td>31650.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>198</td>\n",
       "      <td>4040.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recency  frequency  monetary_value\n",
       "CustomerID_enc                                    \n",
       "0                   325          1        77183.60\n",
       "1                     2        103         4196.01\n",
       "2                     0       4397        31650.78\n",
       "3                     3        198         4040.88\n",
       "4                     3         59          942.34"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get those RFM data quentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantiles = rfmTable.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles = quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a segmented table from the RFM table quentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "segmented_rfm = rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "def FMScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "segmented_rfm['r_quartile'] = segmented_rfm['recency'].apply(RScore, args=('recency',quantiles,))\n",
    "segmented_rfm['f_quartile'] = segmented_rfm['frequency'].apply(FMScore, args=('frequency',quantiles,))\n",
    "segmented_rfm['m_quartile'] = segmented_rfm['monetary_value'].apply(FMScore, args=('monetary_value',quantiles,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID_enc</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>0</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>31650.78</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>198</td>\n",
       "      <td>4040.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID_enc                                                               \n",
       "0                   325          1        77183.60           4           4   \n",
       "1                     2        103         4196.01           1           1   \n",
       "2                     0       4397        31650.78           1           1   \n",
       "3                     3        198         4040.88           1           1   \n",
       "4                     3         59          942.34           1           2   \n",
       "\n",
       "                m_quartile  \n",
       "CustomerID_enc              \n",
       "0                        1  \n",
       "1                        1  \n",
       "2                        1  \n",
       "3                        1  \n",
       "4                        2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine RDF score into one value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4397</td>\n",
       "      <td>31650.78</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>198</td>\n",
       "      <td>4040.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID_enc                                                               \n",
       "0                   325          1        77183.60           4           4   \n",
       "1                     2        103         4196.01           1           1   \n",
       "2                     0       4397        31650.78           1           1   \n",
       "3                     3        198         4040.88           1           1   \n",
       "4                     3         59          942.34           1           2   \n",
       "\n",
       "                m_quartile RFMScore  \n",
       "CustomerID_enc                       \n",
       "0                        1      441  \n",
       "1                        1      111  \n",
       "2                        1      111  \n",
       "3                        1      111  \n",
       "4                        2      122  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_rfm['RFMScore'] = segmented_rfm.r_quartile.map(str) + segmented_rfm.f_quartile.map(str) + segmented_rfm.m_quartile.map(str)\n",
    "segmented_rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save dataframe for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "segmented_rfm.to_csv(\"data/customers_rfm.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3) New Dataset by customer : Bulk and Wealthy buyers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Amount</th>\n",
       "      <th>Bulk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID_enc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77183.600000</td>\n",
       "      <td>36379.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.737961</td>\n",
       "      <td>3.524379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.198267</td>\n",
       "      <td>3.167508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.408485</td>\n",
       "      <td>2.778461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.971864</td>\n",
       "      <td>6.182251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Amount          Bulk\n",
       "CustomerID_enc                            \n",
       "0               77183.600000  36379.901961\n",
       "1                  40.737961      3.524379\n",
       "2                   7.198267      3.167508\n",
       "3                  20.408485      2.778461\n",
       "4                  15.971864      6.182251"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_bulk_df = data_stock_codes.groupby('CustomerID_enc').agg({'Amount': lambda x: x.mean(),\n",
    "                                                                   'Bulk': lambda x: x.mean()})\n",
    "customer_bulk_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(customer_bulk_df.index.values, customer_bulk_df.Bulk);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One outlier in bulk ranking\n",
    "\n",
    "Let's delete it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From main df :\n",
    "customers_to_drop = customer_bulk_df[customer_bulk_df.Bulk > 30000].index.values\n",
    "data_stock_codes = data_stock_codes[~data_stock_codes.CustomerID_enc.isin(customers_to_drop)]\n",
    "# From bulk df :\n",
    "customer_bulk_df = customer_bulk_df[customer_bulk_df.Bulk < 30000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(customer_bulk_df.index.values, customer_bulk_df.Bulk);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about amount ranking ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(customer_bulk_df.index.values, customer_bulk_df.Amount);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two outliers in Amount ranking\n",
    "\n",
    "Let's delete those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From main df :\n",
    "customers_to_drop = customer_bulk_df[customer_bulk_df.Amount > 10000].index.values\n",
    "data_stock_codes = data_stock_codes[~data_stock_codes.CustomerID_enc.isin(customers_to_drop)]\n",
    "# From bulk df :\n",
    "customer_bulk_df = customer_bulk_df[customer_bulk_df.Amount < 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD0CAYAAACPUQ0CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHS1JREFUeJzt3X1wE+edB/DvSnaKYplzPZCjtkOCCeVCCBeohrsbHKdXQpRpS/BQOoZ44A/STkknQ0xb1y9gHAc3xuOO76bQTNNM+g8vLSFwnnRyPSd1YDwOYBKPebGauNMOZ2o56UAcX20hsNA+9weVItu7erG02tXj7+cvWK1Xz77ou7u/59FKEUIIEBGRNGxmN4CIiFKLwU5EJBkGOxGRZBjsRESSYbATEUkmy+wG3Lx5E/39/Zg/fz7sdrvZzSEiygjBYBDXrl3D8uXLMWfOnEmvmR7s/f39qKioMLsZREQZ6ciRI3C5XJOmmR7s8+fPB3CncQsWLDC5NUREmeGTTz5BRUVFOEMjmR7sofLLggULUFRUZHJriIgyi1YJm52nRESSYbATEUmGwU5EJBkGOxGRZEzvPCXKdO19XrR2DGB41I+CPAeq3EtRtrLQ7GbRLMZgJ0pCe58XtScvwx8IAgC8o37UnrwMAAx3Mg1LMURJaO0YCId6iD8QRGvHgEktImKwEyVleNSf0HSidGCwEyWhIM+R0HSidGCwEyWhyr0UjuzJ3/xzZNtR5V5qUouI2HlKlJRQBylHxZCVMNiJklS2spBBTpbCUgwRkWQY7EREkmGwExFJhsFORCQZBjsRkWQY7EREkmGwExFJhsFORCQZBjsRkWQY7EREkmGwExFJhsFORCSZuIL9008/xWOPPYY///nPGBwcxJYtW/D000+joaEBqqoCAA4ePIhNmzZh8+bNuHTpEgDozktERMaJGeyBQAB79+7FnDlzAADNzc2orKzE0aNHIYRAZ2cnPB4Pzp8/j+PHj6OtrQ2NjY268xIRkbFiBntLSws2b96Me+65BwDg8XiwevVqAEBpaSnOnDmD3t5elJSUQFEUFBQUIBgMYmRkRHNeIiIyVtRgP3nyJPLz8/Hoo4+GpwkhoCgKACAnJwdjY2MYHx+H0+kMzxOarjUvEREZK+oPbZw4cQKKouDs2bP48MMPUV1djZGRkfDrPp8Pc+fOhdPphM/nmzQ9NzcXNptt2rxERGSsqFfsR44cweHDh3Ho0CE8+OCDaGlpQWlpKXp6egAAXV1dcLlcWLVqFbq7u6GqKoaHh6GqKvLz87Fs2bJp8xIRkbES/mm86upq1NfXo62tDcXFxXC73bDb7XC5XCgvL4eqqti7d6/uvESp1t7n5W+OEkVQhBDCzAYMDQ1h7dq16OzsRFFRkZlNoQzU3udF7cnL8AeC4WmObDuaNz7McCepRctOfkGJMlprx8CkUAcAfyCI1o4Bk1pEZD4GO2W04VF/QtOJZgMGO2W0gjxHQtOJZgMGO2W0KvdSOLLtk6Y5su2oci81qUVE5kt4VAyRlYQ6SDkqhuhzDHbKeGUrCxnkRBFYiiEikgyDnYhIMgx2IiLJMNiJiCTDYCcikgyDnYhIMgx2IiLJMNiJiCTDYCcikgyDnYhIMgx2IiLJMNiJiCTDYCcikgyDnYhIMgx2IiLJMNiJiCTDYCcikgyDnYhIMgx2IiLJMNiJiCTDYCcikkyW2Q0gIpJBe58XrR0DGB71oyDPgSr3UpStLDSlLQx2IqIktfd5UXvyMvyBIADAO+pH7cnLAGBKuLMUQ0SUpNaOgXCoh/gDQbR2DJjSHgY7EVGShkf9CU03GoOdiChJBXmOhKYbbdYHe3ufF2v2v4tFNW9hzf530d7nNbtJRJRhqtxL4ci2T5rmyLajyr3UlPbM6s5Tq3V4EFFmCuUFR8VYQLQODwY7ESWibGWhZXJjVpdirNbhQUSUCjGv2IPBIPbs2YMrV67AbrejubkZQgjU1NRAURQsWbIEDQ0NsNlsOHjwIE6fPo2srCzU1dVhxYoVGBwc1JzXCgryHPBqhLhZHR5ERKkQM2FPnToFAPjNb36DnTt3orm5Gc3NzaisrMTRo0chhEBnZyc8Hg/Onz+P48ePo62tDY2NjQCgOa9VWK3Dg4goFWJesT/++OP46le/CgAYHh7GvHnzcPr0aaxevRoAUFpaivfeew+LFi1CSUkJFEVBQUEBgsEgRkZG4PF4ps27bt0649YoAVbr8CAiSoW4Ok+zsrJQXV2Nd955Bz/72c9w6tQpKIoCAMjJycHY2BjGx8eRl5cX/pvQdCHEtHmtxEodHkREqRB3sbulpQUdHR2or6/HrVu3wtN9Ph/mzp0Lp9MJn883aXpubu6kenpoXiIiMk7MYG9vb8crr7wCAHA4HFAUBcuXL0dPTw8AoKurCy6XC6tWrUJ3dzdUVcXw8DBUVUV+fj6WLVs2bV4iIjJOzFLME088gdraWlRUVOD27duoq6vD4sWLUV9fj7a2NhQXF8PtdsNut8PlcqG8vByqqmLv3r0AgOrq6mnzEhGRcRQhhDCzAUNDQ1i7di06OztRVFRkZlOIiDJGtOy0xoByIiJKGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSYbBTkQkmaxoLwYCAdTV1cHr9WJiYgLPPvssHnjgAdTU1EBRFCxZsgQNDQ2w2Ww4ePAgTp8+jaysLNTV1WHFihUYHBzUnJeIiIwTNWXffPNN5OXl4ejRo3j11Vexb98+NDc3o7KyEkePHoUQAp2dnfB4PDh//jyOHz+OtrY2NDY2AoDmvEREZKyowf7kk0/i+eefD//fbrfD4/Fg9erVAIDS0lKcOXMGvb29KCkpgaIoKCgoQDAYxMjIiOa8RERkrKjBnpOTA6fTifHxcezcuROVlZUQQkBRlPDrY2NjGB8fh9PpnPR3Y2NjmvMSEZGxYha8P/74Y2zbtg0bNmzA+vXrJ9XIfT4f5s6dC6fTCZ/PN2l6bm6u5rxERGSsqMF+/fp1bN++HVVVVdi0aRMAYNmyZejp6QEAdHV1weVyYdWqVeju7oaqqhgeHoaqqsjPz9ecl4iIjBV1VMwvfvEL/O1vf8PLL7+Ml19+GQCwe/duNDU1oa2tDcXFxXC73bDb7XC5XCgvL4eqqti7dy8AoLq6GvX19ZPmJSIiYylCCGFmA4aGhrB27Vp0dnaiqKjIzKYQEWWMaNnJQeVERJJhsBMRSYbBTkQkGQY7EZFkGOxERJJhsBMRSSbqOHYikk97nxetHQMYHvWjIM+BKvdSlK0sNLtZlEIMdqJZpL3Pi9qTl+EPBAEA3lE/ak9eBgCGu0RYiiGaRVo7BsKhHuIPBNHaMWBSi8gIDHaiWWR41J/QdMpMDHaiWaQgz5HQdMpMDHaiWaTKvRSObPukaY5sO6rcS01qERmBnadEs0iog5SjYuTGYCeaZcpWFjLIJcdSDBGRZBjsRESSYbATEUmGwU5EJBkGOxGRZDgqhsgEfBAXGYnBTpRmfBAXGY2lGKI044O4yGgMdqI044O4yGgMdqI044O4yGgMdqI044O4yGjsPCVKMz6Ii4zGYCcyAR/ERUZiKYaISDIMdiIiyTDYiYgkw2AnIpIMg52ISDIMdiIiyTDYiYgkw2AnIpIMg52ISDJxBfvFixexdetWAMDg4CC2bNmCp59+Gg0NDVBVFQBw8OBBbNq0CZs3b8alS5eizktEmaO9z4s1+9/Fopq3sGb/u2jv85rdJIohZrC/+uqr2LNnD27dugUAaG5uRmVlJY4ePQohBDo7O+HxeHD+/HkcP34cbW1taGxs1J2XiDJH6EdBvKN+CHz+oyAMd2uLGewLFy7EgQMHwv/3eDxYvXo1AKC0tBRnzpxBb28vSkpKoCgKCgoKEAwGMTIyojkvEWUO/ihIZor5EDC3242hoaHw/4UQUBQFAJCTk4OxsTGMj48jLy8vPE9outa8RJQ5+KMgM2P2b9om3Hlqs33+Jz6fD3PnzoXT6YTP55s0PTc3V3NeIsoc/FGQxFmhfJVwsC9btgw9PT0AgK6uLrhcLqxatQrd3d1QVRXDw8NQVRX5+fma8xJR5uCPgiTOCuWrhJ/HXl1djfr6erS1taG4uBhutxt2ux0ulwvl5eVQVRV79+7VnZeIMgd/FCRxVihfKUIIkbZ30zA0NIS1a9eis7MTRUVFZjaFiEhXvHXzNfvfhVcjxAvzHHiv5mspa0+07OQXlIiIYkikbm6F8hWDnYgohkTq5mUrC9G88WEU5jmg4M6VevPGh9NavuJvnhIRxZBo3dzs37RlsBORVIwYQ16Q59Csm1t12CdLMUQkDaPGkFuhbp4IBjsRScOoMeRWqJsngqUYIpKGkWPIza6bJ4JX7EQkDT4C4Q5esZO0zH4QUzrMhnVMRJV7KWpPXp5UjrFyLdwoDHaSUqgTLfQBD3WiAZAm+Ky8jmadcMx8BIKVTrIM9iistKMoMdE60WTZh3rr+MKbHlPX0ewTjhm1cK113nXsAj4YHEFT2cNpbQvAGrsuKzx6k2bOCg9iMpreuoz6A6Yep+l6uqGVfrJPa50FgCPnrprSLga7Dis8epNmbjZ0okVbl2SO02QDM1Un1WjtsNqFl966CSS3L2aKwa5jNlzxySyTvlAy0yCNti4zPU5TEZipOKnGaofRF16J7pNo62ZGZkgX7Km6PZsNV3wy0/tCCQDL3L4DyQVp2cpCfPHubM3XZnqcpiIwU3FSjdUOIy+8ZrJPqtxLoei8ZkZmSBXsqbw9y6QrPtJWtrIQ79V8DVf2fyP8HGwr3b4DyQdpw/qHUnqcpiIwU/EtzVjtMPLCayb7pGxlISr+deG0cDcrM6QaFZPKkRD85Rj5WHGkTLJBmurjNFUPu0p2ZEqsdhg5Xn2m+6Sp7GG47su3RGZIFeypvj3LpK8QU2yxjo9khrfO9G9TEaSpPE61AhMAbkzcRnufd9r7GDUkOFZwG3nhlcw+ibUv0jWEWqpgz7RHa1J6RTs+4hl7rfehTGbcttW+KRlq7wtvejDqD4Snf3YjMG1ctpHj1eMJbqMuvIzaJ+kc3y9VjZ11cYom2vERq64arf8mmTq5FZ8aWLayEIpGT+DUcdlGj0yZ2keSrm1i1D5J5xBqqa7YAeALWbbwxrs724YvZNuw69gFtHYMsEY+y0W7Ctx17ILm34TKNNE+lKmok1vpuGzv8+KzGwHN10LjsstWFko9JNiIfZLO7ZXRwR55a/wPjmz4Jm4jEBTh128EVNwIqACs9RyNdOEjEabT+8DGKuNF+1DKVgKMdQUZOTIlnvWW5ThMdj3SeZxkbClm6q3xqD8wKdS1zKZvjiY79DPe7wNY6WvdyYhVxos2vE62EmCsK8jIkSmx1ttq3xCNtKf9MhbX/jfur3kLi2v/G3vaL+vOm4r1SOdxkrFX7Fq3xvEw4rbHilckyQzti7eTJxWdQWZuu6nv/a2vFOLUR9c02xKtQ02rw3FOdnzXTFY8dvSuLIHER6ZYcYgpcCfUD5+7Gv5/UIjw/7Ue2pWK9UjnEOqMDfaZBnSqb3sSCTejPsRay02mnhfvQZzswZ7oiSGV20/rvU/0enU7yeL5UN66rYb//dmNQMyTnNlPQdRrk+/Wbc3Xvnh3NhrWP5TQyJRk68pT9/m//9N83ZNvIn7d8xfN6YfPXcWRc1enLTtV9fF09adkbLBHu6rQo+DOh2fN/ndTFqrxhptRIaa33Ly7szU7wOI5scV7ECd7sCdyYtBaz6rjF9H4Ww9GbwQS/pDrvXdllI72aB/KmZzkGn/rsdTV7NRtHKIV6PFKpq6stc8jr7KTOREGhX7ZNrLUElp2Kuvj6bhLy9gau1a9KhoFd3YYkNo6X7zhlshQp0TqeXrLFQIzrufF+3XtZL/WnciJQWs9A6rAZzcCM6p5Rjv5zOT4SPQkF23kydS/SVc/hl55U4iZ30EkU1eOp9wa+gwluo3sWuM5dZYNAP/+T/NT8riAdPU5ZGywa4011XsgEvB5qIf4A0H88PWLaXtYWLIhpncS0Fvu//kDMx6LG++HMdnOoERODPHcBSTyIY918km0oz3Rk1y0ZUf+TTxBkKrgN+L57smMCY/3zi+0TRIJyy3/cm9cyx4e9aO9z4tj7/9lWoZ86yuJl1XSNZY9Y0sxwPRb4/Y+L3YduzBtB+gJ3Y5Nve1K5FYp3m+pJXIrl8hJINpyZ1rPi7eTp2xlIT4YHMGve/6CoBCwK0pCB3si3/CLt/QW2pexSl56X52PlEj9VGt50Up/0ZYduf6xSjyp7OOJto2TKQ9pHYfxfMbi3ed2RYm5jaa+V6iDNHTs6inIc6Dxtx7NEXdvXfo44V9HStdY9owO9tAO8476YVeUqDsoltAV/AeDIzh2/i8IqJ+H/g+OXdCt5cYbgqkIsdBX36d2Jp3o9cYdKPGKdlKI3O6RJa6gEDjR64XrvnwA8Z0Yos039XsK8dD7kE+tnUe+t154JPq8lsjlaZX+IufT28d5juxJ66/XNu/fryRT2cdT5V6Kyhhf1EqE1uezUOOYjXby1WtPJL3PfehqW2+9m8oe1n08AvD551OvDZ/dCGDN/ncTqpWnayy7IkQSaZgCQ0NDWLt2LTo7O1FUVBT33+l19KSDI9s+o68YTw0qRcG0k0V7n3faczpC7/mtrxROC/HQ9FMfXZsWKFNfN2I0iZZsG5Blt09r59RtFu2qbSb715Ftjzn/1Ha093lRdfxi+EQeXge7gtZN/xx3eyOt2f+u5oe3MM8RfnywXpCE2hbP+kdbXwXAf5Q/Em6vTefCJ7JNALDyxbc1a/9T54slWvunHqPR3uOhvf8D30T0faood/oBtJYHIOa+iGyz1v69v+atqO8fopcLkSe4RP4ulmjZmbFX7DMdx54KyY5cCH2hKiR0V1B1/AIC6vT5Q6MS9EZRnProGt6r+ZpmoPgDQRw5d3XS1WPlsQvY/V+XcWMiqBtQeichvYCIFFCBgBr91njqyWvqVVu8+zcUEnl/b2M8nW2RP/bc2jEwLdQBIMumoLVjALuOXdC8M4pW8oj3KZL+QDB8JRtqf+jxFzcmbse1LnoEMKksqbfPQlf+of2iNdtMOgm1jtXItum1JSR0jMQKdQDhgQJaV9t6j4rQClm9u1S9E9FUobv+0LJC6xHrBD2TWn0sGRvsZj+PIlbtL56SSSQVgKoR6sCdW75ot6ShW069NmkdlKEPjN5TDCMPxsgATqbcpXVrHCky/OPdv3l3Z2PZl3Jx5s8jcfetjPoDWPni22hY/5DuNvMH1PBrU4fZabU3kt7t9pxsGx5pfHva9sy2KZMeh5HoMF498W6P2pOX8cHgiObxGWuoo9YFgN5on1iUvy/vg8ERze0djT8QnBTAoS+I6e2L0HvFU25MRFAIVB67gF3HLqDiXxfirUsfxzxBn/roWkLvEY+MLcXo3e6m05rF+Tjy3X+bNj3dZaJ4ryiiyXNk40LDE3c6oF+/oHnllgpfyLJN+iKPmWy4s92SXdU1i/Pxv5/6U9LXYxa9cgbw+d2QVtkw1cd5Ko7lkGybgvLV9066Y40UKsfsab8csxPVaP9Z/khKSzEZG+xm1tj1hA7KTP1wE8Uj1G9jdhimQipPJMmYSZ09WnZm7Dh2ALgdtE6oA4hZzySSgT8QxOFzV6U4zq2yBqkey254jV1VVbzwwgsYGBjAXXfdhaamJtx3331JLbO9zxvXMCgiokyRytKy4Vfsv//97zExMYFjx47hhz/8Ifbv35/0MhnqRET6DA/23t5ePProowCARx55BP39/Ua/JRHRrGZ4sI+Pj8PpdIb/b7fbcfu29mNBiYgoeYYHu9PphM/nC/9fVVVkZWXs8HkiIsszPNhXrVqFrq4uAMCFCxfw5S9/Oell/mPuXUkvg4jISrJiP0k4boYH+7p163DXXXdh8+bNaG5uRm1tbdLL7Nm9juFORNLIUoA/NX8jdctL2ZJ02Gw2vPjiiylfbs/udSlfJhGRDDL6C0pERDQdg52ISDIMdiIiyTDYiYgkY/qA8uDfH+T1ySefmNwSIqLMEcrMoMbDEE0P9mvX7jxkvqKiwuSWEBFlnmvXrk17sKLpz2O/efMm+vv7MX/+fNjtdjObQkSUMYLBIK5du4bly5djzpw5k14zPdiJiCi12HlKRCQZ02vsM2XED3jMRFlZGXJzcwEARUVFKC8vx09+8hPY7XaUlJTgueeeS2tbL168iJ/+9Kc4dOgQBgcHUVNTA0VRsGTJEjQ0NMBms+HgwYM4ffo0srKyUFdXhxUrVujOa1TbPB4PduzYgfvvvx8AsGXLFnz9619Pa9sCgQDq6urg9XoxMTGBZ599Fg888IAltplW2xYsWGD6NgPulAD27NmDK1euwG63o7m5GUII07ebVrvGxsYssc0A4NNPP8XGjRvxq1/9CllZWcZuL5GhOjo6RHV1tRBCiL6+PrFjx460t+HmzZtiw4YNk6Y99dRTYnBwUKiqKr7zne+I/v7+tLX1l7/8pfjmN78pvv3tbwshhPje974nzp07J4QQor6+Xrz99tuiv79fbN26VaiqKrxer9i4caPuvEa27fXXXxevvfbapHnS3bY33nhDNDU1CSGEGBkZEY899phltplW26ywzYQQ4p133hE1NTVCCCHOnTsnduzYYYntptUuq2yziYkJ8f3vf1888cQT4k9/+pPh2ytjSzFW+AGPjz76CH6/H9u3b8e2bdvw/vvvY2JiAgsXLoSiKCgpKcHZs2fT1taFCxfiwIED4f97PB6sXr0aAFBaWoozZ86gt7cXJSUlUBQFBQUFCAaDGBkZ0ZzXyLb19/fj9OnTqKioQF1dHcbHx9PetieffBLPP/98+P92u90y20yrbVbYZgDw+OOPY9++fQCA4eFhzJs3zxLbTatdVtlmLS0t2Lx5M+655x4Axn82MzbYrfADHnPmzMEzzzyD1157DY2NjaitrYXD4Qi/npOTg7GxsbS11e12T3rWvRACiqJEbUtouta8RrZtxYoV+PGPf4wjR47g3nvvxc9//vO0ty0nJwdOpxPj4+PYuXMnKisrLbPNtNpmhW0WkpWVherqauzbtw9ut9sy221qu6ywzU6ePIn8/PzwxR1g/GczY4PdCj/gsWjRIjz11FNQFAWLFi1Cbm4uRkdHw6/7fD7MnTvXtLZG1uH02uLz+ZCbm6s5r5HWrVuH5cuXh//9hz/8wZS2ffzxx9i2bRs2bNiA9evXW2qbTW2bVbZZSEtLCzo6OlBfX49bt25Nez+z2hbZrpKSEtO32YkTJ3DmzBls3boVH374IaqrqzEyMjLtvVLZrowNdiN+wCNRb7zxRvjHuf/617/C7/fj7rvvxtWrVyGEQHd3N1wul2ltXbZsGXp6egAAXV1d4bZ0d3dDVVUMDw9DVVXk5+drzmukZ555BpcuXQIAnD17Fg899FDa23b9+nVs374dVVVV2LRpEwDrbDOttllhmwFAe3s7XnnlFQCAw+GAoihYvny56dtNq13PPfec6dvsyJEjOHz4MA4dOoQHH3wQLS0tKC0tNXR7Zew49tBIkz/+8Y8QQuCll17C4sWL09qGiYkJ1NbWYnh4GIqi4Ec/+hFsNhteeuklBINBlJSUYNeuXWlt69DQEH7wgx/g9ddfx5UrV1BfX49AIIDi4mI0NTXBbrfjwIED6OrqgqqqqK2thcvl0p3XqLZ5PB7s27cP2dnZmDdvHvbt2wen05nWtjU1NeF3v/sdiouLw9N2796NpqYm07eZVtsqKyvR2tpq6jYDgBs3bqC2thbXr1/H7du38d3vfheLFy82/VjTateXvvQl04+zSFu3bsULL7wAm81m6PbK2GAnIiJtGVuKISIibQx2IiLJMNiJiCTDYCcikgyDnYhIMgx2IiLJMNiJiCTDYCciksz/A3m8WAgJbO2KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(customer_bulk_df.index.values, customer_bulk_df.Amount);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(customer_bulk_df.index.values, customer_bulk_df.Bulk);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save dataframe for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_bulk_df.to_csv(\"data/customer_bulk.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the clean dataframe for classification algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_stock_codes.to_csv(\"data/online_retail_clean.csv\", index=False)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}