01 - Retail Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc101e5e",
   "metadata": {},
   "source": [
    "# Exploratory data analysis with Pandas & Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3523f9",
   "metadata": {},
   "source": [
    "## Topics\n",
    "\n",
    "- Read data from excel\n",
    "- Data Viz: scatter plot, line chart\n",
    "- Statistics: Distribution summaries\n",
    "- Pandas: groupby and MultiIndex\n",
    "\n",
    "\n",
    "### Dataset\n",
    "- Dataset Used: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II#\n",
    "- Download file: https://archive.ics.uci.edu/ml/machine-learning-databases/00502/online_retail_II.xlsx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07097343",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1635a02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1f175f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the file and put it in the data folder\n",
    "df = pd.read_excel(\"data/online_retail_II.xlsx\")\n",
    "\n",
    "# or just read the file directly. \n",
    "# df = pd.read_excel(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00502/online_retail_II.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5767318b",
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f76c0693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(525461, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "749df517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 525461 entries, 0 to 525460\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   Invoice      525461 non-null  object        \n",
      " 1   StockCode    525461 non-null  object        \n",
      " 2   Description  522533 non-null  object        \n",
      " 3   Quantity     525461 non-null  int64         \n",
      " 4   InvoiceDate  525461 non-null  datetime64[ns]\n",
      " 5   Price        525461 non-null  float64       \n",
      " 6   Customer ID  417534 non-null  float64       \n",
      " 7   Country      525461 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 131.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa7b469",
   "metadata": {},
   "source": [
    "# Columns Description\n",
    "\n",
    "- InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.\n",
    "- StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product.\n",
    "- Description: Product (item) name. Nominal.\n",
    "- Quantity: The quantities of each product (item) per transaction. Numeric.\n",
    "- InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated.\n",
    "- UnitPrice: Unit price. Numeric. Product price per unit in sterling (Â£).\n",
    "- CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer.\n",
    "- Country: Country name. Nominal. The name of the country where a customer resides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9a271d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice', 'stockcode', 'description', 'quantity', 'invoicedate',\n",
       "       'price', 'customer_id', 'country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.str.replace(\" \", \"_\").str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4145fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change columns to lowercase and remove whitespace in between \n",
    "# this is useful for using the df.column_name syntax later on\n",
    "df.columns = ['invoice_id', 'item_id', 'description', 'quantity', 'date',\n",
    "              'price', 'customer_id', 'country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74358d82",
   "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>invoice_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_id item_id                          description  quantity  \\\n",
       "0     489434   85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1     489434  79323P                   PINK CHERRY LIGHTS        12   \n",
       "2     489434  79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3     489434   22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4     489434   21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "                 date  price  customer_id         country  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c756a8bf",
   "metadata": {},
   "source": [
    "## 1. Simple Statistics\n",
    "\n",
    "The idea of this is to understand better the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4db5b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28816,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of invoice_ids\n",
    "df.invoice_id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64d3a603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4632,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of item_ids\n",
    "df.item_id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "23f5cbc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4682,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of description\n",
    "df.description.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2357df58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4384,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of customers\n",
    "df.customer_id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c43018a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2009-12-01 07:45:00'), Timestamp('2010-12-09 20:01:00'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start/End of date\n",
    "df.date.min(), df.date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2456d36b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of countries\n",
    "df.country.unique().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503d728d",
   "metadata": {},
   "source": [
    "## 2. Initial data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "929f73ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's create the sales column\n",
    "df['sales'] = df['quantity'] * df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe426bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>100.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_id item_id                          description  quantity  \\\n",
       "0     489434   85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1     489434  79323P                   PINK CHERRY LIGHTS        12   \n",
       "2     489434  79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3     489434   22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4     489434   21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "                 date  price  customer_id         country  sales  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom   83.4  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom   81.0  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom   81.0  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  100.8  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom   30.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "327e7fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00   -53594.36\n",
       "0.25        3.75\n",
       "0.50        9.95\n",
       "0.75       17.70\n",
       "1.00    25111.09\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# global distribution of the time series\n",
    "# negative values are probably refunds\n",
    "df.sales.quantile([0, 0.25, 0.5, 0.75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5cee36d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.boxplot('sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e40cb74e",
   "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>invoice_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>C489449</td>\n",
       "      <td>22087</td>\n",
       "      <td>PAPER BUNTING WHITE LACE</td>\n",
       "      <td>-12</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>C489449</td>\n",
       "      <td>85206A</td>\n",
       "      <td>CREAM FELT EASTER EGG BASKET</td>\n",
       "      <td>-6</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>C489449</td>\n",
       "      <td>21895</td>\n",
       "      <td>POTTING SHED SOW 'N' GROW SET</td>\n",
       "      <td>-4</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>C489449</td>\n",
       "      <td>21896</td>\n",
       "      <td>POTTING SHED TWINE</td>\n",
       "      <td>-6</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>C489449</td>\n",
       "      <td>22083</td>\n",
       "      <td>PAPER CHAIN KIT RETRO SPOT</td>\n",
       "      <td>-12</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    invoice_id item_id                    description  quantity  \\\n",
       "178    C489449   22087       PAPER BUNTING WHITE LACE       -12   \n",
       "179    C489449  85206A   CREAM FELT EASTER EGG BASKET        -6   \n",
       "180    C489449   21895  POTTING SHED SOW 'N' GROW SET        -4   \n",
       "181    C489449   21896             POTTING SHED TWINE        -6   \n",
       "182    C489449   22083     PAPER CHAIN KIT RETRO SPOT       -12   \n",
       "\n",
       "                   date  price  customer_id    country  sales  \n",
       "178 2009-12-01 10:33:00   2.95      16321.0  Australia  -35.4  \n",
       "179 2009-12-01 10:33:00   1.65      16321.0  Australia   -9.9  \n",
       "180 2009-12-01 10:33:00   4.25      16321.0  Australia  -17.0  \n",
       "181 2009-12-01 10:33:00   2.10      16321.0  Australia  -12.6  \n",
       "182 2009-12-01 10:33:00   2.95      16321.0  Australia  -35.4  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# invoice_id starts with C when we have a refund.\n",
    "# Also the quantity is negative\n",
    "df.query(\"sales < 0\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ecad61",
   "metadata": {},
   "source": [
    "## 2.1. Demand curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ce3004e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    df.query(\"price < 10000 & price > 0 & quantity > 0\")\n",
    "    .plot(x='quantity', y='price', \n",
    "          kind='scatter', \n",
    "          loglog=True, \n",
    "          alpha=0.1)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8882afdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.1622776601683795, 10, 100, 1000, 10000)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to read the axis labels\n",
    "10**0.5, 10**1, 10**2, 10**3, 10**4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958bdd3d",
   "metadata": {},
   "source": [
    "## 2.2. Sales at the daily level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5750752d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compact method \n",
    "agg_daily_sales = df.groupby(\"date\")['sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86eef900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2009-12-01 07:45:00     505.30\n",
       "2009-12-01 07:46:00     145.80\n",
       "2009-12-01 09:06:00     630.33\n",
       "2009-12-01 09:08:00     310.75\n",
       "2009-12-01 09:24:00    2286.24\n",
       "                        ...   \n",
       "2010-12-09 18:58:00     298.95\n",
       "2010-12-09 19:23:00     318.70\n",
       "2010-12-09 19:28:00      93.45\n",
       "2010-12-09 19:32:00     317.59\n",
       "2010-12-09 20:01:00     300.64\n",
       "Name: sales, Length: 25296, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one operation by line\n",
    "agg_daily_sales = (\n",
    "    df.groupby(\"date\")   # groupby \n",
    "    ['sales']            # select a column\n",
    "    .sum()               # function to apply\n",
    ")\n",
    "agg_daily_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35391e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(agg_daily_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "85b1b092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total Sales - Daily Frequency'}, xlabel='date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_daily_sales.plot(title = 'Total Sales - Daily Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db567962",
   "metadata": {},
   "source": [
    "## 2.3. Lets aggregate at the weekly level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cdae8c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_daily_sales.resample(\"W\").sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71199b1",
   "metadata": {},
   "source": [
    "## 3.1. Let's compute some statistics at the country level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e374fe3b",
   "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>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>162.761529</td>\n",
       "      <td>8194777.533</td>\n",
       "      <td>16.866819</td>\n",
       "      <td>8.5</td>\n",
       "      <td>485852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>115.264833</td>\n",
       "      <td>352242.730</td>\n",
       "      <td>36.426342</td>\n",
       "      <td>17.7</td>\n",
       "      <td>9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>140.792366</td>\n",
       "      <td>263863.410</td>\n",
       "      <td>95.291950</td>\n",
       "      <td>47.2</td>\n",
       "      <td>2769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Germany</td>\n",
       "      <td>35.994020</td>\n",
       "      <td>196290.351</td>\n",
       "      <td>24.146925</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>77.613575</td>\n",
       "      <td>130769.900</td>\n",
       "      <td>22.655908</td>\n",
       "      <td>15.9</td>\n",
       "      <td>5772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country         std          sum       mean  median   count\n",
       "37  United Kingdom  162.761529  8194777.533  16.866819     8.5  485852\n",
       "10            EIRE  115.264833   352242.730  36.426342    17.7    9670\n",
       "24     Netherlands  140.792366   263863.410  95.291950    47.2    2769\n",
       "13         Germany   35.994020   196290.351  24.146925    17.0    8129\n",
       "12          France   77.613575   130769.900  22.655908    15.9    5772"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_country = (\n",
    "    df.groupby(\"country\")\n",
    "    ['sales']\n",
    "    .agg(['std', 'sum', 'mean', 'median', 'count'])\n",
    ").reset_index()\n",
    "\n",
    "\n",
    "# sort by total\n",
    "agg_country = agg_country.sort_values(\"sum\", ascending=False)\n",
    "agg_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c355fcc7",
   "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>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Korea</td>\n",
       "      <td>19.365294</td>\n",
       "      <td>949.82</td>\n",
       "      <td>15.076508</td>\n",
       "      <td>17.700</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>9.122665</td>\n",
       "      <td>536.41</td>\n",
       "      <td>9.933519</td>\n",
       "      <td>7.530</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.759600</td>\n",
       "      <td>268.27</td>\n",
       "      <td>4.326935</td>\n",
       "      <td>2.775</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>17.488026</td>\n",
       "      <td>172.03</td>\n",
       "      <td>13.233077</td>\n",
       "      <td>15.900</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>20.033587</td>\n",
       "      <td>-6.66</td>\n",
       "      <td>-0.208125</td>\n",
       "      <td>1.950</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country        std     sum       mean  median  count\n",
       "20        Korea  19.365294  949.82  15.076508  17.700     63\n",
       "39  West Indies   9.122665  536.41   9.933519   7.530     54\n",
       "5        Brazil   3.759600  268.27   4.326935   2.775     62\n",
       "21      Lebanon  17.488026  172.03  13.233077  15.900     13\n",
       "25      Nigeria  20.033587   -6.66  -0.208125   1.950     32"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_country.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8889e356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(agg_country\n",
    " .sort_values(\"mean\", ascending=False)\n",
    " .plot(x='country', y='mean', kind='bar')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7ab5c5",
   "metadata": {},
   "source": [
    "## 4.1. Item Level Analysis - MultiIndex example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "21a2b0f4",
   "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>invoice_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>100.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_id item_id                          description  quantity  \\\n",
       "0     489434   85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1     489434  79323P                   PINK CHERRY LIGHTS        12   \n",
       "2     489434  79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3     489434   22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4     489434   21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "                 date  price  customer_id         country  sales  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom   83.4  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom   81.0  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom   81.0  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  100.8  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom   30.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3c835e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00   -53594.36\n",
       "0.25        3.75\n",
       "0.50        9.95\n",
       "0.75       17.70\n",
       "1.00    25111.09\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sales.quantile([0, 0.25, 0.5, 0.75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0b202912",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sales</th>\n",
       "      <th colspan=\"2\" halign=\"left\">quantity</th>\n",
       "      <th colspan=\"2\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>16.680306</td>\n",
       "      <td>10.20</td>\n",
       "      <td>20.651376</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.961804</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10080</th>\n",
       "      <td>1.457143</td>\n",
       "      <td>0.85</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.728571</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10109</th>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10120</th>\n",
       "      <td>2.025918</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-174.346939</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250612</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>8.774253</td>\n",
       "      <td>3.32</td>\n",
       "      <td>9.827586</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.009195</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales           quantity            price       \n",
       "              mean median        mean median      mean median\n",
       "item_id                                                      \n",
       "10002    16.680306  10.20   20.651376   12.0  0.961804   0.85\n",
       "10080     1.457143   0.85   14.571429    1.0  0.728571   0.85\n",
       "10109     0.840000   0.84    0.000000    0.0  0.210000   0.21\n",
       "10120     2.025918   0.84 -174.346939    4.0  0.250612   0.21\n",
       "10125     8.774253   3.32    9.827586    3.0  1.009195   0.85"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_item = (\n",
    "    df.groupby(\"item_id\")\n",
    "    [['sales', 'quantity', 'price']]\n",
    "    .agg(['mean', 'median'])    \n",
    ")\n",
    "agg_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b69d2b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(   'sales',   'mean'),\n",
       "            (   'sales', 'median'),\n",
       "            ('quantity',   'mean'),\n",
       "            ('quantity', 'median'),\n",
       "            (   'price',   'mean'),\n",
       "            (   'price', 'median')],\n",
       "           )"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_item.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d6e91a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sales_mean',\n",
       " 'sales_median',\n",
       " 'quantity_mean',\n",
       " 'quantity_median',\n",
       " 'price_mean',\n",
       " 'price_median']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_columns = [\"_\".join(cols) for cols in agg_item.columns ]\n",
    "new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d464ba43",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_item.columns = new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "52163522",
   "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>sales_mean</th>\n",
       "      <th>sales_median</th>\n",
       "      <th>quantity_mean</th>\n",
       "      <th>quantity_median</th>\n",
       "      <th>price_mean</th>\n",
       "      <th>price_median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>16.680306</td>\n",
       "      <td>10.20</td>\n",
       "      <td>20.651376</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.961804</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10080</th>\n",
       "      <td>1.457143</td>\n",
       "      <td>0.85</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.728571</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10109</th>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10120</th>\n",
       "      <td>2.025918</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-174.346939</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250612</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>8.774253</td>\n",
       "      <td>3.32</td>\n",
       "      <td>9.827586</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.009195</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sales_mean  sales_median  quantity_mean  quantity_median  price_mean  \\\n",
       "item_id                                                                         \n",
       "10002     16.680306         10.20      20.651376             12.0    0.961804   \n",
       "10080      1.457143          0.85      14.571429              1.0    0.728571   \n",
       "10109      0.840000          0.84       0.000000              0.0    0.210000   \n",
       "10120      2.025918          0.84    -174.346939              4.0    0.250612   \n",
       "10125      8.774253          3.32       9.827586              3.0    1.009195   \n",
       "\n",
       "         price_median  \n",
       "item_id                \n",
       "10002            0.85  \n",
       "10080            0.85  \n",
       "10109            0.21  \n",
       "10120            0.21  \n",
       "10125            0.85  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_item.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a2fb5c",
   "metadata": {},
   "source": [
    "## 4.2 Distribution summary - Item Level\n",
    "\n",
    "- The 50th percentile of sales-mean is 10.7 and 4.4 for quantity mean\n",
    "- This gives an idea of the what the average item sold for and the number of sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e3c9179a",
   "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>sales_mean</th>\n",
       "      <th>quantity_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-64.250909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>5.332103</td>\n",
       "      <td>1.585997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>10.711064</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>17.225375</td>\n",
       "      <td>9.382270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.99</th>\n",
       "      <td>85.571713</td>\n",
       "      <td>118.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sales_mean  quantity_mean\n",
       "0.01    0.000000     -64.250909\n",
       "0.25    5.332103       1.585997\n",
       "0.50   10.711064       4.400000\n",
       "0.75   17.225375       9.382270\n",
       "0.99   85.571713     118.450000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute quartiles for all columns\n",
    "(\n",
    "    agg_item\n",
    "    [['sales_mean', 'quantity_mean']]\n",
    "    .quantile([0.01, 0.25, 0.5, 0.75, 0.99])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f66a00e2",
   "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>sales_mean</th>\n",
       "      <th>quantity_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>-45517.340000</td>\n",
       "      <td>-5466.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>4.275300</td>\n",
       "      <td>1.201463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>6.418199</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>8.581955</td>\n",
       "      <td>3.138467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>10.711064</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>12.834038</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>15.464607</td>\n",
       "      <td>7.996620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>19.458343</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>26.883430</td>\n",
       "      <td>17.756985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>3884.000000</td>\n",
       "      <td>2472.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sales_mean  quantity_mean\n",
       "0.0 -45517.340000   -5466.666667\n",
       "0.1      1.250000      -1.000000\n",
       "0.2      4.275300       1.201463\n",
       "0.3      6.418199       2.000000\n",
       "0.4      8.581955       3.138467\n",
       "0.5     10.711064       4.400000\n",
       "0.6     12.834038       6.000000\n",
       "0.7     15.464607       7.996620\n",
       "0.8     19.458343      11.000000\n",
       "0.9     26.883430      17.756985\n",
       "1.0   3884.000000    2472.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Deciles for all columns\n",
    "\n",
    "# increments of 0.1\n",
    "breaks = np.arange(0, 1.1, 0.1)\n",
    "\n",
    "(\n",
    "    agg_item\n",
    "    [['sales_mean', 'quantity_mean']]\n",
    "    .quantile(breaks)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "310ef7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d5d4aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0efdcae3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bff4223",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afcceb23",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa7add0f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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