Sales Data 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "924030d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b486a3d",
   "metadata": {},
   "source": [
    "### Read the file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a237e490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CustKey</th>\n",
       "      <th>DateKey</th>\n",
       "      <th>Discount Amount</th>\n",
       "      <th>Invoice Date</th>\n",
       "      <th>Invoice Number</th>\n",
       "      <th>Item Class</th>\n",
       "      <th>Item Number</th>\n",
       "      <th>Item</th>\n",
       "      <th>Line Number</th>\n",
       "      <th>List Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales Amount</th>\n",
       "      <th>Sales Amount Based on List Price</th>\n",
       "      <th>Sales Cost Amount</th>\n",
       "      <th>Sales Margin Amount</th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Sales Quantity</th>\n",
       "      <th>Sales Rep</th>\n",
       "      <th>U/M</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000481</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>-237.91</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>100012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban Large Eggs</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>237.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237.91</td>\n",
       "      <td>237.910000</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>EA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>U/M = unit of measure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002220</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>368.79</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>100233</td>\n",
       "      <td>P01</td>\n",
       "      <td>20910</td>\n",
       "      <td>Moms Sliced Turkey</td>\n",
       "      <td>1000</td>\n",
       "      <td>824.96</td>\n",
       "      <td>...</td>\n",
       "      <td>456.17</td>\n",
       "      <td>824.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>456.17</td>\n",
       "      <td>456.170000</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>EA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002220</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>109.73</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>116165</td>\n",
       "      <td>P01</td>\n",
       "      <td>38076</td>\n",
       "      <td>Cutting Edge Foot-Long Hot Dogs</td>\n",
       "      <td>1000</td>\n",
       "      <td>548.66</td>\n",
       "      <td>...</td>\n",
       "      <td>438.93</td>\n",
       "      <td>548.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.93</td>\n",
       "      <td>438.930000</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>EA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EA = each</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002489</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>-211.75</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>100096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kiwi Lox</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>211.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.75</td>\n",
       "      <td>211.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>EA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004516</td>\n",
       "      <td>2017-05-27</td>\n",
       "      <td>96627.94</td>\n",
       "      <td>2017-05-27</td>\n",
       "      <td>103341</td>\n",
       "      <td>P01</td>\n",
       "      <td>60776</td>\n",
       "      <td>High Top Sweet Onion</td>\n",
       "      <td>1000</td>\n",
       "      <td>408.52</td>\n",
       "      <td>...</td>\n",
       "      <td>89248.66</td>\n",
       "      <td>185876.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89248.66</td>\n",
       "      <td>196.150901</td>\n",
       "      <td>455</td>\n",
       "      <td>124</td>\n",
       "      <td>SE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE = some SI unit like kgs or gallons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustKey    DateKey  Discount Amount Invoice Date  Invoice Number  \\\n",
       "0  10000481 2017-04-30          -237.91   2017-04-30          100012   \n",
       "1  10002220 2017-07-14           368.79   2017-07-14          100233   \n",
       "2  10002220 2017-10-17           109.73   2017-10-17          116165   \n",
       "3  10002489 2017-06-03          -211.75   2017-06-03          100096   \n",
       "4  10004516 2017-05-27         96627.94   2017-05-27          103341   \n",
       "\n",
       "  Item Class Item Number                             Item  Line Number  \\\n",
       "0        NaN         NaN                 Urban Large Eggs         2000   \n",
       "1        P01       20910               Moms Sliced Turkey         1000   \n",
       "2        P01       38076  Cutting Edge Foot-Long Hot Dogs         1000   \n",
       "3        NaN         NaN                         Kiwi Lox         1000   \n",
       "4        P01       60776             High Top Sweet Onion         1000   \n",
       "\n",
       "   List Price  ...  Sales Amount Sales Amount Based on List Price  \\\n",
       "0        0.00  ...        237.91                             0.00   \n",
       "1      824.96  ...        456.17                           824.96   \n",
       "2      548.66  ...        438.93                           548.66   \n",
       "3        0.00  ...        211.75                             0.00   \n",
       "4      408.52  ...      89248.66                        185876.60   \n",
       "\n",
       "   Sales Cost Amount  Sales Margin Amount  Sales Price  Sales Quantity  \\\n",
       "0                0.0               237.91   237.910000               1   \n",
       "1                0.0               456.17   456.170000               1   \n",
       "2                0.0               438.93   438.930000               1   \n",
       "3                0.0               211.75   211.750000               1   \n",
       "4                0.0             89248.66   196.150901             455   \n",
       "\n",
       "   Sales Rep  U/M  Unnamed: 20                            Unnamed: 21  \n",
       "0        184   EA          NaN                  U/M = unit of measure  \n",
       "1        127   EA          NaN                                    NaN  \n",
       "2        127   EA          NaN                              EA = each  \n",
       "3        160   EA          NaN                                    NaN  \n",
       "4        124   SE          NaN  SE = some SI unit like kgs or gallons  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel(\"SALESDATA.xls\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b78fbeb",
   "metadata": {},
   "source": [
    "#### Calling Calling info() on our dataset tells us tons of information about our data frame like the data type of our features, and the memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2c4f30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 65282 entries, 0 to 65281\n",
      "Data columns (total 22 columns):\n",
      " #   Column                            Non-Null Count  Dtype         \n",
      "---  ------                            --------------  -----         \n",
      " 0   CustKey                           65282 non-null  int64         \n",
      " 1   DateKey                           65282 non-null  datetime64[ns]\n",
      " 2   Discount Amount                   65280 non-null  float64       \n",
      " 3   Invoice Date                      65282 non-null  datetime64[ns]\n",
      " 4   Invoice Number                    65282 non-null  int64         \n",
      " 5   Item Class                        56993 non-null  object        \n",
      " 6   Item Number                       65241 non-null  object        \n",
      " 7   Item                              65282 non-null  object        \n",
      " 8   Line Number                       65282 non-null  int64         \n",
      " 9   List Price                        65282 non-null  float64       \n",
      " 10  Order Number                      65282 non-null  int64         \n",
      " 11  Promised Delivery Date            65282 non-null  datetime64[ns]\n",
      " 12  Sales Amount                      65282 non-null  float64       \n",
      " 13  Sales Amount Based on List Price  65282 non-null  float64       \n",
      " 14  Sales Cost Amount                 65282 non-null  float64       \n",
      " 15  Sales Margin Amount               65282 non-null  float64       \n",
      " 16  Sales Price                       65281 non-null  float64       \n",
      " 17  Sales Quantity                    65282 non-null  int64         \n",
      " 18  Sales Rep                         65282 non-null  int64         \n",
      " 19  U/M                               65282 non-null  object        \n",
      " 20  Unnamed: 20                       0 non-null      float64       \n",
      " 21  Unnamed: 21                       4 non-null      object        \n",
      "dtypes: datetime64[ns](3), float64(8), int64(6), object(5)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc418766",
   "metadata": {},
   "source": [
    "## Seperating features into categorical and numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95ce89c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-Numeric columns:\n",
      "Item Class\n",
      "Item Number\n",
      "Item\n",
      "U/M\n",
      "Unnamed: 21\n",
      "\n",
      "Numeric columns:\n",
      "CustKey\n",
      "DateKey\n",
      "Discount Amount\n",
      "Invoice Date\n",
      "Invoice Number\n",
      "Line Number\n",
      "List Price\n",
      "Order Number\n",
      "Promised Delivery Date\n",
      "Sales Amount\n",
      "Sales Amount Based on List Price\n",
      "Sales Cost Amount\n",
      "Sales Margin Amount\n",
      "Sales Price\n",
      "Sales Quantity\n",
      "Sales Rep\n",
      "Unnamed: 20\n"
     ]
    }
   ],
   "source": [
    "cat_df = df.select_dtypes(include=['object'])\n",
    "num_df = df.select_dtypes(exclude=['object'])\n",
    "\n",
    "def printColumnTypes(non_numeric_df, numeric_df):\n",
    "    '''separates non-numeric and numeric columns'''\n",
    "    print(\"Non-Numeric columns:\")\n",
    "    for col in non_numeric_df:\n",
    "        print(f\"{col}\")\n",
    "    print(\"\")\n",
    "    print(\"Numeric columns:\")\n",
    "    for col in numeric_df:\n",
    "        print(f\"{col}\")\n",
    "        \n",
    "printColumnTypes(cat_df, num_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2fd084",
   "metadata": {},
   "source": [
    "# Clean the Data!\n",
    "## Columns with missing values\n",
    "### The two common methods to deal with missing values are \n",
    "#### 1. Dropping the rows / columns \n",
    "#### 2. Imputing them based on other observations i.e. the mean or median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "474cea25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustKey                                 0\n",
       "DateKey                                 0\n",
       "Discount Amount                         2\n",
       "Invoice Date                            0\n",
       "Invoice Number                          0\n",
       "Item Class                           8289\n",
       "Item Number                            41\n",
       "Item                                    0\n",
       "Line Number                             0\n",
       "List Price                              0\n",
       "Order Number                            0\n",
       "Promised Delivery Date                  0\n",
       "Sales Amount                            0\n",
       "Sales Amount Based on List Price        0\n",
       "Sales Cost Amount                       0\n",
       "Sales Margin Amount                     0\n",
       "Sales Price                             1\n",
       "Sales Quantity                          0\n",
       "Sales Rep                               0\n",
       "U/M                                     0\n",
       "Unnamed: 20                         65282\n",
       "Unnamed: 21                         65278\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2efdd6",
   "metadata": {},
   "source": [
    "#### Since Item Number and Item Class are not necessarily affecting the Profit or Sales Price we can ignore it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2d52a8",
   "metadata": {},
   "source": [
    "#### The Sales price column has one null value; With this method, we impute the missing values with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be031300",
   "metadata": {},
   "outputs": [],
   "source": [
    "price_mean_value = round(df['Sales Price'].mean(), 2)\n",
    "df['Sales Price'].fillna(price_mean_value, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4269dfb8",
   "metadata": {},
   "source": [
    "#### There are two null values in discount amount col; we can replace this with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "734460e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna({'Discount Amount':0}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a323e4",
   "metadata": {},
   "source": [
    "#### Checking if DateKey and Invoice Date Col are identical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76ec36f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DateKey'].equals(df['Invoice Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bf02d8",
   "metadata": {},
   "source": [
    "#### Drop all unecessary Cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "787685cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "colsToDrop = ['DateKey', 'Item Class', 'Item Number', 'U/M', 'Unnamed: 20', 'Unnamed: 21']\n",
    "\n",
    "df.drop(colsToDrop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "814d18fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>CustKey</th>\n",
       "      <th>Discount Amount</th>\n",
       "      <th>Invoice Date</th>\n",
       "      <th>Invoice Number</th>\n",
       "      <th>Item</th>\n",
       "      <th>Line Number</th>\n",
       "      <th>List Price</th>\n",
       "      <th>Order Number</th>\n",
       "      <th>Promised Delivery Date</th>\n",
       "      <th>Sales Amount</th>\n",
       "      <th>Sales Amount Based on List Price</th>\n",
       "      <th>Sales Cost Amount</th>\n",
       "      <th>Sales Margin Amount</th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Sales Quantity</th>\n",
       "      <th>Sales Rep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000481</td>\n",
       "      <td>-237.91</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>100012</td>\n",
       "      <td>Urban Large Eggs</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>200015</td>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>237.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237.91</td>\n",
       "      <td>237.910000</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002220</td>\n",
       "      <td>368.79</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>100233</td>\n",
       "      <td>Moms Sliced Turkey</td>\n",
       "      <td>1000</td>\n",
       "      <td>824.96</td>\n",
       "      <td>200245</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>456.17</td>\n",
       "      <td>824.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>456.17</td>\n",
       "      <td>456.170000</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002220</td>\n",
       "      <td>109.73</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>116165</td>\n",
       "      <td>Cutting Edge Foot-Long Hot Dogs</td>\n",
       "      <td>1000</td>\n",
       "      <td>548.66</td>\n",
       "      <td>213157</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>438.93</td>\n",
       "      <td>548.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.93</td>\n",
       "      <td>438.930000</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002489</td>\n",
       "      <td>-211.75</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>100096</td>\n",
       "      <td>Kiwi Lox</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>200107</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>211.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.75</td>\n",
       "      <td>211.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004516</td>\n",
       "      <td>96627.94</td>\n",
       "      <td>2017-05-27</td>\n",
       "      <td>103341</td>\n",
       "      <td>High Top Sweet Onion</td>\n",
       "      <td>1000</td>\n",
       "      <td>408.52</td>\n",
       "      <td>203785</td>\n",
       "      <td>2017-05-28</td>\n",
       "      <td>89248.66</td>\n",
       "      <td>185876.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89248.66</td>\n",
       "      <td>196.150901</td>\n",
       "      <td>455</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustKey  Discount Amount Invoice Date  Invoice Number  \\\n",
       "0  10000481          -237.91   2017-04-30          100012   \n",
       "1  10002220           368.79   2017-07-14          100233   \n",
       "2  10002220           109.73   2017-10-17          116165   \n",
       "3  10002489          -211.75   2017-06-03          100096   \n",
       "4  10004516         96627.94   2017-05-27          103341   \n",
       "\n",
       "                              Item  Line Number  List Price  Order Number  \\\n",
       "0                 Urban Large Eggs         2000        0.00        200015   \n",
       "1               Moms Sliced Turkey         1000      824.96        200245   \n",
       "2  Cutting Edge Foot-Long Hot Dogs         1000      548.66        213157   \n",
       "3                         Kiwi Lox         1000        0.00        200107   \n",
       "4             High Top Sweet Onion         1000      408.52        203785   \n",
       "\n",
       "  Promised Delivery Date  Sales Amount  Sales Amount Based on List Price  \\\n",
       "0             2017-04-30        237.91                              0.00   \n",
       "1             2017-07-14        456.17                            824.96   \n",
       "2             2017-10-16        438.93                            548.66   \n",
       "3             2017-06-03        211.75                              0.00   \n",
       "4             2017-05-28      89248.66                         185876.60   \n",
       "\n",
       "   Sales Cost Amount  Sales Margin Amount  Sales Price  Sales Quantity  \\\n",
       "0                0.0               237.91   237.910000               1   \n",
       "1                0.0               456.17   456.170000               1   \n",
       "2                0.0               438.93   438.930000               1   \n",
       "3                0.0               211.75   211.750000               1   \n",
       "4                0.0             89248.66   196.150901             455   \n",
       "\n",
       "   Sales Rep  \n",
       "0        184  \n",
       "1        127  \n",
       "2        127  \n",
       "3        160  \n",
       "4        124  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00b03b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0f2749",
   "metadata": {},
   "source": [
    "# Exploratory Analysis of the Data\n",
    "## Characteristics of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "417c435f",
   "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>CustKey</th>\n",
       "      <th>Discount Amount</th>\n",
       "      <th>Invoice Number</th>\n",
       "      <th>Line Number</th>\n",
       "      <th>List Price</th>\n",
       "      <th>Order Number</th>\n",
       "      <th>Sales Amount</th>\n",
       "      <th>Sales Amount Based on List Price</th>\n",
       "      <th>Sales Cost Amount</th>\n",
       "      <th>Sales Margin Amount</th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Sales Quantity</th>\n",
       "      <th>Sales Rep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.528200e+04</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "      <td>65282.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.001770e+07</td>\n",
       "      <td>1855.517987</td>\n",
       "      <td>216223.662020</td>\n",
       "      <td>23713.849790</td>\n",
       "      <td>514.693380</td>\n",
       "      <td>180583.064352</td>\n",
       "      <td>2852.038373</td>\n",
       "      <td>4707.473613</td>\n",
       "      <td>1660.979228</td>\n",
       "      <td>1191.059145</td>\n",
       "      <td>283.615913</td>\n",
       "      <td>45.084311</td>\n",
       "      <td>137.422398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.176148e+03</td>\n",
       "      <td>9037.008289</td>\n",
       "      <td>94992.281866</td>\n",
       "      <td>32664.024053</td>\n",
       "      <td>449.189182</td>\n",
       "      <td>67593.871116</td>\n",
       "      <td>15164.342107</td>\n",
       "      <td>20696.443785</td>\n",
       "      <td>9556.485250</td>\n",
       "      <td>5860.787502</td>\n",
       "      <td>252.874782</td>\n",
       "      <td>429.661793</td>\n",
       "      <td>26.643936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000045e+07</td>\n",
       "      <td>-255820.800000</td>\n",
       "      <td>100012.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100838.000000</td>\n",
       "      <td>200.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3932.930000</td>\n",
       "      <td>-5000.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001272e+07</td>\n",
       "      <td>246.030000</td>\n",
       "      <td>117931.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>181.560000</td>\n",
       "      <td>115321.000000</td>\n",
       "      <td>308.387500</td>\n",
       "      <td>561.040000</td>\n",
       "      <td>167.790000</td>\n",
       "      <td>129.950000</td>\n",
       "      <td>100.070000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.001966e+07</td>\n",
       "      <td>441.760000</td>\n",
       "      <td>222869.500000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>325.190000</td>\n",
       "      <td>203702.000000</td>\n",
       "      <td>553.940000</td>\n",
       "      <td>998.160000</td>\n",
       "      <td>304.500000</td>\n",
       "      <td>246.490000</td>\n",
       "      <td>183.758250</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.002351e+07</td>\n",
       "      <td>999.760000</td>\n",
       "      <td>314318.750000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>803.860000</td>\n",
       "      <td>218576.000000</td>\n",
       "      <td>1280.042500</td>\n",
       "      <td>2315.040000</td>\n",
       "      <td>687.320000</td>\n",
       "      <td>579.530000</td>\n",
       "      <td>448.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.002758e+07</td>\n",
       "      <td>343532.660000</td>\n",
       "      <td>332842.000000</td>\n",
       "      <td>344000.000000</td>\n",
       "      <td>2760.700000</td>\n",
       "      <td>321532.000000</td>\n",
       "      <td>555376.000000</td>\n",
       "      <td>632610.160000</td>\n",
       "      <td>366576.000000</td>\n",
       "      <td>188800.000000</td>\n",
       "      <td>6035.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>185.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CustKey  Discount Amount  Invoice Number    Line Number  \\\n",
       "count  6.528200e+04     65282.000000    65282.000000   65282.000000   \n",
       "mean   1.001770e+07      1855.517987   216223.662020   23713.849790   \n",
       "std    7.176148e+03      9037.008289    94992.281866   32664.024053   \n",
       "min    1.000045e+07   -255820.800000   100012.000000    1000.000000   \n",
       "25%    1.001272e+07       246.030000   117931.000000    3000.000000   \n",
       "50%    1.001966e+07       441.760000   222869.500000   12000.000000   \n",
       "75%    1.002351e+07       999.760000   314318.750000   32000.000000   \n",
       "max    1.002758e+07    343532.660000   332842.000000  344000.000000   \n",
       "\n",
       "         List Price   Order Number   Sales Amount  \\\n",
       "count  65282.000000   65282.000000   65282.000000   \n",
       "mean     514.693380  180583.064352    2852.038373   \n",
       "std      449.189182   67593.871116   15164.342107   \n",
       "min        0.000000  100838.000000     200.010000   \n",
       "25%      181.560000  115321.000000     308.387500   \n",
       "50%      325.190000  203702.000000     553.940000   \n",
       "75%      803.860000  218576.000000    1280.042500   \n",
       "max     2760.700000  321532.000000  555376.000000   \n",
       "\n",
       "       Sales Amount Based on List Price  Sales Cost Amount  \\\n",
       "count                      65282.000000       65282.000000   \n",
       "mean                        4707.473613        1660.979228   \n",
       "std                        20696.443785        9556.485250   \n",
       "min                            0.000000           0.000000   \n",
       "25%                          561.040000         167.790000   \n",
       "50%                          998.160000         304.500000   \n",
       "75%                         2315.040000         687.320000   \n",
       "max                       632610.160000      366576.000000   \n",
       "\n",
       "       Sales Margin Amount   Sales Price  Sales Quantity     Sales Rep  \n",
       "count         65282.000000  65282.000000    65282.000000  65282.000000  \n",
       "mean           1191.059145    283.615913       45.084311    137.422398  \n",
       "std            5860.787502    252.874782      429.661793     26.643936  \n",
       "min           -3932.930000  -5000.000000       -1.000000    103.000000  \n",
       "25%             129.950000    100.070000        2.000000    113.000000  \n",
       "50%             246.490000    183.758250        3.000000    134.000000  \n",
       "75%             579.530000    448.220000        8.000000    160.000000  \n",
       "max          188800.000000   6035.000000    16000.000000    185.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c61fd4",
   "metadata": {},
   "source": [
    "## Overall Sales Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de089085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-12-31 00:00:00')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Invoice Date'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ce81d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting month Year\n",
    "df['Month_Year'] = df['Invoice Date'].apply(lambda x: x.strftime('%Y-%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f244e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = df.groupby('Month_Year').sum()\n",
    "months = [month for month, df in df.groupby('Month_Year')]\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(months,results['Sales Price'], color = '#b80045')\n",
    "plt.xticks(months, rotation='vertical', size = 8)\n",
    "plt.ylabel('Sales in USD')\n",
    "plt.xlabel('Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f698317",
   "metadata": {},
   "source": [
    "## MoM Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f040784",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoM_Data = pd.DataFrame(results['Sales Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4226a709",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoM_Data['Last_Month'] = np.roll(MoM_Data['Sales Price'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c4ade42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Last_Month</th>\n",
       "      <th>Growth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>600460.161869</td>\n",
       "      <td>781317.138966</td>\n",
       "      <td>-0.231477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>741311.980741</td>\n",
       "      <td>600460.161869</td>\n",
       "      <td>0.234573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>535719.783362</td>\n",
       "      <td>741311.980741</td>\n",
       "      <td>-0.277336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>804600.227856</td>\n",
       "      <td>535719.783362</td>\n",
       "      <td>0.501905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>850540.174847</td>\n",
       "      <td>804600.227856</td>\n",
       "      <td>0.057097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales Price     Last_Month    Growth\n",
       "Month_Year                                        \n",
       "2017-02     600460.161869  781317.138966 -0.231477\n",
       "2017-03     741311.980741  600460.161869  0.234573\n",
       "2017-04     535719.783362  741311.980741 -0.277336\n",
       "2017-05     804600.227856  535719.783362  0.501905\n",
       "2017-06     850540.174847  804600.227856  0.057097"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MoM_Data = MoM_Data.drop(MoM_Data.index[0])\n",
    "MoM_Data['Growth'] = (MoM_Data['Sales Price']/MoM_Data['Last_Month'])-1\n",
    "MoM_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e8eb9973",
   "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>Growth</th>\n",
       "      <th>Months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.231477</td>\n",
       "      <td>2017-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.234573</td>\n",
       "      <td>2017-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.277336</td>\n",
       "      <td>2017-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.501905</td>\n",
       "      <td>2017-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.057097</td>\n",
       "      <td>2017-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Growth   Months\n",
       "0 -0.231477  2017-02\n",
       "1  0.234573  2017-03\n",
       "2 -0.277336  2017-04\n",
       "3  0.501905  2017-05\n",
       "4  0.057097  2017-06"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = MoM_Data.drop(columns = [\"Sales Price\", \"Last_Month\"])\n",
    "results['Months'] = results.index\n",
    "results.reset_index(drop=True, inplace=True)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3adfcf79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(results['Months'],results['Growth']*100, color = '#b60095')\n",
    "plt.xticks(results['Months'], rotation='vertical', size = 10)\n",
    "plt.ylabel('% Growth')\n",
    "plt.xlabel('Month')\n",
    "plt.title(\"\\n MoM Growth\\n\", size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068e5a92",
   "metadata": {},
   "source": [
    "## YoY Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e66317d",
   "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",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Last_Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>706622.651847</td>\n",
       "      <td>781317.138966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>620099.987458</td>\n",
       "      <td>600460.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>620615.304779</td>\n",
       "      <td>741311.980741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>270807.438237</td>\n",
       "      <td>535719.783362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>619094.828742</td>\n",
       "      <td>804600.227856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales Price      Last_Year\n",
       "Month_Year                              \n",
       "2018-01     706622.651847  781317.138966\n",
       "2018-02     620099.987458  600460.161869\n",
       "2018-03     620615.304779  741311.980741\n",
       "2019-01     270807.438237  535719.783362\n",
       "2019-02     619094.828742  804600.227856"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YoY_Data = pd.DataFrame(df.groupby('Month_Year').sum()['Sales Price'])\n",
    "YoY_Data['Last_Year'] = np.roll(YoY_Data['Sales Price'],12)\n",
    "YoY_Data = YoY_Data.drop(YoY_Data.index[0:12])\n",
    "YoY_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "40733bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Last_Year</th>\n",
       "      <th>Growth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>706622.651847</td>\n",
       "      <td>781317.138966</td>\n",
       "      <td>-0.095601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>620099.987458</td>\n",
       "      <td>600460.161869</td>\n",
       "      <td>0.032708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>620615.304779</td>\n",
       "      <td>741311.980741</td>\n",
       "      <td>-0.162815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>270807.438237</td>\n",
       "      <td>535719.783362</td>\n",
       "      <td>-0.494498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>619094.828742</td>\n",
       "      <td>804600.227856</td>\n",
       "      <td>-0.230556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales Price      Last_Year    Growth\n",
       "Month_Year                                        \n",
       "2018-01     706622.651847  781317.138966 -0.095601\n",
       "2018-02     620099.987458  600460.161869  0.032708\n",
       "2018-03     620615.304779  741311.980741 -0.162815\n",
       "2019-01     270807.438237  535719.783362 -0.494498\n",
       "2019-02     619094.828742  804600.227856 -0.230556"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YoY_Data['Growth'] = (YoY_Data['Sales Price']/YoY_Data['Last_Year'])-1\n",
    "YoY_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99702fe9",
   "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>Growth</th>\n",
       "      <th>Month_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.095601</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.032708</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.162815</td>\n",
       "      <td>2018-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.494498</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.230556</td>\n",
       "      <td>2019-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Growth Month_Year\n",
       "0 -0.095601    2018-01\n",
       "1  0.032708    2018-02\n",
       "2 -0.162815    2018-03\n",
       "3 -0.494498    2019-01\n",
       "4 -0.230556    2019-02"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = YoY_Data.drop(columns = [\"Sales Price\", \"Last_Year\"])\n",
    "results['Month_Year'] = results.index\n",
    "results.reset_index(drop=True, inplace=True)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "09ca1b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(results['Month_Year'],results['Growth']*100, color = '#b60095')\n",
    "plt.xticks(results['Month_Year'], rotation='vertical', size = 8)\n",
    "plt.ylabel('% Growth')\n",
    "plt.xlabel('\\n Year')\n",
    "plt.title(\"\\n YoY Growth \\n\", size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab3d400",
   "metadata": {},
   "source": [
    "## What are the top 10 Products in terms of Profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4ed4ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Better Fancy Canned Sardines</th>\n",
       "      <td>1263261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Spade Foot-Long Hot Dogs</th>\n",
       "      <td>937366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discover Manicotti</th>\n",
       "      <td>765222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ebony Prepared Salad</th>\n",
       "      <td>723557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagine Popsicles</th>\n",
       "      <td>693747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atomic Mint Chocolate Bar</th>\n",
       "      <td>578217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Spade Turkey Hot Dogs</th>\n",
       "      <td>540077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moms Sliced Turkey</th>\n",
       "      <td>487833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bravo Large Canned Shrimp</th>\n",
       "      <td>471849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Even Better Strawberry Yogurt</th>\n",
       "      <td>391694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Sales Price\n",
       "Item                                      \n",
       "Better Fancy Canned Sardines       1263261\n",
       "Red Spade Foot-Long Hot Dogs        937366\n",
       "Discover Manicotti                  765222\n",
       "Ebony Prepared Salad                723557\n",
       "Imagine Popsicles                   693747\n",
       "Atomic Mint Chocolate Bar           578217\n",
       "Red Spade Turkey Hot Dogs           540077\n",
       "Moms Sliced Turkey                  487833\n",
       "Bravo Large Canned Shrimp           471849\n",
       "Even Better Strawberry Yogurt       391694"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_sales=pd.DataFrame(df.groupby('Item').sum()['Sales Price'])\n",
    "prod_sales=prod_sales.sort_values('Sales Price', ascending = False).astype('int')\n",
    "prod_sales[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1d8712ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SALES PRICE')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_sales[:10].plot(kind='bar' , title = 'TOP 10 PRODUCTS', figsize=(15,5), color = '#7cb600')\n",
    "plt.xlabel(\"ITEM\")\n",
    "plt.ylabel(\"SALES PRICE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65b82c2",
   "metadata": {},
   "source": [
    "##  What are the 10 Products in terms of loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c8f61e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Great Bagels</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great White Bread</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Super Hot Chocolate</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Excellent Cranberry Juice</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pearl Light Wine</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best Choice Salsa Dip</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Club 1% Milk</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Choice Semi-Sweet Chocolate Bar</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gorilla Head Cheese</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Spade Cole Slaw</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sales Price\n",
       "Item                                        \n",
       "Great Bagels                               1\n",
       "Great White Bread                          3\n",
       "Super Hot Chocolate                        8\n",
       "Excellent Cranberry Juice                  9\n",
       "Pearl Light Wine                          10\n",
       "Best Choice Salsa Dip                     13\n",
       "Club 1% Milk                              16\n",
       "Choice Semi-Sweet Chocolate Bar           17\n",
       "Gorilla Head Cheese                       20\n",
       "Red Spade Cole Slaw                       23"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_loss=pd.DataFrame(df.groupby('Item').sum()['Sales Price'])\n",
    "prod_loss=prod_sales.sort_values('Sales Price', ascending = True).astype('int')\n",
    "prod_loss[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "41cf6a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SALES PRICE')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_loss[:10].plot(kind='bar', title = 'PRODUCTS IN TERMS OF LOSS', figsize=(15,5), color = '#7cb600')\n",
    "plt.xlabel(\"ITEM\")\n",
    "plt.ylabel(\"SALES PRICE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7143eead",
   "metadata": {},
   "source": [
    "## What Products sold the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f080c2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Better Large Canned Shrimp</th>\n",
       "      <td>590343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High Top Dried Mushrooms</th>\n",
       "      <td>377259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Better Canned Tuna in Oil</th>\n",
       "      <td>266996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walrus Chardonnay</th>\n",
       "      <td>212022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Spade Pimento Loaf</th>\n",
       "      <td>163296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Sales Quantity\n",
       "Item                                      \n",
       "Better Large Canned Shrimp          590343\n",
       "High Top Dried Mushrooms            377259\n",
       "Better Canned Tuna in Oil           266996\n",
       "Walrus Chardonnay                   212022\n",
       "Red Spade Pimento Loaf              163296"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_prod=pd.DataFrame(df.groupby('Item').sum()['Sales Quantity'])\n",
    "most_prod=most_prod.sort_values('Sales Quantity', ascending=False)\n",
    "most_prod.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fed42e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SALES QUANTITY')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_prod[:5].plot(kind='bar', title = 'MOST SELLING PROUCTS', figsize=(15,5), color = '#7cb600')\n",
    "plt.xlabel(\"ITEM\")\n",
    "plt.ylabel(\"SALES QUANTITY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64defbd",
   "metadata": {},
   "source": [
    "## What products are most often sold together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "618c77b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-a964ed26fa7a>: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",
      "  often_sold['Grouped']=often_sold.groupby('Order Number')['Item'].transform(lambda x: ','.join(x))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Number</th>\n",
       "      <th>Grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203785</td>\n",
       "      <td>High Top Sweet Onion,Best Choice Fudge Brownies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>205213</td>\n",
       "      <td>Tell Tale Garlic,High Top Walnuts,Big Time Fro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>200478</td>\n",
       "      <td>Better Fancy Canned Sardines,Red Spade Low Fat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>202989</td>\n",
       "      <td>Landslide Columbian Coffee,High Top Dried Mush...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>205041</td>\n",
       "      <td>Ebony Prepared Salad,Bravo Canned Yams,Moms Sl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order Number                                            Grouped\n",
       "4         203785    High Top Sweet Onion,Best Choice Fudge Brownies\n",
       "7         205213  Tell Tale Garlic,High Top Walnuts,Big Time Fro...\n",
       "12        200478  Better Fancy Canned Sardines,Red Spade Low Fat...\n",
       "17        202989  Landslide Columbian Coffee,High Top Dried Mush...\n",
       "21        205041  Ebony Prepared Salad,Bravo Canned Yams,Moms Sl..."
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "often_sold=df[df['Order Number'].duplicated(keep=False)]\n",
    "\n",
    "often_sold['Grouped']=often_sold.groupby('Order Number')['Item'].transform(lambda x: ','.join(x))\n",
    "\n",
    "often_sold = often_sold[['Order Number', 'Grouped']].drop_duplicates()\n",
    "\n",
    "often_sold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90918745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Landslide White Sugar', 'Landslide White Sugar') 564\n",
      "('Ebony Prepared Salad', 'Imagine Popsicles') 307\n",
      "('Ebony Prepared Salad', 'Moms Sliced Turkey') 303\n",
      "('Better Fancy Canned Sardines', 'Ebony Prepared Salad') 302\n",
      "('Better Fancy Canned Sardines', 'Imagine Popsicles') 285\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "count = Counter()\n",
    "\n",
    "for row in often_sold['Grouped']:\n",
    "    row_list = row.split(',')\n",
    "    count.update(Counter(combinations(row_list, 2)))\n",
    "\n",
    "for key,value in count.most_common(5):\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a58e9c1",
   "metadata": {},
   "source": [
    "## Which is the most expensive item ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9bb50935",
   "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>Item</th>\n",
       "      <th>Sales Price</th>\n",
       "      <th>Sales Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18267</th>\n",
       "      <td>Atomic Semi-Sweet Chocolate Bar</td>\n",
       "      <td>6035.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Item  Sales Price  Sales Quantity\n",
       "18267  Atomic Semi-Sweet Chocolate Bar       6035.0               1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_expensive=pd.DataFrame(df.sort_values(by = \"Sales Price\", ascending = False))\n",
    "most_expensive = most_expensive[['Item','Sales Price','Sales Quantity']]\n",
    "most_expensive.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2466b92a",
   "metadata": {},
   "source": [
    "## What is the total Revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c3e6243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue was: $186186367.19\n"
     ]
    }
   ],
   "source": [
    "revenue = (df['Sales Quantity']* df['Sales Price']).sum()\n",
    "print('Revenue was: $' + str(np.round(revenue,2)))"
   ]
  }
 ],
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