online_retail.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Project: Online Retail Exploratory Data Analysis with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study\n",
    "In this project, you will be working with transactional data from an online retail store. The dataset contains information about customer purchases, including product details, quantities, prices, and timestamps.\n",
    "\n",
    "By conducting exploratory data analysis, you will identify patterns, outliers, and correlations in the data, allowing you to make data-driven decisions and recommendations to optimize the store's operations and improve customer satisfaction. Through visualizations and statistical analysis, you will uncover key trends, such as the busiest sales months, best-selling products, popular products and the store's most valuable customers. \n",
    "\n",
    "Ultimately, this project aims to provide actionable insights that can drive strategic business decisions and enhance the store's overall performance in the competitive online retail market.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Objectives\n",
    "1. Describe data to answer key questions to uncover insights\n",
    "2. Gain valuable insights that will help improve online retail performance\n",
    "3. Provide analytic insights and data-driven recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "It contains transactional data of an online retail store from 2010 to 2011. The dataset is available as a .xlsx file named `Online Retail.xlsx`.\n",
    "\n",
    "The dataset contains the following columns:\n",
    "\n",
    "- InvoiceNo: Invoice number of the transaction\n",
    "- StockCode: Unique code of the product\n",
    "- Description: Description of the product\n",
    "- Quantity: Quantity of the product in the transaction\n",
    "- InvoiceDate: Date and time of the transaction\n",
    "- UnitPrice: Unit price of the product\n",
    "- CustomerID: Unique identifier of the customer\n",
    "- Country: Country where the transaction occurred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load data\n",
    "path = \"Online_Retail.xlsx\"\n",
    "df = pd.read_excel(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Some Statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2. Perform data cleaning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primeras observaciones\n",
    "\n",
    "1. Hay 541909 registros y 8 columnas.\n",
    "2. \n",
    "   + 4 columnas categóricas\n",
    "   + 2 de tipo float\n",
    "   + 1 Datetime\n",
    "   + 1 integer\n",
    "3. Faltan datos en `Description`, `CustomerID`\n",
    "4. Quantity y UnitPrice tienen valores negativos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count the null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores negativos o 0 en UnitPrice\n",
    "Son valores inesperados para esta variable, con lo cual los observaremos más en detalle para encontrar la razón."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>299983</th>\n",
       "      <td>A563186</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:51:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299984</th>\n",
       "      <td>A563187</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:52:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode      Description  Quantity         InvoiceDate  \\\n",
       "299983   A563186         B  Adjust bad debt         1 2011-08-12 14:51:00   \n",
       "299984   A563187         B  Adjust bad debt         1 2011-08-12 14:52:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  \n",
       "299983  -11062.06         NaN  United Kingdom  \n",
       "299984  -11062.06         NaN  United Kingdom  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (df['UnitPrice']<0)\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>299982</th>\n",
       "      <td>A563185</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:50:00</td>\n",
       "      <td>11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299983</th>\n",
       "      <td>A563186</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:51:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>299984</th>\n",
       "      <td>A563187</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:52:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
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      ],
      "text/plain": [
       "       InvoiceNo StockCode      Description  Quantity         InvoiceDate  \\\n",
       "299982   A563185         B  Adjust bad debt         1 2011-08-12 14:50:00   \n",
       "299983   A563186         B  Adjust bad debt         1 2011-08-12 14:51:00   \n",
       "299984   A563187         B  Adjust bad debt         1 2011-08-12 14:52:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  \n",
       "299982   11062.06         NaN  United Kingdom  \n",
       "299983  -11062.06         NaN  United Kingdom  \n",
       "299984  -11062.06         NaN  United Kingdom  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all product with Description = \"Adjust bad debt\"\n",
    "df.query(\"Description == 'Adjust bad debt'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Adjust bad debt\n",
    "df = df[df['Description'] != 'Adjust bad debt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2515 entries, 622 to 538919\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   InvoiceNo    2515 non-null   object        \n",
      " 1   StockCode    2515 non-null   object        \n",
      " 2   Description  1061 non-null   object        \n",
      " 3   Quantity     2515 non-null   int64         \n",
      " 4   InvoiceDate  2515 non-null   datetime64[ns]\n",
      " 5   UnitPrice    2515 non-null   float64       \n",
      " 6   CustomerID   40 non-null     float64       \n",
      " 7   Country      2515 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 176.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.query('UnitPrice == 0').info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop UnitPrice = 0\n",
    "df = df[df['UnitPrice']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluar registros que no son productos en STOCK CODE \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DOTCOM POSTAGE', 'Manual', 'AMAZON FEE', 'Bank Charges',\n",
       "       'Discount', 'POSTAGE', 'PICNIC BASKET WICKER 60 PIECES', 'SAMPLES',\n",
       "       'CRUK Commission'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = 'UnitPrice > 500'\n",
    "df.query(q1)['Description'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DOT', 'M', 'AMAZONFEE', 'BANK CHARGES', 'D', 'POST', 22502, 'S',\n",
       "       'CRUK'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(q1)['StockCode'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dentro de los `UnitPrice` con valores más altos, encontramos descripciones que no corresponden a productos y su `StockCode` también lo confirma. Los analizaremos brevemente para proceder o no a su eliminación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a list with extras that seems no products\n",
    "extras = ['AMAZON FEE', 'Bank Charges','DOTCOM POSTAGE', 'Manual', 'Discount', \\\n",
    "          'POSTAGE', 'SAMPLES','CRUK Commission']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amazon FEE:  United Kingdom    34\n",
      "Name: Country, dtype: int64\n",
      "Bank Charges:  United Kingdom    37\n",
      "Name: Country, dtype: int64\n",
      "Bank Charges:  United Kingdom    16\n",
      "Name: Country, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Amazon FEE: \", df[df['StockCode'] == 'AMAZONFEE']['Country'].value_counts())\n",
    "\n",
    "print(\"Bank Charges: \", df[df['StockCode'] == 'BANK CHARGES']['Country'].value_counts())\n",
    "\n",
    "print(\"Bank Charges: \", df[df['StockCode'] == 'CRUK']['Country'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>14514</th>\n",
       "      <td>C537600</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-07 12:41:00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15016</th>\n",
       "      <td>C537630</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-07 15:04:00</td>\n",
       "      <td>13541.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15017</th>\n",
       "      <td>537632</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-07 15:08:00</td>\n",
       "      <td>13541.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16232</th>\n",
       "      <td>C537644</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-07 15:34:00</td>\n",
       "      <td>13474.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16313</th>\n",
       "      <td>C537647</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-07 15:41:00</td>\n",
       "      <td>5519.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo  StockCode Description  Quantity         InvoiceDate  \\\n",
       "14514   C537600  AMAZONFEE  AMAZON FEE        -1 2010-12-07 12:41:00   \n",
       "15016   C537630  AMAZONFEE  AMAZON FEE        -1 2010-12-07 15:04:00   \n",
       "15017    537632  AMAZONFEE  AMAZON FEE         1 2010-12-07 15:08:00   \n",
       "16232   C537644  AMAZONFEE  AMAZON FEE        -1 2010-12-07 15:34:00   \n",
       "16313   C537647  AMAZONFEE  AMAZON FEE        -1 2010-12-07 15:41:00   \n",
       "\n",
       "       UnitPrice  CustomerID         Country  \n",
       "14514       1.00         NaN  United Kingdom  \n",
       "15016   13541.33         NaN  United Kingdom  \n",
       "15017   13541.33         NaN  United Kingdom  \n",
       "16232   13474.79         NaN  United Kingdom  \n",
       "16313    5519.25         NaN  United Kingdom  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['StockCode'] == 'AMAZONFEE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14435</th>\n",
       "      <td>C537572</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-07 12:00:00</td>\n",
       "      <td>95.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28992</th>\n",
       "      <td>C538680</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-13 17:10:00</td>\n",
       "      <td>966.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62508</th>\n",
       "      <td>541505</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-18 15:58:00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15939.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64573</th>\n",
       "      <td>C541653</td>\n",
       "      <td>BANK CHARGES</td>\n",
       "      <td>Bank Charges</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-01-20 11:50:00</td>\n",
       "      <td>1050.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo     StockCode   Description  Quantity         InvoiceDate  \\\n",
       "4406     536779  BANK CHARGES  Bank Charges         1 2010-12-02 15:08:00   \n",
       "14435   C537572  BANK CHARGES  Bank Charges        -1 2010-12-07 12:00:00   \n",
       "28992   C538680  BANK CHARGES  Bank Charges        -1 2010-12-13 17:10:00   \n",
       "62508    541505  BANK CHARGES  Bank Charges         1 2011-01-18 15:58:00   \n",
       "64573   C541653  BANK CHARGES  Bank Charges        -1 2011-01-20 11:50:00   \n",
       "\n",
       "       UnitPrice  CustomerID         Country  \n",
       "4406       15.00     15823.0  United Kingdom  \n",
       "14435      95.38         NaN  United Kingdom  \n",
       "28992     966.92         NaN  United Kingdom  \n",
       "62508      15.00     15939.0  United Kingdom  \n",
       "64573    1050.15         NaN  United Kingdom  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['StockCode'] == 'BANK CHARGES'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>317508</th>\n",
       "      <td>C564763</td>\n",
       "      <td>CRUK</td>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-08-30 10:49:00</td>\n",
       "      <td>1.60</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324023</th>\n",
       "      <td>C565382</td>\n",
       "      <td>CRUK</td>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-09-02 15:45:00</td>\n",
       "      <td>13.01</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333779</th>\n",
       "      <td>C566216</td>\n",
       "      <td>CRUK</td>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-09-09 15:17:00</td>\n",
       "      <td>15.96</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338848</th>\n",
       "      <td>C566565</td>\n",
       "      <td>CRUK</td>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-09-13 12:32:00</td>\n",
       "      <td>52.24</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351003</th>\n",
       "      <td>C567655</td>\n",
       "      <td>CRUK</td>\n",
       "      <td>CRUK Commission</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-09-21 14:40:00</td>\n",
       "      <td>608.66</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode      Description  Quantity         InvoiceDate  \\\n",
       "317508   C564763      CRUK  CRUK Commission        -1 2011-08-30 10:49:00   \n",
       "324023   C565382      CRUK  CRUK Commission        -1 2011-09-02 15:45:00   \n",
       "333779   C566216      CRUK  CRUK Commission        -1 2011-09-09 15:17:00   \n",
       "338848   C566565      CRUK  CRUK Commission        -1 2011-09-13 12:32:00   \n",
       "351003   C567655      CRUK  CRUK Commission        -1 2011-09-21 14:40:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  \n",
       "317508       1.60     14096.0  United Kingdom  \n",
       "324023      13.01     14096.0  United Kingdom  \n",
       "333779      15.96     14096.0  United Kingdom  \n",
       "338848      52.24     14096.0  United Kingdom  \n",
       "351003     608.66     14096.0  United Kingdom  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['StockCode'] == 'CRUK'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#About SAMPLES and CustomerID\n",
    "df[df['Description']=='SAMPLES']['CustomerID'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Samples` no tiene asociado ningún `CustomerID`"
   ]
  },
  {
   "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",
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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>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>108088</th>\n",
       "      <td>C545478</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-720</td>\n",
       "      <td>2011-03-03 11:08:00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>16422.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182729</th>\n",
       "      <td>C552569</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-240</td>\n",
       "      <td>2011-05-10 12:06:00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>12901.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196362</th>\n",
       "      <td>C553841</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-48</td>\n",
       "      <td>2011-05-19 12:19:00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>16029.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226396</th>\n",
       "      <td>C556796</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-96</td>\n",
       "      <td>2011-06-14 14:40:00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>16013.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183138</th>\n",
       "      <td>C552650</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-18</td>\n",
       "      <td>2011-05-10 14:03:00</td>\n",
       "      <td>1.45</td>\n",
       "      <td>16672.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode Description  Quantity         InvoiceDate  \\\n",
       "108088   C545478         D    Discount      -720 2011-03-03 11:08:00   \n",
       "182729   C552569         D    Discount      -240 2011-05-10 12:06:00   \n",
       "196362   C553841         D    Discount       -48 2011-05-19 12:19:00   \n",
       "226396   C556796         D    Discount       -96 2011-06-14 14:40:00   \n",
       "183138   C552650         D    Discount       -18 2011-05-10 14:03:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  \n",
       "108088       0.01     16422.0  United Kingdom  \n",
       "182729       0.03     12901.0  United Kingdom  \n",
       "196362       0.20     16029.0  United Kingdom  \n",
       "226396       0.70     16013.0  United Kingdom  \n",
       "183138       1.45     16672.0  United Kingdom  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#About Description\n",
    "df[df['Description']=='Discount'].sort_values('UnitPrice').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description      Country             \n",
       "AMAZON FEE       United Kingdom           34\n",
       "Bank Charges     United Kingdom           37\n",
       "CRUK Commission  United Kingdom           16\n",
       "DOTCOM POSTAGE   United Kingdom          709\n",
       "Discount         United Kingdom           74\n",
       "                 EIRE                      1\n",
       "                 Italy                     1\n",
       "                 Netherlands               1\n",
       "Manual           United Kingdom          479\n",
       "                 Germany                  16\n",
       "                 Portugal                 14\n",
       "                 Singapore                14\n",
       "                 EIRE                     12\n",
       "                 France                   10\n",
       "                 Hong Kong                 6\n",
       "                 Norway                    6\n",
       "                 Japan                     3\n",
       "                 Spain                     3\n",
       "                 Channel Islands           2\n",
       "                 Cyprus                    2\n",
       "                 Finland                   1\n",
       "                 Italy                     1\n",
       "                 Netherlands               1\n",
       "                 RSA                       1\n",
       "                 Sweden                    1\n",
       "POSTAGE          Germany                 383\n",
       "                 France                  311\n",
       "                 United Kingdom          140\n",
       "                 Belgium                  98\n",
       "                 Spain                    62\n",
       "                 Finland                  41\n",
       "                 Netherlands              39\n",
       "                 Switzerland              33\n",
       "                 Portugal                 30\n",
       "                 Sweden                   24\n",
       "                 Norway                   20\n",
       "                 Italy                    18\n",
       "                 Austria                  14\n",
       "                 Denmark                  14\n",
       "                 Poland                    5\n",
       "                 Greece                    4\n",
       "                 Malta                     4\n",
       "                 European Community        3\n",
       "                 Australia                 2\n",
       "                 Czech Republic            2\n",
       "                 Hong Kong                 2\n",
       "                 Canada                    1\n",
       "                 Cyprus                    1\n",
       "                 United Arab Emirates      1\n",
       "SAMPLES          United Kingdom           63\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Description'].isin(extras)].groupby('Description')['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminación de datos que no corresponden a productos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 536639 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    536639 non-null  object        \n",
      " 1   StockCode    536639 non-null  object        \n",
      " 2   Description  536639 non-null  object        \n",
      " 3   Quantity     536639 non-null  int64         \n",
      " 4   InvoiceDate  536639 non-null  datetime64[ns]\n",
      " 5   UnitPrice    536639 non-null  float64       \n",
      " 6   CustomerID   405013 non-null  float64       \n",
      " 7   Country      536639 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 36.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Drop extras from df\n",
    "df = df[~(df['Description'].isin(extras))]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores negativos en Quantity\n",
    "Estos valores podrían corresponder a descuentos o devoluciones. Sería una pregunta para hacer a los responsables de los datos aunque, al no poder proceder con este intercambio de información, intentaré hacer hablar a los datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540422</th>\n",
       "      <td>C581484</td>\n",
       "      <td>23843</td>\n",
       "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
       "      <td>-80995</td>\n",
       "      <td>2011-12-09 09:27:00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>16446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                  Description  Quantity  \\\n",
       "540421    581483     23843  PAPER CRAFT , LITTLE BIRDIE     80995   \n",
       "540422   C581484     23843  PAPER CRAFT , LITTLE BIRDIE    -80995   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "540421 2011-12-09 09:15:00       2.08     16446.0  United Kingdom  \n",
       "540422 2011-12-09 09:27:00       2.08     16446.0  United Kingdom  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([df[df['Quantity']==df['Quantity'].max()], df[df['Quantity']==df['Quantity'].min()]])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esto comprobamos que los valores negativos corresponden a devoluciones o descuentos, las `Invoice` llevan un prefijo 'C' cuando se trata una devolución mientras que las compras no llevan prefijo."
   ]
  },
  {
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8706.000000</td>\n",
       "      <td>8706.000000</td>\n",
       "      <td>8540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-31.246497</td>\n",
       "      <td>4.420997</td>\n",
       "      <td>14996.510656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1183.307228</td>\n",
       "      <td>9.128259</td>\n",
       "      <td>1704.268454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.000000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>13534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>14903.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>16393.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>18282.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Quantity    UnitPrice    CustomerID\n",
       "count   8706.000000  8706.000000   8540.000000\n",
       "mean     -31.246497     4.420997  14996.510656\n",
       "std     1183.307228     9.128259   1704.268454\n",
       "min   -80995.000000     0.030000  12346.000000\n",
       "25%       -6.000000     1.450000  13534.000000\n",
       "50%       -2.000000     2.550000  14903.000000\n",
       "75%       -1.000000     4.950000  16393.000000\n",
       "max       -1.000000   295.000000  18282.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count return and discounts\n",
    "df[df['InvoiceNo'].str[0]=='C'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columnas: Month, Year, WeekDay, SubTotal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create column Month\n",
    "df['Month'] = df['InvoiceDate'].dt.month\n",
    "\n",
    "#Create column Year\n",
    "df['Year'] = df['InvoiceDate'].dt.year\n",
    "\n",
    "#Create column quarter of year\n",
    "df['Quarter'] = df['InvoiceDate'].dt.quarter\n",
    "\n",
    "#Create column for Week Day by number Monday=0, Sunday=6\n",
    "df['WeekDay'] = df['InvoiceDate'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create column SubTotal\n",
    "df['SubTotal'] = df['Quantity'] * df['UnitPrice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3. Explore the basic statistics of the dataset\n",
    "including measures of central tendency and dispersion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>SubTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>536639.0</td>\n",
       "      <td>536639</td>\n",
       "      <td>536639</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>536639</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>405013.000000</td>\n",
       "      <td>536639</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>536639.000000</td>\n",
       "      <td>536639.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>23198.0</td>\n",
       "      <td>3928</td>\n",
       "      <td>4033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>573585.0</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-10-31 14:41:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1113.0</td>\n",
       "      <td>2307</td>\n",
       "      <td>2365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.886061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.297442</td>\n",
       "      <td>15295.017755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.558558</td>\n",
       "      <td>2010.921689</td>\n",
       "      <td>2.836499</td>\n",
       "      <td>2.434471</td>\n",
       "      <td>18.262520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.925492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.565467</td>\n",
       "      <td>1710.211905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.508899</td>\n",
       "      <td>0.268661</td>\n",
       "      <td>1.137102</td>\n",
       "      <td>1.847292</td>\n",
       "      <td>367.889413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>12346.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-168469.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13969.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15159.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80995.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>649.500000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>168469.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        InvoiceNo StockCode                         Description  \\\n",
       "count    536639.0    536639                              536639   \n",
       "unique    23198.0      3928                                4033   \n",
       "top      573585.0    85123A  WHITE HANGING HEART T-LIGHT HOLDER   \n",
       "freq       1113.0      2307                                2365   \n",
       "first         NaN       NaN                                 NaN   \n",
       "last          NaN       NaN                                 NaN   \n",
       "mean          NaN       NaN                                 NaN   \n",
       "std           NaN       NaN                                 NaN   \n",
       "min           NaN       NaN                                 NaN   \n",
       "25%           NaN       NaN                                 NaN   \n",
       "50%           NaN       NaN                                 NaN   \n",
       "75%           NaN       NaN                                 NaN   \n",
       "max           NaN       NaN                                 NaN   \n",
       "\n",
       "             Quantity          InvoiceDate      UnitPrice     CustomerID  \\\n",
       "count   536639.000000               536639  536639.000000  405013.000000   \n",
       "unique            NaN                21311            NaN            NaN   \n",
       "top               NaN  2011-10-31 14:41:00            NaN            NaN   \n",
       "freq              NaN                 1113            NaN            NaN   \n",
       "first             NaN  2010-12-01 08:26:00            NaN            NaN   \n",
       "last              NaN  2011-12-09 12:50:00            NaN            NaN   \n",
       "mean         9.886061                  NaN       3.297442   15295.017755   \n",
       "std        215.925492                  NaN       4.565467    1710.211905   \n",
       "min     -80995.000000                  NaN       0.001000   12346.000000   \n",
       "25%          1.000000                  NaN       1.250000   13969.000000   \n",
       "50%          3.000000                  NaN       2.080000   15159.000000   \n",
       "75%         10.000000                  NaN       4.130000   16794.000000   \n",
       "max      80995.000000                  NaN     649.500000   18287.000000   \n",
       "\n",
       "               Country          Month           Year        Quarter  \\\n",
       "count           536639  536639.000000  536639.000000  536639.000000   \n",
       "unique              38            NaN            NaN            NaN   \n",
       "top     United Kingdom            NaN            NaN            NaN   \n",
       "freq            491431            NaN            NaN            NaN   \n",
       "first              NaN            NaN            NaN            NaN   \n",
       "last               NaN            NaN            NaN            NaN   \n",
       "mean               NaN       7.558558    2010.921689       2.836499   \n",
       "std                NaN       3.508899       0.268661       1.137102   \n",
       "min                NaN       1.000000    2010.000000       1.000000   \n",
       "25%                NaN       5.000000    2011.000000       2.000000   \n",
       "50%                NaN       8.000000    2011.000000       3.000000   \n",
       "75%                NaN      11.000000    2011.000000       4.000000   \n",
       "max                NaN      12.000000    2011.000000       4.000000   \n",
       "\n",
       "              WeekDay       SubTotal  \n",
       "count   536639.000000  536639.000000  \n",
       "unique            NaN            NaN  \n",
       "top               NaN            NaN  \n",
       "freq              NaN            NaN  \n",
       "first             NaN            NaN  \n",
       "last              NaN            NaN  \n",
       "mean         2.434471      18.262520  \n",
       "std          1.847292     367.889413  \n",
       "min          0.000000 -168469.600000  \n",
       "25%          1.000000       3.750000  \n",
       "50%          2.000000       9.840000  \n",
       "75%          4.000000      17.400000  \n",
       "max          6.000000  168469.600000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR\n",
      "Quantity      9.00\n",
      "UnitPrice     2.88\n",
      "SubTotal     13.65\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "num_col = ['Quantity', 'UnitPrice', 'SubTotal']\n",
    "Q1 = df[num_col].quantile(0.25)\n",
    "Q3 = df[num_col].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "print(\"IQR\")\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observaciones\n",
    "\n",
    "+ Hay datos de 38 paises y el más frecuente es United Kingdom\n",
    "+ Se observan outliers en UnitPrice y Quantity\n",
    "+ Sólo se tienen registros de 6 días de la semana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4. Perform data visualization to gain insights into the dataset. \n",
    "Generate appropriate plots, such as histograms, scatter plots, or bar plots, to visualize different aspects of the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x70a21c968ad0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tktz9vaT/XmWvjfBGsmqHsoH8TzXWAEwbAYxGG1f2O/yFblD2YtuSdDn5fUW1Pz6/6O47y/RfKtNX7jKFuf4/c/cv1TgmMGscgkBDuftFZd/yL5eyf49M2ctQ/nONm7ig7J9fmun4P5d02sx+Jxm/3czer+xfTPiD5NrKMrMlZnZjlU0Bs0YAI8JaSVuTv4bxHUlD7v5Ojfd9QdLvlvsQbhp+X9Ifm9lbyp5N8cvu/m1l/8bbv5rZCWWvNzvjoAdqwdXQACAIM2AACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgyP8Ccam5diA6v84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['UnitPrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x70a225dd5a90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ul1vOl8DLo6IByPiLZPOuxm4sT3v1HbZLcDTmfnIXP5+0kz5KgiVa1+reyvwrswcn279Ps5xA/BAZn6+agYdXwywBETEOM0hjnWZ+YPp1pd6wQBLUhGPAUtSEQMsSUUMsCQVMcCSVMQAS1KR/wWPCnQCkhfBDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Quantity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pairplot of the data.\n",
    "\n",
    "# 1. Select the columns\n",
    "# 2. Create a pairplot for this columns only\n",
    "cols = ['Quantity', 'UnitPrice', 'Month', 'Country', 'SubTotal']\n",
    "sns.pairplot(df[cols]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualización de ventas de 2011\n",
    "\n",
    "Los registros sólo poseen datos de Diciembre de 2010 no se podrían compara ambos años, representaremos sólo los datos de 2011 que están completos. Luego podríamos comprar Diciembre de 2010 y Diciembre de 2011."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data per Year using a query\n",
    "q_2 = 'Year == 2011'\n",
    "month_order = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n",
    "               \"July\", \"August\", \"September\", \"October\", \"November\", \n",
    "               \"December\"]\n",
    "\n",
    "results = df.query(q_2).groupby('Month').sum()\n",
    "plt.bar(month_order, results['SubTotal'])\n",
    "\n",
    "plt.title('Solds per month')\n",
    "\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [2010, 2011]\n",
    "\n",
    "results = df.query('Month == 12').groupby('Year').sum()\n",
    "plt.bar(bins, results['Quantity'])\n",
    "\n",
    "plt.xticks(bins)\n",
    "plt.title('Solds in December 2010 vs 2011')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histórico de Compras y devoluciones por País"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Returns</th>\n",
       "      <th>Total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>4654421</td>\n",
       "      <td>-258099.0</td>\n",
       "      <td>4396322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>200258</td>\n",
       "      <td>-324.0</td>\n",
       "      <td>199934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>147168</td>\n",
       "      <td>-4802.0</td>\n",
       "      <td>142366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>118139</td>\n",
       "      <td>-1798.0</td>\n",
       "      <td>116341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>111272</td>\n",
       "      <td>-1579.0</td>\n",
       "      <td>109693.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country    Sales   Returns  Total_sales\n",
       "0  United Kingdom  4654421 -258099.0    4396322.0\n",
       "1     Netherlands   200258    -324.0     199934.0\n",
       "2            EIRE   147168   -4802.0     142366.0\n",
       "3         Germany   118139   -1798.0     116341.0\n",
       "4          France   111272   -1579.0     109693.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns per Country\n",
    "returns = df.query('Quantity <0').groupby('Country')['Quantity'].sum().reset_index().sort_values('Quantity')\n",
    "returns.rename(columns={'Quantity':'Returns'}, inplace=True)\n",
    "\n",
    "#Sales per Country\n",
    "sales= df.query('Quantity > 0').groupby('Country')['Quantity'].sum().reset_index().sort_values('Quantity', ascending=False)\n",
    "sales.rename(columns={'Quantity':'Sales'}, inplace=True)\n",
    "\n",
    "\n",
    "df_1 = sales.merge(returns, on='Country', how='left').fillna(0)\n",
    "df_1['Total_sales'] = df_1['Sales'] + df_1['Returns']\n",
    "df_1.sort_values('Total_sales', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ventas por País"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = df_1.sort_values('Total_sales', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.bar(res['Country'], res['Total_sales'])\n",
    "\n",
    "plt.title('Sales per Country')\n",
    "\n",
    "plt.xticks(rotation='vertical', horizontalalignment='right')\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Devoluciones por País"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returns per Country\n",
    "\n",
    "#Select the top 10 and exclude UK\n",
    "results = df_1.iloc[1:,].sort_values('Returns')\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(results['Country'], results['Returns'])\n",
    "\n",
    "plt.title('Returns per Country')\n",
    "plt.xticks(rotation='vertical', horizontalalignment='right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ventas por día de la semana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = df.groupby('WeekDay')['Quantity'].sum().sort_index()\n",
    "days = ['Monday', 'Tuesday', 'Wednesday','Thursday','Friday','Sunday']\n",
    "\n",
    "\n",
    "plt.bar(days, results.values)\n",
    "\n",
    "plt.title('Sales per Day of Week')\n",
    "\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5. Analyze the sales trends over time. \n",
    "Identify the busiest months and days of the week in terms of sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Invoice in one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Select only duplicated invoices\n",
    "df_dup = df[df['InvoiceNo'].duplicated(keep=False)]\n",
    "\n",
    "# 2. Group Invoice Products in one column Grouped\n",
    "df_dup['Grouped'] = df_dup.groupby('InvoiceNo')['StockCode'].transform(lambda x: ','.join(x.astype(str)))\n",
    "\n",
    "# 3. Calculate Total of Invoice\n",
    "df_dup['Total'] = df_dup.groupby('InvoiceNo')['SubTotal'].transform(lambda x: x.sum())\n",
    "\n",
    "# 4. Calculate Total of Invoice\n",
    "df_dup['TotalQuantity'] = df_dup.groupby('InvoiceNo')['Quantity'].transform(lambda x: x.sum())\n",
    "\n",
    "# 5. Delete duplicated and keep only one\n",
    "df_dup = df_dup.drop_duplicates(['InvoiceNo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete columns Quantity and UnitPrice\n",
    "df_dup.drop(columns=['Quantity', 'UnitPrice'], axis=1, inplace=True)\n",
    "\n",
    "# Rename column for future merge\n",
    "df_dup.rename(columns={'TotalQuantity':'Quantity'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get no duplicated invoices\n",
    "df_nodup = df.drop_duplicates(['InvoiceNo'], keep=False)\n",
    "\n",
    "#Group Invoice Products in one column Grouped\n",
    "df_nodup['Grouped'] = df_nodup['StockCode']\n",
    "\n",
    "#Calculate Total of Invoice\n",
    "df_nodup['Total'] = df_nodup['SubTotal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join both dataFrames\n",
    "df_invoices = pd.merge(df_nodup, df_dup, how='outer')\n",
    "\n",
    "#Drop the unnecessary columns \n",
    "df_invoices.drop(['UnitPrice', 'SubTotal'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save invoices\n",
    "df_invoices.to_csv(\"online_invoice.csv\")\n",
    "\n",
    "# Save cleaned data\n",
    "df.to_csv(\"online_retail_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Delete DataFrames\n",
    "\n",
    "del df_nodup\n",
    "del df_dup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meses con más ventas en 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Quantity</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>737773</td>\n",
       "      <td>1433884.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>597897</td>\n",
       "      <td>1063394.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>562883</td>\n",
       "      <td>1014355.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>409797</td>\n",
       "      <td>703075.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>395423</td>\n",
       "      <td>678538.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>391347</td>\n",
       "      <td>732763.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>381627</td>\n",
       "      <td>725651.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372787</td>\n",
       "      <td>681340.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>307646</td>\n",
       "      <td>580616.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294926</td>\n",
       "      <td>483392.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280650</td>\n",
       "      <td>500792.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>229738</td>\n",
       "      <td>441574.840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity        Total\n",
       "Month                       \n",
       "11       737773  1433884.990\n",
       "10       597897  1063394.330\n",
       "9        562883  1014355.681\n",
       "8        409797   703075.640\n",
       "7        395423   678538.531\n",
       "5        391347   732763.510\n",
       "6        381627   725651.040\n",
       "3        372787   681340.640\n",
       "1        307646   580616.110\n",
       "4        294926   483392.881\n",
       "2        280650   500792.000\n",
       "12       229738   441574.840"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Busiest Month of 2011\n",
    "\n",
    "# 1. Filter the dataset by year 2011, using q_2 defined previuosly\n",
    "# 2. Group the dataframe by 'Month'.\n",
    "# 3. Calculate the sum of 'Quantity' and 'Total' for each group.\n",
    "\n",
    "df_invoices.query(q_2).groupby(['Month'])[['Quantity','Total']].sum().sort_values('Quantity', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Día de la semana con más ventas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeekDay</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1175922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1016504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1004294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>843639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>797458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>467429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Quantity\n",
       "WeekDay          \n",
       "3         1175922\n",
       "1         1016504\n",
       "2         1004294\n",
       "0          843639\n",
       "4          797458\n",
       "6          467429"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Busiest Days of the week\n",
    "\n",
    "# 1. Group the dataframe by 'WeekDay'.\n",
    "# 2. Calculate the sum of 'Quantity' for each group.\n",
    "\n",
    "df.groupby(['WeekDay'])[['Quantity']].sum().sort_values('Quantity',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 7. Identify any outliers or anomalies in the dataset \n",
    "\n",
    "### Otuliers en 'UnitPrice'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>SubTotal</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>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>649.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222680</th>\n",
       "      <td>556444</td>\n",
       "      <td>22502</td>\n",
       "      <td>PICNIC BASKET WICKER 60 PIECES</td>\n",
       "      <td>60</td>\n",
       "      <td>2011-06-10 15:28:00</td>\n",
       "      <td>649.5</td>\n",
       "      <td>15098.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>38970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118769</th>\n",
       "      <td>546480</td>\n",
       "      <td>22656</td>\n",
       "      <td>VINTAGE BLUE KITCHEN CABINET</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-03-14 11:38:00</td>\n",
       "      <td>295.0</td>\n",
       "      <td>13452.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>295.0</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>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>295.0</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>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>295.0</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",
       "222680    556444     22502  PICNIC BASKET WICKER 60 PIECES        60   \n",
       "118769    546480     22656    VINTAGE BLUE KITCHEN CABINET         1   \n",
       "205759    554836     22655     VINTAGE RED KITCHEN CABINET         1   \n",
       "82768     543253     22655     VINTAGE RED KITCHEN CABINET         1   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  Month  \\\n",
       "222682 2011-06-10 15:33:00      649.5     15098.0  United Kingdom      6   \n",
       "222680 2011-06-10 15:28:00      649.5     15098.0  United Kingdom      6   \n",
       "118769 2011-03-14 11:38:00      295.0     13452.0  United Kingdom      3   \n",
       "205759 2011-05-26 16:25:00      295.0     13015.0  United Kingdom      5   \n",
       "82768  2011-02-04 15:32:00      295.0     14842.0  United Kingdom      2   \n",
       "\n",
       "        Year  Quarter  WeekDay  SubTotal  \n",
       "222682  2011        2        4     649.5  \n",
       "222680  2011        2        4   38970.0  \n",
       "118769  2011        1        0     295.0  \n",
       "205759  2011        2        3     295.0  \n",
       "82768   2011        1        4     295.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('UnitPrice', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>SubTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168469.60</td>\n",
       "    </tr>\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>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</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>10</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1008.00</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>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3096.00</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>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3202.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                        Description  Quantity  \\\n",
       "540421    581483     23843        PAPER CRAFT , LITTLE BIRDIE     80995   \n",
       "61619     541431     23166     MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "421632    573008     84077  WORLD WAR 2 GLIDERS ASSTD DESIGNS      4800   \n",
       "206121    554868     22197               SMALL POPCORN HOLDER      4300   \n",
       "97432     544612     22053              EMPIRE DESIGN ROSETTE      3906   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  Month  \\\n",
       "540421 2011-12-09 09:15:00       2.08     16446.0  United Kingdom     12   \n",
       "61619  2011-01-18 10:01:00       1.04     12346.0  United Kingdom      1   \n",
       "421632 2011-10-27 12:26:00       0.21     12901.0  United Kingdom     10   \n",
       "206121 2011-05-27 10:52:00       0.72     13135.0  United Kingdom      5   \n",
       "97432  2011-02-22 10:43:00       0.82     18087.0  United Kingdom      2   \n",
       "\n",
       "        Year  Quarter  WeekDay   SubTotal  \n",
       "540421  2011        4        4  168469.60  \n",
       "61619   2011        1        1   77183.60  \n",
       "421632  2011        4        3    1008.00  \n",
       "206121  2011        2        4    3096.00  \n",
       "97432   2011        1        1    3202.92  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Quantity', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los Outliers en Quantity indicarían compras mayoristas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6. Explore the top-selling products and countries based on the quantity sold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Producto con más unidades vendidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>StockCode</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22197</td>\n",
       "      <td>56450</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84077</td>\n",
       "      <td>53847</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85099B</td>\n",
       "      <td>47359</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84879</td>\n",
       "      <td>36381</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21212</td>\n",
       "      <td>36039</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  StockCode  Quantity                          Description\n",
       "0     22197     56450   WHITE HANGING HEART T-LIGHT HOLDER\n",
       "1     84077     53847                  WHITE METAL LANTERN\n",
       "2    85099B     47359       CREAM CUPID HEARTS COAT HANGER\n",
       "3     84879     36381  KNITTED UNION FLAG HOT WATER BOTTLE\n",
       "4     21212     36039       RED WOOLLY HOTTIE WHITE HEART."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Group the dataframe by 'StockCode'.\n",
    "# 2. Calculate the sum of 'Quantity' for each group.\n",
    "# 3. Reset the index to make 'StockCode' columns again.\n",
    "\n",
    "best_products = df.groupby(['StockCode'])[['Quantity']] \\\n",
    "        .sum().sort_values('Quantity', ascending=False) \\\n",
    "        .reset_index().head()\n",
    "\n",
    "# 4. Use the 'index' attribute of 'best_products' to get the indices.\n",
    "# 5. Use the 'map' method to apply a lookup on 'df['Description']' using these indices.\n",
    "# 6. Assign the resulting descriptions to the 'Description' column in 'best_products'.\n",
    "best_products['Description'] = best_products.index.map(df['Description'])\n",
    "best_products\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 países con más ventas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Country:  United Kingdom\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United Kingdom    4396322\n",
       "Netherlands        199934\n",
       "EIRE               142366\n",
       "Germany            116341\n",
       "France             109693\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the best country by quantity of purchases\n",
    "# 1. Group the dataframe by 'Country'.\n",
    "# 2. Calculate the sum of 'Quantity' for each group.\n",
    "# 3. Get idxmax to print the first Country on the list\n",
    "\n",
    "print(\"Best Country: \", df_invoices.groupby(['Country'])['Quantity'].sum().idxmax())\n",
    "\n",
    "df_invoices.groupby(['Country'])['Quantity'].sum().sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cliente con más compras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best buyer:  (14646.0, 'Netherlands')\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>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14646.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>196556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12415.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>76946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14911.0</td>\n",
       "      <td>EIRE</td>\n",
       "      <td>76931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17450.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>69041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18102.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID         Country  Quantity\n",
       "0     14646.0     Netherlands    196556\n",
       "1     12415.0       Australia     76946\n",
       "2     14911.0            EIRE     76931\n",
       "3     17450.0  United Kingdom     69041\n",
       "4     18102.0  United Kingdom     64124"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the best buyer by quantity of purchases\n",
    "# 1. Group the dataframe by 'CustomerID' and 'Country'.\n",
    "# 2. Calculate the sum of 'Quantity' for each group.\n",
    "# 3. Get idxmax to print the first Country on the list\n",
    "\n",
    "print(\"Best buyer: \", df_invoices.groupby(['CustomerID', 'Country'])['Quantity'].sum().idxmax())\n",
    "\n",
    "df_invoices.groupby(['CustomerID', 'Country'])[['Quantity']].sum() \\\n",
    "            .sort_values('Quantity', ascending=False) \\\n",
    "            .reset_index().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 8. Draw conclusions and summarize your findings from the EDA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Los meses con más ventas (basado en Quantity) son Noviembre, Octubre y Septiembre.\n",
    "+ El mes de Abril presenta un descenso en las ventas que podría evaluarse para considerar campañas de marketing.\n",
    "+ Hay un notable descenso en las ventas de Diciembre comparado con el año anterior.\n",
    "\n",
    "\n",
    "+ Los días de la semana con más ventas son Jueves, Martes y Miércoles\n",
    "+ No se observan datos los sábados, lo cual es muy llamativo y evidencia falta de datos importantes.\n",
    "\n",
    "\n",
    "+ Los países que registran más ventas son: United Kingdom, Netherlands, EIRE (Ireland), Germany and France.\n",
    "+ Hay una notable diferencia entre UK y los siguientes en la lista aunque el cliente con más compras pertenece a Netherlands, el cuarto en la lista procede de UK.\n",
    "\n",
    "+ Se detectaron anomalías en `SAMPLES` al no tener asociado ningún `Customer ID`.\n",
    "\n",
    "+ La mayor correlación entre las variables que encontramos es entre Quantity y Total, lo cual es esperable.\n",
    "+ Sugerimos recolectar datos sobre satisfacción de usuarios. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoría: María Anastasia Livio, 2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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