analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_state_to_abbrev = {\n",
    "    \"Alabama\": \"AL\",\n",
    "    \"Alaska\": \"AK\",\n",
    "    \"Arizona\": \"AZ\",\n",
    "    \"Arkansas\": \"AR\",\n",
    "    \"California\": \"CA\",\n",
    "    \"Colorado\": \"CO\",\n",
    "    \"Connecticut\": \"CT\",\n",
    "    \"Delaware\": \"DE\",\n",
    "    \"Florida\": \"FL\",\n",
    "    \"Georgia\": \"GA\",\n",
    "    \"Hawaii\": \"HI\",\n",
    "    \"Idaho\": \"ID\",\n",
    "    \"Illinois\": \"IL\",\n",
    "    \"Indiana\": \"IN\",\n",
    "    \"Iowa\": \"IA\",\n",
    "    \"Kansas\": \"KS\",\n",
    "    \"Kentucky\": \"KY\",\n",
    "    \"Louisiana\": \"LA\",\n",
    "    \"Maine\": \"ME\",\n",
    "    \"Maryland\": \"MD\",\n",
    "    \"Massachusetts\": \"MA\",\n",
    "    \"Michigan\": \"MI\",\n",
    "    \"Minnesota\": \"MN\",\n",
    "    \"Mississippi\": \"MS\",\n",
    "    \"Missouri\": \"MO\",\n",
    "    \"Montana\": \"MT\",\n",
    "    \"Nebraska\": \"NE\",\n",
    "    \"Nevada\": \"NV\",\n",
    "    \"New Hampshire\": \"NH\",\n",
    "    \"New Jersey\": \"NJ\",\n",
    "    \"New Mexico\": \"NM\",\n",
    "    \"New York\": \"NY\",\n",
    "    \"North Carolina\": \"NC\",\n",
    "    \"North Dakota\": \"ND\",\n",
    "    \"Ohio\": \"OH\",\n",
    "    \"Oklahoma\": \"OK\",\n",
    "    \"Oregon\": \"OR\",\n",
    "    \"Pennsylvania\": \"PA\",\n",
    "    \"Rhode Island\": \"RI\",\n",
    "    \"South Carolina\": \"SC\",\n",
    "    \"South Dakota\": \"SD\",\n",
    "    \"Tennessee\": \"TN\",\n",
    "    \"Texas\": \"TX\",\n",
    "    \"Utah\": \"UT\",\n",
    "    \"Vermont\": \"VT\",\n",
    "    \"Virginia\": \"VA\",\n",
    "    \"Washington\": \"WA\",\n",
    "    \"West Virginia\": \"WV\",\n",
    "    \"Wisconsin\": \"WI\",\n",
    "    \"Wyoming\": \"WY\",\n",
    "    \"District of Columbia\": \"DC\",\n",
    "    \"American Samoa\": \"AS\",\n",
    "    \"Guam\": \"GU\",\n",
    "    \"Northern Mariana Islands\": \"MP\",\n",
    "    \"Puerto Rico\": \"PR\",\n",
    "    \"United States Minor Outlying Islands\": \"UM\",\n",
    "    \"U.S. Virgin Islands\": \"VI\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to show all rows and columns \n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the csv into a pandas dataframe\n",
    "df = pd.read_csv('Data/Sample-Superstore.csv', encoding='windows-1254')\n",
    "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
    "df['Ship Date'] = pd.to_datetime(df['Ship Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alabama 19510.639999999992\n",
      "Arizona 35282.001\n",
      "Arkansas 11678.129999999997\n",
      "California 457687.631500001\n",
      "Colorado 32108.117999999995\n",
      "Connecticut 13384.356999999996\n",
      "Delaware 27451.068999999992\n",
      "District of Columbia 2865.0199999999995\n",
      "Florida 89473.708\n",
      "Georgia 49095.840000000004\n",
      "Idaho 4382.486000000002\n",
      "Illinois 80166.10099999986\n",
      "Indiana 53555.36\n",
      "Iowa 4579.759999999999\n",
      "Kansas 2914.31\n",
      "Kentucky 36591.74999999997\n",
      "Louisiana 9217.029999999999\n",
      "Maine 1270.5300000000002\n",
      "Maryland 23705.523\n",
      "Massachusetts 28634.433999999994\n",
      "Michigan 76269.61400000002\n",
      "Minnesota 29863.149999999994\n",
      "Mississippi 10771.34\n",
      "Missouri 22205.149999999998\n",
      "Montana 5589.351999999997\n",
      "Nebraska 7464.9299999999985\n",
      "Nevada 16729.102\n",
      "New Hampshire 7292.523999999999\n",
      "New Jersey 35764.31200000001\n",
      "New Mexico 4783.521999999999\n",
      "New York 310876.2709999998\n",
      "North Carolina 55603.16399999997\n",
      "North Dakota 919.91\n",
      "Ohio 78258.13599999993\n",
      "Oklahoma 19683.39\n",
      "Oregon 17431.14999999999\n",
      "Pennsylvania 116511.91400000003\n",
      "Rhode Island 22627.955999999995\n",
      "South Carolina 8481.71\n",
      "South Dakota 1315.5600000000002\n",
      "Tennessee 30661.87299999998\n",
      "Texas 170188.04580000002\n",
      "Utah 11220.055999999999\n",
      "Vermont 8929.369999999999\n",
      "Virginia 70636.71999999999\n",
      "Washington 138641.26999999993\n",
      "West Virginia 1209.824\n",
      "Wisconsin 32114.61000000002\n",
      "Wyoming 1603.136\n"
     ]
    }
   ],
   "source": [
    "for x, data in df.groupby([\"State\"]):\n",
    "    print(x, sum(data[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Unit_Price\"] = round(df[\"Sales\"] / df[\"Quantity\"] / (1-df[\"Discount\"]), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequently used Product_ID: OFF-PA-10001970\n",
      "Count: 19\n"
     ]
    }
   ],
   "source": [
    "most_used_product = None\n",
    "most_used_count = 0\n",
    "\n",
    "for pid, data in df.groupby(\"Product ID\"):\n",
    "    group_size = len(data)\n",
    "    if group_size > most_used_count:\n",
    "        most_used_product = pid\n",
    "        most_used_count = group_size\n",
    "\n",
    "print(\"Most frequently used Product_ID:\", most_used_product)\n",
    "print(\"Count:\", most_used_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for productID, data in df.groupby(\"Product ID\"):\n",
    "    if len(data[\"Product Name\"].drop_duplicates()) > 1:\n",
    "        for productName, data2 in data.groupby(\"Product Name\"):\n",
    "            #print(productID, min(data2[\"Order Date\"]), max(data2[\"Order Date\"]), productName)\n",
    "            if len(data2[\"Unit_Price\"].drop_duplicates()) > 1:\n",
    "                print(productID)\n",
    "        #     display(data[[\"Product ID\", \"Category\", \"Sub-Category\", \"Product Name\", \"Unit_Price\"]].drop_duplicates())\n",
    "        #     i += 1\n",
    "#        break\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1894\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for productID, data in df.groupby(\"Product ID\"):\n",
    "    if len(data[\"Product Name\"].drop_duplicates()) > 1:\n",
    "        # print(productID)\n",
    "        # display(data[[\"Product ID\", \"Category\", \"Sub-Category\", \"Product Name\", \"Unit_Price\"]].drop_duplicates())\n",
    "        i += 2\n",
    "#        break\n",
    "    else:\n",
    "        i += 1\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1374\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for x, data in df.groupby([\"Category\", \"Sub-Category\", \"Unit_Price\"]):\n",
    "    i += 1\n",
    "\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Unit_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>351</td>\n",
       "      <td>CA-2016-129714</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2016-09-03</td>\n",
       "      <td>First Class</td>\n",
       "      <td>AB-10060</td>\n",
       "      <td>Adam Bellavance</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10009</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>24.560</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5432</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>353</td>\n",
       "      <td>CA-2016-129714</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2016-09-03</td>\n",
       "      <td>First Class</td>\n",
       "      <td>AB-10060</td>\n",
       "      <td>Adam Bellavance</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10009</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>49.120</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0864</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>412</td>\n",
       "      <td>CA-2017-117457</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>KH-16510</td>\n",
       "      <td>Keith Herrera</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>94110</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>55.980</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.4302</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>438</td>\n",
       "      <td>CA-2016-147375</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19180</td>\n",
       "      <td>Philisse Overcash</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>60623</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>313.488</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>113.6394</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>1072</td>\n",
       "      <td>CA-2016-152814</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-14005</td>\n",
       "      <td>Erica Hernandez</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>80219</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>29.472</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.9468</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>1118</td>\n",
       "      <td>CA-2015-142755</td>\n",
       "      <td>2015-09-04</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12355</td>\n",
       "      <td>Christine Sundaresam</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Roswell</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>30076</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>279.900</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.1510</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>1192</td>\n",
       "      <td>CA-2017-117212</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>BT-11530</td>\n",
       "      <td>Bradley Talbott</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>223.920</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.7208</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663</th>\n",
       "      <td>3664</td>\n",
       "      <td>US-2017-129777</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>FM-14290</td>\n",
       "      <td>Frank Merwin</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Quincy</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>2169</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>24.560</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5432</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3850</th>\n",
       "      <td>3851</td>\n",
       "      <td>CA-2015-142377</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>2015-12-09</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MS-17980</td>\n",
       "      <td>Michael Stewart</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>65807</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>85.960</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.4012</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>3950</td>\n",
       "      <td>CA-2016-119963</td>\n",
       "      <td>2016-11-18</td>\n",
       "      <td>2016-11-22</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SN-20710</td>\n",
       "      <td>Steve Nguyen</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77506</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>19.648</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.6312</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>5283</td>\n",
       "      <td>CA-2015-105158</td>\n",
       "      <td>2015-09-05</td>\n",
       "      <td>2015-09-10</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SP-20860</td>\n",
       "      <td>Sung Pak</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>55901</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>36.840</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.3148</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6334</th>\n",
       "      <td>6335</td>\n",
       "      <td>CA-2014-133305</td>\n",
       "      <td>2014-07-11</td>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MH-17455</td>\n",
       "      <td>Mark Hamilton</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10011</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>49.120</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0864</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6508</th>\n",
       "      <td>6509</td>\n",
       "      <td>CA-2017-102197</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DK-13150</td>\n",
       "      <td>David Kendrick</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10009</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>279.900</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.1510</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7896</th>\n",
       "      <td>7897</td>\n",
       "      <td>CA-2017-124744</td>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-14125</td>\n",
       "      <td>Eugene Hildebrand</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Wheeling</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>26003</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>447.840</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.4416</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8060</th>\n",
       "      <td>8061</td>\n",
       "      <td>CA-2015-156013</td>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TC-20980</td>\n",
       "      <td>Tamara Chand</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Long Beach</td>\n",
       "      <td>New York</td>\n",
       "      <td>11561</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>61.400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.8580</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8134</th>\n",
       "      <td>8135</td>\n",
       "      <td>CA-2015-121776</td>\n",
       "      <td>2015-02-16</td>\n",
       "      <td>2015-02-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RD-19585</td>\n",
       "      <td>Rob Dowd</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90008</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>36.840</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.3148</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9183</th>\n",
       "      <td>9184</td>\n",
       "      <td>CA-2017-156776</td>\n",
       "      <td>2017-08-07</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JL-15505</td>\n",
       "      <td>Jeremy Lonsdale</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>24.560</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5432</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9387</th>\n",
       "      <td>9388</td>\n",
       "      <td>US-2017-127292</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>5408</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>12.280</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.7716</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9505</th>\n",
       "      <td>9506</td>\n",
       "      <td>CA-2016-113607</td>\n",
       "      <td>2016-10-14</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PW-19030</td>\n",
       "      <td>Pauline Webber</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10009</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1908</td>\n",
       "      <td>111.960</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.8604</td>\n",
       "      <td>55.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row ID        Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "350      351  CA-2016-129714 2016-09-01 2016-09-03     First Class   \n",
       "352      353  CA-2016-129714 2016-09-01 2016-09-03     First Class   \n",
       "411      412  CA-2017-117457 2017-12-08 2017-12-12  Standard Class   \n",
       "437      438  CA-2016-147375 2016-06-12 2016-06-14    Second Class   \n",
       "1071    1072  CA-2016-152814 2016-04-28 2016-05-02  Standard Class   \n",
       "1117    1118  CA-2015-142755 2015-09-04 2015-09-08  Standard Class   \n",
       "1191    1192  CA-2017-117212 2017-02-26 2017-02-28    Second Class   \n",
       "3663    3664  US-2017-129777 2017-07-03 2017-07-09  Standard Class   \n",
       "3850    3851  CA-2015-142377 2015-12-04 2015-12-09  Standard Class   \n",
       "3949    3950  CA-2016-119963 2016-11-18 2016-11-22  Standard Class   \n",
       "5282    5283  CA-2015-105158 2015-09-05 2015-09-10  Standard Class   \n",
       "6334    6335  CA-2014-133305 2014-07-11 2014-07-15  Standard Class   \n",
       "6508    6509  CA-2017-102197 2017-12-20 2017-12-26  Standard Class   \n",
       "7896    7897  CA-2017-124744 2017-06-21 2017-06-25  Standard Class   \n",
       "8060    8061  CA-2015-156013 2015-09-20 2015-09-26  Standard Class   \n",
       "8134    8135  CA-2015-121776 2015-02-16 2015-02-21  Standard Class   \n",
       "9183    9184  CA-2017-156776 2017-08-07 2017-08-11  Standard Class   \n",
       "9387    9388  US-2017-127292 2017-01-19 2017-01-23  Standard Class   \n",
       "9505    9506  CA-2016-113607 2016-10-14 2016-10-18  Standard Class   \n",
       "\n",
       "     Customer ID         Customer Name      Segment        Country  \\\n",
       "350     AB-10060       Adam Bellavance  Home Office  United States   \n",
       "352     AB-10060       Adam Bellavance  Home Office  United States   \n",
       "411     KH-16510         Keith Herrera     Consumer  United States   \n",
       "437     PO-19180     Philisse Overcash  Home Office  United States   \n",
       "1071    EH-14005       Erica Hernandez  Home Office  United States   \n",
       "1117    CS-12355  Christine Sundaresam     Consumer  United States   \n",
       "1191    BT-11530       Bradley Talbott  Home Office  United States   \n",
       "3663    FM-14290          Frank Merwin  Home Office  United States   \n",
       "3850    MS-17980       Michael Stewart    Corporate  United States   \n",
       "3949    SN-20710          Steve Nguyen  Home Office  United States   \n",
       "5282    SP-20860              Sung Pak    Corporate  United States   \n",
       "6334    MH-17455         Mark Hamilton     Consumer  United States   \n",
       "6508    DK-13150        David Kendrick    Corporate  United States   \n",
       "7896    EH-14125     Eugene Hildebrand  Home Office  United States   \n",
       "8060    TC-20980          Tamara Chand    Corporate  United States   \n",
       "8134    RD-19585              Rob Dowd     Consumer  United States   \n",
       "9183    JL-15505       Jeremy Lonsdale     Consumer  United States   \n",
       "9387    RM-19375         Raymond Messe     Consumer  United States   \n",
       "9505    PW-19030        Pauline Webber    Corporate  United States   \n",
       "\n",
       "               City          State  Postal Code   Region       Product ID  \\\n",
       "350   New York City       New York        10009     East  OFF-PA-10001970   \n",
       "352   New York City       New York        10009     East  OFF-PA-10001970   \n",
       "411   San Francisco     California        94110     West  OFF-PA-10001970   \n",
       "437         Chicago       Illinois        60623  Central  OFF-PA-10001970   \n",
       "1071         Denver       Colorado        80219     West  OFF-PA-10001970   \n",
       "1117        Roswell        Georgia        30076    South  OFF-PA-10001970   \n",
       "1191    Los Angeles     California        90036     West  OFF-PA-10001970   \n",
       "3663         Quincy  Massachusetts         2169     East  OFF-PA-10001970   \n",
       "3850    Springfield       Missouri        65807  Central  OFF-PA-10001970   \n",
       "3949       Pasadena          Texas        77506  Central  OFF-PA-10001970   \n",
       "5282      Rochester      Minnesota        55901  Central  OFF-PA-10001970   \n",
       "6334  New York City       New York        10011     East  OFF-PA-10001970   \n",
       "6508  New York City       New York        10009     East  OFF-PA-10001970   \n",
       "7896       Wheeling  West Virginia        26003     East  OFF-PA-10001970   \n",
       "8060     Long Beach       New York        11561     East  OFF-PA-10001970   \n",
       "8134    Los Angeles     California        90008     West  OFF-PA-10001970   \n",
       "9183    Westminster     California        92683     West  OFF-PA-10001970   \n",
       "9387     Burlington        Vermont         5408     East  OFF-PA-10001970   \n",
       "9505  New York City       New York        10009     East  OFF-PA-10001970   \n",
       "\n",
       "             Category Sub-Category Product Name    Sales  Quantity  Discount  \\\n",
       "350   Office Supplies        Paper   Xerox 1881   24.560         2       0.0   \n",
       "352   Office Supplies        Paper   Xerox 1881   49.120         4       0.0   \n",
       "411   Office Supplies        Paper   Xerox 1908   55.980         1       0.0   \n",
       "437   Office Supplies        Paper   Xerox 1908  313.488         7       0.2   \n",
       "1071  Office Supplies        Paper   Xerox 1881   29.472         3       0.2   \n",
       "1117  Office Supplies        Paper   Xerox 1908  279.900         5       0.0   \n",
       "1191  Office Supplies        Paper   Xerox 1908  223.920         4       0.0   \n",
       "3663  Office Supplies        Paper   Xerox 1881   24.560         2       0.0   \n",
       "3850  Office Supplies        Paper   Xerox 1881   85.960         7       0.0   \n",
       "3949  Office Supplies        Paper   Xerox 1881   19.648         2       0.2   \n",
       "5282  Office Supplies        Paper   Xerox 1881   36.840         3       0.0   \n",
       "6334  Office Supplies        Paper   Xerox 1881   49.120         4       0.0   \n",
       "6508  Office Supplies        Paper   Xerox 1908  279.900         5       0.0   \n",
       "7896  Office Supplies        Paper   Xerox 1908  447.840         8       0.0   \n",
       "8060  Office Supplies        Paper   Xerox 1881   61.400         5       0.0   \n",
       "8134  Office Supplies        Paper   Xerox 1881   36.840         3       0.0   \n",
       "9183  Office Supplies        Paper   Xerox 1881   24.560         2       0.0   \n",
       "9387  Office Supplies        Paper   Xerox 1881   12.280         1       0.0   \n",
       "9505  Office Supplies        Paper   Xerox 1908  111.960         2       0.0   \n",
       "\n",
       "        Profit  Unit_Price  \n",
       "350    11.5432       12.28  \n",
       "352    23.0864       12.28  \n",
       "411    27.4302       55.98  \n",
       "437   113.6394       55.98  \n",
       "1071    9.9468       12.28  \n",
       "1117  137.1510       55.98  \n",
       "1191  109.7208       55.98  \n",
       "3663   11.5432       12.28  \n",
       "3850   40.4012       12.28  \n",
       "3949    6.6312       12.28  \n",
       "5282   17.3148       12.28  \n",
       "6334   23.0864       12.28  \n",
       "6508  137.1510       55.98  \n",
       "7896  219.4416       55.98  \n",
       "8060   28.8580       12.28  \n",
       "8134   17.3148       12.28  \n",
       "9183   11.5432       12.28  \n",
       "9387    5.7716       12.28  \n",
       "9505   54.8604       55.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df[(df[\"Product ID\"] == \"OFF-PA-10001970\")]) #  & (df.Unit_Price == 35.44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1862,)\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Unit_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "      <td>130.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "      <td>243.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "      <td>348.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "      <td>13.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4        33311  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  Unit_Price  \n",
       "0      0.00   41.9136      130.98  \n",
       "1      0.00  219.5820      243.98  \n",
       "2      0.00    6.8714        7.31  \n",
       "3      0.45 -383.0310      348.21  \n",
       "4      0.20    2.5164       13.98  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[\"Product ID\"].value_counts().shape)\n",
    "df[\"Product ID\"].head()\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Superstore Data\n",
    "### Contains 51290 lines with 24 columns (+ Market, Shipping Cost, Order Priority)\n",
    "### Contains a additional sheet with 2033 returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the csv into a pandas dataframe\n",
    "df_g = pd.read_csv('Data/Global-Superstore-Orders.csv')\n",
    "df_g['Order Date'] = pd.to_datetime(df_g['Order Date'])\n",
    "df_g['Ship Date'] = pd.to_datetime(df_g['Ship Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12952\\1361090034.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf_g\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"State\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Sales\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "for x, data in df_g.groupby([\"State\"]):\n",
    "    print(x, sum(data[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "'Asir 1603.5\n",
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      "Abruzzi 6723.750000000002\n",
      "Abuja Capital Territory 38.016\n",
      "Aceh 337.5057\n",
      "Acre 701.98\n",
      "Ad Dawhah 6049.799999999999\n",
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      "Al Bahr Al Ahmar 2586.66\n",
      "Al Basrah 13257.210000000003\n",
      "Al Buhayrah 709.83\n",
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      "Alabama 19510.64\n",
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      "Arkansas 11678.129999999997\n",
      "Arkhangel'sk 2800.44\n",
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      "Attica 3129.582\n",
      "Auckland 43721.568\n",
      "Australian Capital Territory 33162.81\n",
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      "Ayacucho 608.304\n",
      "Aydin 1165.596\n",
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      "Babil 2009.43\n",
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      "Baden-Württemberg 46979.530499999986\n",
      "Baghdad 28867.619999999988\n",
      "Bahia 17598.15556000001\n",
      "Baja California 27013.103960000004\n",
      "Baja California Sur 593.3599999999999\n",
      "Baki 5631.5099999999975\n",
      "Balearic Islands 6530.714999999999\n",
      "Bali 6841.553100000003\n",
      "Balikesir 2065.5\n",
      "Balti 627.6\n",
      "Baluchistan 1901.19\n",
      "Bamako 6740.13\n",
      "Banaadir 3501.7799999999997\n",
      "Bandundu 1610.85\n",
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      "Bashkortostan 9729.210000000001\n",
      "Basilicata 177.786\n",
      "Basque Country 24218.502000000008\n",
      "Batman 1190.2920000000001\n",
      "Batna 1972.77\n",
      "Bauchi 631.2959999999999\n",
      "Bavaria 58870.80749999996\n",
      "Bay of Plenty 11101.65\n",
      "Bechar 150.06\n",
      "Beijing 32304.494999999995\n",
      "Beirut 1596.8100000000002\n",
      "Bejaia 631.98\n",
      "Bekes 1428.48\n",
      "Belgorod 1166.04\n",
      "Bengkulu 534.6765\n",
      "Benguela 6082.65\n",
      "Benguet 5740.521000000001\n",
      "Beni 700.8260000000001\n",
      "Benue 1790.163\n",
      "Berlin 61917.89850000006\n",
      "Bern 1135.41\n",
      "Bie 885.75\n",
      "Bihar 28615.140000000003\n",
      "Bihor 1031.55\n",
      "Bingol 938.376\n",
      "Biobio 2662.7533999999996\n",
      "Bishkek 4189.199999999998\n",
      "Bissau 2065.44\n",
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      "Borno 777.4110000000001\n",
      "Botosani 2259.12\n",
      "Bourgogne-Franche-Comté 8958.534\n",
      "Boyacá 1350.626\n",
      "Braga 460.98\n",
      "Braila 428.60999999999996\n",
      "Brandenburg 10539.822\n",
      "Brasov 2768.94\n",
      "Bremen 19594.790999999994\n",
      "Brest 4400.789999999999\n",
      "British Columbia 9544.38\n",
      "Brittany 27632.40600000001\n",
      "Brussels 1049.7299999999998\n",
      "Bryansk 2367.0300000000007\n",
      "Budapest 7469.67\n",
      "Buenos Aires 28576.474439999984\n",
      "Bujumbura-Mairie 267.72\n",
      "Bukidnon 2340.3495000000003\n",
      "Bulacan 5365.0830000000005\n",
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      "Burgas 709.05\n",
      "Bursa 3384.2039999999993\n",
      "Busan 2435.739\n",
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      "Cabo Delgado 88.65\n",
      "Cajamarca 174.708\n",
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      "Caldas 2243.7351599999997\n",
      "California 457687.6315000017\n",
      "Callao 162.58800000000002\n",
      "Camagüey 16106.30268\n",
      "Campania 25532.871000000006\n",
      "Campeche 3959.298399999999\n",
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      "Dublin 11407.149000000003\n",
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      "Düzce 65.196\n",
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      "Eastern Cape 11231.850000000004\n",
      "Ebonyi 34.326\n",
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      "Elazig 522.852\n",
      "Elbasan 1619.1\n",
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      "England 485170.9709999993\n",
      "Entre Ríos 376.54240000000004\n",
      "Enugu 1907.343\n",
      "Equateur 2238.9600000000005\n",
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      "Est 1344.96\n",
      "Estelí 8455.075320000004\n",
      "Estuaire 1751.7299999999998\n",
      "Extremadura 228.528\n",
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      "Federal District 12672.638679999998\n",
      "Federation of Bosnia and Herzegovina 2599.2599999999993\n",
      "Finland Proper 2475.54\n",
      "Flemish Brabant 1641.2099999999998\n",
      "Florida 89473.70799999994\n",
      "Francisco Morazán 48048.903919999946\n",
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      "Friesland 491.46900000000005\n",
      "Friuli-Venezia Giulia 1055.43\n",
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      "Fukui 620.64\n",
      "Fukuoka 1463.6100000000001\n",
      "Fès-Boulemane 7030.799999999999\n",
      "Galati 3376.44\n",
      "Galicia 15173.804999999998\n",
      "Galway 238.65\n",
      "Gangwon 22.41\n",
      "Gansu 14270.285999999998\n",
      "Gauteng 50986.86\n",
      "Gaziantep 3514.163999999999\n",
      "Gelderland 4768.845000000001\n",
      "Geneva 1865.43\n",
      "Georgia 49095.84000000001\n",
      "Gezira 2422.1099999999997\n",
      "Gharb-Chrarda-Béni Hssen 494.43000000000006\n",
      "Gilan 5794.3499999999985\n",
      "Gisborne 1433.7599999999998\n",
      "Giurgiu 253.65\n",
      "Goiás 8450.613159999999\n",
      "Golestan 1003.44\n",
      "Gombe 48.105000000000004\n",
      "Gorj 569.28\n",
      "Grad Zagreb 3084.8699999999994\n",
      "Granada 2366.8171200000006\n",
      "Grand Casablanca 30697.979999999985\n",
      "Grand Gedeh 49.41\n",
      "Granma 17002.537319999996\n",
      "Greater Accra 15556.650000000001\n",
      "Greater Poland 4222.83\n",
      "Groningen 6405.87\n",
      "Guadeloupe 1461.6399999999999\n",
      "Guanajuato 36886.414279999975\n",
      "Guangdong 96087.95999999998\n",
      "Guangxi 10540.110000000002\n",
      "Guantánamo 10942.863559999998\n",
      "Guatemala 103647.33867999991\n",
      "Guayas 3742.08\n",
      "Guelma 3664.71\n",
      "Guelmim-Es Semara 38.4\n",
      "Guerrero 4759.998640000001\n",
      "Guizhou 5491.139999999999\n",
      "Gujarat 29290.350000000006\n",
      "Gulu 364.8060000000001\n",
      "Gunma 3566.6850000000004\n",
      "Guárico 920.1454\n",
      "Gyeonggi 7445.192999999998\n",
      "Gyeongsangnam 2481.3030000000003\n",
      "Gyor 4827.54\n",
      "Gôh-Djiboua 1340.76\n",
      "Haifa 3028.290000000001\n",
      "Hainan 4078.713\n",
      "Hainaut 3662.73\n",
      "Hajdu-Bihar 528.36\n",
      "Halland 835.5\n",
      "Hamadan 7181.699999999999\n",
      "Hamburg 40430.22300000001\n",
      "Harare 1565.1899999999998\n",
      "Harjumaa 4095.39\n",
      "Haryana 24657.42\n",
      "Hatay 1025.868\n",
      "Haute-Matsiatra 206.07\n",
      "Hawke's Bay 9465.119999999997\n",
      "Hebei 13674.426000000001\n",
      "Hedmark 269.31\n",
      "Heilongjiang 42024.812999999995\n",
      "Henan 21232.731\n",
      "Hesse 31317.216000000008\n",
      "Hidalgo 2177.78\n",
      "Hims 3636.294\n",
      "Hirat 1695.2400000000002\n",
      "Hiroshima 2662.2599999999998\n",
      "Ho Chí Minh City 42670.04879999995\n",
      "Hokkaido 557.22\n",
      "Holguín 13223.386760000007\n",
      "Homyel' 2216.01\n",
      "Hong Kong 6147.000000000002\n",
      "Hordaland 6852.3\n",
      "Hormozgan 1050.93\n",
      "Hovedstaden 5221.587000000002\n",
      "Hradec Kralove 1342.4699999999998\n",
      "Hrodna 5790.299999999999\n",
      "Huambo 3052.6500000000005\n",
      "Hubei 26638.350000000002\n",
      "Huehuetenango 6596.579080000002\n",
      "Huila 1287.4104\n",
      "Hunan 20400.599999999995\n",
      "Hunedoara 218.34\n",
      "Hyogo 3593.9100000000008\n",
      "Iasi 1820.79\n",
      "Ibaraki 1594.7250000000001\n",
      "Ibb 354.564\n",
      "Ica 1007.0912\n",
      "Idaho 4382.486\n",
      "Igdir 974.388\n",
      "Ilam 91.35\n",
      "Ile-de-France 317822.5440000001\n",
      "Illinois 80166.10100000002\n",
      "Imereti 2162.3100000000004\n",
      "Imo 616.2930000000001\n",
      "Indiana 53555.360000000015\n",
      "Inhambane 23.25\n",
      "Inner Mongolia 13108.109999999999\n",
      "Iowa 4579.759999999999\n",
      "Irbid 158.16\n",
      "Iringa 462.348\n",
      "Irkutsk 4846.139999999999\n",
      "Ishikawa 3081.4199999999996\n",
      "Isla de la Juventud 2602.7\n",
      "Isparta 103.896\n",
      "Istanbul 31037.459999999977\n",
      "Ivano-Frankivsk 662.3699999999999\n",
      "Ivanovo 1787.13\n",
      "Izmir 15161.91599999999\n",
      "Jakarta 94321.32420000002\n",
      "Jalisco 43216.32555999998\n",
      "Jambi 3500.643899999999\n",
      "Jammu and Kashmir 7969.079999999999\n",
      "Jawa Barat 91975.74420000009\n",
      "Jawa Tengah 25363.265400000008\n",
      "Jawa Timur 50361.27539999996\n",
      "Jeju 621.48\n",
      "Jerusalem 3576.8399999999997\n",
      "Jharkhand 12131.580000000002\n",
      "Jiangsu 55333.823999999986\n",
      "Jiangxi 3358.9799999999996\n",
      "Jilin 19035.93\n",
      "Jinja 101.583\n",
      "Jizan 5385.509999999998\n",
      "Jizzakh 502.61999999999995\n",
      "Jubbada Hoose 353.52\n",
      "Jujuy 315.82800000000003\n",
      "Junín 863.5200000000001\n",
      "Kabardino-Balkariya 74.22\n",
      "Kabarole 3.573\n",
      "Kabul 16951.110000000004\n",
      "Kaduna 5810.561999999999\n",
      "Kafr Ash Shaykh 129.33\n",
      "Kagawa 2431.932\n",
      "Kahramanmaras 832.188\n",
      "Kalimantan Barat 1398.6231000000002\n",
      "Kalimantan Selatan 536.4834\n",
      "Kalimantan Timur 16008.034799999998\n",
      "Kaliningrad 3254.8799999999987\n",
      "Kaluga 2632.4700000000007\n",
      "Kampala 1151.9730000000004\n",
      "Kanagawa 6252.989999999999\n",
      "Kandahar 1867.02\n",
      "Kankan 1169.55\n",
      "Kano 7951.8690000000015\n",
      "Kansas 2914.31\n",
      "Kara 33.96\n",
      "Karaman 51.120000000000005\n",
      "Karbala' 3323.55\n",
      "Karnataka 31802.399999999987\n",
      "Kars 331.56\n",
      "Kasai-Occidental 6078.990000000001\n",
      "Kasai-Oriental 4859.459999999999\n",
      "Kassala 656.13\n",
      "Kastamonu 25.776\n",
      "Katanga 25337.790000000005\n",
      "Katsina 232.21800000000002\n",
      "Kaunas 1757.6369999999997\n",
      "Kayes 791.0999999999999\n",
      "Kayseri 1752.4319999999998\n",
      "Kentucky 36591.74999999999\n",
      "Kerala 20696.969999999994\n",
      "Kerman 3837.8399999999997\n",
      "Kermanshah 4985.789999999999\n",
      "Kharkiv 11568.599999999999\n",
      "Khartoum 9205.14\n",
      "Khatlon 242.784\n",
      "Kherson 1162.14\n",
      "Khmel'nyts'kyy 2005.9200000000003\n",
      "Khomas 2899.4700000000003\n",
      "Khorezm 1124.1\n",
      "Khulna 11407.02\n",
      "Khuzestan 7752.449999999998\n",
      "Khyber Pakhtunkhwa 2010.9299999999996\n",
      "Khánh Hòa 1233.1206\n",
      "Kiambu 840.9\n",
      "Kigali 4752.540000000001\n",
      "Kigoma 4826.73\n",
      "Kilimanjaro 496.77\n",
      "Kilis 118.76399999999998\n",
      "Kindia 1104.96\n",
      "Kinshasa 42536.520000000026\n",
      "Kirovohrad 3070.56\n",
      "Kirsehir 1145.04\n",
      "Kisumu 985.05\n",
      "Kiên Giang 4454.8656\n",
      "Klaipeda 121.068\n",
      "Kocaeli 727.4279999999999\n",
      "Konya 945.2040000000001\n",
      "Kordestan 3841.0799999999995\n",
      "Korçë 77.16\n",
      "Kuala Lumpur 17176.23\n",
      "Kumanovo 198.66000000000003\n",
      "Kutahya 2514.912\n",
      "Kwara 1239.3270000000002\n",
      "Kwazulu-natal 5270.640000000001\n",
      "Kymenlaakso 233.79\n",
      "L'viv 4769.940000000001\n",
      "La Libertad 6777.44808\n",
      "La Pampa 229.28399999999996\n",
      "La Paz 879.6800000000001\n",
      "La Rioja 355.38\n",
      "La Romana 7607.076159999999\n",
      "La Vega 2686.41144\n",
      "Laghouat 798.69\n",
      "Lagos 17185.328999999998\n",
      "Laguna 1399.8179999999998\n",
      "Lambayeque 31.536\n",
      "Languedoc-Roussillon-Midi-Pyrénées 53890.48350000003\n",
      "Lara 1444.116\n",
      "Las Tunas 8917.537319999998\n",
      "Lazio 50409.78\n",
      "Lebap 644.958\n",
      "Lesser Poland 1354.56\n",
      "León 17110.512639999997\n",
      "Liaoning 41385.437999999995\n",
      "Liberec 134.01\n",
      "Liguria 6834.552\n",
      "Lilongwe 366.84\n",
      "Lima 64.44\n",
      "Lima (city) 11006.512640000006\n",
      "Limburg 13167.047999999997\n",
      "Limpopo 451.74\n",
      "Lisboa 10659.66\n",
      "Litoral 150.51\n",
      "Littoral 2702.3100000000004\n",
      "Liège 483.24\n",
      "Ljubljana 1469.94\n",
      "Lodz 3264.8700000000003\n",
      "Lombardy 34590.55799999999\n",
      "Lorestan 9295.41\n",
      "Loreto 438.71999999999997\n",
      "Lori 156.75\n",
      "Los Lagos 3431.56\n",
      "Los Rios 972.81048\n",
      "Louisiana 9217.029999999997\n",
      "Lower Saxony 56148.230999999985\n",
      "Luanda 14679.6\n",
      "Lublin 5935.53\n",
      "Lubusz 554.64\n",
      "Lucerne 4538.429999999999\n",
      "Luhans'k 3039.6\n",
      "Lusaka 12767.220000000003\n",
      "Luxembourg 703.845\n",
      "Madhya Pradesh 27069.780000000002\n",
      "Madrid 64385.172000000006\n",
      "Magallanes y Antártica Chilena 1388.42\n",
      "Magdalena 3109.8761600000003\n",
      "Maharashtra 101322.40500000001\n",
      "Maine 1270.53\n",
      "Makkah 24534.86999999998\n",
      "Malanje 137.25\n",
      "Malatya 530.5680000000001\n",
      "Maluku 155.9655\n",
      "Manabi 725.952\n",
      "Managua 88968.50643999994\n",
      "Manawatu-Wanganui 4184.04\n",
      "Mandoul 1317.03\n",
      "Manica 431.64\n",
      "Manicaland 11.853\n",
      "Maniema 858.81\n",
      "Manipur 5429.34\n",
      "Manisa 1146.9359999999995\n",
      "Manitoba 2017.6200000000001\n",
      "Manzini 288.03000000000003\n",
      "Maputo 3709.5900000000006\n",
      "Mara 316.524\n",
      "Maradi 1648.5\n",
      "Maramures 477.21\n",
      "Maranhão 16874.14796\n",
      "Marche 1923.6179999999997\n",
      "Mardin 1653.9\n",
      "Maritime 3250.439999999999\n",
      "Markazi 2555.4900000000007\n",
      "Marlborough 2849.5800000000004\n",
      "Marrakech-Tensift-El Haouz 7464.93\n",
      "Marsabit 967.56\n",
      "Martinique 5968.151039999999\n",
      "Mary 245.097\n",
      "Maryland 23705.522999999994\n",
      "Masaya 4550.604199999999\n",
      "Maseru 6711.990000000001\n",
      "Mashonaland West 104.364\n",
      "Masovia 5780.22\n",
      "Massachusetts 28634.434\n",
      "Matabeleland North 3.465\n",
      "Matagalpa 16354.974439999998\n",
      "Matanzas 4722.18512\n",
      "Mato Grosso 3568.3423200000007\n",
      "Mato Grosso do Sul 3616.6401599999995\n",
      "Matruh 60.03\n",
      "Mayabeque 7073.873199999999\n",
      "Maysan 3579.15\n",
      "Mazandaran 6091.200000000001\n",
      "Mbeya 1346.0699999999997\n",
      "Mecklenburg-Vorpommern 12317.826\n",
      "Medea 122.88\n",
      "Mehedinti 417.51\n",
      "Meknès-Tafilalet 7753.47\n",
      "Melilla 145.44\n",
      "Mendoza 2222.382\n",
      "Mersin 2692.9920000000006\n",
      "Meta 82.84\n",
      "Michigan 76269.61400000002\n",
      "Michoacán 24566.087959999997\n",
      "Mie 2998.53\n",
      "Minas Gerais 32651.181840000016\n",
      "Minnesota 29863.149999999998\n",
      "Miranda 2250.7193999999995\n",
      "Misiones 306.41999999999996\n",
      "Misratah 3914.88\n",
      "Mississippi 10771.339999999997\n",
      "Missouri 22205.150000000005\n",
      "Mombasa 4233.119999999999\n",
      "Monagas 507.81312\n",
      "Mono 44.099999999999994\n",
      "Monseñor Nouel 159.10399999999998\n",
      "Montagne 743.9399999999999\n",
      "Montana 5589.351999999998\n",
      "Montevideo 5604.404\n",
      "Montserrado 3511.4700000000003\n",
      "Mopti 289.02000000000004\n",
      "Moquegua 287.16\n",
      "Moravian-Silesian 304.38\n",
      "Morelos 7518.927600000001\n",
      "Morogoro 159.33\n",
      "Moscow 632.52\n",
      "Mostaganem 439.92\n",
      "Mpumalanga 155.88\n",
      "Mtwara 251.45999999999998\n",
      "Muharraq 669.18\n",
      "Murcia 10665.318000000001\n",
      "Mures 1851.6299999999999\n",
      "Muscat 76.65\n",
      "Mwanza 2529.8880000000004\n",
      "Mykolayiv 1576.0800000000004\n",
      "Mérida 60.96\n",
      "México 34588.40236\n",
      "Nagano 30.9\n",
      "Nagasaki 4125.42\n",
      "Nairobi 7436.700000000002\n",
      "Nakhon Ratchasima 1002.1527\n",
      "Nakuru 3109.7999999999997\n",
      "Namangan 1894.95\n",
      "Namibe 63.42\n",
      "Nampula 2312.4900000000002\n",
      "Namur 5012.339999999999\n",
      "Nangarhar 1159.95\n",
      "Nariño 578.26\n",
      "National Capital 2928.135000000001\n",
      "National Capital Region 149247.22050000008\n",
      "Navarra 4608.1860000000015\n",
      "Nayarit 8047.38968\n",
      "Neamt 2875.32\n",
      "Nebraska 7464.929999999999\n",
      "Negeri Sembilan 6334.68\n",
      "Negros Occidental 3463.361999999999\n",
      "Nelson 2859.21\n",
      "Neuquén 509.076\n",
      "Nevada 16729.102\n",
      "Nevsehir 320.73599999999993\n",
      "New Hampshire 7292.523999999999\n",
      "New Jersey 35764.312\n",
      "New Mexico 4783.522\n",
      "New South Wales 270487.1040000001\n",
      "New York 310876.27100000036\n",
      "Newfoundland 102.84\n",
      "Niamey 4799.31\n",
      "Niari 2713.23\n",
      "Niassa 970.2\n",
      "Nicosia 437.31\n",
      "Nigde 213.84\n",
      "Niger 443.493\n",
      "Ninawa 4915.589999999999\n",
      "Ningxia 129.84\n",
      "Nitra 742.5899999999999\n",
      "Nord 1933.8599999999997\n",
      "Nord-Ouest 1086.66\n",
      "Nord-Pas-de-Calais-Picardie 65176.78499999997\n",
      "Normandy 27705.086999999996\n",
      "Norte de Santander 5097.102320000001\n",
      "North Brabant 15250.217999999999\n",
      "North Carolina 55603.16399999996\n",
      "North Dakota 919.9099999999999\n",
      "North Holland 15219.426000000001\n",
      "North Lebanon 1199.85\n",
      "North Rhine-Westphalia 216451.85100000005\n",
      "North-West 2353.1399999999994\n",
      "Northern 1478.4599999999998\n",
      "Northern Territory 14463.480000000001\n",
      "Northland 10456.530000000002\n",
      "Nouakchott 4020.9299999999994\n",
      "Nova Scotia 382.32\n",
      "Novgorod 413.1\n",
      "Novosibirsk 54.18\n",
      "Nueva Esparta 269.88\n",
      "Nuevo León 41066.779999999984\n",
      "Nusa Tenggara Barat 5010.8181\n",
      "Nusa Tenggara Timur 4207.7031\n",
      "Nzérékoré 1223.7599999999998\n",
      "O'Higgins 4002.86\n",
      "Oaxaca 607.4799999999999\n",
      "Odisha 5280.360000000001\n",
      "Ogun 373.401\n",
      "Ohio 78258.13600000003\n",
      "Oita 7526.64\n",
      "Okayama 145.86\n",
      "Oklahoma 19683.389999999996\n",
      "Olomouc 229.56\n",
      "Ondo 280.75500000000005\n",
      "Ontario 35450.40000000001\n",
      "Opole 1670.4299999999998\n",
      "Oran 7337.460000000001\n",
      "Ordu 125.292\n",
      "Oregon 17431.149999999998\n",
      "Orientale 1972.1100000000004\n",
      "Oruro 1694.6\n",
      "Osaka 7578.585\n",
      "Osh 917.1000000000001\n",
      "Osjecko-Baranjska 25.29\n",
      "Oslo 7801.86\n",
      "Osmaniye 486.15600000000006\n",
      "Osun 247.221\n",
      "Otago 2678.885999999999\n",
      "Oued el Dahab 666.36\n",
      "Ouest 12796.915639999996\n",
      "Ouémé 2129.25\n",
      "Overijssel 4295.621999999999\n",
      "Oyo 2847.3120000000013\n",
      "Pahang 5894.31\n",
      "Pampanga 873.414\n",
      "Panama 41490.18064\n",
      "Panevezys 1472.9130000000002\n",
      "Papua 2756.2833\n",
      "Paramaribo 23.64\n",
      "Parana 23218.07276\n",
      "Paraíba 8933.192799999997\n",
      "Pará 8723.859200000003\n",
      "Pavlodar 1010.799\n",
      "Pays de la Loire 34357.25399999999\n",
      "Paysandú 3473.14\n",
      "Pazardzhik 193.77\n",
      "Pennsylvania 116511.91399999996\n",
      "Perak 9282.300000000001\n",
      "Peravia 705.456\n",
      "Pernambuco 18232.550119999996\n",
      "Pernik 14.82\n",
      "Phnom Penh 17476.019999999997\n",
      "Piauí 1359.59076\n",
      "Pichincha 7021.557879999999\n",
      "Piedmont 24949.39500000001\n",
      "Pinar del Río 13066.577280000001\n",
      "Piura 353.28\n",
      "Plateau 780.6599999999999\n",
      "Plateaux 581.52\n",
      "Pleven 56.46\n",
      "Plovdiv 898.65\n",
      "Plzen 1332.99\n",
      "Podgorica 4004.37\n",
      "Podlaskie 1042.9499999999998\n",
      "Poltava 8155.349999999999\n",
      "Pomerania 6560.7300000000005\n",
      "Porto 1564.68\n",
      "Portuguesa 914.112\n",
      "Potosí 1557.8799999999997\n",
      "Prague 5940.5999999999985\n",
      "Prahova 2067.84\n",
      "Primorsko-Goranska 380.76\n",
      "Provence-Alpes-Côte d'Azur 114372.30149999996\n",
      "Provincia de Buenos Aires 3659.4388399999993\n",
      "Puducherry 2936.6699999999996\n",
      "Puebla 35022.97440000001\n",
      "Punjab 60069.990000000034\n",
      "Puno 342.90000000000003\n",
      "Pwani 256.95\n",
      "Qaraghandy 938.8710000000001\n",
      "Qashqadaryo 874.2900000000001\n",
      "Qazvin 1129.17\n",
      "Qina 466.53\n",
      "Qinghai 5691.45\n",
      "Qom 6132.63\n",
      "Qostanay 652.1669999999999\n",
      "Quebec 10924.170000000004\n",
      "Queensland 238312.73400000017\n",
      "Querétaro 12189.667600000002\n",
      "Quezaltenango 7857.089680000002\n",
      "Quindío 2292.44\n",
      "Quintana Roo 9370.239320000004\n",
      "Ra's Al Khaymah 534.132\n",
      "Rabat-Salé-Zemmour-Zaer 19399.98\n",
      "Rajasthan 30992.790000000008\n",
      "Rajshahi 4986.9\n",
      "Razavi Khorasan 16469.339999999997\n",
      "Red Sea 3271.08\n",
      "Rhineland-Palatinate 25552.256999999994\n",
      "Rhode Island 22627.95599999999\n",
      "Riau 5980.6521\n",
      "Rio Grande do Norte 6966.858719999997\n",
      "Rio Grande do Sul 20848.053799999998\n",
      "Rio de Janeiro 17334.16204000001\n",
      "Risaralda 1901.1599999999999\n",
      "Rivera 60.44\n",
      "Rivers 3824.468999999999\n",
      "Rivne 428.42999999999995\n",
      "Rizal 11695.958999999999\n",
      "Rize 18.528\n",
      "Rogaland 2904.8999999999996\n",
      "Rondônia 560.38832\n",
      "Roraima 1386.27736\n",
      "Ruvuma 273.456\n",
      "Río Negro 409.824\n",
      "Saarland 2560.362\n",
      "Sabah 14890.95\n",
      "Saga 1137.48\n",
      "Saida 3524.460000000001\n",
      "Saint Catherine 6761.7858400000005\n",
      "Saint Michael 7174.273920000001\n",
      "Saint-Louis 298.98\n",
      "Saitama 5209.08\n",
      "Salah Ad Din 678.66\n",
      "Salta 232.66199999999998\n",
      "Salzburg 1685.73\n",
      "Samsun 399.048\n",
      "San Cristóbal 543.3411199999999\n",
      "San Fernando 4915.55544\n",
      "San Juan 6175.218040000002\n",
      "San Luis 2797.464\n",
      "San Luis Potosí 11968.16084\n",
      "San Pedro de Macorís 8015.44936\n",
      "San Salvador 153639.3970000001\n",
      "Sancti Spíritus 3436.0337999999992\n",
      "Sanliurfa 590.1359999999999\n",
      "Santa Ana 10086.535640000006\n",
      "Santa Catarina 20523.651400000002\n",
      "Santa Cruz 2241.0\n",
      "Santa Fe 5696.9800000000005\n",
      "Santander 5573.1464\n",
      "Santiago 32224.080079999996\n",
      "Santiago de Cuba 32452.547759999987\n",
      "Santiago del Estero 483.462\n",
      "Santo Domingo 78713.66344000002\n",
      "Sarawak 2043.8699999999997\n",
      "Sardinia 4092.222\n",
      "Saskatchewan 1955.5800000000002\n",
      "Sassandra-Marahoué 2195.76\n",
      "Satu Mare 1118.76\n",
      "Savane 238.62\n",
      "Saxony 18404.363999999994\n",
      "Saxony-Anhalt 8850.408\n",
      "Schleswig-Holstein 6278.057999999998\n",
      "Scotland 32537.600999999995\n",
      "Selangor 3855.6299999999997\n",
      "Semnan 48.93\n",
      "Seoul 14298.831000000007\n",
      "Sergipe 1576.5799999999997\n",
      "Setúbal 182.295\n",
      "Sevastopol' 1568.64\n",
      "Shaanxi 8490.969\n",
      "Shandong 57398.541\n",
      "Shanghai 31275.791999999998\n",
      "Shanxi 14952.666000000005\n",
      "Shinyanga 2029.329\n",
      "Shizuoka 14593.65\n",
      "Shkodër 593.28\n",
      "Shumen 1272.6\n",
      "Sibiu 1090.14\n",
      "Sichuan 36531.81000000001\n",
      "Sicily 24440.034000000007\n",
      "Sidi Bel Abbes 569.19\n",
      "Siirt 205.368\n",
      "Sikasso 1094.22\n",
      "Silesia 13842.089999999995\n",
      "Sinaloa 22475.762119999992\n",
      "Sindh 5976.812999999998\n",
      "Singapore 40286.25\n",
      "Sirnak 108.98400000000001\n",
      "Sistan Va Baluchestan 3562.2899999999995\n",
      "Sivas 675.4439999999998\n",
      "Skikda 634.9200000000001\n",
      "Skåne 2235.96\n",
      "Sliven 483.48\n",
      "Sofala 3357.12\n",
      "Sofiya-Grad 8106.09\n",
      "Sokoto 85.65299999999999\n",
      "Sonora 13994.537439999998\n",
      "Sonsonate 5715.75848\n",
      "Souss-Massa-Draâ 1580.1000000000001\n",
      "South Australia 75552.73199999997\n",
      "South Carolina 8481.710000000001\n",
      "South Chungcheong 46.8\n",
      "South Dakota 1315.56\n",
      "South Denmark 3140.0909999999994\n",
      "South Holland 14181.330000000002\n",
      "South Khorasan 65.49000000000001\n",
      "South Kivu 1585.5900000000001\n",
      "South-East 617.46\n",
      "Southern 7496.849999999999\n",
      "Southland 3009.06\n",
      "Splitsko-Dalmatinska 144.9\n",
      "St. Gallen 62.64\n",
      "Stara Zagora 58.83\n",
      "Stavropol' 2243.6400000000003\n",
      "Stockholm 21589.749000000014\n",
      "Styria 4670.07\n",
      "Suceava 62.129999999999995\n",
      "Sucre 173.68800000000002\n",
      "Sud 513.12\n",
      "Sud-Ouest 389.16\n",
      "Suhaj 4633.049999999999\n",
      "Sulawesi Tengah 4203.808499999999\n",
      "Sulawesi Tenggara 9814.037999999997\n",
      "Sulawesi Utara 3668.6127000000006\n",
      "Sumatera Barat 5515.149300000001\n",
      "Sumatera Selatan 11982.672299999997\n",
      "Sumatera Utara 39568.80390000002\n",
      "Sumy 1925.49\n",
      "Surxondaryo 2366.16\n",
      "Sylhet 1313.8200000000004\n",
      "São Paulo 98393.32388\n",
      "Södermanland 283.26\n",
      "Ta'izz 2032.1729999999998\n",
      "Tabasco 7897.824000000001\n",
      "Tabora 80.1\n",
      "Tabuk 5047.949999999998\n",
      "Tadla-Azilal 2526.27\n",
      "Taipei City 7647.629999999997\n",
      "Tamaulipas 23058.017880000007\n",
      "Tambov 549.4799999999999\n",
      "Tamil Nadu 51911.46000000002\n",
      "Tanga 517.92\n",
      "Tanger-Tétouan 6334.14\n",
      "Taranaki 5145.809999999999\n",
      "Tarapacá 189.14\n",
      "Tarija 1858.1640000000002\n",
      "Tartumaa 384.45\n",
      "Tasmania 30381.734999999997\n",
      "Taza-Al Hoceima-Taounate 413.03999999999996\n",
      "Tbilisi 3265.1100000000006\n",
      "Tehran 6420.450000000001\n",
      "Tekirdag 1160.016\n",
      "Tel Aviv 6810.660000000001\n",
      "Telangana 15921.750000000007\n",
      "Tennessee 30661.872999999978\n",
      "Ternopil' 515.1600000000001\n",
      "Tete 72.6\n",
      "Texas 170188.0457999998\n",
      "Thies 2103.6900000000005\n",
      "Thuringia 11922.540000000003\n",
      "Thủ Dô Hà Nội 11054.374200000002\n",
      "Tianjin 18700.27499999999\n",
      "Timis 3850.83\n",
      "Tipaza 677.7\n",
      "Tizi Ouzou 198.21\n",
      "Tlemcen 84.72\n",
      "Tocantins 3896.16\n",
      "Tochigi 384.41999999999996\n",
      "Tokat 155.928\n",
      "Tokyo 17472.63000000001\n",
      "Tolima 2166.6\n",
      "Tottori 27.36\n",
      "Trabzon 1313.5199999999998\n",
      "Trengganu 1884.24\n",
      "Trentino-Alto Adige 4169.124\n",
      "Tripura 646.29\n",
      "Tunis 1760.79\n",
      "Tuscany 30569.784000000007\n",
      "Tyrol 6863.550000000001\n",
      "Táchira 32.7\n",
      "Tỉnh Cần Thơ 1983.1797\n",
      "Ucayali 1433.31588\n",
      "Ulaanbaatar 6789.839999999997\n",
      "Ulsan 742.9290000000001\n",
      "Umbria 1982.6279999999997\n",
      "Upper Austria 6189.839999999999\n",
      "Upper Demerara-Berbice 933.8599999999999\n",
      "Uppsala 1340.46\n",
      "Usak 46.92\n",
      "Usti nad Labem 289.92\n",
      "Utah 11220.056000000002\n",
      "Utrecht 10222.107\n",
      "Uttar Pradesh 76295.16000000005\n",
      "Uttarakhand 7752.329999999998\n",
      "Uusimaa 17995.019999999997\n",
      "Valcea 1399.0500000000002\n",
      "Valenciana 30721.047000000002\n",
      "Valle del Cauca 1427.2\n",
      "Vallée Du Bandama 3260.34\n",
      "Valparaíso 2056.5\n",
      "Van 718.7399999999999\n",
      "Vargas 534.876\n",
      "Varna 2393.0699999999997\n",
      "Vaud 1787.5500000000002\n",
      "Veneto 17537.130000000005\n",
      "Veracruz 24046.847199999993\n",
      "Vermont 8929.369999999999\n",
      "Vest-Agder 1785.54\n",
      "Victoria 151785.03000000003\n",
      "Vienna 62023.53000000002\n",
      "Vientiane Prefecture 590.1336\n",
      "Villa Clara 13127.597640000004\n",
      "Vilnius 3276.1530000000002\n",
      "Vinnytsya 5658.059999999999\n",
      "Virginia 70636.72000000004\n",
      "Vitsyebsk 978.9899999999999\n",
      "Vladimir 2789.52\n",
      "Vlorë 986.49\n",
      "Volyn 5360.820000000001\n",
      "Voronezh 4578.06\n",
      "Vrancea 34.2\n",
      "Värmland 514.644\n",
      "Västra Götaland 3691.8299999999995\n",
      "Waikato 20617.859999999997\n",
      "Wales 10867.728000000001\n",
      "Washington 138641.27\n",
      "Wellington 36690.53999999999\n",
      "West Bengal 16235.909999999998\n",
      "West Flanders 1649.34\n",
      "West Virginia 1209.824\n",
      "Western 871.8\n",
      "Western Australia 111090.22800000003\n",
      "Western Cape 23803.77\n",
      "White Nile 784.11\n",
      "Wisconsin 32114.610000000022\n",
      "Woqooyi Galbeed 4329.749999999999\n",
      "Wyoming 1603.136\n",
      "Xinjiang Uygur 5559.4800000000005\n",
      "Yalova 35.46\n",
      "Yamaguchi 6654.780000000001\n",
      "Yambol 638.91\n",
      "Yamoussoukro 1647.03\n",
      "Yangon 34138.87169999998\n",
      "Yaracuy 908.8026\n",
      "Yaroslavl' 5862.000000000001\n",
      "Yazd 567.8100000000001\n",
      "Yevrey 244.08\n",
      "Yobe 516.0600000000001\n",
      "Yogyakarta 20843.86139999999\n",
      "Yoro 1873.6825600000002\n",
      "Yucatán 15081.925919999998\n",
      "Yunnan 11112.599999999999\n",
      "Zacatecas 1466.9119999999998\n",
      "Zagrebacka 825.36\n",
      "Zambezia 1750.3200000000002\n",
      "Zamfara 135.34199999999998\n",
      "Zanjan 3423.2100000000005\n",
      "Zaporizhzhya 4748.700000000001\n",
      "Zealand 242.697\n",
      "Zeeland 208.44\n",
      "Zhambyl 22.05\n",
      "Zhejiang 26953.35599999999\n",
      "Zhytomyr 4563.839999999999\n",
      "Ziguinchor 617.43\n",
      "Zinder 362.73\n",
      "Zulia 4208.93696\n",
      "Zürich 11707.739999999998\n",
      "Šiauliai 96.56099999999999\n",
      "Žilina 60.120000000000005\n"
     ]
    }
   ],
   "source": [
    "df_g[\"Sales\"] = df_g[\"Sales\"].str.replace(',', '.')\n",
    "df_g = df_g.astype({'Sales': 'float64'})\n",
    "\n",
    "for x, data in df_g.groupby([\"State\"]):\n",
    "    print(x, sum(data[\"Sales\"]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different Categories\n",
    "* (base) cat Sample-Superstore.csv | head -n 36 | tail -n 1\n",
    "\n",
    "`35,CA-2017-107727,10/19/2017,10/23/2017,Second Class,MA-17560,Matt Abelman,Home Office,United States,Houston,Texas,77095,Central,OFF-PA-10000249,Office Supplies,Paper,Easy-staple paper,29.472,3,0.2,9.9468`\n",
    "* (base) cat Global-Superstore-Orders.csv | grep \"United States\" | sort -n | head -n 35 | tail -n 1\n",
    "\n",
    "`31331,CA-2017-MA17560140-43028,10/20/2017,10/24/2017,Second Class,MA-175601402,Matt Abelman,Home Office,77095,Houston,Texas,United States,Central US,USCA,OFF-FA-6129,Staples,Fasteners,Office Supplies,\"29,472\",3,\"0,2\",\"9,9468\",\"3,5\",Medium`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO \n",
    "## Different dates for orders in Sample- and Global!!! (1 day apart)\n",
    "```\n",
    "(base) stefan@Zeph:/mnt/c/Users/Coding/Documents/Python Scripts/M1P5S1-SQL/Data$ cat Global-Superstore-Orders.csv | grep \"United States\" | grep \"OFF-ST-6289\" | grep 2014 | grep Mari\n",
    "41009,CA-2014-MD17350140-41947,11/4/2014,11/9/2014,Standard Class,MD-173501406,Maribeth Dona,Consumer,19143,Philadelphia,Pennsylvania,United States,Eastern US,USCA,OFF-ST-6289,Tennsco Double-Tier Lockers,Storage,Office Supplies,\"1080,096\",6,\"0,2\",\"-94,5084\",\"53,84\",Medium\n",
    "(base) stefan@Zeph:/mnt/c/Users/Coding/Documents/Python Scripts/M1P5S1-SQL/Data$ cat Sample-Superstore.csv | grep \",Tennsco Double-Tier\" | grep 2014\n",
    "9713,CA-2014-114251,11/5/2014,11/10/2014,Standard Class,MD-17350,Maribeth Dona,Consumer,United States,Philadelphia,Pennsylvania,19143,East,OFF-ST-10003716,Office Supplies,Storage,Tennsco Double-Tier Lockers,1080.096,6,0.2,-94.5084\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Global DataSet are no orders on February 29th in 2016 (which was leap year)\n",
    "\n",
    "In Sample DataSet there is no 31.12.2017?\n",
    "\n",
    "I checked the original Sample Dataset from Tableau (https://community.tableau.com/s/question/0D54T00000CWeX8SAL/sample-superstore-sales-excelxls) and there is also the 31.12.2017 missing and the first entry is on the 03.01.2014 - meanwhile in the Global data set the first day is the 04.01.2014 and there is also a 31.12.2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03/09/2015\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "serial_number = 42250\n",
    "date = datetime.datetime(1899, 12, 30) + datetime.timedelta(days=serial_number)\n",
    "\n",
    "formatted_date = date.strftime('%d/%m/%Y')\n",
    "\n",
    "print(formatted_date)  # Output: 18/06/2017"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dates with no sold products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41691 41690 2014-02-21 00:00:00\n",
      "41711 41710 2014-03-13 00:00:00\n",
      "41718 41717 2014-03-20 00:00:00\n",
      "41739 41738 2014-04-10 00:00:00\n",
      "41746 41745 2014-04-17 00:00:00\n",
      "41760 41759 2014-05-01 00:00:00\n",
      "41788 41787 2014-05-29 00:00:00\n",
      "41823 41822 2014-07-03 00:00:00\n",
      "41830 41829 2014-07-10 00:00:00\n",
      "41865 41864 2014-08-14 00:00:00\n",
      "41879 41878 2014-08-28 00:00:00\n",
      "41886 41885 2014-09-04 00:00:00\n",
      "41935 41934 2014-10-23 00:00:00\n",
      "41991 41990 2014-12-18 00:00:00\n",
      "42005 42004 2015-01-01 00:00:00\n",
      "42019 42018 2015-01-15 00:00:00\n",
      "42061 42060 2015-02-26 00:00:00\n",
      "42068 42067 2015-03-05 00:00:00\n",
      "42082 42081 2015-03-19 00:00:00\n",
      "42089 42088 2015-03-26 00:00:00\n",
      "42103 42102 2015-04-09 00:00:00\n",
      "42208 42207 2015-07-23 00:00:00\n",
      "42418 42417 2016-02-18 00:00:00\n",
      "42425 42424 2016-02-25 00:00:00\n",
      "42430 42429 2016-03-01 00:00:00\n",
      "42482 42481 2016-04-22 00:00:00\n",
      "42489 42488 2016-04-29 00:00:00\n",
      "42678 42677 2016-11-04 00:00:00\n",
      "42769 42768 2017-02-03 00:00:00\n",
      "42937 42936 2017-07-21 00:00:00\n",
      "42958 42957 2017-08-11 00:00:00\n",
      "43035 43034 2017-10-27 00:00:00\n"
     ]
    }
   ],
   "source": [
    "delta = 41640\n",
    "for date, data in df_g.groupby(\"Order Date\"):\n",
    "    #print(data[\"Order ID\"].first.split('-')[-1])\n",
    "    id = [data[\"Order ID\"]][0]\n",
    "    last_element = int([id.split('-')[-1] for id in data[\"Order ID\"]][0])\n",
    "    new_date = datetime.datetime(1899, 12, 30) + datetime.timedelta(days=int(last_element))\n",
    "    #print(date)\n",
    "\n",
    "\n",
    "    if  last_element != delta:\n",
    "        print(last_element, delta, date)\n",
    "        delta = last_element\n",
    "\n",
    "    if str(date) != str(new_date):\n",
    "        print(id)\n",
    "        print(last_element, date, \"!=\", date.strftime('%Y-%m-%m 00:00:00'))     \n",
    "        break\n",
    "    \n",
    "    delta = delta + 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Black Friday"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Freitag, 28. November\n",
    "Black Friday 2014\n",
    "\n",
    "Freitag, 27. November\n",
    "Black Friday 2015\n",
    "\n",
    "Freitag, 25. November\n",
    "Black Friday 2016\n",
    "\n",
    "Freitag, 24. November\n",
    "Black Friday 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# for year in range(2014,2018):\n",
    "#     print(year)\n",
    "#     grouped1_df = df[(df[\"Order Date\"] >= str(year) + \"-11-23\") & (df[\"Order Date\"] <= str(year) + \"-11-29\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "#     grouped2_df = df_g[(df_g[\"Country\"] == \"United States\") & (df_g[\"Order Date\"] >= str(year) + \"-11-23\") & (df_g[\"Order Date\"] <= str(year) + \"-11-29\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "\n",
    "#     display(grouped1_df)\n",
    "#     display(grouped2_df)\n",
    "\n",
    "#     # Plot the graph\n",
    "#     plt.figure(figsize=(10, 6))  # Set the size of the plot (optional)\n",
    "#     grouped1_df.plot(kind='line')\n",
    "#     grouped2_df.plot(kind='line')\n",
    "#     plt.title('Order Count per Date')\n",
    "#     plt.xlabel('Order Date')\n",
    "#     plt.ylabel('Count')\n",
    "#     plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# for year in range(2014,2018):\n",
    "#     print(year)\n",
    "#     grouped1_df = df[(df[\"Order Date\"] >= str(year) + \"-01-01\") & (df[\"Order Date\"] <= str(year) + \"-12-31\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "#     grouped2_df = df_g[(df_g[\"Country\"] == \"United States\") & (df_g[\"Order Date\"] >= str(year) + \"-01-01\") & (df_g[\"Order Date\"] <= str(year) + \"-12-31\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "\n",
    "#     #display(grouped1_df)\n",
    "#     #display(grouped2_df)\n",
    "\n",
    "#     # Plot the graph\n",
    "#     plt.figure(figsize=(10, 6))  # Set the size of the plot (optional)\n",
    "#     grouped1_df.plot(kind='line', label=\"Sample\")\n",
    "#     grouped2_df.plot(kind='line', label=\"Global\")\n",
    "#     plt.legend(loc=\"upper left\")\n",
    "#     plt.title('Order Count per Date')\n",
    "#     plt.xlabel('Order Date')\n",
    "#     plt.ylabel('Count')\n",
    "#     plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Order Date\n",
      "2014-02-23     6\n",
      "2014-02-24     7\n",
      "2014-02-27     4\n",
      "2014-03-01    40\n",
      "2014-03-02    11\n",
      "2014-03-03    35\n",
      "2014-03-04     8\n",
      "2014-03-05    16\n",
      "Name: Quantity, dtype: int64\n",
      "Global Order Date\n",
      "2014-02-24     6\n",
      "2014-02-25     7\n",
      "2014-02-28    44\n",
      "2014-03-01    11\n",
      "2014-03-02    35\n",
      "2014-03-03     8\n",
      "2014-03-04    16\n",
      "Name: Quantity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "grouped1_df = df[(df[\"Order Date\"] >= \"2014-02-23\") & (df[\"Order Date\"] <= \"2014-03-05\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "grouped2_df = df_g[(df_g[\"Country\"] == \"United States\") & (df_g[\"Order Date\"] >= \"2014-02-24\") & (df_g[\"Order Date\"] <= \"2014-03-05\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "\n",
    "print(\"Sample\", grouped1_df)\n",
    "print(\"Global\", grouped2_df)\n",
    "\n",
    "# Plot the graph\n",
    "# plt.figure(figsize=(10, 6))  # Set the size of the plot (optional)\n",
    "# grouped1_df.plot(kind='line')\n",
    "# grouped2_df.plot(kind='line')\n",
    "# plt.title('Dateshift 2014')\n",
    "# plt.xlabel('Order Date')\n",
    "# plt.ylabel('Count')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Order Date\n",
      "2015-02-23     8\n",
      "2015-02-25     5\n",
      "2015-02-27    27\n",
      "2015-02-28    10\n",
      "2015-03-01    23\n",
      "2015-03-02    10\n",
      "2015-03-05    57\n",
      "Name: Quantity, dtype: int64\n",
      "Global Order Date\n",
      "2015-02-22     8\n",
      "2015-02-24     5\n",
      "2015-02-26    27\n",
      "2015-02-27    10\n",
      "2015-02-28     9\n",
      "2015-03-01    14\n",
      "2015-03-02    10\n",
      "2015-03-05    57\n",
      "Name: Quantity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "grouped1_df = df[(df[\"Order Date\"] >= \"2015-02-23\") & (df[\"Order Date\"] <= \"2015-03-05\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "grouped2_df = df_g[(df_g[\"Country\"] == \"United States\") & (df_g[\"Order Date\"] >= \"2015-02-22\") & (df_g[\"Order Date\"] <= \"2015-03-05\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "\n",
    "print(\"Sample\", grouped1_df)\n",
    "print(\"Global\", grouped2_df)\n",
    "\n",
    "# Plot the graph\n",
    "# plt.figure(figsize=(10, 6))  # Set the size of the plot (optional)\n",
    "# grouped1_df.plot(kind='line')\n",
    "# grouped2_df.plot(kind='line')\n",
    "# plt.title('Dateshift 2015')\n",
    "# plt.xlabel('Order Date')\n",
    "# plt.ylabel('Count')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Order Date\n",
      "2016-02-25     5\n",
      "2016-02-27     8\n",
      "2016-02-28     6\n",
      "2016-02-29     7\n",
      "2016-03-01    20\n",
      "2016-03-03    59\n",
      "2016-03-04    10\n",
      "2016-03-05     4\n",
      "Name: Quantity, dtype: int64\n",
      "Global Order Date\n",
      "2016-02-25     5\n",
      "2016-02-27     8\n",
      "2016-02-28     6\n",
      "2016-03-01     7\n",
      "2016-03-02    20\n",
      "2016-03-04    59\n",
      "2016-03-05    10\n",
      "2016-03-06     4\n",
      "Name: Quantity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "grouped1_df = df[(df[\"Order Date\"] >= \"2016-02-25\") & (df[\"Order Date\"] <= \"2016-03-05\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "grouped2_df = df_g[(df_g[\"Country\"] == \"United States\") & (df_g[\"Order Date\"] >= \"2016-02-25\") & (df_g[\"Order Date\"] <= \"2016-03-06\")].groupby('Order Date')[\"Quantity\"].sum()\n",
    "\n",
    "print(\"Sample\", grouped1_df)\n",
    "print(\"Global\", grouped2_df)\n",
    "\n",
    "# Plot the graph\n",
    "# plt.figure(figsize=(10, 6))  # Set the size of the plot (optional)\n",
    "# grouped1_df.plot(kind='line')\n",
    "# grouped2_df.plot(kind='line')\n",
    "# plt.title('Dateshift 2016')\n",
    "# plt.xlabel('Order Date')\n",
    "# plt.ylabel('Count')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5769, CA-2015-154900,          2/25/2015,3/1/2015,Standard Class,SS-20875,    Sung Shariari,Consumer,United States,Leominster,Massachusetts,1453,East,OFF-LA-10001641,Office Supplies,Labels,Avery 518,3.15,1,0,1.512\n",
    "# 37066,CA-2015-SS20875140-42059,2/24/2015,3/1/2015,Standard Class,SS-208751406,Sung Shariari,Consumer,1453,Leominster,Massachusetts,United States,Eastern US,USCA,OFF-PA-3010,\"Adams Telephone Message Book W/Dividers/Space For Phone Numbers, 5 1/4\"\"X8 1/2\"\", 200/Messages\",Paper,Office Supplies,\"22,72\",4,0,\"10,224\",\"1,69\",Medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5768</th>\n",
       "      <td>5769</td>\n",
       "      <td>CA-2015-154900</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SS-20875</td>\n",
       "      <td>Sung Shariari</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Leominster</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>1453</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-LA-10001641</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Avery 518</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>5770</td>\n",
       "      <td>CA-2015-154900</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SS-20875</td>\n",
       "      <td>Sung Shariari</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Leominster</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>1453</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10002377</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Adams Telephone Message Book W/Dividers/Space ...</td>\n",
       "      <td>22.72</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row ID        Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "5768    5769  CA-2015-154900 2015-02-25 2015-03-01  Standard Class   \n",
       "5769    5770  CA-2015-154900 2015-02-25 2015-03-01  Standard Class   \n",
       "\n",
       "     Customer ID  Customer Name   Segment        Country        City  \\\n",
       "5768    SS-20875  Sung Shariari  Consumer  United States  Leominster   \n",
       "5769    SS-20875  Sung Shariari  Consumer  United States  Leominster   \n",
       "\n",
       "              State  Postal Code Region       Product ID         Category  \\\n",
       "5768  Massachusetts         1453   East  OFF-LA-10001641  Office Supplies   \n",
       "5769  Massachusetts         1453   East  OFF-PA-10002377  Office Supplies   \n",
       "\n",
       "     Sub-Category                                       Product Name  Sales  \\\n",
       "5768       Labels                                          Avery 518   3.15   \n",
       "5769        Paper  Adams Telephone Message Book W/Dividers/Space ...  22.72   \n",
       "\n",
       "      Quantity  Discount  Profit  \n",
       "5768         1       0.0   1.512  \n",
       "5769         4       0.0  10.224  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Order Date\"] == \"2015-02-25\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Market</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48507</th>\n",
       "      <td>31526</td>\n",
       "      <td>US-2015-VD21670140-42062</td>\n",
       "      <td>2015-02-27</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>VD-216701408</td>\n",
       "      <td>Valerie Dominguez</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>38401.0</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>FUR-CH-4421</td>\n",
       "      <td>Global Push Button Manager's Chair, Indigo</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>389,696</td>\n",
       "      <td>8</td>\n",
       "      <td>0,2</td>\n",
       "      <td>43,8408</td>\n",
       "      <td>28,62</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48508</th>\n",
       "      <td>31525</td>\n",
       "      <td>US-2015-VD21670140-42062</td>\n",
       "      <td>2015-02-27</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>VD-216701408</td>\n",
       "      <td>Valerie Dominguez</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>38401.0</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>FUR-CH-4419</td>\n",
       "      <td>Global Low Back Tilter Chair</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>161,568</td>\n",
       "      <td>2</td>\n",
       "      <td>0,2</td>\n",
       "      <td>-28,2744</td>\n",
       "      <td>4,89</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "48507   31526  US-2015-VD21670140-42062 2015-02-27 2015-03-04  Standard Class   \n",
       "48508   31525  US-2015-VD21670140-42062 2015-02-27 2015-03-04  Standard Class   \n",
       "\n",
       "        Customer ID      Customer Name   Segment  Postal Code      City  \\\n",
       "48507  VD-216701408  Valerie Dominguez  Consumer      38401.0  Columbia   \n",
       "48508  VD-216701408  Valerie Dominguez  Consumer      38401.0  Columbia   \n",
       "\n",
       "           State        Country       Region Market   Product ID  \\\n",
       "48507  Tennessee  United States  Southern US   USCA  FUR-CH-4421   \n",
       "48508  Tennessee  United States  Southern US   USCA  FUR-CH-4419   \n",
       "\n",
       "                                     Product Name Sub-Category   Category  \\\n",
       "48507  Global Push Button Manager's Chair, Indigo       Chairs  Furniture   \n",
       "48508                Global Low Back Tilter Chair       Chairs  Furniture   \n",
       "\n",
       "         Sales  Quantity Discount    Profit Shipping Cost Order Priority  \n",
       "48507  389,696         8      0,2   43,8408         28,62         Medium  \n",
       "48508  161,568         2      0,2  -28,2744          4,89         Medium  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g[(df_g[\"Order Date\"] == \"2015-02-27\") & (df_g[\"Country\"] == \"United States\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "675     Sample Company A\n",
       "1723        Sue Ann Reed\n",
       "2318       Paul Van Hugh\n",
       "3482     Darrin Van Huff\n",
       "3743       Mark Van Huff\n",
       "Name: Customer Name, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(name.split()) > 2\n",
    "df_g[df_g['Customer Name'].str.split().str.len() > 2][\"Customer Name\"].drop_duplicates()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What Happend with Corey-Lock?\n",
    "Since the Customer Corey-Lock doesn't have a Surname or Space (no clue how this is even possible) the Customer ID is containing a \"Co\" (for \"Corey\") instead of the initials (CL). As a result there a Customer ID for every combination of Country/Region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Customer ID Customer Name          Country             Region\n",
      "2027      Co-126407    Corey-Lock        Australia            Oceania\n",
      "5500     Co-1264018    Corey-Lock           Brazil      South America\n",
      "7679     Co-1264027    Corey-Lock            China       Eastern Asia\n",
      "11562     Co-264038    Corey-Lock            Egypt       North Africa\n",
      "12509    Co-1264039    Corey-Lock      El Salvador    Central America\n",
      "12833    Co-1264045    Corey-Lock           France     Western Europe\n",
      "19544    Co-1264058    Corey-Lock            India      Southern Asia\n",
      "24522    Co-1264066    Corey-Lock            Japan       Eastern Asia\n",
      "26494    Co-1264082    Corey-Lock           Mexico    Central America\n",
      "28489     Co-264086    Corey-Lock          Morocco       North Africa\n",
      "28673    Co-1264088    Corey-Lock  Myanmar (Burma)  Southeastern Asia\n",
      "29940    Co-1264093    Corey-Lock        Nicaragua    Central America\n",
      "30983     Co-264095    Corey-Lock          Nigeria     Western Africa\n",
      "33848    Co-2640108    Corey-Lock           Russia     Eastern Europe\n",
      "37668    Co-2640134    Corey-Lock           Turkey       Western Asia\n",
      "38978   Co-12640139    Corey-Lock   United Kingdom    Northern Europe\n",
      "44333  Co-126401402    Corey-Lock    United States         Central US\n",
      "47932  Co-126401406    Corey-Lock    United States         Eastern US\n",
      "48446  Co-126401408    Corey-Lock    United States        Southern US\n",
      "50073  Co-126401404    Corey-Lock    United States         Western US\n",
      "50736   Co-12640143    Corey-Lock        Venezuela      South America\n"
     ]
    }
   ],
   "source": [
    "#df_g['Customer Name'].drop_duplicates().sort_values()\n",
    "print(df_g[~df_g['Customer Name'].str.contains(\" \")][['Customer ID', 'Customer Name', \"Country\", \"Region\"]].drop_duplicates().sort_values([\"Customer Name\", \"Country\", \"Region\"]))\n",
    "# for id, data in df_g[df_g['Customer Name'] == \"Corey-Lock\"].groupby(\"Customer ID\"):\n",
    "#     display(data[[\"Customer ID\",\"Country\", \"Region\"]].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    .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>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>JF-152955</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>JF-152957</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5910</th>\n",
       "      <td>JF-1529518</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>JF-529523</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9231</th>\n",
       "      <td>JF-529530</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9450</th>\n",
       "      <td>JF-1529531</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10559</th>\n",
       "      <td>JF-1529536</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12330</th>\n",
       "      <td>JF-1529539</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13727</th>\n",
       "      <td>JF-1529545</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>France</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15516</th>\n",
       "      <td>JF-1529548</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22201</th>\n",
       "      <td>JF-529560</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Iran</td>\n",
       "      <td>Southern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23743</th>\n",
       "      <td>JF-1529564</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24919</th>\n",
       "      <td>JF-529570</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30674</th>\n",
       "      <td>JF-529595</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32048</th>\n",
       "      <td>JF-1529598</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Panama</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34450</th>\n",
       "      <td>JF-15295113</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35797</th>\n",
       "      <td>JF-15295120</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36654</th>\n",
       "      <td>JF-5295129</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36900</th>\n",
       "      <td>JF-15295130</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40340</th>\n",
       "      <td>JF-15295139</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Northern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>JF-152951402</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>United States</td>\n",
       "      <td>Central US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41501</th>\n",
       "      <td>JF-152951404</td>\n",
       "      <td>Jason Fortune-</td>\n",
       "      <td>United States</td>\n",
       "      <td>Western US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>JB-160005</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3394</th>\n",
       "      <td>JB-160007</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>JB-1600018</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7984</th>\n",
       "      <td>JB-1600027</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>China</td>\n",
       "      <td>Eastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8845</th>\n",
       "      <td>JB-1600028</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9330</th>\n",
       "      <td>JB-1600031</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10397</th>\n",
       "      <td>JB-600033</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>Central Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13194</th>\n",
       "      <td>JB-1600045</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>France</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16597</th>\n",
       "      <td>JB-1600048</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17761</th>\n",
       "      <td>JB-1600051</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21248</th>\n",
       "      <td>JB-1600059</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23514</th>\n",
       "      <td>JB-1600064</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25402</th>\n",
       "      <td>JB-1600082</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29982</th>\n",
       "      <td>JB-1600093</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30946</th>\n",
       "      <td>JB-600095</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32506</th>\n",
       "      <td>JB-16000102</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34261</th>\n",
       "      <td>JB-6000110</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Western Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34993</th>\n",
       "      <td>JB-6000117</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35306</th>\n",
       "      <td>JB-16000120</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38395</th>\n",
       "      <td>JB-6000134</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Western Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38532</th>\n",
       "      <td>JB-6000137</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39119</th>\n",
       "      <td>JB-16000139</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Northern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45191</th>\n",
       "      <td>JB-160001402</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>United States</td>\n",
       "      <td>Central US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44827</th>\n",
       "      <td>JB-160001406</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eastern US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41541</th>\n",
       "      <td>JB-160001404</td>\n",
       "      <td>Joy Bell-</td>\n",
       "      <td>United States</td>\n",
       "      <td>Western US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>SC-200505</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>SC-200507</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4768</th>\n",
       "      <td>SC-2005018</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8111</th>\n",
       "      <td>SC-2005027</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>China</td>\n",
       "      <td>Eastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8855</th>\n",
       "      <td>SC-2005028</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>SC-2005058</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>India</td>\n",
       "      <td>Southern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22314</th>\n",
       "      <td>SC-1005060</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Iran</td>\n",
       "      <td>Southern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>SC-1005061</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>Western Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24558</th>\n",
       "      <td>SC-2005066</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Eastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25003</th>\n",
       "      <td>SC-1005074</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Libya</td>\n",
       "      <td>North Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27985</th>\n",
       "      <td>SC-2005082</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28564</th>\n",
       "      <td>SC-1005087</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28934</th>\n",
       "      <td>SC-2005091</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31501</th>\n",
       "      <td>SC-2005097</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32367</th>\n",
       "      <td>SC-20050102</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33696</th>\n",
       "      <td>SC-10050108</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35384</th>\n",
       "      <td>SC-20050120</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Southern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36670</th>\n",
       "      <td>SC-10050129</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37867</th>\n",
       "      <td>SC-10050134</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Western Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39821</th>\n",
       "      <td>SC-20050139</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Northern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45159</th>\n",
       "      <td>SC-200501402</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>United States</td>\n",
       "      <td>Central US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43149</th>\n",
       "      <td>SC-200501406</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eastern US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43340</th>\n",
       "      <td>SC-200501408</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40965</th>\n",
       "      <td>SC-200501404</td>\n",
       "      <td>Sample Company A</td>\n",
       "      <td>United States</td>\n",
       "      <td>Western US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Customer ID     Customer Name                           Country  \\\n",
       "617       JF-152955    Jason Fortune-                         Argentina   \n",
       "2997      JF-152957    Jason Fortune-                         Australia   \n",
       "5910     JF-1529518    Jason Fortune-                            Brazil   \n",
       "6432      JF-529523    Jason Fortune-                            Canada   \n",
       "9231      JF-529530    Jason Fortune-                           Croatia   \n",
       "9450     JF-1529531    Jason Fortune-                              Cuba   \n",
       "10559    JF-1529536    Jason Fortune-                Dominican Republic   \n",
       "12330    JF-1529539    Jason Fortune-                       El Salvador   \n",
       "13727    JF-1529545    Jason Fortune-                            France   \n",
       "15516    JF-1529548    Jason Fortune-                           Germany   \n",
       "22201     JF-529560    Jason Fortune-                              Iran   \n",
       "23743    JF-1529564    Jason Fortune-                             Italy   \n",
       "24919     JF-529570    Jason Fortune-                        Kyrgyzstan   \n",
       "30674     JF-529595    Jason Fortune-                           Nigeria   \n",
       "32048    JF-1529598    Jason Fortune-                            Panama   \n",
       "34450   JF-15295113    Jason Fortune-                         Singapore   \n",
       "35797   JF-15295120    Jason Fortune-                             Spain   \n",
       "36654    JF-5295129    Jason Fortune-                          Tanzania   \n",
       "36900   JF-15295130    Jason Fortune-                          Thailand   \n",
       "40340   JF-15295139    Jason Fortune-                    United Kingdom   \n",
       "45204  JF-152951402    Jason Fortune-                     United States   \n",
       "41501  JF-152951404    Jason Fortune-                     United States   \n",
       "670       JB-160005         Joy Bell-                         Argentina   \n",
       "3394      JB-160007         Joy Bell-                         Australia   \n",
       "4691     JB-1600018         Joy Bell-                            Brazil   \n",
       "7984     JB-1600027         Joy Bell-                             China   \n",
       "8845     JB-1600028         Joy Bell-                          Colombia   \n",
       "9330     JB-1600031         Joy Bell-                              Cuba   \n",
       "10397     JB-600033         Joy Bell-  Democratic Republic of the Congo   \n",
       "13194    JB-1600045         Joy Bell-                            France   \n",
       "16597    JB-1600048         Joy Bell-                           Germany   \n",
       "17761    JB-1600051         Joy Bell-                         Guatemala   \n",
       "21248    JB-1600059         Joy Bell-                         Indonesia   \n",
       "23514    JB-1600064         Joy Bell-                             Italy   \n",
       "25402    JB-1600082         Joy Bell-                            Mexico   \n",
       "29982    JB-1600093         Joy Bell-                         Nicaragua   \n",
       "30946     JB-600095         Joy Bell-                           Nigeria   \n",
       "32506   JB-16000102         Joy Bell-                       Philippines   \n",
       "34261    JB-6000110         Joy Bell-                      Saudi Arabia   \n",
       "34993    JB-6000117         Joy Bell-                      South Africa   \n",
       "35306   JB-16000120         Joy Bell-                             Spain   \n",
       "38395    JB-6000134         Joy Bell-                            Turkey   \n",
       "38532    JB-6000137         Joy Bell-                           Ukraine   \n",
       "39119   JB-16000139         Joy Bell-                    United Kingdom   \n",
       "45191  JB-160001402         Joy Bell-                     United States   \n",
       "44827  JB-160001406         Joy Bell-                     United States   \n",
       "41541  JB-160001404         Joy Bell-                     United States   \n",
       "675       SC-200505  Sample Company A                         Argentina   \n",
       "802       SC-200507  Sample Company A                         Australia   \n",
       "4768     SC-2005018  Sample Company A                            Brazil   \n",
       "8111     SC-2005027  Sample Company A                             China   \n",
       "8855     SC-2005028  Sample Company A                          Colombia   \n",
       "20633    SC-2005058  Sample Company A                             India   \n",
       "22314    SC-1005060  Sample Company A                              Iran   \n",
       "22976    SC-1005061  Sample Company A                              Iraq   \n",
       "24558    SC-2005066  Sample Company A                             Japan   \n",
       "25003    SC-1005074  Sample Company A                             Libya   \n",
       "27985    SC-2005082  Sample Company A                            Mexico   \n",
       "28564    SC-1005087  Sample Company A                        Mozambique   \n",
       "28934    SC-2005091  Sample Company A                       Netherlands   \n",
       "31501    SC-2005097  Sample Company A                          Pakistan   \n",
       "32367   SC-20050102  Sample Company A                       Philippines   \n",
       "33696   SC-10050108  Sample Company A                            Russia   \n",
       "35384   SC-20050120  Sample Company A                             Spain   \n",
       "36670   SC-10050129  Sample Company A                          Tanzania   \n",
       "37867   SC-10050134  Sample Company A                            Turkey   \n",
       "39821   SC-20050139  Sample Company A                    United Kingdom   \n",
       "45159  SC-200501402  Sample Company A                     United States   \n",
       "43149  SC-200501406  Sample Company A                     United States   \n",
       "43340  SC-200501408  Sample Company A                     United States   \n",
       "40965  SC-200501404  Sample Company A                     United States   \n",
       "\n",
       "                  Region  \n",
       "617        South America  \n",
       "2997             Oceania  \n",
       "5910       South America  \n",
       "6432              Canada  \n",
       "9231     Southern Europe  \n",
       "9450           Caribbean  \n",
       "10559          Caribbean  \n",
       "12330    Central America  \n",
       "13727     Western Europe  \n",
       "15516     Western Europe  \n",
       "22201      Southern Asia  \n",
       "23743    Southern Europe  \n",
       "24919       Central Asia  \n",
       "30674     Western Africa  \n",
       "32048    Central America  \n",
       "34450  Southeastern Asia  \n",
       "35797    Southern Europe  \n",
       "36654     Eastern Africa  \n",
       "36900  Southeastern Asia  \n",
       "40340    Northern Europe  \n",
       "45204         Central US  \n",
       "41501         Western US  \n",
       "670        South America  \n",
       "3394             Oceania  \n",
       "4691       South America  \n",
       "7984        Eastern Asia  \n",
       "8845       South America  \n",
       "9330           Caribbean  \n",
       "10397     Central Africa  \n",
       "13194     Western Europe  \n",
       "16597     Western Europe  \n",
       "17761    Central America  \n",
       "21248  Southeastern Asia  \n",
       "23514    Southern Europe  \n",
       "25402    Central America  \n",
       "29982    Central America  \n",
       "30946     Western Africa  \n",
       "32506  Southeastern Asia  \n",
       "34261       Western Asia  \n",
       "34993    Southern Africa  \n",
       "35306    Southern Europe  \n",
       "38395       Western Asia  \n",
       "38532     Eastern Europe  \n",
       "39119    Northern Europe  \n",
       "45191         Central US  \n",
       "44827         Eastern US  \n",
       "41541         Western US  \n",
       "675        South America  \n",
       "802              Oceania  \n",
       "4768       South America  \n",
       "8111        Eastern Asia  \n",
       "8855       South America  \n",
       "20633      Southern Asia  \n",
       "22314      Southern Asia  \n",
       "22976       Western Asia  \n",
       "24558       Eastern Asia  \n",
       "25003       North Africa  \n",
       "27985    Central America  \n",
       "28564     Eastern Africa  \n",
       "28934     Western Europe  \n",
       "31501      Southern Asia  \n",
       "32367  Southeastern Asia  \n",
       "33696     Eastern Europe  \n",
       "35384    Southern Europe  \n",
       "36670     Eastern Africa  \n",
       "37867       Western Asia  \n",
       "39821    Northern Europe  \n",
       "45159         Central US  \n",
       "43149         Eastern US  \n",
       "43340        Southern US  \n",
       "40965         Western US  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CHECK IF Joe Lock \n",
    "display(df_g[~df_g['Customer Name'].str.contains(r'[a-z]$', regex=True)][[\"Customer ID\", \"Customer Name\", \"Country\", \"Region\"]].drop_duplicates().sort_values([\"Customer Name\", \"Country\", \"Region\"]))\n",
    "# for id, data in df_g[df_g['Customer Name'].str.contains(\"Joy Bell-\")].groupby(\"Customer ID\"):\n",
    "#     print(data[[\"Customer ID\", \"Customer Name\", \"Country\", \"Region\"]].drop_duplicates())\n",
    "#     print(\"From:\", min(data[\"Order Date\"]), \" / To:\", max(data[\"Order Date\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The last three digits of the Customer ID seems to be a ID for the \"Country\" / \"Region\"\n",
    "#df_g[(df_g[\"Country\"] == \"Turkey\") & (df_g[\"Region\"] == \"Western Asia\")][[\"Customer ID\", \"Country\", \"Region\", \"Customer Name\"]].drop_duplicates().sort_values([\"Customer Name\", \"Country\", \"Region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "675    Sample Company A\n",
       "Name: Customer Name, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g[df_g['Customer Name'].str.contains('Sample') | df_g['Customer Name'].str.contains('Test') | df_g['Customer Name'].str.contains('Test')]['Customer Name'].drop_duplicates()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How is the field Customer ID build?\n",
    "* `SS-208751406` (Customer Name: `Sung Shariari`)\n",
    "* `JF-1549082` (Somehow the CountryID for this Costa Rica should be 29 but it is only shown as a 2 at the end)\n",
    "* `[INITIALS]-[3-5DIGITS][3D Code of Country][1D Code for Region]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's generate the Initials on our own\n",
    "# We need to take extra care for Names with more than words or just one word  \n",
    "df_g['_Initials'] = df_g['Customer Name'].apply(lambda name: str((''.join(word[:1] for word in name.split()) if len(name.split()) > 1 else name)[0:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Customer ID</th>\n",
       "      <th>_Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SC-20050</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SC-20050</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-12055</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BD-11605</td>\n",
       "      <td>BD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID _Initials\n",
       "0    SC-20050        KR\n",
       "1    SC-20050        KR\n",
       "2    CA-12055        CA\n",
       "3    BD-11605        BD"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Customer ID _Initials\n",
      "0    SC-20050        KR\n",
      "1    SC-20050        KR\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create the DataFrame\n",
    "data = {\n",
    "    'Customer ID': ['SC-20050', 'SC-20050', 'CA-12055', 'BD-11605'],\n",
    "    '_Initials': ['KR', 'KR', 'CA', 'BD']\n",
    "}\n",
    "\n",
    "dfX = pd.DataFrame(data)\n",
    "\n",
    "display(dfX)\n",
    "# Filter rows where Customer ID doesn't start with _Initials\n",
    "#filtered_df = dfX[~dfX['Customer ID'].str.startswith(dfX['_Initials'])]\n",
    "# Filter rows where Customer ID doesn't start with _Initials\n",
    "filtered_df = dfX[dfX.apply(lambda row: not row['Customer ID'].startswith(row['_Initials']), axis=1)]\n",
    "\n",
    "# Print the filtered DataFrame\n",
    "print(filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Market</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>_Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18659</th>\n",
       "      <td>33</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>TEC-CO-5998</td>\n",
       "      <td>Sharp Fax Machine, Digital</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>Technology</td>\n",
       "      <td>941,77824</td>\n",
       "      <td>8</td>\n",
       "      <td>0,402</td>\n",
       "      <td>-601,74176</td>\n",
       "      <td>80,281</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18728</th>\n",
       "      <td>32</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>OFF-AP-3582</td>\n",
       "      <td>Breville Toaster, Black</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>152,28</td>\n",
       "      <td>5</td>\n",
       "      <td>0,4</td>\n",
       "      <td>-99,02</td>\n",
       "      <td>16,245</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18759</th>\n",
       "      <td>28</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>OFF-BI-6383</td>\n",
       "      <td>Wilson Jones Binding Machine, Durable</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>141,288</td>\n",
       "      <td>7</td>\n",
       "      <td>0,4</td>\n",
       "      <td>-73,052</td>\n",
       "      <td>9,465</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18805</th>\n",
       "      <td>30</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>TEC-PH-5262</td>\n",
       "      <td>Motorola Office Telephone, with Caller ID</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Technology</td>\n",
       "      <td>114,816</td>\n",
       "      <td>4</td>\n",
       "      <td>0,4</td>\n",
       "      <td>-49,824</td>\n",
       "      <td>5,923</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18919</th>\n",
       "      <td>34</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>OFF-AR-6118</td>\n",
       "      <td>Stanley Pencil Sharpener, Easy-Erase</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>10,416</td>\n",
       "      <td>1</td>\n",
       "      <td>0,4</td>\n",
       "      <td>-0,704</td>\n",
       "      <td>1,931</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18929</th>\n",
       "      <td>31</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>OFF-AR-3488</td>\n",
       "      <td>Binney &amp; Smith Markers, Blue</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>19,272</td>\n",
       "      <td>2</td>\n",
       "      <td>0,4</td>\n",
       "      <td>2,872</td>\n",
       "      <td>1,843</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19008</th>\n",
       "      <td>29</td>\n",
       "      <td>US-2016-SC2005055-42514</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SC-2005055</td>\n",
       "      <td>Kai Rey</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Francisco Morazán</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Central America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>OFF-FA-6208</td>\n",
       "      <td>Stockwell Thumb Tacks, Metal</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>21,84</td>\n",
       "      <td>4</td>\n",
       "      <td>0,4</td>\n",
       "      <td>-3,28</td>\n",
       "      <td>1,091</td>\n",
       "      <td>Medium</td>\n",
       "      <td>KR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                 Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "18659      33  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "18728      32  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "18759      28  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "18805      30  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "18919      34  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "18929      31  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "19008      29  US-2016-SC2005055-42514 2016-05-24 2016-05-31  Standard Class   \n",
       "\n",
       "      Customer ID Customer Name      Segment  Postal Code         City  \\\n",
       "18659  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "18728  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "18759  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "18805  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "18919  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "18929  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "19008  SC-2005055       Kai Rey  Home Office          NaN  Tegucigalpa   \n",
       "\n",
       "                   State   Country           Region Market   Product ID  \\\n",
       "18659  Francisco Morazán  Honduras  Central America  LATAM  TEC-CO-5998   \n",
       "18728  Francisco Morazán  Honduras  Central America  LATAM  OFF-AP-3582   \n",
       "18759  Francisco Morazán  Honduras  Central America  LATAM  OFF-BI-6383   \n",
       "18805  Francisco Morazán  Honduras  Central America  LATAM  TEC-PH-5262   \n",
       "18919  Francisco Morazán  Honduras  Central America  LATAM  OFF-AR-6118   \n",
       "18929  Francisco Morazán  Honduras  Central America  LATAM  OFF-AR-3488   \n",
       "19008  Francisco Morazán  Honduras  Central America  LATAM  OFF-FA-6208   \n",
       "\n",
       "                                    Product Name Sub-Category  \\\n",
       "18659                 Sharp Fax Machine, Digital      Copiers   \n",
       "18728                    Breville Toaster, Black   Appliances   \n",
       "18759      Wilson Jones Binding Machine, Durable      Binders   \n",
       "18805  Motorola Office Telephone, with Caller ID       Phones   \n",
       "18919       Stanley Pencil Sharpener, Easy-Erase          Art   \n",
       "18929               Binney & Smith Markers, Blue          Art   \n",
       "19008               Stockwell Thumb Tacks, Metal    Fasteners   \n",
       "\n",
       "              Category      Sales  Quantity Discount      Profit  \\\n",
       "18659       Technology  941,77824         8    0,402  -601,74176   \n",
       "18728  Office Supplies     152,28         5      0,4      -99,02   \n",
       "18759  Office Supplies    141,288         7      0,4     -73,052   \n",
       "18805       Technology    114,816         4      0,4     -49,824   \n",
       "18919  Office Supplies     10,416         1      0,4      -0,704   \n",
       "18929  Office Supplies     19,272         2      0,4       2,872   \n",
       "19008  Office Supplies      21,84         4      0,4       -3,28   \n",
       "\n",
       "      Shipping Cost Order Priority _Initials  \n",
       "18659        80,281         Medium        KR  \n",
       "18728        16,245         Medium        KR  \n",
       "18759         9,465         Medium        KR  \n",
       "18805         5,923         Medium        KR  \n",
       "18919         1,931         Medium        KR  \n",
       "18929         1,843         Medium        KR  \n",
       "19008         1,091         Medium        KR  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if Customer ID contains our generated Initials\n",
    "filtered_df = df_g[df_g.apply(lambda row: not row['Customer ID'].startswith(row['_Initials']), axis=1)]\n",
    "display(filtered_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States 40553    Southern US\n",
      "40614     Western US\n",
      "43081     Eastern US\n",
      "43857     Central US\n",
      "Name: Region, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# The last Digit of the Customer ID can be Region if there is more than one Region per Country\n",
    "for country, data in df_g.groupby(\"Country\"):\n",
    "    if len(data[\"Region\"].drop_duplicates()) > 1:\n",
    "        print(country, data[\"Region\"].drop_duplicates())\n",
    "        #break\n",
    "# => Only the USA seems to have more than 1 Region\n",
    "\n",
    "#df_g[df_g[\"Region\"] == \"Southern US\"][[\"Country\", \"Customer ID\", \"Order ID\", \"Order Date\", \"City\"]].head().drop_duplicates().sort_values([\"Country\", \"Order Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Market</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>_Initials</th>\n",
       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40553</th>\n",
       "      <td>33312</td>\n",
       "      <td>CA-2016-AM10705140-42632</td>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AM-10705140</td>\n",
       "      <td>Anne McFarland</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>36830.0</td>\n",
       "      <td>Auburn</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>OFF-ST-6289</td>\n",
       "      <td>Tennsco Double-Tier Lockers</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>900,08</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>117,0104</td>\n",
       "      <td>53,84</td>\n",
       "      <td>Medium</td>\n",
       "      <td>AM</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40554</th>\n",
       "      <td>33310</td>\n",
       "      <td>CA-2016-AM10705140-42632</td>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AM-10705140</td>\n",
       "      <td>Anne McFarland</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>36830.0</td>\n",
       "      <td>Auburn</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>FUR-CH-5431</td>\n",
       "      <td>Office Star - Professional Matrix Back Chair w...</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>350,98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84,2352</td>\n",
       "      <td>17,04</td>\n",
       "      <td>Medium</td>\n",
       "      <td>AM</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "40553   33312  CA-2016-AM10705140-42632 2016-09-19 2016-09-24  Standard Class   \n",
       "40554   33310  CA-2016-AM10705140-42632 2016-09-19 2016-09-24  Standard Class   \n",
       "\n",
       "       Customer ID   Customer Name   Segment  Postal Code    City    State  \\\n",
       "40553  AM-10705140  Anne McFarland  Consumer      36830.0  Auburn  Alabama   \n",
       "40554  AM-10705140  Anne McFarland  Consumer      36830.0  Auburn  Alabama   \n",
       "\n",
       "             Country       Region Market   Product ID  \\\n",
       "40553  United States  Southern US   USCA  OFF-ST-6289   \n",
       "40554  United States  Southern US   USCA  FUR-CH-5431   \n",
       "\n",
       "                                            Product Name Sub-Category  \\\n",
       "40553                        Tennsco Double-Tier Lockers      Storage   \n",
       "40554  Office Star - Professional Matrix Back Chair w...       Chairs   \n",
       "\n",
       "              Category   Sales  Quantity Discount    Profit Shipping Cost  \\\n",
       "40553  Office Supplies  900,08         4        0  117,0104         53,84   \n",
       "40554        Furniture  350,98         1        0   84,2352         17,04   \n",
       "\n",
       "      Order Priority _Initials  _Region ID  \n",
       "40553         Medium        AM           8  \n",
       "40554         Medium        AM           8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Market</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>_Initials</th>\n",
       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24599</td>\n",
       "      <td>IN-2017-CA120551-42816</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>2017-03-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CA-120551</td>\n",
       "      <td>Cathy Armstrong</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Herat</td>\n",
       "      <td>Hirat</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>FUR-BO-4861</td>\n",
       "      <td>Ikea Library with Doors, Mobile</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>731,82</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>102,42</td>\n",
       "      <td>39,66</td>\n",
       "      <td>Medium</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29465</td>\n",
       "      <td>ID-2015-BD116051-42248</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>2015-09-04</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>BD-116051</td>\n",
       "      <td>Brian Dahlen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Herat</td>\n",
       "      <td>Hirat</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-SU-2988</td>\n",
       "      <td>Acme Scissors, Easy Grip</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>243,54</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>104,49</td>\n",
       "      <td>18,72</td>\n",
       "      <td>Medium</td>\n",
       "      <td>BD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID                Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "0   24599  IN-2017-CA120551-42816 2017-03-22 2017-03-29  Standard Class   \n",
       "1   29465  ID-2015-BD116051-42248 2015-09-01 2015-09-04    Second Class   \n",
       "\n",
       "  Customer ID    Customer Name      Segment  Postal Code   City  State  \\\n",
       "0   CA-120551  Cathy Armstrong  Home Office          NaN  Herat  Hirat   \n",
       "1   BD-116051     Brian Dahlen     Consumer          NaN  Herat  Hirat   \n",
       "\n",
       "       Country         Region        Market   Product ID  \\\n",
       "0  Afghanistan  Southern Asia  Asia Pacific  FUR-BO-4861   \n",
       "1  Afghanistan  Southern Asia  Asia Pacific  OFF-SU-2988   \n",
       "\n",
       "                      Product Name Sub-Category         Category   Sales  \\\n",
       "0  Ikea Library with Doors, Mobile    Bookcases        Furniture  731,82   \n",
       "1         Acme Scissors, Easy Grip     Supplies  Office Supplies  243,54   \n",
       "\n",
       "   Quantity Discount  Profit Shipping Cost Order Priority _Initials  \\\n",
       "0         2        0  102,42         39,66         Medium        CA   \n",
       "1         9        0  104,49         18,72         Medium        BD   \n",
       "\n",
       "   _Region ID  \n",
       "0           0  \n",
       "1           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    3203\n",
      "6    2848\n",
      "2    2323\n",
      "8    1620\n",
      "Name: _Region ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "# Let's remove the last digit if the Country is \"United States\"\n",
    "df_g['_Region ID'] = df_g.apply(lambda row: int(row['Customer ID'][-1]) if row['Country'] == 'United States' else 0, axis=1)\n",
    "df_g['Customer ID'] = df_g.apply(lambda row: row['Customer ID'][:-1] if row['Country'] == 'United States' else row['Customer ID'], axis=1)\n",
    "display(df_g[df_g[\"Country\"] == \"United States\"].head(2))\n",
    "display(df_g[df_g[\"Country\"] != \"United States\"].head(2))\n",
    "print(df_g[df_g[\"Country\"] == \"United States\"][\"_Region ID\"].value_counts())\n",
    "\n",
    "if df_g[df_g[\"Country\"] == \"United States\"][\"_Region ID\"].sum() != 47506:\n",
    "    warnings.warn(\"The sum() of all RegionIDs should be 47506\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found CountyID for Afghanistan = 1\n",
      "Found CountyID for Albania = 2\n",
      "Found CountyID for Algeria = 3\n",
      "Found CountyID for Angola = 4\n",
      "Found CountyID for Argentina = 5\n",
      "Found CountyID for Armenia = 6\n",
      "Found CountyID for Australia = 7\n",
      "Found CountyID for Austria = 8\n",
      "Found CountyID for Azerbaijan = 9\n",
      "Manual CountyID for Bahrain = 10\n",
      "Found CountyID for Bangladesh = 11\n",
      "Found CountyID for Barbados = 12\n",
      "Found CountyID for Belarus = 13\n",
      "Found CountyID for Belgium = 14\n",
      "Manual CountyID for Belize = 82\n",
      "Found CountyID for Benin = 15\n",
      "Manual CountyID for Bhutan = 58\n",
      "Found CountyID for Bolivia = 16\n",
      "Found CountyID for Bosnia and Herzegovina = 17\n",
      "Manual CountyID for Botswana = 117\n",
      "Found CountyID for Brazil = 18\n",
      "Found CountyID for Bulgaria = 19\n",
      "Manual CountyID for Burkina Faso = 111\n",
      "Manual CountyID for Burundi = 20\n",
      "Found CountyID for Cambodia = 21\n",
      "Found CountyID for Cameroon = 22\n",
      "Found CountyID for Canada = 23\n",
      "Found CountyID for Central African Republic = 24\n",
      "Manual CountyID for Chad = 25\n",
      "Found CountyID for Chile = 26\n",
      "Found CountyID for China = 27\n",
      "Found CountyID for Colombia = 28\n",
      "Manual CountyID for Costa Rica = 28\n",
      "Found CountyID for Croatia = 30\n",
      "Found CountyID for Cuba = 31\n",
      "Manual CountyID for Cyprus = 64\n",
      "Found CountyID for Czech Republic = 32\n",
      "Found CountyID for Côte-d'Ivoire = 29\n",
      "Found CountyID for Democratic Republic of the Congo = 33\n",
      "Found CountyID for Denmark = 34\n",
      "Found CountyID for Djibouti = 35\n",
      "Found CountyID for Dominican Republic = 36\n",
      "Found CountyID for Ecuador = 37\n",
      "Found CountyID for Egypt = 38\n",
      "Found CountyID for El Salvador = 39\n",
      "Manual CountyID for Equatorial Guinea = 40\n",
      "Manual CountyID for Eritrea = 41\n",
      "Found CountyID for Estonia = 42\n",
      "Found CountyID for Ethiopia = 43\n",
      "Found CountyID for Finland = 44\n",
      "Found CountyID for France = 45\n",
      "Manual CountyID for French Guiana = 18\n",
      "Found CountyID for Gabon = 46\n",
      "Found CountyID for Georgia = 47\n",
      "Found CountyID for Germany = 48\n",
      "Found CountyID for Ghana = 49\n",
      "Manual CountyID for Greece = 148\n",
      "Manual CountyID for Guadeloupe = 50\n",
      "Found CountyID for Guatemala = 51\n",
      "Found CountyID for Guinea = 52\n",
      "Found CountyID for Guinea-Bissau = 53\n",
      "Manual CountyID for Guyana = 54\n",
      "Found CountyID for Haiti = 54\n",
      "Found CountyID for Honduras = 55\n",
      "Found CountyID for Hong Kong = 56\n",
      "Found CountyID for Hungary = 57\n",
      "Found CountyID for India = 58\n",
      "Found CountyID for Indonesia = 59\n",
      "Found CountyID for Iran = 60\n",
      "Found CountyID for Iraq = 61\n",
      "Found CountyID for Ireland = 62\n",
      "Found CountyID for Israel = 63\n",
      "Found CountyID for Italy = 64\n",
      "Found CountyID for Jamaica = 65\n",
      "Found CountyID for Japan = 66\n",
      "Found CountyID for Jordan = 67\n",
      "Found CountyID for Kazakhstan = 68\n",
      "Found CountyID for Kenya = 69\n",
      "Manual CountyID for Kuwait = 110\n",
      "Found CountyID for Kyrgyzstan = 70\n",
      "Manual CountyID for Laos = 144\n",
      "Found CountyID for Lebanon = 71\n",
      "Found CountyID for Lesotho = 72\n",
      "Found CountyID for Liberia = 73\n",
      "Found CountyID for Libya = 74\n",
      "Found CountyID for Lithuania = 75\n",
      "Manual CountyID for Luxembourg = 48\n",
      "Found CountyID for Macedonia = 76\n",
      "Found CountyID for Madagascar = 77\n",
      "Manual CountyID for Malawi = 87\n",
      "Found CountyID for Malaysia = 78\n",
      "Found CountyID for Mali = 79\n",
      "Found CountyID for Martinique = 80\n",
      "Found CountyID for Mauritania = 81\n",
      "Found CountyID for Mexico = 82\n",
      "Found CountyID for Moldova = 83\n",
      "Found CountyID for Mongolia = 84\n",
      "Manual CountyID for Montenegro = 85\n",
      "Found CountyID for Morocco = 86\n",
      "Found CountyID for Mozambique = 87\n",
      "Found CountyID for Myanmar (Burma) = 88\n",
      "Found CountyID for Namibia = 89\n",
      "Found CountyID for Nepal = 90\n",
      "Found CountyID for Netherlands = 91\n",
      "Found CountyID for New Zealand = 92\n",
      "Found CountyID for Nicaragua = 93\n",
      "Found CountyID for Niger = 94\n",
      "Found CountyID for Nigeria = 95\n",
      "Found CountyID for Norway = 96\n",
      "Manual CountyID for Oman = 110\n",
      "Found CountyID for Pakistan = 97\n",
      "Found CountyID for Panama = 98\n",
      "Found CountyID for Papua New Guinea = 99\n",
      "Found CountyID for Paraguay = 100\n",
      "Found CountyID for Peru = 101\n",
      "Found CountyID for Philippines = 102\n",
      "Found CountyID for Poland = 103\n",
      "Found CountyID for Portugal = 104\n",
      "Found CountyID for Qatar = 105\n",
      "Manual CountyID for Republic of the Congo = 106\n",
      "Found CountyID for Romania = 107\n",
      "Found CountyID for Russia = 108\n",
      "Found CountyID for Rwanda = 109\n",
      "Found CountyID for Saudi Arabia = 110\n",
      "Found CountyID for Senegal = 111\n",
      "Manual CountyID for Serbia = 19\n",
      "Found CountyID for Sierra Leone = 112\n",
      "Found CountyID for Singapore = 113\n",
      "Found CountyID for Slovakia = 114\n",
      "Found CountyID for Slovenia = 115\n",
      "Found CountyID for Somalia = 116\n",
      "Found CountyID for South Africa = 117\n",
      "Found CountyID for South Korea = 118\n",
      "Manual CountyID for South Sudan = 119\n",
      "Found CountyID for Spain = 120\n",
      "Found CountyID for Sri Lanka = 121\n",
      "Found CountyID for Sudan = 122\n",
      "Manual CountyID for Suriname = 18\n",
      "Found CountyID for Swaziland = 123\n",
      "Found CountyID for Sweden = 124\n",
      "Found CountyID for Switzerland = 125\n",
      "Found CountyID for Syria = 126\n",
      "Found CountyID for Taiwan = 127\n",
      "Manual CountyID for Tajikistan = 128\n",
      "Found CountyID for Tanzania = 129\n",
      "Found CountyID for Thailand = 130\n",
      "Manual CountyID for The Gambia = 111\n",
      "Found CountyID for Togo = 131\n",
      "Found CountyID for Trinidad and Tobago = 132\n",
      "Found CountyID for Tunisia = 133\n",
      "Found CountyID for Turkey = 134\n",
      "Found CountyID for Turkmenistan = 135\n",
      "Found CountyID for Uganda = 136\n",
      "Found CountyID for Ukraine = 137\n",
      "Found CountyID for United Arab Emirates = 138\n",
      "Found CountyID for United Kingdom = 139\n",
      "Found CountyID for United States = 140\n",
      "Found CountyID for Uruguay = 141\n",
      "Found CountyID for Uzbekistan = 142\n",
      "Found CountyID for Venezuela = 143\n",
      "Found CountyID for Vietnam = 144\n",
      "Manual CountyID for Western Sahara = 29\n",
      "Found CountyID for Yemen = 145\n",
      "Found CountyID for Zambia = 146\n",
      "Found CountyID for Zimbabwe = 147\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "prev_id = 0\n",
    "out = \"\"\n",
    "\n",
    "matching = {}\n",
    "manual_matching = {\n",
    "    \"Bahrain\": 10,\n",
    "    \"Belize\": 82, # Matches Mexico (!)\n",
    "    \"Bhutan\": 58, # Matches India (!)\n",
    "    \"Botswana\": 117, # Matches South Africa (!)\n",
    "    \"Burkina Faso\": 111, # Matches Senegal (1)\n",
    "    \"Burundi\": 20,\n",
    "    \"Chad\": 25,\n",
    "    \"Costa Rica\": 28, # Matches Mexico (!)\n",
    "    \"Cyprus\": 64, # Matches Italy (!!!)\n",
    "    \"Equatorial Guinea\": 40,\n",
    "    \"Eritrea\": 41,\n",
    "    \"French Guiana\": 18, # Matches Brazil (!)\n",
    "    \"Greece\": 148, # No zf found(!!!!!) / Must be added very late since it has the highest CountryID\n",
    "    \"Guadeloupe\": 50,\n",
    "    \"Guyana\": 54, # Matches Brazil (!)\n",
    "    \"Kuwait\": 110, # Matches Saudi Arabia (!)\n",
    "    \"Laos\": 144, # Matches Laos (1)\n",
    "    \"Luxembourg\": 48, # Matches Germany (!)\n",
    "    \"Malawi\": 87, # Matches Mozambique (!)\n",
    "    \"Montenegro\": 85,\n",
    "    \"Oman\": 110, # Matches Saudi Arabia (!)\n",
    "    \"Republic of the Congo\": 106,\n",
    "    \"Serbia\": 19, # Matches Bulgaria (!)\n",
    "    \"South Sudan\": 119,\n",
    "    \"Suriname\": 18, # Matches Brazil (!)\n",
    "    \"Tajikistan\": 128, # [128]\n",
    "    \"The Gambia\": 111, # Matches Senegal (!)\n",
    "    \"Western Sahara\": 29, # No clue why western sahara has such a low CountryID\n",
    "}\n",
    "\n",
    "for country, data in df_g.groupby(\"Country\"):\n",
    "    #print(country)\n",
    "    zf_found = False\n",
    "    for i in reversed(range(1,5)):\n",
    "\n",
    "        ids = (data['Customer ID'].str[-i:].drop_duplicates().astype(\"int\").values)\n",
    "        #print(i, prev_id, ids)\n",
    "\n",
    "        # lets see if we can find the \"0\" and \"5\" before each countryID\n",
    "        if (len(ids) == 2) and (abs(ids[0]-ids[-1]) in [50, 500, 5000]):\n",
    "            zf_found = True\n",
    "\n",
    "\n",
    "        if (zf_found == True) & (len(ids) == 1) & (int(ids[0]) not in matching):\n",
    "            prev_id = int(ids[0])\n",
    "            print(\"Found CountyID for\", country, \"=\", prev_id)\n",
    "            matching[country] = prev_id\n",
    "            #display(data[[\"Row ID\", \"Customer ID\", \"Country\"]].head())\n",
    "            df_g.loc[df_g['Country'] == country, 'Customer ID'] = df_g.loc[df_g['Country'] == country, 'Customer ID'].str[:-i]\n",
    "            break\n",
    "\n",
    "    if country in manual_matching:\n",
    "        print(\"Manual CountyID for\", country, \"=\", manual_matching[country])\n",
    "        matching[country] = manual_matching[country]\n",
    "        df_g.loc[df_g['Country'] == country, 'Customer ID'] = df_g.loc[df_g['Country'] == country, 'Customer ID'].str[:-len(str(manual_matching[country]))]\n",
    "    elif zf_found == False:\n",
    "        print(\"Coudn't find a CountryID for\", country)\n",
    "        ids = (data['Customer ID'].str[-4:].drop_duplicates().astype(\"int\").values)\n",
    "        print(ids, len(ids))\n",
    "        out = out + \"\\n\" + '\"' + country + '\": ' + str(prev_id + 1) + ', # ' + ids[0]\n",
    "print(out)\n",
    "#display(matching)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Medium\n",
       "5         High\n",
       "12         Low\n",
       "18    Critical\n",
       "Name: Order Priority, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g[\"Order Priority\"].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Customer Name</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>_Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>Aaron Bergman</td>\n",
       "      <td>AB-10015</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6252</th>\n",
       "      <td>Aaron Bergman</td>\n",
       "      <td>AB-15</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>Aaron Hawkins</td>\n",
       "      <td>AH-10030</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Aaron Smayling</td>\n",
       "      <td>AS-10045</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>Adam Bellavance</td>\n",
       "      <td>AB-10060</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Adam Hart</td>\n",
       "      <td>AH-10075</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2185</th>\n",
       "      <td>Adam Shillingsburg</td>\n",
       "      <td>AS-10090</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Adrian Barton</td>\n",
       "      <td>AB-10105</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6598</th>\n",
       "      <td>Adrian Barton</td>\n",
       "      <td>AB-105</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Adrian Hane</td>\n",
       "      <td>AH-10120</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11640</th>\n",
       "      <td>Adrian Hane</td>\n",
       "      <td>AH-120</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7018</th>\n",
       "      <td>Adrian Shami</td>\n",
       "      <td>AS-10135</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>Adrian Shami</td>\n",
       "      <td>AS-135</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>Aimee Bixby</td>\n",
       "      <td>AB-10150</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22144</th>\n",
       "      <td>Aimee Bixby</td>\n",
       "      <td>AB-150</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>Alan Barnes</td>\n",
       "      <td>AB-10165</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6468</th>\n",
       "      <td>Alan Barnes</td>\n",
       "      <td>AB-165</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Alan Dominguez</td>\n",
       "      <td>AD-10180</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11715</th>\n",
       "      <td>Alan Dominguez</td>\n",
       "      <td>AD-180</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>Alan Haines</td>\n",
       "      <td>AH-10195</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>Alan Haines</td>\n",
       "      <td>AH-195</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Alan Hwang</td>\n",
       "      <td>AH-10210</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Alan Hwang</td>\n",
       "      <td>AH-210</td>\n",
       "      <td>AH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Alan Schoenberger</td>\n",
       "      <td>AS-10225</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4419</th>\n",
       "      <td>Alan Schoenberger</td>\n",
       "      <td>AS-225</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>Alan Shonely</td>\n",
       "      <td>AS-10240</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6109</th>\n",
       "      <td>Alan Shonely</td>\n",
       "      <td>AS-240</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>Alejandro Ballentine</td>\n",
       "      <td>AB-10255</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>Alejandro Ballentine</td>\n",
       "      <td>AB-255</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>Alejandro Grove</td>\n",
       "      <td>AG-10270</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6267</th>\n",
       "      <td>Alejandro Grove</td>\n",
       "      <td>AG-270</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>Alejandro Savely</td>\n",
       "      <td>AS-10285</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Alejandro Savely</td>\n",
       "      <td>AS-285</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2611</th>\n",
       "      <td>Aleksandra Gannaway</td>\n",
       "      <td>AG-10300</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24685</th>\n",
       "      <td>Aleksandra Gannaway</td>\n",
       "      <td>AG-300</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17661</th>\n",
       "      <td>Aleksandra Gannaway</td>\n",
       "      <td>AG-88888</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Alex Avila</td>\n",
       "      <td>AA-10315</td>\n",
       "      <td>AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4211</th>\n",
       "      <td>Alex Avila</td>\n",
       "      <td>AA-315</td>\n",
       "      <td>AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>Alex Grayson</td>\n",
       "      <td>AG-10330</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28536</th>\n",
       "      <td>Alex Grayson</td>\n",
       "      <td>AG-330</td>\n",
       "      <td>AG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Customer Name Customer ID _Initials\n",
       "730           Aaron Bergman    AB-10015        AB\n",
       "6252          Aaron Bergman       AB-15        AB\n",
       "725           Aaron Hawkins    AH-10030        AH\n",
       "999          Aaron Smayling    AS-10045        AS\n",
       "771         Adam Bellavance    AB-10060        AB\n",
       "217               Adam Hart    AH-10075        AH\n",
       "2185     Adam Shillingsburg    AS-10090        AS\n",
       "641           Adrian Barton    AB-10105        AB\n",
       "6598          Adrian Barton      AB-105        AB\n",
       "1020            Adrian Hane    AH-10120        AH\n",
       "11640           Adrian Hane      AH-120        AH\n",
       "7018           Adrian Shami    AS-10135        AS\n",
       "6359           Adrian Shami      AS-135        AS\n",
       "1407            Aimee Bixby    AB-10150        AB\n",
       "22144           Aimee Bixby      AB-150        AB\n",
       "807             Alan Barnes    AB-10165        AB\n",
       "6468            Alan Barnes      AB-165        AB\n",
       "563          Alan Dominguez    AD-10180        AD\n",
       "11715        Alan Dominguez      AD-180        AD\n",
       "2667            Alan Haines    AH-10195        AH\n",
       "6632            Alan Haines      AH-195        AH\n",
       "575              Alan Hwang    AH-10210        AH\n",
       "83               Alan Hwang      AH-210        AH\n",
       "577       Alan Schoenberger    AS-10225        AS\n",
       "4419      Alan Schoenberger      AS-225        AS\n",
       "2183           Alan Shonely    AS-10240        AS\n",
       "6109           Alan Shonely      AS-240        AS\n",
       "1944   Alejandro Ballentine    AB-10255        AB\n",
       "6633   Alejandro Ballentine      AB-255        AB\n",
       "716         Alejandro Grove    AG-10270        AG\n",
       "6267        Alejandro Grove      AG-270        AG\n",
       "2066       Alejandro Savely    AS-10285        AS\n",
       "168        Alejandro Savely      AS-285        AS\n",
       "2611    Aleksandra Gannaway    AG-10300        AG\n",
       "24685   Aleksandra Gannaway      AG-300        AG\n",
       "17661   Aleksandra Gannaway    AG-88888        AG\n",
       "1628             Alex Avila    AA-10315        AA\n",
       "4211             Alex Avila      AA-315        AA\n",
       "2535           Alex Grayson    AG-10330        AG\n",
       "28536          Alex Grayson      AG-330        AG"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oh wow - the customers are sorted alphabetically and added 15 each times? WTF!!!\n",
    "# And please beware that the entries for Greece got a 88888\n",
    "df_g[[\"Customer Name\", \"Customer ID\", \"_Initials\"]].sort_values([\"Customer Name\", \"Customer ID\"]).drop_duplicates().head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[41291, 41294, 41296, 41298, 41300, 41302, 41304, 41307, 41308, 41310, 41311, 41314, 41315, 41316, 41320, 41331, 41332, 41333, 41336, 41337, 41338, 41340, 41342, 41346, 41348, 41352, 41356, 41357, 41358, 41359, 41361, 41365, 41367, 41368, 41370, 41371, 41372, 41375, 41376, 41377, 41378, 41379, 41380, 41384, 41385, 41386, 41388, 41391, 41392, 41393, 41396, 41397, 41398, 41400, 41404, 41406, 41407, 41408, 41409, 41410, 41412, 41415, 41417, 41419, 41424, 41425, 41426, 41432, 41433, 41435, 41437, 41438, 41439, 41440, 41442, 41443, 41445, 41446, 41447, 41448, 41449, 41450, 41451, 41459, 41460, 41463, 41465, 41466, 41467, 41473, 41474, 41476, 41479, 41481, 41482, 41483, 41484, 41486, 41487, 41488, 41493, 41497, 41499, 41503, 41506, 41507, 41508, 41510, 41512, 41513, 41514, 41518, 41519, 41520, 41522, 41525, 41528, 41529, 41533, 41535, 41536, 41540, 41542, 41543, 41544, 41546, 41549, 41550, 41561, 41563, 41564, 41565, 41569, 41576, 41578, 41581, 41582, 41583, 41586, 41587, 41590, 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42509, 42511, 42512, 42513, 42520, 42523, 42526, 42529, 42534, 42541, 42547, 42554, 42558, 42565, 42568, 42570, 42580, 42581, 42584, 42587, 42589, 42590, 42592, 42593, 42594, 42595, 42598, 42601, 42603, 42609, 42629, 42631, 42633, 42640, 42650, 42657, 42669, 42670, 42671, 42674, 42675, 42682, 42686, 42689, 42718, 42723, 42730, 42738, 42745, 42754, 42755, 42763, 42770, 42773, 42774, 42783, 42785, 42794, 42806, 42809, 42813, 42823, 42824, 42828, 42837, 42847, 42848, 42856, 42859, 42861, 42865, 42869, 42871, 42881, 42892, 42893, 42894, 42897, 42912, 42922, 42923, 42941, 42942, 42943, 42944, 42945, 42947, 42952, 42953, 42954, 42964, 42968, 42980, 42982, 42984, 42992, 42993, 42998, 43001, 43004, 43011, 43017, 43020, 43021, 43036, 43041, 43042, 43048, 43049, 43052, 43062, 43064, 43070, 43072, 43074, 43076, 43115, 43123, 43128, 43139, 43159, 43165, 43166, 43173, 43181, 43182, 43185, 43215, 43219, 43238, 43239, 43241, 43246, 43247, 43257, 43262, 43275, 43277, 43283, 43285, 43297, 43299, 43306, 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45056, 45070, 45114, 45144, 45182, 45200, 45201, 45209, 45237, 45247, 45253, 45304, 45340, 45343, 45354, 45358, 45366, 45368, 45385, 45393, 45417, 45422, 45502, 45583, 45643, 45650, 45704, 45744, 45794, 45903, 45910, 46010, 46069, 46118, 46182, 46186, 46232, 46250, 46254, 46261, 46385, 46417, 46537, 46544, 46574, 46621, 46641, 46648, 46717, 46718, 46721, 46725, 46778, 46946, 47058, 47093, 47320, 47328, 47384, 47400, 47453, 47554, 47632, 47657, 47768, 48066, 48428, 48430, 48506, 48548, 48851, 48920, 49559, 50126, 50338, 50895]\n",
      "41291\n"
     ]
    }
   ],
   "source": [
    "row_ids = []\n",
    "for person, data in df_g[[\"Customer Name\", \"Customer ID\", \"_Initials\", \"Row ID\"]].sort_values([\"Customer Name\", \"Customer ID\", \"Row ID\"]).groupby(\"Customer Name\"):\n",
    "    ids = data[\"Customer ID\"].drop_duplicates().values\n",
    "    #print(cleaned_ids, i)\n",
    "    \n",
    "    # Find the ID with the lower RowID and than search the max RowID for this CustomerID!!!\n",
    "    if person == \"Susan Vittorini\":\n",
    "        # I have no clue what happened with \"Susan Vittorini\" (for Row ID 31295) which is the only person where multiple IDs changes happend:\n",
    "        # 321,6/22/2014,SV-2093555\n",
    "        # [...]\n",
    "        # 29801,9/21/2016,SV-209357\n",
    "        # 31295,12/20/2017,SV-1093559\n",
    "        # 31296,12/20/2017,SV-1093559\n",
    "        # 33604,11/6/2017,SV-209351406\n",
    "        # [...]\n",
    "        # 39938,12/3/2017,SV-209351404\n",
    "        # 43885,9/25/2015,SV-1093595\n",
    "        continue\n",
    "    elif len(ids) > 1:\n",
    "        # Find the customer ID with the highest row ID\n",
    "        max_row_id = data['Row ID'].max()\n",
    "        highest_row_id_customer_id = data.loc[data['Row ID'] == max_row_id, 'Customer ID'].iloc[0]\n",
    "\n",
    "        # Find the highest row ID for the customer ID with the lowest row ID\n",
    "        lowest_row_id = data.loc[data['Customer ID'] == highest_row_id_customer_id, 'Row ID'].min()\n",
    "\n",
    "        # print()\n",
    "        # print(person)\n",
    "        # print(\"Lowest Row ID Customer ID:\", highest_row_id_customer_id)\n",
    "        # print(\"Highest Row ID for Lowest Row ID Customer ID:\", lowest_row_id)\n",
    "        row_ids.append(lowest_row_id)\n",
    "    #break\n",
    "row_ids.sort()\n",
    "print(row_ids)\n",
    "print(min(row_ids))\n",
    "\n",
    "changeRowID = min(row_ids)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Check if there are any rowIDs that are larger than the changeRowID (just to be safe!)\n",
    "for person, data in df_g[[\"Customer Name\", \"Customer ID\", \"_Initials\", \"Row ID\"]].sort_values([\"Customer Name\", \"Customer ID\", \"Row ID\"]).groupby(\"Customer Name\"):\n",
    "    ids = data[\"Customer ID\"].drop_duplicates().values\n",
    "\n",
    "    if person == \"Susan Vittorini\":\n",
    "        continue\n",
    "    elif len(ids) > 1:\n",
    "        min_row_id = data['Row ID'].min()\n",
    "        lowest_row_id_customer_id = data.loc[data['Row ID'] == min_row_id, 'Customer ID'].iloc[0]\n",
    "\n",
    "        # Find the highest row ID for the customer ID with the lowest row ID\n",
    "        max_row_id = data.loc[data['Customer ID'] == lowest_row_id_customer_id, 'Row ID'].max()\n",
    "        if max_row_id > changeRowID:\n",
    "            print(person, max_row_id, \">\", changeRowID)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'wild_mode' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14508\\518178860.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"WTF:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m     \u001b[1;32melif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mwild_mode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mtemp_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m         \u001b[1;31m# print(\"Treffer:\", name, len(str(temp_id)), temp_id, ids)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[0mlast_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemp_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'wild_mode' is not defined"
     ]
    }
   ],
   "source": [
    "last_id = 0\n",
    "\n",
    "# TODO The reason why there a sometimes multiple ids (i.e.:AG-270 and AG-10270) has something to do with the row ID? \n",
    "# Somewhere over the ID 40.000 the ID in Customer ID changes by 10.000 \n",
    "\n",
    "for name, data in df_g.groupby(\"Customer Name\"):\n",
    "    \n",
    "    temp_id = last_id + 15\n",
    "    # if ( last_id < 1000 and temp_id > 1000):\n",
    "    #     temp_id += 6000 # Please don't ask me why\n",
    "\n",
    "    ids = (data['Customer ID'].str[3:].drop_duplicates().astype(\"int\").values)\n",
    "    ids = ids[~((ids == 88887) | (ids == 88888))]  # Just needed for \"Aleksandra Gannaway\" and \"Denny Joy\" in Greece\n",
    "\n",
    "    \n",
    "    if (len(ids) == 1) & (temp_id == ids[0]):\n",
    "        # print(\"Treffer:\", name, len(str(temp_id)), temp_id, ids)\n",
    "        last_id = temp_id\n",
    "    elif (len(ids) == 1) & (temp_id + 10000 == ids[0]):\n",
    "        # print(\"Treffer:\", name, len(str(temp_id)), temp_id, ids)\n",
    "        last_id = temp_id\n",
    "    elif name == \"Kai Rey\":\n",
    "        # TODO No idea what happened here, but Kai Rey shares the same ID than the stupid Customer Name \"Sample Company A\"\n",
    "        # Since Kai Rey also has the Initials \"SC\" (same as Sample Company A) I guess Kai Rey shoud be renamed \"Sample Company A\"\n",
    "        print(\"WTF:\", name, len(str(temp_id)), temp_id, ids)\n",
    "        continue\n",
    "    elif (len(ids) == 2) & (abs(ids[0] - ids[-1]) == 10000) & (wild_mode == True) & (ids.min() == temp_id):\n",
    "        # print(\"Treffer:\", name, len(str(temp_id)), temp_id, ids)\n",
    "        last_id = temp_id\n",
    "        #break\n",
    "    else:\n",
    "        print(\"Unknown:\", name, len(str(temp_id)), temp_id, ids)\n",
    "    #break\n",
    "    #for country, data2 in data.group(\"Country\"):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kai Rey 3 420 [50]\n"
     ]
    }
   ],
   "source": [
    "last_id = 0\n",
    "wild_mode = False\n",
    "\n",
    "for name, data in df_g.groupby(\"Customer Name\"):\n",
    "    \n",
    "    temp_id = last_id + 15\n",
    "    if ( last_id < 1000 and temp_id > 1000):\n",
    "        temp_id += 6000 # Please don't ask me why\n",
    "    elif ( last_id < 10000 and temp_id > 10000):\n",
    "        wild_mode = True    \n",
    "\n",
    "    ids = (data['Customer ID'].str[-len(str(temp_id)):].drop_duplicates().astype(\"int\").values)\n",
    "    \n",
    "    if (len(ids) == 1) & (temp_id == ids[0]):\n",
    "        last_id = temp_id\n",
    "    elif name == \"Kai Rey\":\n",
    "        # TODO No idea what happened here, but Kai Rey shares the same ID than the stupid Customer Name \"Sample Company A\"\n",
    "        # Since Kai Rey also has the Initials \"SC\" (same as Sample Company A) I guess Kai Rey shoud be renamed \"Sample Company A\"\n",
    "        print(name, len(str(temp_id)), temp_id, ids)\n",
    "        continue\n",
    "    elif (len(ids) == 2) & (abs(ids[0] - ids[-1]) == 10000) & (wild_mode == True) & (ids.min() == temp_id):\n",
    "        last_id = temp_id\n",
    "        #break\n",
    "    #break\n",
    "    #for country, data2 in data.group(\"Country\"):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greece [7, 8]\n"
     ]
    }
   ],
   "source": [
    "# We already removed RegionIDs for Unites States\n",
    "# We already removed CountryID for all states\n",
    "# Now we look for the last letter which is supposed to be a 0 or a 5\n",
    "for country, data in df_g.groupby(\"Country\"):\n",
    "    ids = (data['Customer ID'].str[-1:].drop_duplicates().astype(\"int\").values)\n",
    "    ids = [num for num in ids if num != 0 and num != 5]\n",
    "    if len(ids) > 0:\n",
    "        print(country, ids)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How is the field Order ID build?\n",
    "* `IN-2017-CA120551-42816`\n",
    "* `[Country Code]-[Year]-[Customer ID]-[Days since 1899, 12, 30]`\n",
    "\n",
    "The part with the \"days since 1899, 12, 30 is the way that Excel calculates the date internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# for date, data in df_g.groupby([\"Order Date\"]).sort_values('Order Date'):\n",
    "#     print(date, len(data))\n",
    "\n",
    "# Group the dataframe by 'Date' and count the occurrences\n",
    "grouped_df = df_g.groupby('Order Date').size().reset_index(name='Count')\n",
    "\n",
    "# Sort the dataframe by 'Date' in chronological order\n",
    "sorted_df = grouped_df.sort_values('Order Date')\n",
    "\n",
    "# Print the sorted dataframe\n",
    "# print(sorted_df[sorted_df[\"Order Date\"] > \"2016-02-28\"])\n",
    "# print(sorted_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
    "# for date, data in df_g.groupby([\"Order Date\"]).sort_values('Order Date'):\n",
    "#     print(date, len(data))\n",
    "\n",
    "# Group the dataframe by 'Date' and count the occurrences\n",
    "grouped_df = df.groupby('Order Date').size().reset_index(name='Count')\n",
    "\n",
    "# Sort the dataframe by 'Date' in chronological order\n",
    "sorted_df = grouped_df.sort_values('Order Date')\n",
    "\n",
    "# Print the sorted dataframe\n",
    "# print(sorted_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Order ID</th>\n",
       "      <th>Customer ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Order ID, Customer ID]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are there any entries for Country 'United States' without a '140-' in the Order ID?\n",
      "What are the numbers in the Order ID?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "40553    CA-2016-AM10705140-42632\n",
       "40554    CA-2016-AM10705140-42632\n",
       "40555    CA-2016-AM10705140-42632\n",
       "40556    CA-2017-KN16705140-42898\n",
       "40557    CA-2015-MS17710140-42079\n",
       "Name: Order ID, dtype: object"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display(df_g[[\"Customer ID\", \"Order ID\"]])\n",
    "\n",
    "display(df_g[df_g.apply(lambda row: row['Customer ID'].replace('-', '') not in row['Order ID'], axis=1)][[\"Order ID\", \"Customer ID\"]])\n",
    "\n",
    "for c, data in (df_g[df_g.apply(lambda row: row['Customer ID'].replace('-', '') not in row['Order ID'], axis=1)].groupby([\"Country\"])):\n",
    "    print(f\"{c}: {len(data)} lines\")\n",
    "\n",
    "print(\"Are there any entries for Country 'United States' without a '140-' in the Order ID?\") \n",
    "df_g[(df_g[\"Country\"] == \"United States\") & (\"140-\" not in df_g[\"Order ID\"].to_string())]\n",
    "\n",
    "print(\"What are the numbers in the Order ID?\")\n",
    "df_g[(df_g[\"Country\"] == \"United States\")][\"Order ID\"].head()\n",
    "\n",
    "#display(df_g[(df_g['Customer_ID_Contained'] == False) & (\"140-\" in df_g[\"Order ID\"].to_string()) & (df_g.Country != \"United States\")][[\"Order ID\", \"Customer ID\", \"Country\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9994, 21)\n",
      "(51290, 26)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df_g.shape) # + Market, Shipping Cost, Order Priority"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How are the ShippingCosts calculated?\n",
    "* 2 pieces cost 2.88 = 5,76\n",
    "* 3 pieces cost 1.85 = 5,55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 32060,CA-2014-CS12250140-41653,1/14/2014,1/16/2014,Second Class,CS-122501408,Chris Selesnick,Corporate,71111,Bossier City,Louisiana,United States,Southern US,USCA,OFF-FA-6129,Staples,Fasteners,Office Supplies,\"11,36\",2,0,\"5,3392\",\"2,88\",Critical\n",
    "# 32063,CA-2014-CS12250140-41653,1/14/2014,1/16/2014,Second Class,CS-122501408,Chris Selesnick,Corporate,71111,Bossier City,Louisiana,United States,Southern US,USCA,OFF-FA-6129,Staples,Fasteners,Office Supplies,\"5,64\",3,0,\"2,7072\",\"1,85\",Critical"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Orders are duplicated due to different shipping costs (!!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Timestamp('2014-01-14 00:00:00'), 'Chris Selesnick', 'OFF-FA-6129', 'Bossier City')\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Market</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>_Initials</th>\n",
       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44694</th>\n",
       "      <td>32060</td>\n",
       "      <td>CA-2014-CS12250140-41653</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CS-12250</td>\n",
       "      <td>Chris Selesnick</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>71111.0</td>\n",
       "      <td>Bossier City</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>OFF-FA-6129</td>\n",
       "      <td>Staples</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>11,36</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5,3392</td>\n",
       "      <td>2,88</td>\n",
       "      <td>Critical</td>\n",
       "      <td>CS</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44695</th>\n",
       "      <td>32063</td>\n",
       "      <td>CA-2014-CS12250140-41653</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CS-12250</td>\n",
       "      <td>Chris Selesnick</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>71111.0</td>\n",
       "      <td>Bossier City</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>United States</td>\n",
       "      <td>Southern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>OFF-FA-6129</td>\n",
       "      <td>Staples</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>5,64</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2,7072</td>\n",
       "      <td>1,85</td>\n",
       "      <td>Critical</td>\n",
       "      <td>CS</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
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       "       Row ID                  Order ID Order Date  Ship Date     Ship Mode  \\\n",
       "44694   32060  CA-2014-CS12250140-41653 2014-01-14 2014-01-16  Second Class   \n",
       "44695   32063  CA-2014-CS12250140-41653 2014-01-14 2014-01-16  Second Class   \n",
       "\n",
       "      Customer ID    Customer Name    Segment  Postal Code          City  \\\n",
       "44694    CS-12250  Chris Selesnick  Corporate      71111.0  Bossier City   \n",
       "44695    CS-12250  Chris Selesnick  Corporate      71111.0  Bossier City   \n",
       "\n",
       "           State        Country       Region Market   Product ID Product Name  \\\n",
       "44694  Louisiana  United States  Southern US   USCA  OFF-FA-6129      Staples   \n",
       "44695  Louisiana  United States  Southern US   USCA  OFF-FA-6129      Staples   \n",
       "\n",
       "      Sub-Category         Category  Sales  Quantity Discount  Profit  \\\n",
       "44694    Fasteners  Office Supplies  11,36         2        0  5,3392   \n",
       "44695    Fasteners  Office Supplies   5,64         3        0  2,7072   \n",
       "\n",
       "      Shipping Cost Order Priority _Initials  _Region ID  \n",
       "44694          2,88       Critical        CS           8  \n",
       "44695          1,85       Critical        CS           8  "
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      "(Timestamp('2014-04-01 00:00:00'), 'Hunter Lopez', 'TEC-PH-5839', 'Rajshahi')\n"
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       "      Row ID                 Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "4135   23445  IN-2014-HL1504011-41730 2014-04-01 2014-04-07  Standard Class   \n",
       "4136   23447  IN-2014-HL1504011-41730 2014-04-01 2014-04-07  Standard Class   \n",
       "\n",
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       "4136    HL-15040  Hunter Lopez  Consumer          NaN  Rajshahi  Rajshahi   \n",
       "\n",
       "         Country         Region        Market   Product ID  \\\n",
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       "4136  Bangladesh  Southern Asia  Asia Pacific  TEC-PH-5839   \n",
       "\n",
       "                       Product Name Sub-Category    Category Sales  Quantity  \\\n",
       "4135  Samsung Smart Phone, Cordless       Phones  Technology  1278         2   \n",
       "4136  Samsung Smart Phone, Cordless       Phones  Technology  3195         5   \n",
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       "47270   34703  US-2014-LB16795140-41751 2014-04-22 2014-04-26  Standard Class   \n",
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       "47270    LB-16795  Laurel Beltran  Home Office      43229.0  Columbus  Ohio   \n",
       "\n",
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       "      <td>Singapore</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Singapore</td>\n",
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       "      <td>4</td>\n",
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       "      <td>146,76</td>\n",
       "      <td>31,75</td>\n",
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       "      <td>SJ</td>\n",
       "      <td>0</td>\n",
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       "      <th>34480</th>\n",
       "      <td>29965</td>\n",
       "      <td>IN-2014-SJ20500113-41868</td>\n",
       "      <td>2014-08-17</td>\n",
       "      <td>2014-08-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SJ-20500</td>\n",
       "      <td>Shirley Jackson</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Singapore</td>\n",
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       "      <td>Technology</td>\n",
       "      <td>262,14</td>\n",
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       "      <td>73,38</td>\n",
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       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "34461   29966  IN-2014-SJ20500113-41868 2014-08-17 2014-08-21  Standard Class   \n",
       "34480   29965  IN-2014-SJ20500113-41868 2014-08-17 2014-08-21  Standard Class   \n",
       "\n",
       "      Customer ID    Customer Name   Segment  Postal Code       City  \\\n",
       "34461    SJ-20500  Shirley Jackson  Consumer          NaN  Singapore   \n",
       "34480    SJ-20500  Shirley Jackson  Consumer          NaN  Singapore   \n",
       "\n",
       "           State    Country             Region        Market   Product ID  \\\n",
       "34461  Singapore  Singapore  Southeastern Asia  Asia Pacific  TEC-PH-5271   \n",
       "34480  Singapore  Singapore  Southeastern Asia  Asia Pacific  TEC-PH-5271   \n",
       "\n",
       "                            Product Name Sub-Category    Category   Sales  \\\n",
       "34461  Motorola Speaker Phone, Full Size       Phones  Technology  524,28   \n",
       "34480  Motorola Speaker Phone, Full Size       Phones  Technology  262,14   \n",
       "\n",
       "       Quantity Discount  Profit Shipping Cost Order Priority _Initials  \\\n",
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       "34480         2        0   73,38         22,27         Medium        SJ   \n",
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      "(Timestamp('2014-10-14 00:00:00'), 'Fred Wasserman', 'OFF-LA-5402', 'Dhaka')\n"
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       "      Row ID                 Order ID Order Date  Ship Date       Ship Mode  \\\n",
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       "4114   27934  IN-2014-FW1439511-41926 2014-10-14 2014-10-19  Standard Class   \n",
       "\n",
       "     Customer ID   Customer Name    Segment  Postal Code   City  State  \\\n",
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       "4114    FW-14395  Fred Wasserman  Corporate          NaN  Dhaka  Dhaka   \n",
       "\n",
       "         Country         Region        Market   Product ID  \\\n",
       "4092  Bangladesh  Southern Asia  Asia Pacific  OFF-LA-5402   \n",
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       "\n",
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       "4092  Novimex Shipping Labels, 5000 Label Set       Labels  Office Supplies   \n",
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      "(Timestamp('2014-12-26 00:00:00'), 'Chad McGuire', 'OFF-BI-3255', 'Ho Chi Minh City')\n"
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       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
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       "50960   28135  ID-2014-CM12115144-41999 2014-12-26 2014-12-31  Standard Class   \n",
       "\n",
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       "\n",
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       "50960  Ho Chí Minh City  Vietnam  Southeastern Asia  Asia Pacific   \n",
       "\n",
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       "\n",
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       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "41904   39072  CA-2014-CD12790140-41999 2014-12-26 2014-12-30  Standard Class   \n",
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       "\n",
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       "41905    CD-12790  Cynthia Delaney  Home Office      92553.0  Moreno Valley   \n",
       "\n",
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       "41905  California  United States  Western US   USCA  OFF-FA-6129      Staples   \n",
       "\n",
       "      Sub-Category         Category  Sales  Quantity Discount  Profit  \\\n",
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       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
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       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>_Initials</th>\n",
       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31461</th>\n",
       "      <td>16804</td>\n",
       "      <td>ES-2015-BW1111096-42066</td>\n",
       "      <td>2015-03-03</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BW-11110</td>\n",
       "      <td>Bart Watters</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-ST-4294</td>\n",
       "      <td>Fellowes Trays, Industrial</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>173,61</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>41,58</td>\n",
       "      <td>11,27</td>\n",
       "      <td>Medium</td>\n",
       "      <td>BW</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31462</th>\n",
       "      <td>16805</td>\n",
       "      <td>ES-2015-BW1111096-42066</td>\n",
       "      <td>2015-03-03</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BW-11110</td>\n",
       "      <td>Bart Watters</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-ST-4294</td>\n",
       "      <td>Fellowes Trays, Industrial</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>115,74</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>27,72</td>\n",
       "      <td>10,49</td>\n",
       "      <td>Medium</td>\n",
       "      <td>BW</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                 Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "31461   16804  ES-2015-BW1111096-42066 2015-03-03 2015-03-09  Standard Class   \n",
       "31462   16805  ES-2015-BW1111096-42066 2015-03-03 2015-03-09  Standard Class   \n",
       "\n",
       "      Customer ID Customer Name    Segment  Postal Code  City State Country  \\\n",
       "31461    BW-11110  Bart Watters  Corporate          NaN  Oslo  Oslo  Norway   \n",
       "31462    BW-11110  Bart Watters  Corporate          NaN  Oslo  Oslo  Norway   \n",
       "\n",
       "                Region  Market   Product ID                Product Name  \\\n",
       "31461  Northern Europe  Europe  OFF-ST-4294  Fellowes Trays, Industrial   \n",
       "31462  Northern Europe  Europe  OFF-ST-4294  Fellowes Trays, Industrial   \n",
       "\n",
       "      Sub-Category         Category   Sales  Quantity Discount Profit  \\\n",
       "31461      Storage  Office Supplies  173,61         3        0  41,58   \n",
       "31462      Storage  Office Supplies  115,74         2        0  27,72   \n",
       "\n",
       "      Shipping Cost Order Priority _Initials  _Region ID  \n",
       "31461         11,27         Medium        BW           0  \n",
       "31462         10,49         Medium        BW           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Timestamp('2015-05-04 00:00:00'), 'Arthur Gainer', 'OFF-AP-4508', 'Mexico City')\n"
     ]
    },
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       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25957</th>\n",
       "      <td>2378</td>\n",
       "      <td>MX-2015-AG1090082-42128</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>Standard Class</td>\n",
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       "      <td>Arthur Gainer</td>\n",
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       "    <tr>\n",
       "      <th>25984</th>\n",
       "      <td>2377</td>\n",
       "      <td>MX-2015-AG1090082-42128</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                 Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "25957    2378  MX-2015-AG1090082-42128 2015-05-04 2015-05-09  Standard Class   \n",
       "25984    2377  MX-2015-AG1090082-42128 2015-05-04 2015-05-09  Standard Class   \n",
       "\n",
       "      Customer ID  Customer Name   Segment  Postal Code         City  \\\n",
       "25957    AG-10900  Arthur Gainer  Consumer          NaN  Mexico City   \n",
       "25984    AG-10900  Arthur Gainer  Consumer          NaN  Mexico City   \n",
       "\n",
       "                  State Country           Region Market   Product ID  \\\n",
       "25957  Distrito Federal  Mexico  Central America  LATAM  OFF-AP-4508   \n",
       "25984  Distrito Federal  Mexico  Central America  LATAM  OFF-AP-4508   \n",
       "\n",
       "                      Product Name Sub-Category         Category    Sales  \\\n",
       "25957  Hamilton Beach Stove, White   Appliances  Office Supplies  3242,88   \n",
       "25984  Hamilton Beach Stove, White   Appliances  Office Supplies   720,64   \n",
       "\n",
       "       Quantity Discount  Profit Shipping Cost Order Priority _Initials  \\\n",
       "25957         9        0  324,18        279,53         Medium        AG   \n",
       "25984         2        0   72,04         65,58         Medium        AG   \n",
       "\n",
       "       _Region ID  \n",
       "25957           0  \n",
       "25984           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Timestamp('2015-07-05 00:00:00'), 'Muhammed Yedwab', 'OFF-BI-3734', 'Tel Aviv')\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
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       "      <th>_Region ID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23267</th>\n",
       "      <td>51176</td>\n",
       "      <td>IS-2015-MY829563-42190</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>2015-07-07</td>\n",
       "      <td>First Class</td>\n",
       "      <td>MY-8295</td>\n",
       "      <td>Muhammed Yedwab</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>Tel Aviv</td>\n",
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       "      <td>Asia Pacific</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23274</th>\n",
       "      <td>51174</td>\n",
       "      <td>IS-2015-MY829563-42190</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>2015-07-07</td>\n",
       "      <td>First Class</td>\n",
       "      <td>MY-8295</td>\n",
       "      <td>Muhammed Yedwab</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>Israel</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-BI-3734</td>\n",
       "      <td>Cardinal Hole Reinforcements, Durable</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>6,84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1,41</td>\n",
       "      <td>1,73</td>\n",
       "      <td>High</td>\n",
       "      <td>MY</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                Order ID Order Date  Ship Date    Ship Mode  \\\n",
       "23267   51176  IS-2015-MY829563-42190 2015-07-05 2015-07-07  First Class   \n",
       "23274   51174  IS-2015-MY829563-42190 2015-07-05 2015-07-07  First Class   \n",
       "\n",
       "      Customer ID    Customer Name    Segment  Postal Code      City  \\\n",
       "23267     MY-8295  Muhammed Yedwab  Corporate          NaN  Tel Aviv   \n",
       "23274     MY-8295  Muhammed Yedwab  Corporate          NaN  Tel Aviv   \n",
       "\n",
       "          State Country        Region        Market   Product ID  \\\n",
       "23267  Tel Aviv  Israel  Western Asia  Asia Pacific  OFF-BI-3734   \n",
       "23274  Tel Aviv  Israel  Western Asia  Asia Pacific  OFF-BI-3734   \n",
       "\n",
       "                                Product Name Sub-Category         Category  \\\n",
       "23267  Cardinal Hole Reinforcements, Durable      Binders  Office Supplies   \n",
       "23274  Cardinal Hole Reinforcements, Durable      Binders  Office Supplies   \n",
       "\n",
       "       Sales  Quantity Discount Profit Shipping Cost Order Priority _Initials  \\\n",
       "23267  41,04         6        0   8,46           4,5           High        MY   \n",
       "23274   6,84         1        0   1,41          1,73           High        MY   \n",
       "\n",
       "       _Region ID  \n",
       "23267           0  \n",
       "23274           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "for key, data in df_g.groupby([\"Order Date\", 'Customer Name', \"Product ID\", \"City\"]):\n",
    "    if len(data) > 1:\n",
    "        print(key)\n",
    "        display(data)\n",
    "        i += 1\n",
    "        if i > 10:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
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       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
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       "      <th>_Initials</th>\n",
       "      <th>_Region ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>44647</td>\n",
       "      <td>AG-2017-CD27903-42967</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CD-2790</td>\n",
       "      <td>Cynthia Delaney</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oran</td>\n",
       "      <td>Oran</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>TEC-AC-5219</td>\n",
       "      <td>Memorex Mouse, USB</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Technology</td>\n",
       "      <td>56,76</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10,74</td>\n",
       "      <td>5,43</td>\n",
       "      <td>High</td>\n",
       "      <td>CD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>44649</td>\n",
       "      <td>AG-2017-CD27903-42967</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CD-2790</td>\n",
       "      <td>Cynthia Delaney</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oran</td>\n",
       "      <td>Oran</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>TEC-AC-5219</td>\n",
       "      <td>Memorex Mouse, USB</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Technology</td>\n",
       "      <td>56,76</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10,74</td>\n",
       "      <td>2,77</td>\n",
       "      <td>High</td>\n",
       "      <td>CD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>27443</td>\n",
       "      <td>ID-2014-AR105107-41761</td>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>2014-05-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AR-10510</td>\n",
       "      <td>Andrew Roberts</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Armidale</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-BI-3736</td>\n",
       "      <td>Cardinal Hole Reinforcements, Recycled</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>15,39</td>\n",
       "      <td>3</td>\n",
       "      <td>0,1</td>\n",
       "      <td>2,52</td>\n",
       "      <td>1,7</td>\n",
       "      <td>Medium</td>\n",
       "      <td>AR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>27445</td>\n",
       "      <td>ID-2014-AR105107-41761</td>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>2014-05-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AR-10510</td>\n",
       "      <td>Andrew Roberts</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Armidale</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-BI-3736</td>\n",
       "      <td>Cardinal Hole Reinforcements, Recycled</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>15,39</td>\n",
       "      <td>3</td>\n",
       "      <td>0,1</td>\n",
       "      <td>2,52</td>\n",
       "      <td>1,64</td>\n",
       "      <td>Medium</td>\n",
       "      <td>AR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>27358</td>\n",
       "      <td>IN-2016-SF202007-42527</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>SF-20200</td>\n",
       "      <td>Sarah Foster</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-AR-6118</td>\n",
       "      <td>Stanley Pencil Sharpener, Easy-Erase</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>23,436</td>\n",
       "      <td>1</td>\n",
       "      <td>0,1</td>\n",
       "      <td>9,876</td>\n",
       "      <td>6,68</td>\n",
       "      <td>Medium</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>27357</td>\n",
       "      <td>IN-2016-SF202007-42527</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>SF-20200</td>\n",
       "      <td>Sarah Foster</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>OFF-AR-6118</td>\n",
       "      <td>Stanley Pencil Sharpener, Easy-Erase</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>23,436</td>\n",
       "      <td>1</td>\n",
       "      <td>0,1</td>\n",
       "      <td>9,876</td>\n",
       "      <td>1,85</td>\n",
       "      <td>Medium</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4295</th>\n",
       "      <td>13944</td>\n",
       "      <td>ES-2015-RF1984014-42245</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>2015-09-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RF-19840</td>\n",
       "      <td>Roy Französisch</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Antwerp</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-AR-5926</td>\n",
       "      <td>Sanford Pens, Fluorescent</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>37,08</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10,35</td>\n",
       "      <td>2,98</td>\n",
       "      <td>Medium</td>\n",
       "      <td>RF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4296</th>\n",
       "      <td>13946</td>\n",
       "      <td>ES-2015-RF1984014-42245</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>2015-09-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RF-19840</td>\n",
       "      <td>Roy Französisch</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Antwerp</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-AR-5926</td>\n",
       "      <td>Sanford Pens, Fluorescent</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>37,08</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10,35</td>\n",
       "      <td>1,84</td>\n",
       "      <td>Medium</td>\n",
       "      <td>RF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6756</th>\n",
       "      <td>8003</td>\n",
       "      <td>MX-2015-LS1720026-42341</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LS-17200</td>\n",
       "      <td>Luke Schmidt</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rancagua</td>\n",
       "      <td>O'Higgins</td>\n",
       "      <td>Chile</td>\n",
       "      <td>South America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>FUR-CH-5775</td>\n",
       "      <td>SAFCO Executive Leather Armchair, Red</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>615,24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12,28</td>\n",
       "      <td>68,727</td>\n",
       "      <td>Medium</td>\n",
       "      <td>LS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6759</th>\n",
       "      <td>8007</td>\n",
       "      <td>MX-2015-LS1720026-42341</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LS-17200</td>\n",
       "      <td>Luke Schmidt</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rancagua</td>\n",
       "      <td>O'Higgins</td>\n",
       "      <td>Chile</td>\n",
       "      <td>South America</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>FUR-CH-5775</td>\n",
       "      <td>SAFCO Executive Leather Armchair, Red</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>615,24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12,28</td>\n",
       "      <td>54,907</td>\n",
       "      <td>Medium</td>\n",
       "      <td>LS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24802</th>\n",
       "      <td>49443</td>\n",
       "      <td>KE-2016-PC900069-42423</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PC-9000</td>\n",
       "      <td>Pauline Chand</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thika</td>\n",
       "      <td>Kiambu</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>OFF-EN-3656</td>\n",
       "      <td>Cameo Business Envelopes, Set of 50</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>20,49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8,19</td>\n",
       "      <td>2,58</td>\n",
       "      <td>High</td>\n",
       "      <td>PC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24804</th>\n",
       "      <td>49444</td>\n",
       "      <td>KE-2016-PC900069-42423</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PC-9000</td>\n",
       "      <td>Pauline Chand</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thika</td>\n",
       "      <td>Kiambu</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>OFF-EN-3656</td>\n",
       "      <td>Cameo Business Envelopes, Set of 50</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>20,49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8,19</td>\n",
       "      <td>2,41</td>\n",
       "      <td>High</td>\n",
       "      <td>PC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35930</th>\n",
       "      <td>10449</td>\n",
       "      <td>ES-2016-SR20740120-42535</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>2016-06-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SR-20740</td>\n",
       "      <td>Steven Roelle</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-BI-6371</td>\n",
       "      <td>Wilson Jones 3-Hole Punch, Economy</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>83,97</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27,63</td>\n",
       "      <td>3,88</td>\n",
       "      <td>Medium</td>\n",
       "      <td>SR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35939</th>\n",
       "      <td>10446</td>\n",
       "      <td>ES-2016-SR20740120-42535</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>2016-06-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SR-20740</td>\n",
       "      <td>Steven Roelle</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>OFF-BI-6371</td>\n",
       "      <td>Wilson Jones 3-Hole Punch, Economy</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>83,97</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27,63</td>\n",
       "      <td>3,14</td>\n",
       "      <td>Medium</td>\n",
       "      <td>SR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36724</th>\n",
       "      <td>29853</td>\n",
       "      <td>IN-2015-RM19375130-42313</td>\n",
       "      <td>2015-11-05</td>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>First Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>FUR-CH-5450</td>\n",
       "      <td>Office Star Steel Folding Chair, Adjustable</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>343,83</td>\n",
       "      <td>5</td>\n",
       "      <td>0,27</td>\n",
       "      <td>89,43</td>\n",
       "      <td>72,95</td>\n",
       "      <td>Medium</td>\n",
       "      <td>RM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36734</th>\n",
       "      <td>29854</td>\n",
       "      <td>IN-2015-RM19375130-42313</td>\n",
       "      <td>2015-11-05</td>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>First Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Southeastern Asia</td>\n",
       "      <td>Asia Pacific</td>\n",
       "      <td>FUR-CH-5450</td>\n",
       "      <td>Office Star Steel Folding Chair, Adjustable</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>343,83</td>\n",
       "      <td>5</td>\n",
       "      <td>0,27</td>\n",
       "      <td>89,43</td>\n",
       "      <td>55,8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>RM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47269</th>\n",
       "      <td>34702</td>\n",
       "      <td>US-2014-LB16795140-41751</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>2014-04-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>43229.0</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eastern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>FUR-CH-4417</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>281,372</td>\n",
       "      <td>2</td>\n",
       "      <td>0,3</td>\n",
       "      <td>-12,0588</td>\n",
       "      <td>21,59</td>\n",
       "      <td>Medium</td>\n",
       "      <td>LB</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47270</th>\n",
       "      <td>34703</td>\n",
       "      <td>US-2014-LB16795140-41751</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>2014-04-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>43229.0</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eastern US</td>\n",
       "      <td>USCA</td>\n",
       "      <td>FUR-CH-4417</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>281,372</td>\n",
       "      <td>2</td>\n",
       "      <td>0,3</td>\n",
       "      <td>-12,0588</td>\n",
       "      <td>3,46</td>\n",
       "      <td>Medium</td>\n",
       "      <td>LB</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID                  Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "223     44647     AG-2017-CD27903-42967 2017-08-20 2017-08-25  Standard Class   \n",
       "231     44649     AG-2017-CD27903-42967 2017-08-20 2017-08-25  Standard Class   \n",
       "972     27443    ID-2014-AR105107-41761 2014-05-02 2014-05-08  Standard Class   \n",
       "973     27445    ID-2014-AR105107-41761 2014-05-02 2014-05-08  Standard Class   \n",
       "1170    27358    IN-2016-SF202007-42527 2016-06-06 2016-06-06        Same Day   \n",
       "1190    27357    IN-2016-SF202007-42527 2016-06-06 2016-06-06        Same Day   \n",
       "4295    13944   ES-2015-RF1984014-42245 2015-08-29 2015-09-05  Standard Class   \n",
       "4296    13946   ES-2015-RF1984014-42245 2015-08-29 2015-09-05  Standard Class   \n",
       "6756     8003   MX-2015-LS1720026-42341 2015-12-03 2015-12-07  Standard Class   \n",
       "6759     8007   MX-2015-LS1720026-42341 2015-12-03 2015-12-07  Standard Class   \n",
       "24802   49443    KE-2016-PC900069-42423 2016-02-23 2016-02-25    Second Class   \n",
       "24804   49444    KE-2016-PC900069-42423 2016-02-23 2016-02-25    Second Class   \n",
       "35930   10449  ES-2016-SR20740120-42535 2016-06-14 2016-06-21  Standard Class   \n",
       "35939   10446  ES-2016-SR20740120-42535 2016-06-14 2016-06-21  Standard Class   \n",
       "36724   29853  IN-2015-RM19375130-42313 2015-11-05 2015-11-06     First Class   \n",
       "36734   29854  IN-2015-RM19375130-42313 2015-11-05 2015-11-06     First Class   \n",
       "47269   34702  US-2014-LB16795140-41751 2014-04-22 2014-04-26  Standard Class   \n",
       "47270   34703  US-2014-LB16795140-41751 2014-04-22 2014-04-26  Standard Class   \n",
       "\n",
       "      Customer ID    Customer Name      Segment  Postal Code       City  \\\n",
       "223       CD-2790  Cynthia Delaney  Home Office          NaN       Oran   \n",
       "231       CD-2790  Cynthia Delaney  Home Office          NaN       Oran   \n",
       "972      AR-10510   Andrew Roberts     Consumer          NaN   Armidale   \n",
       "973      AR-10510   Andrew Roberts     Consumer          NaN   Armidale   \n",
       "1170     SF-20200     Sarah Foster     Consumer          NaN  Newcastle   \n",
       "1190     SF-20200     Sarah Foster     Consumer          NaN  Newcastle   \n",
       "4295     RF-19840  Roy Französisch     Consumer          NaN   Mechelen   \n",
       "4296     RF-19840  Roy Französisch     Consumer          NaN   Mechelen   \n",
       "6756     LS-17200     Luke Schmidt    Corporate          NaN   Rancagua   \n",
       "6759     LS-17200     Luke Schmidt    Corporate          NaN   Rancagua   \n",
       "24802     PC-9000    Pauline Chand  Home Office          NaN      Thika   \n",
       "24804     PC-9000    Pauline Chand  Home Office          NaN      Thika   \n",
       "35930    SR-20740    Steven Roelle  Home Office          NaN     Madrid   \n",
       "35939    SR-20740    Steven Roelle  Home Office          NaN     Madrid   \n",
       "36724    RM-19375    Raymond Messe     Consumer          NaN    Bangkok   \n",
       "36734    RM-19375    Raymond Messe     Consumer          NaN    Bangkok   \n",
       "47269    LB-16795   Laurel Beltran  Home Office      43229.0   Columbus   \n",
       "47270    LB-16795   Laurel Beltran  Home Office      43229.0   Columbus   \n",
       "\n",
       "                 State        Country             Region        Market  \\\n",
       "223               Oran        Algeria       North Africa        Africa   \n",
       "231               Oran        Algeria       North Africa        Africa   \n",
       "972    New South Wales      Australia            Oceania  Asia Pacific   \n",
       "973    New South Wales      Australia            Oceania  Asia Pacific   \n",
       "1170   New South Wales      Australia            Oceania  Asia Pacific   \n",
       "1190   New South Wales      Australia            Oceania  Asia Pacific   \n",
       "4295           Antwerp        Belgium     Western Europe        Europe   \n",
       "4296           Antwerp        Belgium     Western Europe        Europe   \n",
       "6756         O'Higgins          Chile      South America         LATAM   \n",
       "6759         O'Higgins          Chile      South America         LATAM   \n",
       "24802           Kiambu          Kenya     Eastern Africa        Africa   \n",
       "24804           Kiambu          Kenya     Eastern Africa        Africa   \n",
       "35930           Madrid          Spain    Southern Europe        Europe   \n",
       "35939           Madrid          Spain    Southern Europe        Europe   \n",
       "36724          Bangkok       Thailand  Southeastern Asia  Asia Pacific   \n",
       "36734          Bangkok       Thailand  Southeastern Asia  Asia Pacific   \n",
       "47269             Ohio  United States         Eastern US          USCA   \n",
       "47270             Ohio  United States         Eastern US          USCA   \n",
       "\n",
       "        Product ID                                       Product Name  \\\n",
       "223    TEC-AC-5219                                 Memorex Mouse, USB   \n",
       "231    TEC-AC-5219                                 Memorex Mouse, USB   \n",
       "972    OFF-BI-3736             Cardinal Hole Reinforcements, Recycled   \n",
       "973    OFF-BI-3736             Cardinal Hole Reinforcements, Recycled   \n",
       "1170   OFF-AR-6118               Stanley Pencil Sharpener, Easy-Erase   \n",
       "1190   OFF-AR-6118               Stanley Pencil Sharpener, Easy-Erase   \n",
       "4295   OFF-AR-5926                          Sanford Pens, Fluorescent   \n",
       "4296   OFF-AR-5926                          Sanford Pens, Fluorescent   \n",
       "6756   FUR-CH-5775              SAFCO Executive Leather Armchair, Red   \n",
       "6759   FUR-CH-5775              SAFCO Executive Leather Armchair, Red   \n",
       "24802  OFF-EN-3656                Cameo Business Envelopes, Set of 50   \n",
       "24804  OFF-EN-3656                Cameo Business Envelopes, Set of 50   \n",
       "35930  OFF-BI-6371                 Wilson Jones 3-Hole Punch, Economy   \n",
       "35939  OFF-BI-6371                 Wilson Jones 3-Hole Punch, Economy   \n",
       "36724  FUR-CH-5450        Office Star Steel Folding Chair, Adjustable   \n",
       "36734  FUR-CH-5450        Office Star Steel Folding Chair, Adjustable   \n",
       "47269  FUR-CH-4417  Global Leather Highback Executive Chair with P...   \n",
       "47270  FUR-CH-4417  Global Leather Highback Executive Chair with P...   \n",
       "\n",
       "      Sub-Category         Category    Sales  Quantity Discount    Profit  \\\n",
       "223    Accessories       Technology    56,76         2        0     10,74   \n",
       "231    Accessories       Technology    56,76         2        0     10,74   \n",
       "972        Binders  Office Supplies    15,39         3      0,1      2,52   \n",
       "973        Binders  Office Supplies    15,39         3      0,1      2,52   \n",
       "1170           Art  Office Supplies   23,436         1      0,1     9,876   \n",
       "1190           Art  Office Supplies   23,436         1      0,1     9,876   \n",
       "4295           Art  Office Supplies    37,08         3        0     10,35   \n",
       "4296           Art  Office Supplies    37,08         3        0     10,35   \n",
       "6756        Chairs        Furniture   615,24         2        0     12,28   \n",
       "6759        Chairs        Furniture   615,24         2        0     12,28   \n",
       "24802    Envelopes  Office Supplies    20,49         1        0      8,19   \n",
       "24804    Envelopes  Office Supplies    20,49         1        0      8,19   \n",
       "35930      Binders  Office Supplies    83,97         3        0     27,63   \n",
       "35939      Binders  Office Supplies    83,97         3        0     27,63   \n",
       "36724       Chairs        Furniture   343,83         5     0,27     89,43   \n",
       "36734       Chairs        Furniture   343,83         5     0,27     89,43   \n",
       "47269       Chairs        Furniture  281,372         2      0,3  -12,0588   \n",
       "47270       Chairs        Furniture  281,372         2      0,3  -12,0588   \n",
       "\n",
       "      Shipping Cost Order Priority _Initials  _Region ID  \n",
       "223            5,43           High        CD           0  \n",
       "231            2,77           High        CD           0  \n",
       "972             1,7         Medium        AR           0  \n",
       "973            1,64         Medium        AR           0  \n",
       "1170           6,68         Medium        SF           0  \n",
       "1190           1,85         Medium        SF           0  \n",
       "4295           2,98         Medium        RF           0  \n",
       "4296           1,84         Medium        RF           0  \n",
       "6756         68,727         Medium        LS           0  \n",
       "6759         54,907         Medium        LS           0  \n",
       "24802          2,58           High        PC           0  \n",
       "24804          2,41           High        PC           0  \n",
       "35930          3,88         Medium        SR           0  \n",
       "35939          3,14         Medium        SR           0  \n",
       "36724         72,95         Medium        RM           0  \n",
       "36734          55,8         Medium        RM           0  \n",
       "47269         21,59         Medium        LB           6  \n",
       "47270          3,46         Medium        LB           6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select all columns except 'Row ID'\n",
    "columns_to_check = df_g.columns.drop(['Row ID', 'Shipping Cost'])\n",
    "\n",
    "# Check for duplicate rows based on selected columns\n",
    "duplicates = df_g.duplicated(subset=columns_to_check, keep=False)\n",
    "\n",
    "# Filter the dataframe to show only the duplicate rows\n",
    "duplicate_rows = df_g[duplicates]\n",
    "\n",
    "# Print the duplicate rows\n",
    "display(duplicate_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AG-2017-CD27903-42967|ID-2014-AR105107-41761|IN-2016-SF202007-42527|ES-2015-RF1984014-42245|MX-2015-LS1720026-42341|KE-2016-PC900069-42423|ES-2016-SR20740120-42535|IN-2015-RM19375130-42313|US-2014-LB16795140-41751'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display('|'.join(duplicate_rows[\"Order ID\"].drop_duplicates().values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orders dataframe:\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4        33311  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders_df = df[['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode', 'Customer ID']]\n",
    "\n",
    "# Splitting Products dataframe\n",
    "products_df = df[['Product ID', 'Product Name', 'Sub-Category', 'Category']]\n",
    "\n",
    "# Splitting Customers dataframe\n",
    "customers_df = df[['Customer ID', 'Customer Name', 'Segment', 'Postal Code', 'City', 'State', 'Country', 'Region' ]]\n",
    "\n",
    "# Print the first few rows of each dataframe (optional)\n",
    "print(\"Orders dataframe:\")\n",
    "# print(orders_df.head())\n",
    "# print(products_df.head())\n",
    "# print(customers_df.head())\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Postal Code</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4997.500000</td>\n",
       "      <td>55190.379428</td>\n",
       "      <td>229.858001</td>\n",
       "      <td>3.789574</td>\n",
       "      <td>0.156203</td>\n",
       "      <td>28.656896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2885.163629</td>\n",
       "      <td>32063.693350</td>\n",
       "      <td>623.245101</td>\n",
       "      <td>2.225110</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>234.260108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2499.250000</td>\n",
       "      <td>23223.000000</td>\n",
       "      <td>17.280000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4997.500000</td>\n",
       "      <td>56430.500000</td>\n",
       "      <td>54.490000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>8.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7495.750000</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>209.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>29.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8399.976000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row ID   Postal Code         Sales     Quantity     Discount  \\\n",
       "count  9994.000000   9994.000000   9994.000000  9994.000000  9994.000000   \n",
       "mean   4997.500000  55190.379428    229.858001     3.789574     0.156203   \n",
       "std    2885.163629  32063.693350    623.245101     2.225110     0.206452   \n",
       "min       1.000000   1040.000000      0.444000     1.000000     0.000000   \n",
       "25%    2499.250000  23223.000000     17.280000     2.000000     0.000000   \n",
       "50%    4997.500000  56430.500000     54.490000     3.000000     0.200000   \n",
       "75%    7495.750000  90008.000000    209.940000     5.000000     0.200000   \n",
       "max    9994.000000  99301.000000  22638.480000    14.000000     0.800000   \n",
       "\n",
       "            Profit  \n",
       "count  9994.000000  \n",
       "mean     28.656896  \n",
       "std     234.260108  \n",
       "min   -6599.978000  \n",
       "25%       1.728750  \n",
       "50%       8.666500  \n",
       "75%      29.364000  \n",
       "max    8399.976000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('Customer ID', 'Customer Name')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Coding\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3628\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3629\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Coding\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Coding\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('Customer ID', 'Customer Name')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12680\\358492500.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcustomer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Customer ID\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Customer Name\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\Coding\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3505\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3506\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Coding\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3629\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3631\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3632\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3633\u001b[0m                 \u001b[1;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('Customer ID', 'Customer Name')"
     ]
    }
   ],
   "source": [
    "customer = df[\"Customer ID\", \"Customer Name\"]"
   ]
  }
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