eda_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Online Retail.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_duplicate = df.copy(deep=True)\n",
    "df_duplicate.loc[df_duplicate[df_duplicate.Country != 'United Kingdom'].index, 'Country'] = 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 8 columns</p>\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",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0      2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2      2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4      2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "...                    ...        ...         ...             ...  \n",
       "541904 2011-12-09 12:50:00       0.85     12680.0           Other  \n",
       "541905 2011-12-09 12:50:00       2.10     12680.0           Other  \n",
       "541906 2011-12-09 12:50:00       4.15     12680.0           Other  \n",
       "541907 2011-12-09 12:50:00       4.15     12680.0           Other  \n",
       "541908 2011-12-09 12:50:00       4.95     12680.0           Other  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Order by Country')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_duplicate, x='Country').set_title('Number of Order by Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392687</th>\n",
       "      <td>541904</td>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392688</th>\n",
       "      <td>541905</td>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392689</th>\n",
       "      <td>541906</td>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392690</th>\n",
       "      <td>541907</td>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392691</th>\n",
       "      <td>541908</td>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392692 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index InvoiceNo StockCode                          Description  \\\n",
       "0            0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER   \n",
       "1            1    536365     71053                  WHITE METAL LANTERN   \n",
       "2            2    536365    84406B       CREAM CUPID HEARTS COAT HANGER   \n",
       "3            3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE   \n",
       "4            4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.   \n",
       "...        ...       ...       ...                                  ...   \n",
       "392687  541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS   \n",
       "392688  541905    581587     22899         CHILDREN'S APRON DOLLY GIRL    \n",
       "392689  541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL    \n",
       "392690  541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE   \n",
       "392691  541908    581587     22138        BAKING SET 9 PIECE RETROSPOT    \n",
       "\n",
       "        Quantity         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0              6 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1              6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2              8 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3              6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4              6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "...          ...                 ...        ...         ...             ...  \n",
       "392687        12 2011-12-09 12:50:00       0.85     12680.0          France  \n",
       "392688         6 2011-12-09 12:50:00       2.10     12680.0          France  \n",
       "392689         4 2011-12-09 12:50:00       4.15     12680.0          France  \n",
       "392690         4 2011-12-09 12:50:00       4.15     12680.0          France  \n",
       "392691         3 2011-12-09 12:50:00       4.95     12680.0          France  \n",
       "\n",
       "[392692 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df.CustomerID.isna()].index, inplace=True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.drop(df[df[\"UnitPrice\"] == 0].index, inplace=True)\n",
    "df.drop(df[(df[\"InvoiceNo\"].str[0]=='C')|(df[\"InvoiceNo\"].str[0]=='A')].index, inplace=True)\n",
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Quantity')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df, y='Quantity').set_title('Distribution of Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df.Quantity.mean()\n",
    "std = df.Quantity.std()\n",
    "drop = df[df['Quantity'] >= mean+3*std]\n",
    "\n",
    "df.drop(drop.index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Quantity (Post Dropping)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BgYFKTU3Vk08+KUmeXUr1/Rf7xo0b9dVXX3mNZWVlKTw8XGeddZakw4sPStLXX3/tNe+dd96pdHsV/xI8mt69e+ujjz7yhI5yL730kpo3b64ePXrU9GFUqUWLFkpNTdX8+fO9+iorK9PcuXOVkJCgk046qda3e8MNNyguLk7jxo3zuato+vTp+u6773TLLbd4fmc1fR7LPxzLr1fu3//+d637PFJtfjfVvc7+8Y9/6Pfff9ell16qP/74o8oDVCs65ZRT9PPPP9es4Qp69+4tSV4HzErSm2++qf3793vqR2qM91FRUZFuvPFGHTp0SGPGjKl2/nnnnSep8uPIzs7Wt99+W+lx7N27t9JrJCsrSwEBAfqf//kfr/H58+frwIEDXtd999139Ze//EWBgYG1ely+pKWlacOGDfrmm2+8xqsKkYcOHdLWrVtrfEA66ldQ9VOAutmwYYMOHTqkQ4cOaceOHfrvf/+r2bNnKzAwUAsWLKi0Cf5I//rXv/TRRx9pwIABSkpK0oEDB/TCCy9Ikvr06SPp8D74du3a6e2331bv3r0VHR2tmJgYzwdpbcXHx2vgwIGaOHGi2rZtq7lz52rZsmWaNm2amjdvLknq1q2bTj75ZI0ePVqHDh1SVFSUFixYoE8//bTS7XXs2FHz58/X008/rS5duiggIEBdu3b1ed8TJkzQwoULde655+ree+9VdHS0XnnlFS1atEjTp09XZGRknR5TRVOnTlXfvn117rnnavTo0QoJCdFTTz2lDRs26NVXX63TMTWtWrXSm2++qYyMDHXp0kV33nmnzjjjDBUUFOi1117TK6+8or59+3qtQlzT5/GUU05R+/btddddd8kYo+joaL377rtatmyZX89Dx44dtXz5cr377rtq27atwsPDdfLJJ1c5V5KmTZum/v37KzAwUJ06dVJISIgk6aSTTtIFF1ygxYsX65xzzql03FdV0tPTNXnyZBUWFnpeXzXVt29fnX/++Ro7dqwKCgp09tln6+uvv9aECRPUuXNnDRkyRFLDvo9ycnK0evVqlZWVKT8/X19++aVeeOEFbdmyRY888oj69etX7eM4+eSTddNNN+mJJ57wnC21efNmjR8/XomJiV5n0UmHt6bceuutysnJ0UknnaT33ntPzz77rG699VYlJSV5zQ0MDFTfvn01atQolZWVadq0aSooKNCkSZNq+jQfVWZmpl544QX1799fkydPVlxcnLKysvTdd99JOnxM15G+/vprFRYW6txzz62X+0ctOXhwNCxVfoZD+SUkJMS0adPGpKWlmSlTppgdO3ZUuk7FM6RWrVpl/va3v5l27dqZ0NBQ07p1a5OWlmbeeecdr+t98MEHpnPnziY0NNTnOjsV12LxdV/G/N86O2+88YY5/fTTTUhIiDn++ON9LoT3ww8/mH79+pmIiAgTGxtrRo4c6VkD5cgzMHbv3m0uvfRS06pVK+NyuWq0zs5FF11kIiMjTUhIiDnjjDPM7NmzveaUny1UflZTufIzUCrO96V8nZ0WLVqYZs2amR49eph3333X5+3V5Gysclu2bDHDhg0zycnJJjg42PP7nzx5stc6KOVq+jx+8803pm/fviY8PNxERUWZv//97yYnJ6fSc1jV79zXGTfr1q0zZ599tmnevPlR19kxxpji4mJz4403mtjYWM/vseJZZHPmzDGSzLx582r8fP3000/G5XKZ//znPz77PdrZjMYcXmdn7Nixpl27diY4ONi0bdvW3HrrrV7r7Pj7PvKl4to8gYGBJioqynTp0sVkZmZWOjupusdUvs7OSSedZIKDg01MTIy5+uqrq1xnZ/ny5aZr164mNDTUtG3b1tx9992e9ZuO7G/atGlm0qRJJiEhwYSEhJjOnTub999/32dfvtbZqWjo0KGVzq7csGGD6dOnjwkLCzPR0dHmhhtuMC+++KKRZL766iuvuePHjzcxMTENvlozfHMZU81pMQBQB+vXr9df/vIXnXnmmVq8eLGaNWvmdEsN5pJLLtHq1au1efNmBQcH1/h6F110kQ4dOqTFixc3YHd2SE9P1++//64NGzYcdd7mzZuVnJyshx56SKNHj26k7v7PTTfdpFdffVW7du3ybP0rLS3ViSeeqMGDB+uBBx5o9J7AbiwADaRjx456++23df7552vQoEF6++23Pf/426C4uFhffPGFPvvsMy1YsEAzZsyoVdCRDu9W7Ny5s7Kzs9WtW7cG6hQNZfLkyYqPj9cJJ5ygffv2aeHChXruuef0z3/+0+u1PnfuXO3bt0933nmng902bYQdAA0mLS3N6yBRm+Tm5qpXr16KiIjQzTffrJEjR9b6NlJSUjR79uyjnsGDY1dwcLAeeughbdu2TYcOHVKHDh00Y8aMSis6l5WV6ZVXXqnRWXpoGOzGAgAAVuPUcwAAYDXCDgAAsBphBwAAWI0DlHX44LHt27crPDy8TouqAQCAxmeM0d69exUfH19pIccjEXZ0+DtYavsFhQAA4NiwdetWJSQkVFkn7OjwcunS4ScrIiLC4W4AAEBNFBQUKDEx0fM5XhXCjv7vywYjIiIIOwAA/MlUdwgKBygDAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7ACw2sqVK3X55Zdr5cqVTrcCwCGEHQDWOnDggGbMmKHffvtNM2bM0IEDB5xuCYADCDsArPXKK69o165dkqRdu3YpKyvL4Y4AOIGwA8BK27ZtU1ZWlowxkiRjjLKysrRt2zaHOwPQ2Ag7AKxjjNHMmTOrHC8PQACaBsIOAOvk5OQoOztbpaWlXuOlpaXKzs5WTk6OQ50BcAJhB4B1kpKS1K1bNwUGBnqNBwYGqnv37kpKSnKoMwBOIOwAsI7L5dJtt91W5bjL5XKgKwBOIewAsFJCQoIGDx7sNTZ48GAdd9xxDnUEwCmEHQDWysjI8Pp5wIABDnUCwEmEHQDWuv/++71+fuCBBxzqBICTCDsArPT5559r/fr1XmNff/21Pv/8c4c6AuAUwg4A65SVlWny5Mk+a5MnT1ZZWVkjdwTASYQdANZZs2aNCgoKfNYKCgq0Zs2aRu4IgJMIOwCsk5qaqoiICJ+1yMhIpaamNnJHAJxE2AFgnYCAAN17770+axMmTFBAAP/0AU0J73gAVuratatOP/10r7GUlBSdddZZDnUEwCmEHQDWOu200476M4CmgbADwErbtm3T/Pnzvcbmz5+vbdu2OdQRAKcQdgBYxxijmTNnVjlujHGgKwBOIewAsE5OTo6ys7NVWlrqNV5aWqrs7Gzl5OQ41BkAJxB2AFgnKSlJ3bp1U2BgoNd4YGCgunfvrqSkJIc6A+AEwg4A67hcLt12221VjrtcLge6AuAUwg4AKyUkJGjw4MGeYONyuTR48GAdd9xxDncGoLERdgBY66qrrlLr1q0lSTExMRo8eLDDHQFwAmEHgLXCwsLUv39/BQQE6IILLlBYWJjTLQFwAGEHgLUOHDigxYsXq6ysTIsXL9aBAwecbgmAAwg7AKz1yiuvaNeuXZKkXbt2KSsry+GOADiBsAPAStu2bVNWVpZnAUFjjLKyslhBGWiCCDsArMMKygCORNgBYB1WUAZwJMIOAOuUr6DsCysoA00PYQeAdapaQVkSKygDTZCjYWfixIlyuVxeF7fb7akbYzRx4kTFx8erWbNmSk9P18aNG71uo7i4WCNHjlRMTIxatGihgQMHcgAiAI0cOdLn+IgRIxq5EwBOc3zLzumnn67c3FzPZf369Z7a9OnTNWPGDM2aNUvZ2dlyu93q27ev9u7d65mTmZmpBQsWaN68efr000+1b98+ZWRkVNpXD6DpyM/P1549e3zW9uzZo/z8/EbuCICTHA87QUFBcrvdnktsbKykw1t1HnvsMd1zzz0aNGiQUlJS9OKLL6qwsNCzVkZ+fr6ef/55PfLII+rTp486d+6suXPnav369frggw+cfFgAHFTd10LwtRFA0+J42Pnxxx8VHx+v5ORkXXHFFfrll18kSZs2bVJeXp769evnmRsaGqq0tDStXLlSkrR27VqVlJR4zYmPj1dKSopnji/FxcUqKCjwugCwx8MPP+xXHYBdHA07qampeumll/T+++/r2WefVV5ennr16qVdu3YpLy9PkhQXF+d1nbi4OE8tLy9PISEhioqKqnKOL1OnTlVkZKTnkpiYWM+PDICTbr31Vr/qAOziaNjp37+/LrnkEnXs2FF9+vTRokWLJEkvvviiZ07FsyaMMdWeSVHdnHHjxik/P99z2bp1qx+PAsCxZu7cuX7VAdjF8d1YR2rRooU6duyoH3/80XNWVsUtNDt27PBs7XG73Tp48GClAxGPnONLaGioIiIivC4A7FHdHzD8gQM0LcdU2CkuLta3336rtm3bKjk5WW63W8uWLfPUDx48qBUrVqhXr16SpC5duig4ONhrTm5urjZs2OCZA6Dp6d69uwIDA33WAgMD1b1790buCICTgpy889GjR+uiiy5SUlKSduzYofvvv18FBQUaOnSoXC6XMjMzNWXKFHXo0EEdOnTQlClT1Lx5c8+ZFJGRkbrhhht0xx13qHXr1oqOjtbo0aM9u8UANE1bt26tcvmJ0tJSbd26Vccff3zjNgXAMY6GnW3btunKK6/U77//rtjYWPXo0UOrV69Wu3btJEljxoxRUVGRhg0bpj179ig1NVVLly5VeHi45zYeffRRBQUF6bLLLlNRUZF69+6tOXPmVPlXHQD7VfdFn3wRKNC0uAzvehUUFCgyMlL5+fkcvwNYYPPmzbr22murrM+ZM4ctO4AFavr5fUwdswMA9aFdu3bq2LGjz1qnTp08W48BNA2EHQDWcblcGjt2rM/a2LFj+SJQoIkh7ACwUkJCgs/x4447rpE7AeA0wg4AK6Wnp9dqHIC9CDsArLNw4UK/6gDsQtgBYB2+CBTAkQg7AKwzevRov+oA7ELYAWCdjIwMv+oA7ELYAWCl5cuX12ocgL0IOwAAwGqEHQDWqriKcqdOnRzqBICTCDsArPT5559r/fr1XmNff/21Pv/8c4c6AuAUwg4A65SVlWny5Mk+a5MnT1ZZWVkjdwTASYQdANZZs2aNCgoKfNYKCgq0Zs2aRu4IgJMIOwCsk5qaqpYtW/qstWzZUqmpqY3cEQAnEXYAWMflcik+Pt5nLT4+nm89B5oYwg4A6+Tk5OiHH37wWfvhhx+Uk5PTyB0BcBJhB4B1kpKS1K1bN5+17t27KykpqZE7AuAkwg4A67hcLrVp08ZnLTY2lt1YQBND2AFgnZKSEi1atMhnbdGiRSopKWnkjgA4ibADwDpPPPGEX3UAdiHsALDOyJEj/aoDsAthB4B1goODNWDAAJ+1jIwMBQcHN3JHAJxE2AFgHWOMfvzxR5+1H374QcaYRu4IgJMIOwCss3nz5qOus7N58+bGbQiAowg7AKyTm5vrVx2AXQg7AKzTo0ePo343Vo8ePRq5IwBOIuwAsE5AQIAmTpzoszZ58mQFBPBPH9CU8I4HYKWuXbvq9NNP9xpLSUnRWWed5VBHAJxC2AFgrQceeMDz1RAul0v333+/wx0BcAJhB4C1WrVq5TnN3BijVq1aOdsQAEcQdgBYKz09/ag/A2gaCDsAAMBqhB0AVqpqKw5bd4Cmh7ADwDoLFizwqw7ALoQdANaZOXOmX3UAdiHsALDOpZde6lcdgF0IOwCsk5GR4VcdgF0IOwCs065dO3Xs2NFnrVOnTmrXrl0jdwTASYQdANZxuVwaO3asz9rYsWM9qyoDaBoIOwCstGHDBp/j69evb+ROADiNsAPAOqWlpXrooYd81h566CGVlpY2ckcAnETYAWCdhQsXVhloSktLtXDhwkbuCICTCDsArHP++ef7VQdgF8IOAOs89dRTftUB2IWwA8A6I0eO9KsOwC6EHQDWCQ4O1oABA3zWMjIyFBwc3MgdAXASYQeAdYwxWrJkic/a4sWLZYxp5I4AOImwA8A6GzZsOOrZWFWtwQPAToQdANapavXkmtYB2OWYCTtTp06Vy+VSZmamZ8wYo4kTJyo+Pl7NmjVTenq6Nm7c6HW94uJijRw5UjExMWrRooUGDhyobdu2NXL3AI4l06ZN86sOwC7HRNjJzs7WM888o06dOnmNT58+XTNmzNCsWbOUnZ0tt9utvn37au/evZ45mZmZWrBggebNm6dPP/1U+/btU0ZGBiukAk1YSkqKAgJ8//MWEBCglJSURu4IgJMcDzv79u3TVVddpWeffVZRUVGecWOMHnvsMd1zzz0aNGiQUlJS9OKLL6qwsFBZWVmSpPz8fD3//PN65JFH1KdPH3Xu3Flz587V+vXr9cEHHzj1kAAcA8rKymo1DsBejoed4cOHa8CAAerTp4/X+KZNm5SXl6d+/fp5xkJDQ5WWlqaVK1dKktauXauSkhKvOfHx8UpJSfHM8aW4uFgFBQVeFwD2eOedd/yqA7CLo2Fn3rx5+uKLLzR16tRKtby8PElSXFyc13hcXJynlpeXp5CQEK8tQhXn+DJ16lRFRkZ6LomJif4+FADHkEcffdSvOgC7OBZ2tm7dqttuu01z585VWFhYlfNcLpfXz8aYSmMVVTdn3Lhxys/P91y2bt1au+YBHNNmzJjhVx2AXRwLO2vXrtWOHTvUpUsXBQUFKSgoSCtWrNDjjz+uoKAgzxadiltoduzY4am53W4dPHhQe/bsqXKOL6GhoYqIiPC6ALBHcXGxX3UAdnEs7PTu3Vvr16/XunXrPJeuXbvqqquu0rp163TCCSfI7XZr2bJlnuscPHhQK1asUK9evSRJXbp0UXBwsNec3NxcbdiwwTMHQNOTmpqqli1b+qy1bNlSqampjdwRACcFOXXH4eHhlU7/bNGihVq3bu0Zz8zM1JQpU9ShQwd16NBBU6ZMUfPmzTV48GBJUmRkpG644Qbdcccdat26taKjozV69Gh17Nix0gHPAJoOl8ul+Ph4/fDDD5Vq8fHx1e4KB2AXx8JOTYwZM0ZFRUUaNmyY9uzZo9TUVC1dulTh4eGeOY8++qiCgoJ02WWXqaioSL1799acOXMUGBjoYOcAnJSTk+Mz6EjSDz/8oJycHLVr166RuwLgFJfhG/FUUFCgyMhI5efnc/wOYIGysjJdfPHFPpeViIiI0FtvvVXlooMA/jxq+vnNux2AdbZu3Vrl+lkFBQWcgQk0MYQdANZJSkryqw7ALoQdANap+IXBta0DsAthB4B1RowY4VcdgF0IOwCsM2vWLL/qAOxC2AFgnVNPPdWvOgC7EHYAWGfhwoV+1QHYhbADwDoZGRlVLiwaFBSkjIyMRu4IgJMIOwCsExgYqDvvvNNnbcyYMaywDjQxhB0AVrrgggsUGxvrNdamTRv169fPoY4AOIWwA8BaFc+6euKJJxzqBICTCDsArBUXF6f09HRJUnp6uuLi4pxtCIAjCDsArLZ8+XKv/wJoegg7AKx14YUXHvVnAE0DYQeAtQoLC4/6M4CmgbADwErlx+rUdByAvQg7AKyzatUqv+oA7ELYAWCdcePG+VUHYBfCDgDrTJ061a86ALsQdgBYp2fPnn7VAdiFsAPASg8//HCtxgHYi7ADwDplZWWaPHmyz9rkyZNVVlbWyB0BcBJhB4B11qxZo4KCAp+1goICrVmzppE7AuAkwg4A63Tp0sWvOgC7EHYAWGfu3Ll+1QHYhbADwDppaWl+1QHYhbADwDonnHCC3G63z1rbtm11wgknNHJHAJxE2AFgpdjYWJ/jMTExjdwJAKcRdgBYJycnR+vXr/dZW79+vXJychq5IwBOIuwAsE5SUpK6devms9a9e3clJSU1ckcAnETYAWAdl8sll8t11DqApoOwA8A6xcXF+uyzz3zWPvvsMxUXFzdyRwCcRNgBYJ3x48f7VQdgF8IOAOvcd999ftUB2IWwA8A6oaGh6tSpk8/amWeeqdDQ0EbuCICTCDsArFNWVqbNmzf7rP3yyy986znQxBB2AFiHbz0HcCTCDgDrpKam+lUHYBfCDgDr7N271686ALsQdgBYZ/DgwX7VAdiFsAPAOlOnTvWrDsAudQo7EydO1JYtW+q7FwCoF1V943lN6wDsUqew8+6776p9+/bq3bu3srKydODAgfruCwDq7Morr/SrDsAudQo7a9eu1RdffKFOnTrp9ttvV9u2bXXrrbcqOzu7vvsDgFqbN2+eX3UAdqnzMTudOnXSo48+ql9//VUvvPCCfv31V5199tnq2LGjZs6cqfz8/PrsEwBqzO12KyDA9z9vAQEBcrvdjdwRACf5fYByWVmZDh48qOLiYhljFB0draefflqJiYl67bXX6qNHAKi1yy67rFbjAOxV57Czdu1ajRgxQm3bttXtt9+uzp0769tvv9WKFSv03XffacKECfrHP/5Rn70CQI2UlJRUuatq3rx5KikpaeSOADipTmGnU6dO6tGjhzZt2qTnn39eW7du1YMPPqgTTzzRM+eaa67Rzp07661RAKipJ554wq86ALu4jDGmtle67777dP311+u4445riJ4aXUFBgSIjI5Wfn6+IiAin2wHgp5KSEvXt27fK+rJlyxQcHNyIHQFoCDX9/K7Tlh1jjKKioiqNFxUVafLkyXW5SQCoN0FBQVUehNy2bVsFBQU1ckcAnFSnsDNp0iTt27ev0nhhYaEmTZpU49t5+umn1alTJ0VERCgiIkI9e/bU4sWLPXVjjCZOnKj4+Hg1a9ZM6enp2rhxo9dtFBcXa+TIkYqJiVGLFi00cOBAbdu2rS4PC4AlcnJylJeX57OWm5urnJycRu4IgJPqvGXH5XJVGv/qq68UHR1d49tJSEjQgw8+qM8//1yff/65zjvvPP31r3/1BJrp06drxowZmjVrlrKzs+V2u9W3b1+vL/HLzMzUggULNG/ePH366afat2+fMjIyVFpaWpeHBsACCQkJCgwM9FkLDAxUQkJCI3cEwEm1OmYnKipKLpfLs2/syMBTWlqqffv26ZZbbtGTTz5Z54aio6P10EMP6frrr1d8fLwyMzM1duxYSYe34sTFxWnatGm6+eablZ+fr9jYWL388su6/PLLJUnbt29XYmKi3nvvPZ1//vk1uk+O2QHssmrVKo0bN67K+tSpU9WzZ89G7AhAQ6jp53etdlw/9thjMsbo+uuv16RJkxQZGemphYSE6Pjjj6/zPyClpaV6/fXXtX//fvXs2VObNm1SXl6e+vXr55kTGhqqtLQ0rVy5UjfffLPWrl2rkpISrznx8fFKSUnRypUrqww7xcXFKi4u9vxcUFBQp54BHJvWrl1bbZ2wAzQdtQo7Q4cOlSQlJyerV69e9XI2w/r169WzZ08dOHBALVu21IIFC3Taaadp5cqVkqS4uDiv+XFxcZ4vIc3Ly1NISEilg6Xj4uKq3F8vHf6rrjbHFgH4c3njjTeqrY8YMaKRugHgtBofs3Pk1o/OnTurqKhIBQUFPi+1cfLJJ2vdunVavXq1br31Vg0dOlTffPONp17x2KCqjheqzZxx48YpPz/fc9m6dWutegZwbKtulWRWUQaalhqHnaioKO3YsUOS1KpVK0VFRVW6lI/XRkhIiE488UR17dpVU6dO1RlnnKGZM2d6ThutuIVmx44dnq09brdbBw8e1J49e6qc40toaKjnDLDyCwB7DBs2zK86ALvUeDfWRx995DnT6uOPP26whowxKi4uVnJystxut5YtW6bOnTtLkg4ePKgVK1Zo2rRpkqQuXbooODhYy5Yt8/yllpubqw0bNmj69OkN1iOAY9/cuXN19dVX+xwH0LTUOOykpaV5/j85OVmJiYk+dzHVZpfQ3Xffrf79+ysxMVF79+7VvHnztHz5ci1ZskQul0uZmZmaMmWKOnTooA4dOmjKlClq3ry5Bg8eLEmKjIzUDTfcoDvuuEOtW7dWdHS0Ro8erY4dO6pPnz417gOAXYwxmjlzps/azJkzNX369Gp3hwOwR52WEU1OTlZubq7atGnjNb57924lJyfXeI2b3377TUOGDFFubq4iIyPVqVMnLVmyxLPM+5gxY1RUVKRhw4Zpz549Sk1N1dKlSxUeHu65jUcffVRBQUG67LLLVFRUpN69e2vOnDlVrrEBwH45OTnKzs72WcvOzlZOTo7atWvXyF0BcEqdvhsrICBAv/32m2JjY73Gt2zZotNOO0379++vtwYbA+vsAHZJT0+vds7y5csbvA8ADatB1tkZNWqUpMNnSI0fP17Nmzf31EpLS7VmzRqdeeaZdesYAACgAdQq7Hz55ZeSDu8PX79+vUJCQjy1kJAQnXHGGRo9enT9dggAAOCHOu3Guu666zRz5kxrdvmwGwuwz9F2ZbELC7BDTT+/6/RFoLNnzyYUADimVRV2anI8DwC71OlsrP379+vBBx/Uhx9+qB07dqisrMyr/ssvv9RLcwBQF8XFxVVuvVm+fLmKi4sVGhrauE0BcEydws6NN96oFStWaMiQIWrbti3rVQA4powfP77aOguPAk1HnY7ZadWqlRYtWqSzzz67IXpqdByzA9iluLhY559/fpX1999/ny07gAUa9JidqKgoz1dHAMCxJjg4uMqFRQMDAxUcHNzIHQFwUp3Czn333ad7771XhYWF9d0PAPht9erVVa7kXlpaqtWrVzdyRwCcVKdjdh555BH9/PPPiouL0/HHH1/pr6QvvviiXpoDgLqo+FU2ta0DsEudws7FF19cz20AQP1Zt25dtfUTTzyxcZoB4Lg6hZ0JEybUdx8AUG/++OMPv+oA7FKnY3YA4Fh27bXX+lUHYJc6hZ3S0lI9/PDD6t69u9xut6Kjo70uAOCkoKAgde/e3WetR48eCgqq00ZtAH9SdQo7kyZN0owZM3TZZZcpPz9fo0aN0qBBgxQQEKCJEyfWc4sAUDuHDh3SZ5995rO2evVqHTp0qJE7AuCkOoWdV155Rc8++6xGjx6toKAgXXnllXruued07733ckonAMe99NJLftUB2KVOYScvL08dO3aUJLVs2VL5+fmSpIyMDC1atKj+ugOAOhgyZIhfdQB2qVPYSUhIUG5uriTpxBNP1NKlSyVJ2dnZLMEOwHHbt2/3qw7ALnUKO3/729/04YcfSpJuu+02jR8/Xh06dNA111yj66+/vl4bBIDaSkpK8qsOwC51OiXhwQcf9Pz/pZdeqoSEBK1cuVInnniiBg4cWG/NAUBd/Prrr9XWExISGqkbAE6r07ee24ZvPQfskp6eXu2c5cuXN3gfABpWTT+/67Rlp7ozGa655pq63CwA1ItnnnlGN91001HrAJqOOm3ZiYqK8vq5pKREhYWFCgkJUfPmzbV79+56a7AxsGUHsMvFF1981K+EaNWqld56661G6wdAw6jp53edDlDes2eP12Xfvn36/vvvdc455+jVV1+tc9MAUB/4biwAR6q378bq0KGDHnzwQd122231dZMAUCcvvPCCX3UAdqnXLwINDAxk/QoAjsvLy/OrDsAudTpA+Z133vH62Rij3NxczZo1S2effXa9NAYAdeV2u/2qA7BLncLOxRdf7PWzy+VSbGyszjvvPD3yyCP10RcA1FlAwNE3WldXB2CXOoWdsrIySdLOnTsVEhKiyMjIem0KAPzRrl07v+oA7FLrP2/++OMPDR8+XDExMXK73YqOjpbb7da4ceNUWFjYED0CQK2ce+65ftUB2KVWW3Z2796tnj176tdff9VVV12lU089VcYYffvtt3riiSe0bNkyffrpp/rqq6+0Zs0a/eMf/2iovgEAAGqkVmFn8uTJCgkJ0c8//6y4uLhKtX79+mnIkCFaunSpHn/88XptFAAAoC5qtRvrrbfe0sMPP1wp6EiHz26YPn263nzzTY0aNUpDhw6ttyYBoDamTJniVx2AXWoVdnJzc3X66adXWU9JSVFAQIAmTJjgd2MAUFeceg7gSLUKOzExMdq8eXOV9U2bNqlNmzb+9gQAfuHUcwBHqtU7/oILLtA999yjgwcPVqoVFxdr/PjxuuCCC+qtOQCoi6SkJIWFhfmshYWFKSkpqZE7AuCkWh2gPGnSJHXt2lUdOnTQ8OHDdcopp0iSvvnmGz311FMqLi7WSy+91CCNAkBNbdmyRQcOHPBZO3DggLZs2aLk5ORG7gqAU2oVdhISErRq1SoNGzZM48aNkzFG0uEVlPv27atZs2bxFxMAx1X3HX3bt28n7ABNSK1XUE5OTtbixYu1Z88e/fjjj5KkE088UdHR0fXeHADURWxsrF91AHap81F6UVFR6t69u7p3707QAXBMWbhwoV91AHbhlAQA1hk2bJhfdQB2IewAsM6iRYv8qgOwC2EHgHX27NnjVx2AXQg7AKzz+++/+1UHYBfCDgDrLFmyxK86ALsQdgBYJyMjw686ALsQdgBYZ/To0X7VAdiFsAPAOsYYnXTSST5rJ510kmf1dwBNA2EHgHU2b96sH374wWfthx9+0ObNmxu3IQCOIuwAsE5ubq5fdQB2cTTsTJ06Vd26dVN4eLjatGmjiy++WN9//73XHGOMJk6cqPj4eDVr1kzp6enauHGj15zi4mKNHDlSMTExatGihQYOHKht27Y15kMBcAzp0aOHX3UAdnE07KxYsULDhw/X6tWrtWzZMh06dEj9+vXT/v37PXOmT5+uGTNmaNasWcrOzpbb7Vbfvn21d+9ez5zMzEwtWLBA8+bN06effqp9+/YpIyNDpaWlTjwsAA4rLCz0qw7ALi5zDB2pt3PnTrVp00YrVqzQ//zP/8gYo/j4eGVmZmrs2LGSDm/FiYuL07Rp03TzzTcrPz9fsbGxevnll3X55ZdLkrZv367ExES99957Ov/886u934KCAkVGRio/P18REREN+hgBNLy///3v2rlzZ5X12NhYvf76643YEYCGUNPP72PqmJ38/HxJ8nyL+qZNm5SXl6d+/fp55oSGhiotLU0rV66UJK1du1YlJSVec+Lj45WSkuKZU1FxcbEKCgq8LgDscbSgU5M6ALscM2HHGKNRo0bpnHPOUUpKiiQpLy9PkhQXF+c1Ny4uzlPLy8tTSEiIoqKiqpxT0dSpUxUZGem5JCYm1vfDAQAAx4hjJuyMGDFCX3/9tV599dVKNZfL5fWzMabSWEVHmzNu3Djl5+d7Llu3bq174wAA4Jh2TISdkSNH6p133tHHH3+shIQEz7jb7ZakSltoduzY4dna43a7dfDgwUrfYnzknIpCQ0MVERHhdQFgj9mzZ/tVB2AXR8OOMUYjRozQ/Pnz9dFHHyk5OdmrnpycLLfbrWXLlnnGDh48qBUrVqhXr16SpC5duig4ONhrTm5urjZs2OCZA6BpOf744/2qA7BLkJN3Pnz4cGVlZentt99WeHi4ZwtOZGSkmjVrJpfLpczMTE2ZMkUdOnRQhw4dNGXKFDVv3lyDBw/2zL3hhht0xx13qHXr1oqOjtbo0aPVsWNH9enTx8mHB8Ah5557brX15cuXN04zABznaNh5+umnJUnp6ele47Nnz9a1114rSRozZoyKioo0bNgw7dmzR6mpqVq6dKnCw8M98x999FEFBQXpsssuU1FRkXr37q05c+YoMDCwsR4KAAA4Rh1T6+w4hXV2ALtU/APKF7bsAH9+f8p1dgCgPrz44ot+1QHYhbADwDrx8fF+1QHYhbADwDovv/yyX3UAdiHsALBOWlqaX3UAdiHsALDOgQMH/KoDsAthB4B1hg8f7lcdgF0IOwCs89RTT/lVB2AXwg4A65x88sl+1QHYhbADwDoLFy70qw7ALoQdANbJyMio8utigoKClJGR0cgdAXASYQeAdQIDA3XnnXf6rI0ZM4bvzQOaGMIOACs9/vjjPscfe+yxxm0EgOMIOwCss3v3bhUWFvqsFRYWavfu3Y3cEQAnEXYAWOfyyy/3qw7ALoQdANZ57bXX/KoDsAthB4B1oqKi/KoDsAthB4B1nnzySb/qAOxC2AFgnTfeeMOvOgC7EHYAWOfmm2/2qw7ALoQdANbp0qWLX3UAdiHsALBOVQsK1rQOwC6EHQDWmTZtml91AHYh7ACwzsMPP+xXHYBdCDsArPPxxx/7VQdgF8IOAOtMmDDBrzoAuxB2AFjnhBNO8KsOwC6EHQDWGTp0qF91AHYh7AAAAKsRdgAAgNUIOwCs8+yzz/pVB2AXwg4A62RlZflVB2AXwg4A69x1111+1QHYhbADwDp5eXl+1QHYhbADwDpt27b1qw7ALoQdANaZNWuWX3UAdiHsALDOzTff7FcdgF0IOwCsM2DAAL/qAOxC2AEAAFYj7ACwjsvl8qsOwC6EHQDWMcb4VQdgF8IOAOu8+eabftUB2IWwA8A6l1xyiV91AHYh7AAAAKsRdgAAgNUIOwCsM2nSJL/qAOxC2AFgneXLl/tVB2AXwg4A64wYMcKvOgC7EHYAWOe6667zqw7ALoQdANYpKCjwqw7ALoQdAABgNUfDzieffKKLLrpI8fHxcrlceuutt7zqxhhNnDhR8fHxatasmdLT07Vx40avOcXFxRo5cqRiYmLUokULDRw4UNu2bWvERwHgWNOjRw+/6gDs4mjY2b9/v8444wzNmjXLZ3369OmaMWOGZs2apezsbLndbvXt21d79+71zMnMzNSCBQs0b948ffrpp9q3b58yMjJUWlraWA8DwDHm/vvv96sOwC6Ohp3+/fvr/vvv16BBgyrVjDF67LHHdM8992jQoEFKSUnRiy++qMLCQmVlZUmS8vPz9fzzz+uRRx5Rnz591LlzZ82dO1fr16/XBx980NgPB8AxYs6cOX7VAdjlmD1mZ9OmTcrLy1O/fv08Y6GhoUpLS9PKlSslSWvXrlVJSYnXnPj4eKWkpHjm+FJcXKyCggKvCwB7zJ071686ALscs2EnLy9PkhQXF+c1HhcX56nl5eUpJCREUVFRVc7xZerUqYqMjPRcEhMT67l7AABwrDhmw045l8vl9bMxptJYRdXNGTdunPLz8z2XrVu31kuvAADg2HPMhh232y1JlbbQ7Nixw7O1x+126+DBg9qzZ0+Vc3wJDQ1VRESE1wUAANjpmA07ycnJcrvdWrZsmWfs4MGDWrFihXr16iVJ6tKli4KDg73m5ObmasOGDZ45AACgaXM07Ozbt0/r1q3TunXrJB0+KHndunXKycmRy+VSZmampkyZogULFmjDhg269tpr1bx5cw0ePFiSFBkZqRtuuEF33HGHPvzwQ3355Ze6+uqr1bFjR/Xp08fBRwbASZx6DuBIQU7e+eeff65zzz3X8/OoUaMkSUOHDtWcOXM0ZswYFRUVadiwYdqzZ49SU1O1dOlShYeHe67z6KOPKigoSJdddpmKiorUu3dvzZkzR4GBgY3+eAAcG3799Ve/6gDs4jLGGKebcFpBQYEiIyOVn5/P8TuABdLT06uds3z58gbvA0DDqunn9zF7zA4A1FVsbKxfdQB2IewAsM69997rVx2AXQg7AKzzzDPP+FUHYBfCDgDrrF+/3q86ALsQdgAAgNUIOwAAwGqEHQAAYDXCDgDrTJgwwa86ALuwqKBYVBCwDYsKAk0DiwoCAACIsAMAACxH2AEAAFYj7ACwzowZM/yqA7ALYQeAdcLCwvyqA7ALYQeAdYYNG+ZXHYBdCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAPAOt26dfOrDsAuhB0A1snOzvarDsAuhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqwU53QBgG2OMDhw44HQbqEZRUZHTLTRZYWFhcrlcTreBJoSwA9SzAwcOqH///k63gWrwO3LO4sWL1axZM6fbQBPCbiwAAGA1tuwA9SwsLEyLFy92uo0m7cknn9TChQurrGdkZGj48OGN2BGOFBYW5nQLaGJcxhjjdBNOKygoUGRkpPLz8xUREeF0OwDqQXp6epW15cuXN1ofABpOTT+/2Y0FwEpVBRqCDtD0EHYAAIDVCDsArFXx2Cm26gBNE2EHQJPAQeNA00XYAQAAVuPUc0uwai9Q2ZHvCd4fQGVNZTVrwo4lWLUXOLq//e1vTrcAHHOaymrW7MYCAABWY8uOJY5cG3Jfx7/LBAQ62A1wjDCSyg4d/v+AIMn+rfVAtVxlpWq5/nVJ3p8dNiPsWKK4uNjz/+UvYgAAjqa4uFjNmzd3uo0GZ81urKeeekrJyckKCwtTly5d9N///tfplgAAwDHAii07r732mjIzM/XUU0/p7LPP1r///W/1799f33zzjZKSkpxur1GEhoZ6/p/dWA47ctcJgMrYpeioI3djHfnZYTMrvgg0NTVVZ511lp5++mnP2KmnnqqLL75YU6dOrfb6NnwRaFFREWdjAQBq5c9+NlaT+SLQgwcPau3aterXr5/XeL9+/bRy5Uqf1ykuLlZBQYHXBQAA2OlPvxvr999/V2lpqeLi4rzG4+LilJeX5/M6U6dO1aRJkxqjvUYTFhbGcvjHCGOM1wHjALyFhoY2iYXs/gzCwsKcbqFR/OnDTrmKbxxjTJVvpnHjxmnUqFGenwsKCpSYmNig/TU0l8v1p94UaZumcHYDAPxZ/OnDTkxMjAIDAyttxdmxY0elrT3lQkNDm8xBWQAANHV/+mN2QkJC1KVLFy1btsxrfNmyZerVq5dDXQEAgGPFn37LjiSNGjVKQ4YMUdeuXdWzZ08988wzysnJ0S233OJ0awAAwGFWhJ3LL79cu3bt0uTJk5Wbm6uUlBS99957ateundOtAQAAh1mxzo6/bFhnBwCApqbJrLMDAABwNIQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVrFhB2V/l6yoWFBQ43AkAAKip8s/t6tZHJuxI2rt3ryQpMTHR4U4AAEBt7d27V5GRkVXW+boISWVlZdq+fbvCw8PlcrmcbgdAPSooKFBiYqK2bt3K18EAljHGaO/evYqPj1dAQNVH5hB2AFiN774DwAHKAADAaoQdAABgNcIOAKuFhoZqwoQJCg0NdboVAA7hmB0AAGA1tuwAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFb7/4OyVKpGxScdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df, y='Quantity').set_title('Distribution of Quantity (Post Dropping)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Unit Price')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df, y='Quantity').set_title('Distribution of Unit Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 392306 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    392306 non-null  object        \n",
      " 1   StockCode    392306 non-null  object        \n",
      " 2   Description  392306 non-null  object        \n",
      " 3   Quantity     392306 non-null  int64         \n",
      " 4   InvoiceDate  392306 non-null  datetime64[ns]\n",
      " 5   UnitPrice    392306 non-null  float64       \n",
      " 6   CustomerID   392306 non-null  float64       \n",
      " 7   Country      392306 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 26.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom          348867\n",
       "Germany                   9024\n",
       "France                    8324\n",
       "EIRE                      7223\n",
       "Spain                     2479\n",
       "Netherlands               2348\n",
       "Belgium                   2031\n",
       "Switzerland               1841\n",
       "Portugal                  1453\n",
       "Australia                 1171\n",
       "Norway                    1071\n",
       "Italy                      758\n",
       "Channel Islands            747\n",
       "Finland                    685\n",
       "Cyprus                     603\n",
       "Sweden                     439\n",
       "Austria                    398\n",
       "Denmark                    380\n",
       "Poland                     330\n",
       "Japan                      309\n",
       "Israel                     245\n",
       "Unspecified                241\n",
       "Singapore                  222\n",
       "Iceland                    182\n",
       "USA                        179\n",
       "Canada                     151\n",
       "Greece                     145\n",
       "Malta                      112\n",
       "United Arab Emirates        68\n",
       "European Community          60\n",
       "RSA                         57\n",
       "Lebanon                     45\n",
       "Lithuania                   35\n",
       "Brazil                      32\n",
       "Czech Republic              25\n",
       "Bahrain                     17\n",
       "Saudi Arabia                 9\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85123A    2018\n",
       "22423     1713\n",
       "85099B    1612\n",
       "84879     1392\n",
       "47566     1389\n",
       "          ... \n",
       "90125E       1\n",
       "90041        1\n",
       "21310        1\n",
       "37471        1\n",
       "90033        1\n",
       "Name: StockCode, Length: 3663, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.StockCode.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3663"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.StockCode.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.95    1646\n",
       "2.55     357\n",
       "5.79       9\n",
       "3.20       4\n",
       "3.24       2\n",
       "Name: UnitPrice, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.StockCode == '85123A'].UnitPrice.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-12-01 08:26:00\n",
      "2011-12-09 12:50:00\n"
     ]
    }
   ],
   "source": [
    "print(df.InvoiceDate.min())\n",
    "print(df.InvoiceDate.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other             36\n",
       "United Kingdom     1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_customers = pd.DataFrame(df.groupby('Country').nunique()).reset_index()\n",
    "df_country_customers.loc[df_country_customers[df_country_customers.CustomerID <= 100].index ,'Country'] = 'Other'\n",
    "df_country_customers.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Customers in UK vs Elsewhere')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df_country_customers, x='Country', y='CustomerID').set_title(\"Customers in UK vs Elsewhere\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392306.000000</td>\n",
       "      <td>392306.000000</td>\n",
       "      <td>392306.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.807589</td>\n",
       "      <td>3.127962</td>\n",
       "      <td>15287.412369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.036754</td>\n",
       "      <td>22.252653</td>\n",
       "      <td>1713.316282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>12347.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>552.000000</td>\n",
       "      <td>8142.750000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  392306.000000  392306.000000  392306.000000\n",
       "mean       11.807589       3.127962   15287.412369\n",
       "std        26.036754      22.252653    1713.316282\n",
       "min         1.000000       0.001000   12347.000000\n",
       "25%         2.000000       1.250000   13955.000000\n",
       "50%         6.000000       1.950000   15150.000000\n",
       "75%        12.000000       3.750000   16791.000000\n",
       "max       552.000000    8142.750000   18287.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uk = df[df['Country'] == 'United Kingdom'].drop('Country', axis=1)\n",
    "df_uk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348867, 7)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StockCode\n",
       "85099B    38678\n",
       "85123A    28217\n",
       "84077     27966\n",
       "84879     27580\n",
       "22197     27417\n",
       "22178     19956\n",
       "21212     18722\n",
       "23203     16972\n",
       "22386     16575\n",
       "84946     16220\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uk.groupby(\"StockCode\")[\"Quantity\"].sum().sort_values(ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_uk.to_csv('online retail v2.csv')"
   ]
  }
 ],
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