pre_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pré Processamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É feita a leitura do Dataset `.xlsx` usando `read_excel`, em seguida, o mesmo é convertido para `.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx = pd.read_excel('data/Online_Retail.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx.to_csv('data/online_retail.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A leitura do Dataframe armazenada na variável `df`. Logo de início já se pode observar que o dataframe contém 541909 registros com 8 colunas cada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/online_retail.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo é possível ter uma ideia de como o Dataset está estruturado e o que cada coluna representa. De acordo com a descrição da UCI e pelo que se pode ver nesses primeiros registros, a coluna `Country` representa a origem do pedido.\n",
    "\n",
    "`InvoiceNo` é um número que se repete, portanto empiricamente e pelo seu nome pode-se relacioná-lo como sendo um identificador da transação ocorrida.\n",
    "\n",
    "Cada registro representa uma transação, então pelos nomes das colunas podemos identificar `StockCode` e `CustomerID` como sendo código de identificação do produto no estoque e identificador do cliente que realizou a compra.\n",
    "\n",
    "Por conseguinte, em `Description` temos uma descrição do produto, em `Quantity` a quantidade comprada na transação, `InvoiceDate` a data da mesma e `UnitPrice` busca representar o preço unitário.\n",
    "\n",
    "Outro aspecto interessante à ser observado é que uma transação é a relação entre a compra de N unidade de um produto com um cliente, então uma compra com diversos items tem diversas transações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "    </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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>536365</td>\n",
       "      <td>22752</td>\n",
       "      <td>SET 7 BABUSHKA NESTING BOXES</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>7.65</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",
       "5    536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \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  \n",
       "5  2010-12-01 08:26:00       7.65     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      541909 non-null object\n",
      "StockCode      541909 non-null object\n",
      "Description    540455 non-null object\n",
      "Quantity       541909 non-null int64\n",
      "InvoiceDate    541909 non-null object\n",
      "UnitPrice      541909 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        541909 non-null object\n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  541909.000000  541909.000000  406829.000000\n",
       "mean        9.552250       4.611114   15287.690570\n",
       "std       218.081158      96.759853    1713.600303\n",
       "min    -80995.000000  -11062.060000   12346.000000\n",
       "25%         1.000000       1.250000   13953.000000\n",
       "50%         3.000000       2.080000   15152.000000\n",
       "75%        10.000000       4.130000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É feita a busca por valores nulos. \n",
    "\n",
    "Logo ao plotar o mapa de calor, é possível observar que a coluna `CustomerId` tem bastante ocorrência de valores nulos, mais especificamente `135080`, cerca de `24,9%` dos registros.\n",
    "\n",
    "Já o campo `Description` possui `1454` valores nulos, representando `0,26%` do total de registros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113e0f128>"
      ]
     },
     "execution_count": 10,
     "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": [
    "sns.heatmap(df.isnull(), cbar=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descartando todos os registros com valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao observar o dataframe modificado, é possível notar que foram removidos todos os registros com valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1247b3320>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.heatmap(df.isnull(), cbar=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      406829 non-null object\n",
      "StockCode      406829 non-null object\n",
      "Description    406829 non-null object\n",
      "Quantity       406829 non-null int64\n",
      "InvoiceDate    406829 non-null object\n",
      "UnitPrice      406829 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        406829 non-null object\n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(406829, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Busca e remoção de registros duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5225"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401604, 8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao fim destas seleções supracitadas, o dataframe fica com `401604` registros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adicionando coluna de Pedido Cancelado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível observar nos registros que os registros em que o `InvoiceNo` contém um C, também contém uma quantidade negativa de produtos, ou seja... Pode ser entendido como devolução/cancelamento de transação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OrderCanceled'] = df['InvoiceNo'].apply(lambda invoice : int('C' in invoice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>OrderCanceled</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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  OrderCanceled  \n",
       "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom              0  \n",
       "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom              0  \n",
       "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom              0  \n",
       "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom              0  \n",
       "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom              0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de produtos cancelados: 8872\n"
     ]
    }
   ],
   "source": [
    "print('Quantidade de produtos cancelados:', df['OrderCanceled'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verificando total de Pedidos Cancelados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>OrderCanceled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>536367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>536368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>536369</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo  OrderCanceled\n",
       "0     536365              0\n",
       "7     536366              0\n",
       "9     536367              0\n",
       "21    536368              0\n",
       "25    536369              0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'InvoiceNo': df['InvoiceNo'], 'OrderCanceled': df['OrderCanceled'] }\n",
    "order_canceled = pd.DataFrame(data=d)\n",
    "order_canceled.drop_duplicates(inplace = True)\n",
    "order_canceled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de pedidos cancelados: 3654\n"
     ]
    }
   ],
   "source": [
    "print('Total de pedidos cancelados:', sum(order_canceled['OrderCanceled']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explorando o Código do Produto\n",
    "\n",
    "Assim como na **InvoiceNo** que contem o C para representar o cancelamento, foi verificado que há varios registros que continham strings em seu **StockCode**, como pode ser verificado abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>OrderCanceled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>536370</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-12-01 08:45:00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12583.0</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>C536379</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-01 09:41:00</td>\n",
       "      <td>27.50</td>\n",
       "      <td>14527.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>536403</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:27:00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>12791.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>536527</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 13:04:00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12662.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>536540</td>\n",
       "      <td>C2</td>\n",
       "      <td>CARRIAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 14:05:00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>EIRE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>536569</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 15:35:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16274.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>536569</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 15:35:00</td>\n",
       "      <td>18.95</td>\n",
       "      <td>16274.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>536779</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-02 15:08:00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15823.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073</th>\n",
       "      <td>536840</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-02 18:27:00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12738.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5258</th>\n",
       "      <td>536852</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-03 09:51:00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12686.0</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     InvoiceNo     StockCode   Description  Quantity          InvoiceDate  \\\n",
       "45      536370          POST       POSTAGE         3  2010-12-01 08:45:00   \n",
       "141    C536379             D      Discount        -1  2010-12-01 09:41:00   \n",
       "386     536403          POST       POSTAGE         1  2010-12-01 11:27:00   \n",
       "1123    536527          POST       POSTAGE         1  2010-12-01 13:04:00   \n",
       "1423    536540            C2      CARRIAGE         1  2010-12-01 14:05:00   \n",
       "2239    536569             M        Manual         1  2010-12-01 15:35:00   \n",
       "2250    536569             M        Manual         1  2010-12-01 15:35:00   \n",
       "4406    536779  BANK CHARGES  Bank Charges         1  2010-12-02 15:08:00   \n",
       "5073    536840          POST       POSTAGE         1  2010-12-02 18:27:00   \n",
       "5258    536852          POST       POSTAGE         1  2010-12-03 09:51:00   \n",
       "\n",
       "      UnitPrice  CustomerID         Country  OrderCanceled  \n",
       "45        18.00     12583.0          France              0  \n",
       "141       27.50     14527.0  United Kingdom              1  \n",
       "386       15.00     12791.0     Netherlands              0  \n",
       "1123      18.00     12662.0         Germany              0  \n",
       "1423      50.00     14911.0            EIRE              0  \n",
       "2239       1.25     16274.0  United Kingdom              0  \n",
       "2250      18.95     16274.0  United Kingdom              0  \n",
       "4406      15.00     15823.0  United Kingdom              0  \n",
       "5073      18.00     12738.0         Germany              0  \n",
       "5258      18.00     12686.0          France              0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exists_char = df[df['StockCode'].str.contains('^[a-zA-Z]+', regex=True)]\n",
    "exists_char.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nota-se que, na tabela, a coluna **Stock Code** e **Description** tem valores similares. Assim, pode-se afirmar que, nestes casos, há relação entre as duas colunas, onde o código é explicado através de sua descrição"
   ]
  },
  {
   "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>Description</th>\n",
       "      <th>StockCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>POST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Discount</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>CARRIAGE</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>Manual</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157195</th>\n",
       "      <td>PADS TO MATCH ALL CUSHIONS</td>\n",
       "      <td>PADS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317507</th>\n",
       "      <td>DOTCOM POSTAGE</td>\n",
       "      <td>DOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317508</th>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>CRUK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Description     StockCode\n",
       "45                         POSTAGE          POST\n",
       "141                       Discount             D\n",
       "1423                      CARRIAGE            C2\n",
       "2239                        Manual             M\n",
       "4406                  Bank Charges  BANK CHARGES\n",
       "157195  PADS TO MATCH ALL CUSHIONS          PADS\n",
       "317507              DOTCOM POSTAGE           DOT\n",
       "317508             CRUK Commission          CRUK"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = { 'StockCode': exists_char['StockCode'], 'Description': exists_char['Description'] }\n",
    "code = pd.DataFrame(data=c)\n",
    "code.drop_duplicates(inplace = True)\n",
    "code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazendo uma contagem de valores únicos é possível observar que foram realizadas 22190 compras (contém várias transações) por 4372 clientes e podem contar-se 3684 produtos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Compras</th>\n",
       "      <th>Produtos</th>\n",
       "      <th>Clientes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantidade</th>\n",
       "      <td>22190</td>\n",
       "      <td>3684</td>\n",
       "      <td>4372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Compras  Produtos  Clientes\n",
       "Quantidade    22190      3684      4372"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([{'Compras': len(df['InvoiceNo'].value_counts()),\n",
    "               'Produtos': len(df['StockCode'].value_counts()),    \n",
    "               'Clientes': len(df['CustomerID'].value_counts()),  \n",
    "              }], columns = ['Compras', 'Produtos', 'Clientes'], index = ['Quantidade'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupamento por compra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo é feito um agrupamento por compra e feita a ordenação de acordo com o número de produtos em cada compra em ordem crescente. De acordo com o início da lista, já é possível observar que poucas transações tem menos que uma dezena de items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Número de produtos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536369</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536371</td>\n",
       "      <td>13748.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>536374</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536366</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536372</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536368</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536367</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536373</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>536370</td>\n",
       "      <td>12583.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo  CustomerID  Número de produtos\n",
       "4    536369     13047.0                   1\n",
       "6    536371     13748.0                   1\n",
       "9    536374     15100.0                   1\n",
       "1    536366     17850.0                   2\n",
       "7    536372     17850.0                   2\n",
       "3    536368     13047.0                   4\n",
       "0    536365     17850.0                   7\n",
       "2    536367     13047.0                  12\n",
       "8    536373     17850.0                  16\n",
       "5    536370     12583.0                  20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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</th>\n",
       "      <th>Número de produtos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22190.000000</td>\n",
       "      <td>22190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15238.498738</td>\n",
       "      <td>18.098423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1733.149624</td>\n",
       "      <td>23.363888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13755.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15136.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16746.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18287.000000</td>\n",
       "      <td>542.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID  Número de produtos\n",
       "count  22190.000000        22190.000000\n",
       "mean   15238.498738           18.098423\n",
       "std     1733.149624           23.363888\n",
       "min    12346.000000            1.000000\n",
       "25%    13755.000000            3.000000\n",
       "50%    15136.000000           12.000000\n",
       "75%    16746.000000           24.000000\n",
       "max    18287.000000          542.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy = df.groupby(by=['InvoiceNo', 'CustomerID'], as_index=False)['InvoiceDate'].count().rename(columns = {'InvoiceDate':'Número de produtos'})\n",
    "display(buy.head(10).sort_values('Número de produtos'))\n",
    "buy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>356728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>8475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>7475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>1471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Community</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSA</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      InvoiceNo\n",
       "Country                        \n",
       "United Kingdom           356728\n",
       "Germany                    9480\n",
       "France                     8475\n",
       "EIRE                       7475\n",
       "Spain                      2528\n",
       "Netherlands                2371\n",
       "Belgium                    2069\n",
       "Switzerland                1877\n",
       "Portugal                   1471\n",
       "Australia                  1258\n",
       "Norway                     1086\n",
       "Italy                       803\n",
       "Channel Islands             757\n",
       "Finland                     695\n",
       "Cyprus                      611\n",
       "Sweden                      461\n",
       "Austria                     401\n",
       "Denmark                     389\n",
       "Japan                       358\n",
       "Poland                      341\n",
       "USA                         291\n",
       "Israel                      247\n",
       "Unspecified                 241\n",
       "Singapore                   229\n",
       "Iceland                     182\n",
       "Canada                      151\n",
       "Greece                      146\n",
       "Malta                       127\n",
       "United Arab Emirates         68\n",
       "European Community           61\n",
       "RSA                          58\n",
       "Lebanon                      45\n",
       "Lithuania                    35\n",
       "Brazil                       32\n",
       "Czech Republic               30\n",
       "Bahrain                      17\n",
       "Saudi Arabia                 10"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country = df[['Country', 'InvoiceNo']]\n",
    "country = country.pivot_table(columns='Country', aggfunc='count')\n",
    "country.sort_values('InvoiceNo', axis=1, ascending=False).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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