Pythn_Assignment2_BI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "color = sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>12/1/2010 8:26</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>12/1/2010 8:26</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>12/1/2010 8:26</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>12/1/2010 8:26</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>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>536365</td>\n",
       "      <td>22752</td>\n",
       "      <td>SET 7 BABUSHKA NESTING BOXES</td>\n",
       "      <td>2</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>7.65</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536365</td>\n",
       "      <td>21730</td>\n",
       "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>22633</td>\n",
       "      <td>HAND WARMER UNION JACK</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:28</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536366</td>\n",
       "      <td>22632</td>\n",
       "      <td>HAND WARMER RED POLKA DOT</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:28</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>536367</td>\n",
       "      <td>84879</td>\n",
       "      <td>ASSORTED COLOUR BIRD ORNAMENT</td>\n",
       "      <td>32</td>\n",
       "      <td>12/1/2010 8:34</td>\n",
       "      <td>1.69</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>536367</td>\n",
       "      <td>22745</td>\n",
       "      <td>POPPY'S PLAYHOUSE BEDROOM</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:34</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0     536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365     71053                  WHITE METAL LANTERN         6   \n",
       "2     536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "5     536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
       "6     536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
       "7     536366     22633               HAND WARMER UNION JACK         6   \n",
       "8     536366     22632            HAND WARMER RED POLKA DOT         6   \n",
       "9     536367     84879        ASSORTED COLOUR BIRD ORNAMENT        32   \n",
       "10    536367     22745           POPPY'S PLAYHOUSE BEDROOM          6   \n",
       "\n",
       "       InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0   12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1   12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2   12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3   12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4   12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "5   12/1/2010 8:26       7.65     17850.0  United Kingdom  \n",
       "6   12/1/2010 8:26       4.25     17850.0  United Kingdom  \n",
       "7   12/1/2010 8:28       1.85     17850.0  United Kingdom  \n",
       "8   12/1/2010 8:28       1.85     17850.0  United Kingdom  \n",
       "9   12/1/2010 8:34       1.69     13047.0  United Kingdom  \n",
       "10  12/1/2010 8:34       2.10     13047.0  United Kingdom  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Ecommerce.csv',encoding= 'unicode_escape')\n",
    "df.head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleanning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove negative values in colmn 'Quantity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_new = df[(df['Quantity'] >= 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for missing values in all columns and replace them with the appropriate metric\n",
    "#### In our data there is no missing/blank value in main column 'Quantity','UnitPrice','Country','InvoiceDate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 531285 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    531285 non-null  object \n",
      " 1   StockCode    531285 non-null  object \n",
      " 2   Description  530693 non-null  object \n",
      " 3   Quantity     531285 non-null  int64  \n",
      " 4   InvoiceDate  531285 non-null  object \n",
      " 5   UnitPrice    531285 non-null  float64\n",
      " 6   CustomerID   397924 non-null  float64\n",
      " 7   Country      531285 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 36.5+ MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add the columns - Month, Day and Hour for the invoice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-a7d6cb534259>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['InvoiceDate'] = df_new['InvoiceDate'].astype('datetime64[ns]')\n",
      "<ipython-input-12-a7d6cb534259>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['Year'] = df_new.InvoiceDate.dt.year\n",
      "<ipython-input-12-a7d6cb534259>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['Month'] = df_new.InvoiceDate.dt.month\n",
      "<ipython-input-12-a7d6cb534259>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['year_month']=df_new.InvoiceDate.map(lambda x: 100*x.year + x.month)\n",
      "<ipython-input-12-a7d6cb534259>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['Day']=(df_new.InvoiceDate.dt.dayofweek)+1 # +1 to make Monday=1.....until Sunday=7\n",
      "<ipython-input-12-a7d6cb534259>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['Hour'] = df_new.InvoiceDate.dt.hour\n",
      "<ipython-input-12-a7d6cb534259>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['Revenue'] = df_new['Quantity'] * df_new['UnitPrice']\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>year_month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>201012</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>201012</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>201012</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>201012</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>201012</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>20.34</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  Year  Month  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  2010     12   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  2010     12   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  2010     12   \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  2010     12   \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  2010     12   \n",
       "\n",
       "   year_month  Day  Hour  Revenue  \n",
       "0      201012    3     8    15.30  \n",
       "1      201012    3     8    20.34  \n",
       "2      201012    3     8    22.00  \n",
       "3      201012    3     8    20.34  \n",
       "4      201012    3     8    20.34  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_new['InvoiceDate'] = df_new['InvoiceDate'].astype('datetime64[ns]')\n",
    "df_new['Year'] = df_new.InvoiceDate.dt.year\n",
    "df_new['Month'] = df_new.InvoiceDate.dt.month\n",
    "df_new['year_month']=df_new.InvoiceDate.map(lambda x: 100*x.year + x.month)\n",
    "df_new['Day']=(df_new.InvoiceDate.dt.dayofweek)+1 # +1 to make Monday=1.....until Sunday=7\n",
    "df_new['Hour'] = df_new.InvoiceDate.dt.hour\n",
    "df_new['Revenue'] = df_new['Quantity'] * df_new['UnitPrice']\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Perform Basic EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Kingdom', 'WHITE HANGING HEART T-LIGHT HOLDER', '85123A',\n",
       "       ..., 'CREAM HANGING HEART T-LIGHT HOLDER',\n",
       "       'PAPER CRAFT , LITTLE BIRDIE', '23843'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique values across colmn\n",
    "#.ravel(): as below\n",
    "#returns contiguous flattened array(1D array with all the input-array elements and with the same type as it)\n",
    "pd.unique(df_new[['Country','Description','StockCode','Country']].values.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>year_month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>397924.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.000000</td>\n",
       "      <td>531285.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.655262</td>\n",
       "      <td>3.857296</td>\n",
       "      <td>15294.315171</td>\n",
       "      <td>2010.921543</td>\n",
       "      <td>7.560244</td>\n",
       "      <td>201099.714549</td>\n",
       "      <td>3.435561</td>\n",
       "      <td>13.076259</td>\n",
       "      <td>20.03550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156.830323</td>\n",
       "      <td>41.810047</td>\n",
       "      <td>1713.169877</td>\n",
       "      <td>0.268890</td>\n",
       "      <td>3.508717</td>\n",
       "      <td>25.800406</td>\n",
       "      <td>1.849816</td>\n",
       "      <td>2.438303</td>\n",
       "      <td>270.91237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>201012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-11062.06000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13969.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>201103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15159.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>201107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16795.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>201110.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>13541.330000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>201112.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>168469.60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID           Year  \\\n",
       "count  531285.000000  531285.000000  397924.000000  531285.000000   \n",
       "mean       10.655262       3.857296   15294.315171    2010.921543   \n",
       "std       156.830323      41.810047    1713.169877       0.268890   \n",
       "min         1.000000  -11062.060000   12346.000000    2010.000000   \n",
       "25%         1.000000       1.250000   13969.000000    2011.000000   \n",
       "50%         3.000000       2.080000   15159.000000    2011.000000   \n",
       "75%        10.000000       4.130000   16795.000000    2011.000000   \n",
       "max     80995.000000   13541.330000   18287.000000    2011.000000   \n",
       "\n",
       "               Month     year_month            Day           Hour  \\\n",
       "count  531285.000000  531285.000000  531285.000000  531285.000000   \n",
       "mean        7.560244  201099.714549       3.435561      13.076259   \n",
       "std         3.508717      25.800406       1.849816       2.438303   \n",
       "min         1.000000  201012.000000       1.000000       6.000000   \n",
       "25%         5.000000  201103.000000       2.000000      11.000000   \n",
       "50%         8.000000  201107.000000       3.000000      13.000000   \n",
       "75%        11.000000  201110.000000       5.000000      15.000000   \n",
       "max        12.000000  201112.000000       7.000000      20.000000   \n",
       "\n",
       "            Revenue  \n",
       "count  531285.00000  \n",
       "mean       20.03550  \n",
       "std       270.91237  \n",
       "min    -11062.06000  \n",
       "25%         3.75000  \n",
       "50%         9.90000  \n",
       "75%        17.70000  \n",
       "max    168469.60000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Aggregation for all numerical Columns\n",
    "df_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line chart to show the trend of data \n",
    "\n",
    "plt.plot(df_new['Day'],df_new['Revenue'])\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribition plot (Skewed +ve)\n",
    "# If a data set has a positive skew, \n",
    "# but the mean of the returns is negative, it means that overall performance is negative\n",
    "# but here mean is positive hence performance is considerd as to be good\n",
    "plt.subplots(figsize=(10,8))\n",
    "sns.distplot(df_new.Quantity[df_new.Quantity < 50], label='Unit Price').legend()\n",
    "\n",
    "plt.xlabel('Unit Price')\n",
    "plt.ylabel('Distribution')\n",
    "plt.title('Price Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Plot \n",
    "result=df_new.groupby('Month').sum()\n",
    "month=range(1,13)\n",
    "plt.bar(month,result['Revenue'])\n",
    "plt.xticks(month)\n",
    "plt.xlabel('month in number')\n",
    "plt.ylabel('Revenue USD($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantity      460.130967\n",
       "UnitPrice      60.552895\n",
       "CustomerID      0.025776\n",
       "Year           -3.135448\n",
       "Month          -0.417780\n",
       "year_month     -3.047498\n",
       "Day             0.466762\n",
       "Hour            0.000429\n",
       "Revenue       502.218534\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skewness \n",
    "df_new.skew(axis=0,skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2.957284\n",
       "1         2.957288\n",
       "2         2.957290\n",
       "3         2.957288\n",
       "4         2.957288\n",
       "            ...   \n",
       "541904    2.978223\n",
       "541905    2.978222\n",
       "541906    2.978224\n",
       "541907    2.978224\n",
       "541908    2.978223\n",
       "Length: 531285, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.skew(axis=1,skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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aET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVnbOegLA1rRnz55ZTwFgU4gfYFWvfe1rZz0FgE3hZS8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALRSY4xHv3LVA0k+t3nT2VRPTvKVWU+CVTk2W5djs3U5NluXY7N1PHOMccnKwQ3Fz7msqo6MMXbPeh58N8dm63Jsti7HZutybLY+L3sBAK2IHwCglU7xc+OsJ8CaHJuty7HZuhybrcux2eLa/MwPAEDS68wPAMC5Gz9V9TNV9cmq+nZV7V6x7NeraqGqPlVVVy0bf2FV3TVZ9o6qqsn4+VV182T8f1XV3LLb7Kuqv5xc9n3PdnCbqKrrq+r/VtVHJ5eXLls2tePE9FXVnsmxWaiqN8x6Ph1U1dHJ9/5Hq+rIZOyiqrpt8hx0W1VduGz9DT2G2Jiq+o9VdX9VfWLZ2NSOh+e0GRpjnJOXJJcm+aEkdyTZvWz8siQfS3J+kmcl+XSSHZNlH07yd5NUkluT7J2MvzrJOyfXr05y8+T6RUk+M/l44eT6hbPe93PpkuT6JK9fZXxqx8llU47bjskxeXaS75scq8tmPa/tfklyNMmTV4y9LckbJtffkOStk+sbfgy5bPh4/IMkL0jyic04Hp7TZnc5Z8/8jDHuGWN8apVF/zDJ744xHh5jfDbJQpIXVdVTkzxxjPE/x+J32nuS/KNltzk0uf57SS6flPlVSW4bY3x1jPFXSW5Lsmfz9qqVaR4npu9FSRbGGJ8ZY3wrye9m8evP997y7/tDOfXxsNHHEBswxvjvSb66Yniax8Nz2oycs/Gzjqcn+fyyz++bjD19cn3l+Cm3GWM8kuSvk1y8zrbYmNdU1ccnp5BPniKe5nFi+nzvz8ZI8sdVdWdV7Z+M/cAY44tJMvn4/ZPxM3kMcfameTw8p83IzllPYD1V9cEkT1ll0XVjjFvWutkqY2Od8TO9DRPrHack/z7Jb2Tx6/YbSf5Nkl/KdI8T0+drPRt/b4zxhar6/iS3VdVfrLOu562txXPaOWRLx88Y44ozuNl9SX5w2efPSPKFyfgzVhlffpv7qmpnkidl8VTnfUnmV9zmjjOY07b2aI9TVf12kj+cfDrN48T0rXV82ERjjC9MPt5fVb+fxZcfv1xVTx1jfHHyEsr9k9XP5DHE2Zvm8fCcNiPb8WWv9ye5evJT9M9K8pwkH56cnvx6Vf2dyWuq1yS5ZdltTv4m108nuX3y2uwfJfnxqrpw8nLNj0/GeJQmTw4n/VSSk781Mc3jxPR9JMlzqupZVfV9WfxhzPfPeE7bWlU9vqqecPJ6Fp9vPpFTv+/35dTHw0YfQ5y9aR4Pz2mzMuufuD7TSxb/Ib0vycNJvpzkj5Ytuy6LP2n/qSz7LYcku7P4ZPLpJL+Z77zJ499I8r4s/oDah5M8e9ltfmkyvpDkF2e93+faJcnvJLkrycez+EB/6mYcJ5dNOXYvTXLv5DhcN+v5bPdLFn+z7mOTyydPfs2z+DMgf5LkLycfL1p2mw09hlw2fEzem+SLSY5P/r355WkeD89ps7t4h2cAoJXt+LIXAMCaxA8A0Ir4AQBaET8AQCviBwBoRfwAp1VVc8v/svVk7Pqqev1pbre7qt4xuT5fVT+6xnq/UFUPTP6a+d1V9atrrPeT/sI8cLa29Ds8A+e2McaRJEcmn84neTDJn6+x+s1jjNdM/rTDJ6vq/WOML59cWFU7xxjvjzdbBM6SMz/AWauqO6rqrVX14aq6t6p+bDI+X1V/WFVzSf5Jkn82ObvzY2tta4xxfxbfDO6ZVfXuqvq3VfWhJG+dnCH6zcm2f6Cqfr+qPja5/Ohk/Ocm8/hoVb2rqnZs9v4D5xbxA0zLzjHGi5Jcm+RfLl8wxjia5J1J/t0Y4/ljjD9dayNV9ewsvtvxwmTouUmuGGP82opV35Hkv40x/naSF2TxbNGlSX42i38g9PlJTiR5xVnuF7DNeNkLeDTWeiv45eP/ZfLxziRzZ3AfP1tVfz+Lf7LmlWOMry7+KaS8b4xxYpX1X5LFv5OUyfK/rqqfT/LCJB+Z3Pax+c4fngRIIn6AR+dYkgtXjF2U5LPLPn948vFEzuy55eYxxmtWGf/GBrZRSQ6NMX79DO4faMLLXsBpjTEeTPLFqro8SarqoiR7kvzZBjbz9SRPmOK0/iTJqybz2VFVT5yM/fTkh6ZTVRdV1TOneJ/ANiB+gEfrmiQHquqjSW5P8q/GGJ/ewO3/IMlPne4HnjfgnyZ5cVXdlcWX2v7WGOPuJAeS/HFVfTzJbUmeOoX7ArYRf9UdAGjFmR8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArfx/q+uDasBYeLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot\n",
    "plt.subplots(figsize=(10,8))\n",
    "sns.boxplot(df_new.UnitPrice)\n",
    "\n",
    "plt.xlabel('Unit Price')\n",
    "plt.title('Unit Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pair plot allows us to see both distribution of single variables and relationships between two variables.\n",
    "sns.pairplot(df_new,vars=[\"Quantity\",\"UnitPrice\",\"Revenue\",\"Month\",\"Day\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Histogram (graphical representation of numerical data distribution)\n",
    "plt.hist(df_new['Hour'], color = 'blue', edgecolor = 'black',\n",
    "         bins = int(180/5))\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Revenue USD($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(df_new['Day'], color = 'blue', edgecolor = 'black',\n",
    "         bins = int(180/5))\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Revenue USD($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(df_new['Month'], color = 'blue', edgecolor = 'black',\n",
    "         bins = int(180/5))\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Revenue USD($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Heat Map\n",
    "sns.heatmap(df_new.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. How many orders made by the customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.Index(df_new['Quantity'])\n",
    "print(f\"Total orders made by the customers = {index.value_counts().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_order=df_new.groupby(by=['CustomerID','Country'], as_index=False)['InvoiceNo'].count().head(10)\n",
    "print(total_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. TOP 5 customers with higher number of orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_order.sort_values(by='InvoiceNo', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.How much money spent by the customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "money_spent = df_new.groupby(by=['CustomerID','Country'], as_index=False)['Revenue'].sum()\n",
    "print(money_spent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. TOP 5 customers with highest money spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "money_spent.sort_values(by='Revenue', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. How many orders per month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yum = df_new.groupby('InvoiceNo')['year_month'].unique().value_counts().sort_index().plot(kind='bar',color=color[0],figsize=(20,6))\n",
    "yum.set_xlabel('Month',fontsize=15)\n",
    "yum.set_ylabel('Number of Orders',fontsize=15)\n",
    "yum.set_title('Number of orders for different Months (1st Dec 2010 - 9th Dec 2011)',fontsize=20)\n",
    "yum.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','Jun_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11','Dec_11'), rotation='horizontal', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. How many orders per day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = df_new.groupby('InvoiceNo')['Day'].unique().value_counts().sort_index().plot(kind='bar',color=color[0],figsize=(15,6))\n",
    "ax.set_xlabel('Day',fontsize=15)\n",
    "ax.set_ylabel('Orders',fontsize=15)\n",
    "ax.set_xticklabels(('Mon','Tue','Wed','Thur','Fri','Sun'), rotation='horizontal', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. How many orders per hour?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = df_new.groupby('InvoiceNo')['Hour'].unique().value_counts().iloc[:-1].sort_index().plot(kind='bar',color=color[0],figsize=(15,6))\n",
    "ax.set_xlabel('Hour',fontsize=13)\n",
    "ax.set_ylabel('Number of Orders',fontsize=13)\n",
    "ax.set_title('Number of Hours',fontsize=13)\n",
    "ax.set_xticklabels(range(6,21), rotation='horizontal', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 8. How many orders for each country?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_country_orders = df_new.groupby('Country')['InvoiceNo'].count().sort_values()\n",
    "del group_country_orders['United Kingdom']\n",
    "\n",
    "# number of unique customers in each country (without UK)\n",
    "plt.subplots(figsize=(15,8))\n",
    "group_country_orders.plot(kind='barh', fontsize=14, color=color[0])\n",
    "plt.xlabel('Number of Orders', fontsize=14)\n",
    "plt.ylabel('Country', fontsize=14)\n",
    "plt.title('Number of Orders for different Countries', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_country_orders = df_new.groupby('Country')['InvoiceNo'].count().sort_values()\n",
    "#del group_country_orders['United Kingdom']\n",
    "\n",
    "# number of unique customers in each country (with UK)\n",
    "plt.subplots(figsize=(15,8))\n",
    "group_country_orders.plot(kind='barh', fontsize=14, color=color[0])\n",
    "plt.xlabel('Number of Orders', fontsize=14)\n",
    "plt.ylabel('Country', fontsize=14)\n",
    "plt.title('Number of Orders for different Countries', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 9.Orders trend across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trend plot\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "x1 = fig.add_subplot(111)\n",
    "\n",
    "x1.set_xlabel('Month',fontsize=15)\n",
    "x1.set_ylabel('Total Amount Spent ($)',fontsize=15)\n",
    "x1.set_title('Total Amount Spent for Months',fontsize=15)\n",
    "\n",
    "x1.bar(df_new.groupby(by=['year_month'])['Revenue'].sum().index.tolist()[1:],\n",
    "        df_new.groupby(by=['year_month'])['Revenue'].sum()[1:],\n",
    "        alpha=0.85,#If you want to make the graph plot more transparent, then you can make alpha less than 1, such as 0.5 or 0.25.\n",
    "        label='Amount Spent by Month')\n",
    "\n",
    "x2 = x1.twinx()# tread line\n",
    "\n",
    "x2.set_ylabel('Percentage Change of Total Amount Spent (%)',fontsize=15)\n",
    "\n",
    "#pct_change()Percentage change between the current and a prior element.\n",
    "x2.plot(df_new.groupby(by=['year_month'])['Revenue'].sum().index.tolist()[1:], \n",
    "         df_new.groupby(by=['year_month'])['Revenue'].sum()[1:].pct_change().fillna(0)*100,\n",
    "         label='Percentage change of total amount spent (%)',\n",
    "         color='red')\n",
    "\n",
    "x1.legend(loc='upper left')\n",
    "x2.legend(loc='upper right')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 10. How much money spent by each country?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_country_amount_spent = df_new.groupby('Country')['Revenue'].sum().sort_values()\n",
    "del group_country_amount_spent['United Kingdom']\n",
    "\n",
    "# plot total money spent by each country (without UK)\n",
    "plt.subplots(figsize=(15,8))\n",
    "group_country_amount_spent.plot(kind='barh', fontsize=12, color=color[0])\n",
    "plt.xlabel('Money Spent (Dollar)', fontsize=12)\n",
    "plt.ylabel('Country', fontsize=12)\n",
    "plt.title('Money Spent by different Countries', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_country_amount_spent = df_new.groupby('Country')['Revenue'].sum().sort_values()\n",
    "#del group_country_amount_spent['United Kingdom']\n",
    "\n",
    "# plot total money spent by each country (without UK)\n",
    "plt.subplots(figsize=(15,8))\n",
    "group_country_amount_spent.plot(kind='barh', fontsize=12, color=color[0])\n",
    "plt.xlabel('Money Spent (Dollar)', fontsize=12)\n",
    "plt.ylabel('Country', fontsize=12)\n",
    "plt.title('Money Spent by different Countries', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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