Task Assigned in Full Stack Data Science Class -- 31-07-2022[Task2 - Superstore_USA].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6d137db",
   "metadata": {},
   "source": [
    "### TASK 2 - Superstore_USA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5845b03",
   "metadata": {},
   "source": [
    "#### 1. Load this data in sql and in pandas with a relation in sql "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d6a9b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mysql\n",
    "import mysql.connector as conn\n",
    "\n",
    "mydb = conn.connect(host = \"localhost\", user = \"root\", passwd = \"password\")\n",
    "cursor = mydb.cursor()\n",
    "cursor.execute(\"create database Superstore_USA\")\n",
    "cursor.execute(\"show databases\")\n",
    "for i in cursor.fetchall():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57d682f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d48a2943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the 'Superstore_USA' dataset in pandas\n",
    "\n",
    "orders = pd.read_excel(r'C:\\Users\\Computers\\Downloads\\Superstore_USA.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6770769",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>...</th>\n",
       "      <th>Region</th>\n",
       "      <th>State or Province</th>\n",
       "      <th>City</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity ordered new</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18606</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>Janice Fletcher</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Addison</td>\n",
       "      <td>60101</td>\n",
       "      <td>2012-05-28</td>\n",
       "      <td>2012-05-30</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>2</td>\n",
       "      <td>5.90</td>\n",
       "      <td>88525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20847</td>\n",
       "      <td>High</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.93</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>2010-07-08</td>\n",
       "      <td>4.560000</td>\n",
       "      <td>4</td>\n",
       "      <td>13.01</td>\n",
       "      <td>88522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23086</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.03</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.15</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-28</td>\n",
       "      <td>-47.640000</td>\n",
       "      <td>7</td>\n",
       "      <td>49.92</td>\n",
       "      <td>88523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23087</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-28</td>\n",
       "      <td>-30.510000</td>\n",
       "      <td>7</td>\n",
       "      <td>41.64</td>\n",
       "      <td>88523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23088</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.99</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Technology</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>998.202300</td>\n",
       "      <td>8</td>\n",
       "      <td>1446.67</td>\n",
       "      <td>88523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9421</th>\n",
       "      <td>20275</td>\n",
       "      <td>Critical</td>\n",
       "      <td>0.06</td>\n",
       "      <td>35.89</td>\n",
       "      <td>14.72</td>\n",
       "      <td>3402</td>\n",
       "      <td>Frederick Cole</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>25314</td>\n",
       "      <td>2013-05-14</td>\n",
       "      <td>2013-05-15</td>\n",
       "      <td>137.860000</td>\n",
       "      <td>13</td>\n",
       "      <td>447.87</td>\n",
       "      <td>87532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9422</th>\n",
       "      <td>20276</td>\n",
       "      <td>Critical</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.34</td>\n",
       "      <td>7.49</td>\n",
       "      <td>3402</td>\n",
       "      <td>Frederick Cole</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>25314</td>\n",
       "      <td>2013-05-14</td>\n",
       "      <td>2013-05-14</td>\n",
       "      <td>-39.070000</td>\n",
       "      <td>3</td>\n",
       "      <td>13.23</td>\n",
       "      <td>87532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9423</th>\n",
       "      <td>24491</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.08</td>\n",
       "      <td>550.98</td>\n",
       "      <td>45.70</td>\n",
       "      <td>3402</td>\n",
       "      <td>Frederick Cole</td>\n",
       "      <td>Delivery Truck</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>25314</td>\n",
       "      <td>2013-09-12</td>\n",
       "      <td>2013-09-14</td>\n",
       "      <td>-1225.029097</td>\n",
       "      <td>4</td>\n",
       "      <td>2215.93</td>\n",
       "      <td>87533</td>\n",
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       "    <tr>\n",
       "      <th>9424</th>\n",
       "      <td>25914</td>\n",
       "      <td>High</td>\n",
       "      <td>0.10</td>\n",
       "      <td>105.98</td>\n",
       "      <td>13.99</td>\n",
       "      <td>3403</td>\n",
       "      <td>Tammy Buckley</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cheyenne</td>\n",
       "      <td>82001</td>\n",
       "      <td>2010-02-08</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>349.485000</td>\n",
       "      <td>5</td>\n",
       "      <td>506.50</td>\n",
       "      <td>87530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9425</th>\n",
       "      <td>24492</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.09</td>\n",
       "      <td>7.78</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3403</td>\n",
       "      <td>Tammy Buckley</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cheyenne</td>\n",
       "      <td>82001</td>\n",
       "      <td>2013-09-12</td>\n",
       "      <td>2013-09-14</td>\n",
       "      <td>78.062400</td>\n",
       "      <td>23</td>\n",
       "      <td>172.48</td>\n",
       "      <td>87533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9426 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row ID Order Priority  Discount  Unit Price  Shipping Cost  Customer ID  \\\n",
       "0      18606  Not Specified      0.01        2.88           0.50            2   \n",
       "1      20847           High      0.01        2.84           0.93            3   \n",
       "2      23086  Not Specified      0.03        6.68           6.15            3   \n",
       "3      23087  Not Specified      0.01        5.68           3.60            3   \n",
       "4      23088  Not Specified      0.00      205.99           2.50            3   \n",
       "...      ...            ...       ...         ...            ...          ...   \n",
       "9421   20275       Critical      0.06       35.89          14.72         3402   \n",
       "9422   20276       Critical      0.00        3.34           7.49         3402   \n",
       "9423   24491  Not Specified      0.08      550.98          45.70         3402   \n",
       "9424   25914           High      0.10      105.98          13.99         3403   \n",
       "9425   24492  Not Specified      0.09        7.78           2.50         3403   \n",
       "\n",
       "        Customer Name       Ship Mode Customer Segment Product Category  ...  \\\n",
       "0     Janice Fletcher     Regular Air        Corporate  Office Supplies  ...   \n",
       "1       Bonnie Potter     Express Air        Corporate  Office Supplies  ...   \n",
       "2       Bonnie Potter     Express Air        Corporate  Office Supplies  ...   \n",
       "3       Bonnie Potter     Regular Air        Corporate  Office Supplies  ...   \n",
       "4       Bonnie Potter     Express Air        Corporate       Technology  ...   \n",
       "...               ...             ...              ...              ...  ...   \n",
       "9421   Frederick Cole     Regular Air         Consumer  Office Supplies  ...   \n",
       "9422   Frederick Cole     Regular Air         Consumer  Office Supplies  ...   \n",
       "9423   Frederick Cole  Delivery Truck         Consumer        Furniture  ...   \n",
       "9424    Tammy Buckley     Express Air         Consumer        Furniture  ...   \n",
       "9425    Tammy Buckley     Express Air         Consumer  Office Supplies  ...   \n",
       "\n",
       "       Region State or Province        City  Postal Code Order Date  \\\n",
       "0     Central          Illinois     Addison        60101 2012-05-28   \n",
       "1        West        Washington   Anacortes        98221 2010-07-07   \n",
       "2        West        Washington   Anacortes        98221 2011-07-27   \n",
       "3        West        Washington   Anacortes        98221 2011-07-27   \n",
       "4        West        Washington   Anacortes        98221 2011-07-27   \n",
       "...       ...               ...         ...          ...        ...   \n",
       "9421     East     West Virginia  Charleston        25314 2013-05-14   \n",
       "9422     East     West Virginia  Charleston        25314 2013-05-14   \n",
       "9423     East     West Virginia  Charleston        25314 2013-09-12   \n",
       "9424     West           Wyoming    Cheyenne        82001 2010-02-08   \n",
       "9425     West           Wyoming    Cheyenne        82001 2013-09-12   \n",
       "\n",
       "      Ship Date       Profit  Quantity ordered new    Sales Order ID  \n",
       "0    2012-05-30     1.320000                     2     5.90    88525  \n",
       "1    2010-07-08     4.560000                     4    13.01    88522  \n",
       "2    2011-07-28   -47.640000                     7    49.92    88523  \n",
       "3    2011-07-28   -30.510000                     7    41.64    88523  \n",
       "4    2011-07-27   998.202300                     8  1446.67    88523  \n",
       "...         ...          ...                   ...      ...      ...  \n",
       "9421 2013-05-15   137.860000                    13   447.87    87532  \n",
       "9422 2013-05-14   -39.070000                     3    13.23    87532  \n",
       "9423 2013-09-14 -1225.029097                     4  2215.93    87533  \n",
       "9424 2010-02-11   349.485000                     5   506.50    87530  \n",
       "9425 2013-09-14    78.062400                    23   172.48    87533  \n",
       "\n",
       "[9426 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12d7bdb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the second sheet in the 'Superstore_USA' dataset called Returns\n",
    "\n",
    "returns = pd.read_excel(r'C:\\Users\\Computers\\Downloads\\Superstore_USA.xlsx',sheet_name = \"Returns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfa11d51",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Order ID</th>\n",
       "      <th>Status</th>\n",
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       "      <th>3</th>\n",
       "      <td>678</td>\n",
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       "      <th>4</th>\n",
       "      <td>710</td>\n",
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       "      <th>...</th>\n",
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       "<p>1634 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order ID    Status\n",
       "0           65  Returned\n",
       "1          612  Returned\n",
       "2          614  Returned\n",
       "3          678  Returned\n",
       "4          710  Returned\n",
       "...        ...       ...\n",
       "1629    182681  Returned\n",
       "1630    182683  Returned\n",
       "1631    182750  Returned\n",
       "1632    182781  Returned\n",
       "1633    182906  Returned\n",
       "\n",
       "[1634 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    "returns"
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   "cell_type": "code",
   "execution_count": 6,
   "id": "2c019467",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the third sheet in the 'Superstore_USA' dataset called Users\n",
    "\n",
    "users = pd.read_excel(r'C:\\Users\\Computers\\Downloads\\Superstore_USA.xlsx',sheet_name = \"Users\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56c5c68b",
   "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>Region</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East</td>\n",
       "      <td>Erin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>Sam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>William</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Region  Manager\n",
       "0  Central    Chris\n",
       "1     East     Erin\n",
       "2    South      Sam\n",
       "3     West  William"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b20c488",
   "metadata": {},
   "source": [
    "#### 2. While loading this data you dont have to create a table manually you can use any automated approach to create a table and load a data in bulk in table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "754d9000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To bulk load data in MySQ, we need to follow the process which is given below:\n",
    "\n",
    "# First to load excel file directly into database, we have to convert it into CSV file format. \n",
    "\n",
    "# Then do as follows:\n",
    "\n",
    "# STEP 1 : Right click on Tables  \n",
    "# STEP 2 : Select \"Table Data Import Wizard\"\n",
    "# STEP 3 : Select the dataset which we have to load (here, it is 'SuperStore_USA.csv') and press next.\n",
    "# STEP 4 : Select create new table and give the table name and press next.\n",
    "# STEP 5 : Select the required conditions and press next. The data is loaded Successfully."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e2713f",
   "metadata": {},
   "source": [
    "#### 3. Find out how much return that we have recived and with a product id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43d6462d",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_returns = pd.merge(orders, returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de90cb51",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame(orders_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6548178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_returns.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "999ad0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      9895\n",
       "1     13959\n",
       "2     13959\n",
       "3     36038\n",
       "4     39490\n",
       "      ...  \n",
       "93     7107\n",
       "94     7107\n",
       "95    42823\n",
       "96    13638\n",
       "97    47109\n",
       "Name: Order ID, Length: 98, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[(d['Status']=='Returned')]['Order ID'] \n",
    "# Using 'Order ID' since there is no column named as 'Product ID' available in the given dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "de561cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d[(d['Status']=='Returned')]['Order ID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bda7539",
   "metadata": {},
   "source": [
    "#### 4. Try to join order and return data both in sql and pandas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "ab6e266a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>...</th>\n",
       "      <th>City</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity ordered new</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>difference_date</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1359</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.85</td>\n",
       "      <td>2.27</td>\n",
       "      <td>21</td>\n",
       "      <td>Tony Wilkins Winters</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10012</td>\n",
       "      <td>2011-04-20</td>\n",
       "      <td>2011-04-24</td>\n",
       "      <td>-6.8200</td>\n",
       "      <td>9</td>\n",
       "      <td>54.79</td>\n",
       "      <td>9895</td>\n",
       "      <td>-4 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.91</td>\n",
       "      <td>0.50</td>\n",
       "      <td>117</td>\n",
       "      <td>Linda Weiss</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>98103</td>\n",
       "      <td>2010-04-04</td>\n",
       "      <td>2010-04-06</td>\n",
       "      <td>112.0600</td>\n",
       "      <td>47</td>\n",
       "      <td>228.46</td>\n",
       "      <td>13959</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1951</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0.09</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.30</td>\n",
       "      <td>117</td>\n",
       "      <td>Linda Weiss</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>98103</td>\n",
       "      <td>2010-04-04</td>\n",
       "      <td>2010-04-06</td>\n",
       "      <td>16.7900</td>\n",
       "      <td>19</td>\n",
       "      <td>77.61</td>\n",
       "      <td>13959</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5055</td>\n",
       "      <td>High</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.28</td>\n",
       "      <td>5.00</td>\n",
       "      <td>117</td>\n",
       "      <td>Linda Weiss</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>98103</td>\n",
       "      <td>2013-08-16</td>\n",
       "      <td>2013-08-18</td>\n",
       "      <td>-89.0600</td>\n",
       "      <td>52</td>\n",
       "      <td>190.52</td>\n",
       "      <td>36038</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5573</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.02</td>\n",
       "      <td>11.97</td>\n",
       "      <td>5.81</td>\n",
       "      <td>272</td>\n",
       "      <td>Eleanor Swain</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>28204</td>\n",
       "      <td>2013-08-12</td>\n",
       "      <td>2013-08-12</td>\n",
       "      <td>-81.9413</td>\n",
       "      <td>76</td>\n",
       "      <td>912.06</td>\n",
       "      <td>39490</td>\n",
       "      <td>0 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>986</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.06</td>\n",
       "      <td>37.94</td>\n",
       "      <td>5.08</td>\n",
       "      <td>3075</td>\n",
       "      <td>Gordon Brandt</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90061</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>-33.9680</td>\n",
       "      <td>3</td>\n",
       "      <td>113.14</td>\n",
       "      <td>7107</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>987</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.10</td>\n",
       "      <td>55.29</td>\n",
       "      <td>5.08</td>\n",
       "      <td>3075</td>\n",
       "      <td>Gordon Brandt</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90061</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>163.1200</td>\n",
       "      <td>35</td>\n",
       "      <td>1886.52</td>\n",
       "      <td>7107</td>\n",
       "      <td>-1 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6041</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.28</td>\n",
       "      <td>6.18</td>\n",
       "      <td>3075</td>\n",
       "      <td>Gordon Brandt</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90061</td>\n",
       "      <td>2013-06-25</td>\n",
       "      <td>2013-06-27</td>\n",
       "      <td>-22.5600</td>\n",
       "      <td>12</td>\n",
       "      <td>73.71</td>\n",
       "      <td>42823</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1910</td>\n",
       "      <td>High</td>\n",
       "      <td>0.05</td>\n",
       "      <td>14.48</td>\n",
       "      <td>1.99</td>\n",
       "      <td>3079</td>\n",
       "      <td>Andrew Levine</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Technology</td>\n",
       "      <td>...</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>19112</td>\n",
       "      <td>2012-09-30</td>\n",
       "      <td>2012-10-02</td>\n",
       "      <td>165.2000</td>\n",
       "      <td>96</td>\n",
       "      <td>1337.67</td>\n",
       "      <td>13638</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6625</td>\n",
       "      <td>High</td>\n",
       "      <td>0.06</td>\n",
       "      <td>128.24</td>\n",
       "      <td>12.65</td>\n",
       "      <td>3251</td>\n",
       "      <td>Peter Brooks</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>...</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10112</td>\n",
       "      <td>2012-09-11</td>\n",
       "      <td>2012-09-13</td>\n",
       "      <td>1844.9600</td>\n",
       "      <td>92</td>\n",
       "      <td>12002.16</td>\n",
       "      <td>47109</td>\n",
       "      <td>-2 days</td>\n",
       "      <td>Returned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Row ID Order Priority  Discount  Unit Price  Shipping Cost  Customer ID  \\\n",
       "0     1359            Low      0.05        5.85           2.27           21   \n",
       "1     1950         Medium      0.01        4.91           0.50          117   \n",
       "2     1951         Medium      0.09        4.00           1.30          117   \n",
       "3     5055           High      0.02        3.28           5.00          117   \n",
       "4     5573            Low      0.02       11.97           5.81          272   \n",
       "..     ...            ...       ...         ...            ...          ...   \n",
       "93     986  Not Specified      0.06       37.94           5.08         3075   \n",
       "94     987  Not Specified      0.10       55.29           5.08         3075   \n",
       "95    6041         Medium      0.05        4.28           6.18         3075   \n",
       "96    1910           High      0.05       14.48           1.99         3079   \n",
       "97    6625           High      0.06      128.24          12.65         3251   \n",
       "\n",
       "           Customer Name    Ship Mode Customer Segment Product Category  ...  \\\n",
       "0   Tony Wilkins Winters  Regular Air   Small Business  Office Supplies  ...   \n",
       "1            Linda Weiss  Regular Air      Home Office  Office Supplies  ...   \n",
       "2            Linda Weiss  Express Air      Home Office  Office Supplies  ...   \n",
       "3            Linda Weiss  Regular Air      Home Office  Office Supplies  ...   \n",
       "4          Eleanor Swain  Regular Air   Small Business  Office Supplies  ...   \n",
       "..                   ...          ...              ...              ...  ...   \n",
       "93         Gordon Brandt  Regular Air        Corporate  Office Supplies  ...   \n",
       "94         Gordon Brandt  Regular Air        Corporate  Office Supplies  ...   \n",
       "95         Gordon Brandt  Express Air        Corporate  Office Supplies  ...   \n",
       "96         Andrew Levine  Regular Air         Consumer       Technology  ...   \n",
       "97          Peter Brooks  Regular Air   Small Business        Furniture  ...   \n",
       "\n",
       "             City Postal Code Order Date  Ship Date     Profit  \\\n",
       "0   New York City       10012 2011-04-20 2011-04-24    -6.8200   \n",
       "1         Seattle       98103 2010-04-04 2010-04-06   112.0600   \n",
       "2         Seattle       98103 2010-04-04 2010-04-06    16.7900   \n",
       "3         Seattle       98103 2013-08-16 2013-08-18   -89.0600   \n",
       "4       Charlotte       28204 2013-08-12 2013-08-12   -81.9413   \n",
       "..            ...         ...        ...        ...        ...   \n",
       "93    Los Angeles       90061 2013-01-04 2013-01-06   -33.9680   \n",
       "94    Los Angeles       90061 2013-01-04 2013-01-05   163.1200   \n",
       "95    Los Angeles       90061 2013-06-25 2013-06-27   -22.5600   \n",
       "96   Philadelphia       19112 2012-09-30 2012-10-02   165.2000   \n",
       "97  New York City       10112 2012-09-11 2012-09-13  1844.9600   \n",
       "\n",
       "   Quantity ordered new     Sales  Order ID difference_date    Status  \n",
       "0                     9     54.79      9895         -4 days  Returned  \n",
       "1                    47    228.46     13959         -2 days  Returned  \n",
       "2                    19     77.61     13959         -2 days  Returned  \n",
       "3                    52    190.52     36038         -2 days  Returned  \n",
       "4                    76    912.06     39490          0 days  Returned  \n",
       "..                  ...       ...       ...             ...       ...  \n",
       "93                    3    113.14      7107         -2 days  Returned  \n",
       "94                   35   1886.52      7107         -1 days  Returned  \n",
       "95                   12     73.71     42823         -2 days  Returned  \n",
       "96                   96   1337.67     13638         -2 days  Returned  \n",
       "97                   92  12002.16     47109         -2 days  Returned  \n",
       "\n",
       "[98 rows x 26 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joining order and return data in pandas\n",
    "\n",
    "pd.merge(orders, returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fd1c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# joining order and return data in sql\n",
    "\n",
    "select * from orders INNER JOIN returns ON orders.Order ID = returns.order ID;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e65114",
   "metadata": {},
   "source": [
    "#### 5. Try to find out how many unique customer that we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6291493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2,    3,    5, ..., 3400, 3402, 3403], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Customer ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba5a43b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2703"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders['Customer ID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba9f161",
   "metadata": {},
   "source": [
    "#### 6. Try to find out in how many regions we are selling a product and who is a manager for a respective region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3fc3d7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Central', 'East', 'South', 'West'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4a8634f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(users['Region'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "0cab5069",
   "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>Region</th>\n",
       "      <th>Manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East</td>\n",
       "      <td>Erin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>Sam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>William</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Region  Manager\n",
       "0  Central    Chris\n",
       "1     East     Erin\n",
       "2    South      Sam\n",
       "3     West  William"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60673313",
   "metadata": {},
   "source": [
    "#### 7. Find out how many different differnet shipement mode that we have and what is a percentage usablity of all the shipment mode with respect to dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e94ab687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Regular Air', 'Express Air', 'Delivery Truck'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Ship Mode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e133ba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders['Ship Mode'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f1ef1dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regular Air       7036\n",
       "Delivery Truck    1283\n",
       "Express Air       1107\n",
       "Name: Ship Mode, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Ship Mode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ec37b154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regular Air       7036\n",
       "Delivery Truck    1283\n",
       "Express Air       1107\n",
       "Name: Ship Mode, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "04a34d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9426"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0a964688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.26"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.sum()/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d56a4e",
   "metadata": {},
   "source": [
    "#### 8. Create a new coulmn and try to find our a diffrence between order date and shipment date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b2758bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d568d0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders['difference_date'] = orders['Order Date'] - orders['Ship Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "028491ea",
   "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>Row ID</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>...</th>\n",
       "      <th>State or Province</th>\n",
       "      <th>City</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity ordered new</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>difference_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18606</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>Janice Fletcher</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Addison</td>\n",
       "      <td>60101</td>\n",
       "      <td>2012-05-28</td>\n",
       "      <td>2012-05-30</td>\n",
       "      <td>1.3200</td>\n",
       "      <td>2</td>\n",
       "      <td>5.90</td>\n",
       "      <td>88525</td>\n",
       "      <td>-2 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20847</td>\n",
       "      <td>High</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.93</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>2010-07-08</td>\n",
       "      <td>4.5600</td>\n",
       "      <td>4</td>\n",
       "      <td>13.01</td>\n",
       "      <td>88522</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23086</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.03</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.15</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-28</td>\n",
       "      <td>-47.6400</td>\n",
       "      <td>7</td>\n",
       "      <td>49.92</td>\n",
       "      <td>88523</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23087</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Regular Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>...</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-28</td>\n",
       "      <td>-30.5100</td>\n",
       "      <td>7</td>\n",
       "      <td>41.64</td>\n",
       "      <td>88523</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23088</td>\n",
       "      <td>Not Specified</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.99</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Bonnie Potter</td>\n",
       "      <td>Express Air</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Technology</td>\n",
       "      <td>...</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>98221</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>998.2023</td>\n",
       "      <td>8</td>\n",
       "      <td>1446.67</td>\n",
       "      <td>88523</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID Order Priority  Discount  Unit Price  Shipping Cost  Customer ID  \\\n",
       "0   18606  Not Specified      0.01        2.88           0.50            2   \n",
       "1   20847           High      0.01        2.84           0.93            3   \n",
       "2   23086  Not Specified      0.03        6.68           6.15            3   \n",
       "3   23087  Not Specified      0.01        5.68           3.60            3   \n",
       "4   23088  Not Specified      0.00      205.99           2.50            3   \n",
       "\n",
       "     Customer Name    Ship Mode Customer Segment Product Category  ...  \\\n",
       "0  Janice Fletcher  Regular Air        Corporate  Office Supplies  ...   \n",
       "1    Bonnie Potter  Express Air        Corporate  Office Supplies  ...   \n",
       "2    Bonnie Potter  Express Air        Corporate  Office Supplies  ...   \n",
       "3    Bonnie Potter  Regular Air        Corporate  Office Supplies  ...   \n",
       "4    Bonnie Potter  Express Air        Corporate       Technology  ...   \n",
       "\n",
       "  State or Province       City Postal Code  Order Date  Ship Date    Profit  \\\n",
       "0          Illinois    Addison       60101  2012-05-28 2012-05-30    1.3200   \n",
       "1        Washington  Anacortes       98221  2010-07-07 2010-07-08    4.5600   \n",
       "2        Washington  Anacortes       98221  2011-07-27 2011-07-28  -47.6400   \n",
       "3        Washington  Anacortes       98221  2011-07-27 2011-07-28  -30.5100   \n",
       "4        Washington  Anacortes       98221  2011-07-27 2011-07-27  998.2023   \n",
       "\n",
       "  Quantity ordered new    Sales Order ID difference_date  \n",
       "0                    2     5.90    88525         -2 days  \n",
       "1                    4    13.01    88522         -1 days  \n",
       "2                    7    49.92    88523         -1 days  \n",
       "3                    7    41.64    88523         -1 days  \n",
       "4                    8  1446.67    88523          0 days  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "bdc5938d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -2 days\n",
       "1      -1 days\n",
       "2      -1 days\n",
       "3      -1 days\n",
       "4       0 days\n",
       "         ...  \n",
       "9421   -1 days\n",
       "9422    0 days\n",
       "9423   -2 days\n",
       "9424   -3 days\n",
       "9425   -2 days\n",
       "Name: difference_date, Length: 9426, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['difference_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c746d2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631bb564",
   "metadata": {},
   "source": [
    "#### 9. Based on question number 8 find out for which order id we have shipment duration more than 10 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "20f78b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "shipment_duration = orders['difference_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "150c68d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -2 days\n",
       "1      -1 days\n",
       "2      -1 days\n",
       "3      -1 days\n",
       "4       0 days\n",
       "         ...  \n",
       "9421   -1 days\n",
       "9422    0 days\n",
       "9423   -2 days\n",
       "9424   -3 days\n",
       "9425   -2 days\n",
       "Name: difference_date, Length: 9426, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shipment_duration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "40e5b82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(shipment_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83e1a0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders[(orders['difference_date'].dt.components['days']>10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "678b9fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders[(orders['difference_date'].dt.components['days']>10)]['OrderID']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650ae4c2",
   "metadata": {},
   "source": [
    "#### 10. Try to find out a list of a returned order which shipment duration was more then 15 days and find out that region manager as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "08e9a3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_returns.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "016e4c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_returns1 = pd.merge(orders,returns, how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "be80a053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date',\n",
       "       'Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_returns1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "0c244f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Returned\n",
       "1     Returned\n",
       "2     Returned\n",
       "3     Returned\n",
       "4     Returned\n",
       "        ...   \n",
       "93    Returned\n",
       "94    Returned\n",
       "95    Returned\n",
       "96    Returned\n",
       "97    Returned\n",
       "Name: Status, Length: 98, dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_returns1['Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d68e21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_returns1[(orders_returns1['difference_date'].dt.components['days']>15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "71681305",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_users = pd.merge(orders, users, how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "bf65ae89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date',\n",
       "       'Manager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_users.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "bf4589cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chris', 'William', 'Erin', 'Sam'], dtype=object)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_users['Manager'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a59e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_users[(orders_users['difference_date'].dt.components['days']>15)]['Manager']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7608a4",
   "metadata": {},
   "source": [
    "#### 11. Gorup by region and find out which region is more profitable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4800395d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b2b5e6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID                            int64\n",
       "Order Priority                   object\n",
       "Discount                        float64\n",
       "Unit Price                      float64\n",
       "Shipping Cost                   float64\n",
       "Customer ID                       int64\n",
       "Customer Name                    object\n",
       "Ship Mode                        object\n",
       "Customer Segment                 object\n",
       "Product Category                 object\n",
       "Product Sub-Category             object\n",
       "Product Container                object\n",
       "Product Name                     object\n",
       "Product Base Margin             float64\n",
       "Region                           object\n",
       "State or Province                object\n",
       "City                             object\n",
       "Postal Code                       int64\n",
       "Order Date               datetime64[ns]\n",
       "Ship Date                datetime64[ns]\n",
       "Profit                          float64\n",
       "Quantity ordered new              int64\n",
       "Sales                           float64\n",
       "Order ID                          int64\n",
       "difference_date         timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "65f2db8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Central    2899\n",
       "East       2289\n",
       "South      1954\n",
       "West       2284\n",
       "Name: Profit, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3582b779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>Region</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2899</td>\n",
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       "      <th>1</th>\n",
       "      <td>East</td>\n",
       "      <td>2289</td>\n",
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       "      <th>2</th>\n",
       "      <td>South</td>\n",
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       "      <th>3</th>\n",
       "      <td>West</td>\n",
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      ],
      "text/plain": [
       "    Region  Profit\n",
       "0  Central    2899\n",
       "1     East    2289\n",
       "2    South    1954\n",
       "3     West    2284"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d6b5b288",
   "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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       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
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       "      <th>3</th>\n",
       "      <td>West</td>\n",
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       "      <td>Central</td>\n",
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      "text/plain": [
       "    Region  Profit\n",
       "2    South    1954\n",
       "3     West    2284\n",
       "1     East    2289\n",
       "0  Central    2899"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count').reset_index().sort_values(\"Profit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "8df69b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>Region</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2899</td>\n",
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       "      <td>South</td>\n",
       "      <td>1954</td>\n",
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       "</div>"
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      "text/plain": [
       "    Region  Profit\n",
       "0  Central    2899\n",
       "1     East    2289\n",
       "3     West    2284\n",
       "2    South    1954"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count').reset_index().sort_values(\"Profit\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "cefa655c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Region  Profit\n",
       "0  Central    2899"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count').reset_index().sort_values(\"Profit\",ascending = False).iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "91928c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region    Central\n",
       "Profit       2899\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Profit'].aggregate('count').reset_index().sort_values(\"Profit\",ascending = False).iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5861de4a",
   "metadata": {},
   "source": [
    "#### 12. Try to find out overalll in which country we are giving more didscount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "ccb48263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Central    2899\n",
       "East       2289\n",
       "South      1954\n",
       "West       2284\n",
       "Name: Discount, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Discount'].aggregate('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5ff88059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Discount</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>2899</td>\n",
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      ],
      "text/plain": [
       "    Region  Discount\n",
       "0  Central      2899\n",
       "1     East      2289\n",
       "2    South      1954\n",
       "3     West      2284"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Discount'].aggregate('count').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "27fd6089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2899</td>\n",
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       "</table>\n",
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      "text/plain": [
       "    Region  Discount\n",
       "0  Central      2899"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Discount'].aggregate('count').reset_index().sort_values(\"Discount\",ascending = False).iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "68c9eaaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region      Central\n",
       "Discount       2899\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Region'])['Discount'].aggregate('count').reset_index().sort_values(\"Discount\",ascending = False).iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "d12f2d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01, 0.03, 0.  , 0.09, 0.08, 0.02, 0.07, 0.1 , 0.05, 0.06, 0.04,\n",
       "       0.11, 0.17, 0.16, 0.25, 0.21])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Discount'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "825760fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8649    Central\n",
       " Name: Region, dtype: object]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[orders[orders['Discount']>0.21]['Region']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa28bdc",
   "metadata": {},
   "source": [
    "#### 13. Give me a list of unique postal code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "e1538f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "96efef1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "postal_code_unique = orders['Postal Code'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "45b72d1f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       " 24541,\n",
       " ...]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(postal_code_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb99fc0",
   "metadata": {},
   "source": [
    "#### 14. Which customer segement is more profitalble find it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "61e3bccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer Segment\n",
       "Consumer          1894\n",
       "Corporate         3375\n",
       "Home Office       2316\n",
       "Small Business    1841\n",
       "Name: Profit, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Customer Segment'])['Profit'].aggregate('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4fe1ec08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Corporate</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home Office</td>\n",
       "      <td>2316</td>\n",
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       "      <th>3</th>\n",
       "      <td>Small Business</td>\n",
       "      <td>1841</td>\n",
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      ],
      "text/plain": [
       "  Customer Segment  Profit\n",
       "0         Consumer    1894\n",
       "1        Corporate    3375\n",
       "2      Home Office    2316\n",
       "3   Small Business    1841"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Customer Segment'])['Profit'].aggregate('count').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "2b96b88b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Corporate</td>\n",
       "      <td>3375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home Office</td>\n",
       "      <td>2316</td>\n",
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       "      <td>1894</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  Customer Segment  Profit\n",
       "1        Corporate    3375\n",
       "2      Home Office    2316\n",
       "0         Consumer    1894\n",
       "3   Small Business    1841"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Customer Segment'])['Profit'].aggregate('count').reset_index().sort_values('Profit', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "45320489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Corporate</td>\n",
       "      <td>3375</td>\n",
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       "  Customer Segment  Profit\n",
       "1        Corporate    3375"
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     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby(['Customer Segment'])['Profit'].aggregate('count').reset_index().sort_values('Profit', ascending = False).iloc[[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09ab401",
   "metadata": {},
   "source": [
    "#### 15. Try to find out the 10th most loss making product catagory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "8b25600a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "fe10c443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       Office Supplies\n",
       "3       Office Supplies\n",
       "8       Office Supplies\n",
       "10      Office Supplies\n",
       "14            Furniture\n",
       "             ...       \n",
       "9415         Technology\n",
       "9418    Office Supplies\n",
       "9420          Furniture\n",
       "9422    Office Supplies\n",
       "9423          Furniture\n",
       "Name: Product Category, Length: 4558, dtype: object"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['Profit']<0]['Product Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d11a26cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Office Supplies'"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['Profit']<0]['Product Category'].iloc[9]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7edd5a4",
   "metadata": {},
   "source": [
    "#### 16. Try to find out 10 top  product with highest margins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "ac59d015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order Priority', 'Discount', 'Unit Price', 'Shipping Cost',\n",
       "       'Customer ID', 'Customer Name', 'Ship Mode', 'Customer Segment',\n",
       "       'Product Category', 'Product Sub-Category', 'Product Container',\n",
       "       'Product Name', 'Product Base Margin', 'Region', 'State or Province',\n",
       "       'City', 'Postal Code', 'Order Date', 'Ship Date', 'Profit',\n",
       "       'Quantity ordered new', 'Sales', 'Order ID', 'difference_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "4995dba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2431              GBC DocuBind 200 Manual Binding Machine\n",
       "8248       Peel & Seel® Recycled Catalog Envelopes, Brown\n",
       "1515       Peel & Seel® Recycled Catalog Envelopes, Brown\n",
       "5050       Peel & Seel® Recycled Catalog Envelopes, Brown\n",
       "413                                           Accessory31\n",
       "5034                          Prang Dustless Chalk Sticks\n",
       "8308         Staples Brown Kraft Recycled Clasp Envelopes\n",
       "8311     Storex DuraTech Recycled Plastic Frosted Binders\n",
       "8318                                          Accessory31\n",
       "1553    Adams Telephone Message Book w/Frequently-Call...\n",
       "Name: Product Name, dtype: object"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.sort_values(['Product Base Margin'])['Product Name'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "ee1f77f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9145                                          Accessory27\n",
       "4756                                           Accessory4\n",
       "7607                                          Accessory27\n",
       "5672                                           Accessory4\n",
       "4168                    Sterling Rubber Bands by Alliance\n",
       "807                                           Accessory27\n",
       "816                                           Accessory27\n",
       "3994    Office Impressions Heavy Duty Welded Shelving ...\n",
       "2138                    Sterling Rubber Bands by Alliance\n",
       "7202    Office Impressions Heavy Duty Welded Shelving ...\n",
       "Name: Product Name, dtype: object"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.sort_values(['Product Base Margin'], ascending = False)['Product Name'].head(10)"
   ]
  }
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