Concatenate_Merge_Join_Module.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "week1 = pd.read_csv(\"Restaurant - Week 1 Sales.csv\")\n",
    "week2 = pd.read_csv(\"Restaurant - Week 2 Sales.csv\")\n",
    "customers = pd.read_csv(\"Restaurant - Customers.csv\")\n",
    "foods = pd.read_csv(\"Restaurant - Foods.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The __pd.concat()__ method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This method is used to combine two or more dataFrames\n",
    "- Its first param is a list of the DFs that you want to combine\n",
    "- It has a _ignore_index_ param that you can use to reset the index to make everything uniform(default: False)\n",
    "- To be able to differentiate between the two combined DFs, we use a param called _keys_ . Note: when you do this, you do not need to use _ignore_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
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       "      <td>10</td>\n",
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       "      <td>9</td>\n",
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       "      <td>252</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     Customer ID  Food ID\n",
       "495          783       10\n",
       "496          556       10\n",
       "497          547        9\n",
       "498          252        9\n",
       "499          249        6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([week1,week2], ignore_index= True).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">W1</th>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>9</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "      Customer ID  Food ID\n",
       "W1 0          537        9\n",
       "   1           97        4\n",
       "   2          658        1\n",
       "   3          202        2\n",
       "   4          155        9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([week1,week2], keys=[\"W1\", \"W2\"]).head()\n",
    "#now we know where the data comes from"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The .append() Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference between this method and pd.concat() method is that you call .append() method on the DF object.\n",
    "However, you call .concat() method on the pd object itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>688</td>\n",
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       "      <th>1</th>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          688       10\n",
       "1          813        7\n",
       "2          495       10\n",
       "3          189        5\n",
       "4          267        3"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "week2.append(week1, ignore_index= True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Sql Joins](http://1.bp.blogspot.com/-E3Hj3JNrCuM/T-gRz-tToWI/AAAAAAAAAOI/Azi7Hr7IWV0/s1600/SQL+Joinsjpg.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID-Week1</th>\n",
       "      <th>Food ID-Week2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>503</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Customer ID  Food ID-Week1  Food ID-Week2\n",
       "0          537              9              5\n",
       "1          155              9              3\n",
       "2          155              1              3\n",
       "3          503              5              8\n",
       "4          503              5              9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "week1.merge(right=week2, how=\"inner\", on=\"Customer ID\", suffixes= [\"-Week1\", \"-Week2\"]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how we joined both DataFrames:\n",
    "- we used the __.merge()__ method\n",
    "- the first param: __right__ which indicates which Df you want to merge with\n",
    "- the second param: __how__ which sets what kind of join you want i.e. inner,outer e.t.c.\n",
    "- the third param:  __on__ which means on what common field do you want to merge both DFs (this is the _foreign_key in SQL)\n",
    "- the __suffixes__ param is an indicator for different fields when the DFs are joined. To differentiate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "    Customer ID  Food ID\n",
       "94          233        3"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mask = week1[\"Customer ID\"] == 233\n",
    "week1[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
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      "text/plain": [
       "     Customer ID  Food ID\n",
       "238          233        3"
      ]
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "mask1 = week2[\"Customer ID\"] == 233\n",
    "week2[mask1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outter Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID-Week1</th>\n",
       "      <th>Food ID-Week2</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Customer ID  Food ID-Week1  Food ID-Week2     _merge\n",
       "0          537            9.0            5.0       both\n",
       "1           97            4.0            NaN  left_only\n",
       "2          658            1.0            NaN  left_only\n",
       "3          202            2.0            NaN  left_only\n",
       "4          155            9.0            3.0       both"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(week2, how=\"outer\", on = \"Customer ID\", suffixes= [\"-Week1\", \"-Week2\"], indicator= True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- outter joins combine data that are common to both DFs and inserts NaN to data that are not common\n",
    "- as opposed to inner joins that only care about the intersections i.e. data that are only present in both DFs\n",
    "- we can also add an __indicator__ param field that will give us a visiual idea of the type of merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9\n",
       "1           97        4\n",
       "2          658        1"
      ]
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "week1.head(3)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          688       10\n",
       "1          813        7\n",
       "2          495       10"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "week2.head(3)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = week1.merge(week2, how=\"outer\", on = \"Customer ID\", suffixes= [\"-Week1\", \"-Week2\"], indicator= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>Food ID-Week1</th>\n",
       "      <th>Food ID-Week2</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>213</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>71</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "      <th>12</th>\n",
       "      <td>174</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>961</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>966</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>641</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>288</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>149</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>954</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>147</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>147</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>549</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>78</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>514</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>833</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>833</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>329</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>586</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>519</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>680</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>419</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>822</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>226</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
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       "      <th>424</th>\n",
       "      <td>622</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>959</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>426</th>\n",
       "      <td>959</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>810</td>\n",
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       "      <th>428</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID-Week1  Food ID-Week2      _merge\n",
       "1             97            4.0            NaN   left_only\n",
       "2            658            1.0            NaN   left_only\n",
       "3            202            2.0            NaN   left_only\n",
       "6            213            8.0            NaN   left_only\n",
       "7            600            1.0            NaN   left_only\n",
       "10            71            3.0            NaN   left_only\n",
       "11            71            8.0            NaN   left_only\n",
       "12           174            3.0            NaN   left_only\n",
       "13           961            9.0            NaN   left_only\n",
       "14           966            5.0            NaN   left_only\n",
       "15           641            4.0            NaN   left_only\n",
       "16           288            2.0            NaN   left_only\n",
       "17           149            4.0            NaN   left_only\n",
       "18           954            2.0            NaN   left_only\n",
       "19           147            9.0            NaN   left_only\n",
       "20           147            8.0            NaN   left_only\n",
       "23           549            6.0            NaN   left_only\n",
       "25            78            7.0            NaN   left_only\n",
       "26           514            5.0            NaN   left_only\n",
       "27           833            7.0            NaN   left_only\n",
       "28           833            3.0            NaN   left_only\n",
       "29           329            8.0            NaN   left_only\n",
       "30           586           10.0            NaN   left_only\n",
       "31           341            1.0            NaN   left_only\n",
       "32           519           10.0            NaN   left_only\n",
       "33           680            9.0            NaN   left_only\n",
       "34           419            7.0            NaN   left_only\n",
       "35            20            1.0            NaN   left_only\n",
       "36           822            6.0            NaN   left_only\n",
       "37           226           10.0            NaN   left_only\n",
       "..           ...            ...            ...         ...\n",
       "424          622            NaN            9.0  right_only\n",
       "425          959            NaN            4.0  right_only\n",
       "426          959            NaN            2.0  right_only\n",
       "427          810            NaN            2.0  right_only\n",
       "428          488            NaN            2.0  right_only\n",
       "429          188            NaN            8.0  right_only\n",
       "430          305            NaN            2.0  right_only\n",
       "431          869            NaN            7.0  right_only\n",
       "432          131            NaN            1.0  right_only\n",
       "433          208            NaN            4.0  right_only\n",
       "434          145            NaN            5.0  right_only\n",
       "435          794            NaN            6.0  right_only\n",
       "436          734            NaN           10.0  right_only\n",
       "437          734            NaN            1.0  right_only\n",
       "438          735            NaN            2.0  right_only\n",
       "439          940            NaN            8.0  right_only\n",
       "440          571            NaN            7.0  right_only\n",
       "441          888            NaN            2.0  right_only\n",
       "442          664            NaN            6.0  right_only\n",
       "443          143            NaN            4.0  right_only\n",
       "444          505            NaN            3.0  right_only\n",
       "445           54            NaN            8.0  right_only\n",
       "446          367            NaN            8.0  right_only\n",
       "447          883            NaN            8.0  right_only\n",
       "448          251            NaN            9.0  right_only\n",
       "449          855            NaN            4.0  right_only\n",
       "450          559            NaN           10.0  right_only\n",
       "451          276            NaN            4.0  right_only\n",
       "452          556            NaN           10.0  right_only\n",
       "453          252            NaN            9.0  right_only\n",
       "\n",
       "[392 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask1 = merged[\"_merge\"] == \"both\"\n",
    "mask2 = merged[\"_merge\"] == \"left_only\"\n",
    "mask3 = merged[\"_merge\"] == \"right_only\"\n",
    "\n",
    "merged[~mask1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- left join is like vlookup in excel\n",
    "- it takes into consideration all data that are in the left DF and also data in the right DF (that are also present in the left DF) that we want to look up extra info on\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9\n",
       "1           97        4\n",
       "2          658        1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.head(3)"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID Food Item  Price\n",
       "0        1     Sushi   3.99\n",
       "1        2   Burrito   9.99\n",
       "2        3      Taco   2.99"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(3)"
   ]
  },
  {
   "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",
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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>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID   Food Item  Price\n",
       "0          537        9       Donut   0.99\n",
       "1           97        4  Quesadilla   4.25\n",
       "2          658        1       Sushi   3.99\n",
       "3          202        2     Burrito   9.99\n",
       "4          155        9       Donut   0.99"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(foods, how=\"left\", on = \"Food ID\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how it merged the data sets: <br>\n",
    "- the right DF is the foods data that has all the info about different food IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The left_on and right_on Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We use these params when the two DFs to be merged do not have a common field label to merge on\n",
    "- We have to specify on each DF which fields to do the merge\n",
    "- You can drop the duplicate field during the merger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>688</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          688       10\n",
       "1          813        7\n",
       "2          495       10\n",
       "3          189        5\n",
       "4          267        3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Evans</td>\n",
       "      <td>Male</td>\n",
       "      <td>Fatz</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Judy</td>\n",
       "      <td>Morrison</td>\n",
       "      <td>Female</td>\n",
       "      <td>Demivee</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID First Name Last Name  Gender  Company                     Occupation\n",
       "0   1     Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "1   2   Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer\n",
       "2   3      Roger     Black    Male  Tagfeed              Account Executive\n",
       "3   4     Steven     Evans    Male     Fatz               Registered Nurse\n",
       "4   5       Judy  Morrison  Female  Demivee                Legal Assistant"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \"Roger\" in list(customers[\"First Name\"])\n",
    "#This is how you do a quick check if a string is in a Series - You must encapsulate the Series with a List\n",
    "#You do not need to do this with numbers(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>688</td>\n",
       "      <td>10</td>\n",
       "      <td>688</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Graphic Designer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
       "      <td>813</td>\n",
       "      <td>Johnny</td>\n",
       "      <td>Walker</td>\n",
       "      <td>Male</td>\n",
       "      <td>Kayveo</td>\n",
       "      <td>Developer II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "      <td>495</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>Little</td>\n",
       "      <td>Female</td>\n",
       "      <td>Babbleblab</td>\n",
       "      <td>VP Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "      <td>189</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>Male</td>\n",
       "      <td>Skilith</td>\n",
       "      <td>Operator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "      <td>267</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Male</td>\n",
       "      <td>Agimba</td>\n",
       "      <td>Product Engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID   ID First Name   Last Name  Gender     Company  \\\n",
       "0          688       10  688       Carl  Williamson    Male  Thoughtmix   \n",
       "1          813        7  813     Johnny      Walker    Male      Kayveo   \n",
       "2          495       10  495    Deborah      Little  Female  Babbleblab   \n",
       "3          189        5  189      Roger      Gordon    Male     Skilith   \n",
       "4          267        3  267    Matthew        Wood    Male      Agimba   \n",
       "\n",
       "         Occupation  \n",
       "0  Graphic Designer  \n",
       "1      Developer II  \n",
       "2     VP Accounting  \n",
       "3          Operator  \n",
       "4  Product Engineer  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week2.merge(customers, how=\"left\", left_on= \"Customer ID\", right_on= \"ID\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above see how we specified the left_on and right_on fields. However, see how \"Customer ID\" and \"ID\" fields are the same.<br> You can drop one of them during the merger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>688</td>\n",
       "      <td>10</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Graphic Designer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
       "      <td>Johnny</td>\n",
       "      <td>Walker</td>\n",
       "      <td>Male</td>\n",
       "      <td>Kayveo</td>\n",
       "      <td>Developer II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>Little</td>\n",
       "      <td>Female</td>\n",
       "      <td>Babbleblab</td>\n",
       "      <td>VP Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>Male</td>\n",
       "      <td>Skilith</td>\n",
       "      <td>Operator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Male</td>\n",
       "      <td>Agimba</td>\n",
       "      <td>Product Engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name   Last Name  Gender     Company  \\\n",
       "0          688       10       Carl  Williamson    Male  Thoughtmix   \n",
       "1          813        7     Johnny      Walker    Male      Kayveo   \n",
       "2          495       10    Deborah      Little  Female  Babbleblab   \n",
       "3          189        5      Roger      Gordon    Male     Skilith   \n",
       "4          267        3    Matthew        Wood    Male      Agimba   \n",
       "\n",
       "         Occupation  \n",
       "0  Graphic Designer  \n",
       "1      Developer II  \n",
       "2     VP Accounting  \n",
       "3          Operator  \n",
       "4  Product Engineer  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week2.merge(customers, how=\"left\", left_on= \"Customer ID\", right_on= \"ID\").drop(\"ID\", axis = \"columns\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging by Indexes with left_index and right_index parameters"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID Food Item  Price\n",
       "0        1     Sushi   3.99\n",
       "1        2   Burrito   9.99\n",
       "2        3      Taco   2.99"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers.set_index(\"ID\", inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Last Name  Gender  Company                     Occupation\n",
       "ID                                                                     \n",
       "1      Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "2    Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer\n",
       "3       Roger     Black    Male  Tagfeed              Account Executive"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</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>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9\n",
       "1           97        4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</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>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Account Representative III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name Last Name  Gender    Company  \\\n",
       "0          537        9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1           97        4     Amanda   Watkins  Female        Ozu   \n",
       "2          658        1    Patrick      Webb    Male  Browsebug   \n",
       "3          202        2      Louis  Campbell    Male  Rhynoodle   \n",
       "4          155        9    Carolyn      Diaz  Female   Gigazoom   \n",
       "\n",
       "                      Occupation  \n",
       "0               Registered Nurse  \n",
       "1            Account Coordinator  \n",
       "2  Community Outreach Specialist  \n",
       "3     Account Representative III  \n",
       "4     Database Administrator III  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = week1.merge(customers, how=\"left\", left_on='Customer ID', right_index= True)\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- see how we merged both DFs on the left's \"Customer ID\" label and the right's index values\n",
    "- one advantage of doing this is that we do not get duplicate fields - where we have to drop a field during merger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Food ID_x</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Food ID_y</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Drink</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Drink</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID_x First Name Last Name  Gender    Company  \\\n",
       "0          537          9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1           97          4     Amanda   Watkins  Female        Ozu   \n",
       "2          658          1    Patrick      Webb    Male  Browsebug   \n",
       "3          202          2      Louis  Campbell    Male  Rhynoodle   \n",
       "4          155          9    Carolyn      Diaz  Female   Gigazoom   \n",
       "\n",
       "                      Occupation  Food ID_y Food Item  Price  \n",
       "0               Registered Nurse       10.0     Drink   1.75  \n",
       "1            Account Coordinator        5.0     Pizza   2.49  \n",
       "2  Community Outreach Specialist        2.0   Burrito   9.99  \n",
       "3     Account Representative III        3.0      Taco   2.99  \n",
       "4     Database Administrator III       10.0     Drink   1.75  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now to get the Food data by their IDs , we will merge the above DF with the foods DF\n",
    "sales.merge(foods, how=\"left\", left_on=\"Food ID\", right_index=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - You can also use .join() method when you want to merge to DFs with similar index fields\n",
    " - You can also do a pd.merge() for merging operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practice with Live Data - Summit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "reps = pd.read_excel(\"SalesReps.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(222, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>SLS</th>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th>SALES</th>\n",
       "      <th>COST</th>\n",
       "      <th>PROFIT</th>\n",
       "      <th>PFT %</th>\n",
       "      <th>ORDER</th>\n",
       "      <th>AVGOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>STRATIFICA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>106</td>\n",
       "      <td>DORMANT LO</td>\n",
       "      <td>42467.85</td>\n",
       "      <td>36671.41</td>\n",
       "      <td>5796.44</td>\n",
       "      <td>13.65</td>\n",
       "      <td>44.0</td>\n",
       "      <td>965.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>111</td>\n",
       "      <td>DORMANT LO</td>\n",
       "      <td>15312.95</td>\n",
       "      <td>13763.90</td>\n",
       "      <td>1549.05</td>\n",
       "      <td>10.12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>765.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Period  SLS  SLSRP NAME     SALES      COST   PROFIT  PFT %  \\\n",
       "0         1      12    5  STRATIFICA       NaN       NaN      NaN    NaN   \n",
       "1         1      12  106  DORMANT LO  42467.85  36671.41  5796.44  13.65   \n",
       "2         1      12  111  DORMANT LO  15312.95  13763.90  1549.05  10.12   \n",
       "\n",
       "   ORDER   AVGOR  \n",
       "0    NaN     NaN  \n",
       "1   44.0  965.18  \n",
       "2   20.0  765.65  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reps.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location        3\n",
       "Period          1\n",
       "SLS           127\n",
       "SLSRP NAME     98\n",
       "SALES         155\n",
       "COST          155\n",
       "PROFIT        155\n",
       "PFT %         146\n",
       "ORDER          89\n",
       "AVGOR         152\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reps.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#mask100 = (100 >= reps[\"SLS\"]) & (reps[\"SLS\"] <= 199)\n",
    "#mask500 = 500 >= reps[\"SLS\"] <= 599\n",
    "salesReps = reps[(reps[\"SLS\"] >= 500) & (reps[\"SLS\"] <= 599)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>SLS</th>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th>SALES</th>\n",
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       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>506</td>\n",
       "      <td>RYAN IRWIN</td>\n",
       "      <td>125120.13</td>\n",
       "      <td>106614.50</td>\n",
       "      <td>18505.63</td>\n",
       "      <td>14.79</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1009.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>508</td>\n",
       "      <td>MICHAEL DA</td>\n",
       "      <td>145300.46</td>\n",
       "      <td>126236.30</td>\n",
       "      <td>19064.16</td>\n",
       "      <td>13.12</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1357.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>511</td>\n",
       "      <td>RHYS LLOYD</td>\n",
       "      <td>148565.73</td>\n",
       "      <td>123527.03</td>\n",
       "      <td>25038.70</td>\n",
       "      <td>16.85</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1227.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period  SLS  SLSRP NAME      SALES       COST    PROFIT  PFT %  \\\n",
       "33         1      12  506  RYAN IRWIN  125120.13  106614.50  18505.63  14.79   \n",
       "34         1      12  508  MICHAEL DA  145300.46  126236.30  19064.16  13.12   \n",
       "35         1      12  511  RHYS LLOYD  148565.73  123527.03  25038.70  16.85   \n",
       "\n",
       "    ORDER    AVGOR  \n",
       "33  124.0  1009.03  \n",
       "34  107.0  1357.95  \n",
       "35  121.0  1227.82  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salesReps.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "salesReps[\"SLSRP NAME\"] = salesReps[\"SLSRP NAME\"].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = salesReps.groupby(\"Location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>SLS</th>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th>SALES</th>\n",
       "      <th>COST</th>\n",
       "      <th>PROFIT</th>\n",
       "      <th>PFT %</th>\n",
       "      <th>ORDER</th>\n",
       "      <th>AVGOR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>506</td>\n",
       "      <td>Ryan Irwin</td>\n",
       "      <td>125120.13</td>\n",
       "      <td>106614.50</td>\n",
       "      <td>18505.63</td>\n",
       "      <td>14.79</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1009.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>540</td>\n",
       "      <td>Inside Sal</td>\n",
       "      <td>1869.61</td>\n",
       "      <td>1502.62</td>\n",
       "      <td>366.99</td>\n",
       "      <td>19.63</td>\n",
       "      <td>3.0</td>\n",
       "      <td>623.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>517</td>\n",
       "      <td>Joanne Dec</td>\n",
       "      <td>135481.81</td>\n",
       "      <td>113793.75</td>\n",
       "      <td>21688.06</td>\n",
       "      <td>16.01</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1575.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Period  SLS  SLSRP NAME      SALES       COST    PROFIT  PFT %  \\\n",
       "Location                                                                   \n",
       "1             12  506  Ryan Irwin  125120.13  106614.50  18505.63  14.79   \n",
       "2             12  540  Inside Sal    1869.61    1502.62    366.99  19.63   \n",
       "3             12  517  Joanne Dec  135481.81  113793.75  21688.06  16.01   \n",
       "\n",
       "          ORDER    AVGOR  \n",
       "Location                  \n",
       "1         124.0  1009.03  \n",
       "2           3.0   623.20  \n",
       "3          86.0  1575.37  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "1     785.67\n",
       "2    1869.61\n",
       "3     555.43\n",
       "Name: SALES, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc[\"SALES\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>SLS</th>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th>SALES</th>\n",
       "      <th>COST</th>\n",
       "      <th>PROFIT</th>\n",
       "      <th>PFT %</th>\n",
       "      <th>ORDER</th>\n",
       "      <th>AVGOR</th>\n",
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       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>506</td>\n",
       "      <td>Aron Leves</td>\n",
       "      <td>785.67</td>\n",
       "      <td>662.76</td>\n",
       "      <td>122.91</td>\n",
       "      <td>13.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>653.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>540</td>\n",
       "      <td>Channing N</td>\n",
       "      <td>1869.61</td>\n",
       "      <td>1502.62</td>\n",
       "      <td>366.99</td>\n",
       "      <td>12.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>623.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>517</td>\n",
       "      <td>Channing N</td>\n",
       "      <td>555.43</td>\n",
       "      <td>483.44</td>\n",
       "      <td>71.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>405.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Period  SLS  SLSRP NAME    SALES     COST  PROFIT  PFT %  ORDER  \\\n",
       "Location                                                                    \n",
       "1             12  506  Aron Leves   785.67   662.76  122.91  13.07    1.0   \n",
       "2             12  540  Channing N  1869.61  1502.62  366.99  12.26    2.0   \n",
       "3             12  517  Channing N   555.43   483.44   71.99  12.79    1.0   \n",
       "\n",
       "           AVGOR  \n",
       "Location          \n",
       "1         653.69  \n",
       "2         623.20  \n",
       "3         405.49  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "salesYear = pd.read_excel(\"sum_sales.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2774, 10)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salesYear.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>SLS</th>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th>SALES</th>\n",
       "      <th>COST</th>\n",
       "      <th>PROFIT</th>\n",
       "      <th>PFT %</th>\n",
       "      <th>ORDER</th>\n",
       "      <th>AVGOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>STRATIFICA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>RHYS LLOYD</td>\n",
       "      <td>9246.7</td>\n",
       "      <td>8274.79</td>\n",
       "      <td>971.91</td>\n",
       "      <td>10.51</td>\n",
       "      <td>9</td>\n",
       "      <td>1027.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>PAUL CRELL</td>\n",
       "      <td>106257</td>\n",
       "      <td>91695.4</td>\n",
       "      <td>14561.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>80</td>\n",
       "      <td>1328.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Period  SLS  SLSRP NAME   SALES     COST    PROFIT  PFT % ORDER  \\\n",
       "0         1       1    5  STRATIFICA     NaN      NaN       NaN    NaN     1   \n",
       "1         1       1  111  RHYS LLOYD  9246.7  8274.79    971.91  10.51     9   \n",
       "2         1       1  114  PAUL CRELL  106257  91695.4  14561.45   13.7    80   \n",
       "\n",
       "     AVGOR  \n",
       "0      NaN  \n",
       "1  1027.41  \n",
       "2  1328.21  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salesYear.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2774 entries, 0 to 2773\n",
      "Data columns (total 10 columns):\n",
      "Location      2774 non-null int64\n",
      "Period        2774 non-null int64\n",
      "SLS           2774 non-null int64\n",
      "SLSRP NAME    2774 non-null object\n",
      "SALES         2184 non-null object\n",
      "COST          2184 non-null object\n",
      "PROFIT        2175 non-null float64\n",
      "PFT %         2164 non-null object\n",
      "ORDER         2140 non-null object\n",
      "AVGOR         2136 non-null float64\n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 216.8+ KB\n"
     ]
    }
   ],
   "source": [
    "salesYear.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "salesYear.dropna(how=\"any\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reps[(reps[\"SLS\"] >= 500) & (reps[\"SLS\"] <= 599)]\n",
    "newReps = salesYear[(salesYear[\"SLS\"] >= 500) & (salesYear[\"SLS\"] <= 599)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(521, 10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location        3\n",
       "Period         13\n",
       "SLS            41\n",
       "SLSRP NAME     48\n",
       "SALES         521\n",
       "COST          521\n",
       "PROFIT        521\n",
       "PFT %         402\n",
       "ORDER         162\n",
       "AVGOR         520\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "repGroup = newReps.groupby(\"SLSRP NAME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13 entries, 31 to 945\n",
      "Data columns (total 10 columns):\n",
      "Location      13 non-null int64\n",
      "Period        13 non-null int64\n",
      "SLS           13 non-null int64\n",
      "SLSRP NAME    13 non-null object\n",
      "SALES         13 non-null object\n",
      "COST          13 non-null object\n",
      "PROFIT        13 non-null float64\n",
      "PFT %         13 non-null object\n",
      "ORDER         13 non-null object\n",
      "AVGOR         13 non-null float64\n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "Aron = repGroup.get_group(\"ARON LEVES\")\n",
    "Aron.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminco\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\adminco\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "Aron[\"SALES\"] = Aron[\"SALES\"].astype(\"int\")\n",
    "Aron[\"COST\"] = Aron[\"COST\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>25999.27</td>\n",
       "      <td>14.3</td>\n",
       "      <td>115</td>\n",
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       "      <td>ARON LEVES</td>\n",
       "      <td>168883</td>\n",
       "      <td>144593</td>\n",
       "      <td>24290.19</td>\n",
       "      <td>14.38</td>\n",
       "      <td>120</td>\n",
       "      <td>1407.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>513</td>\n",
       "      <td>ARON LEVES</td>\n",
       "      <td>165990</td>\n",
       "      <td>142261</td>\n",
       "      <td>23729.26</td>\n",
       "      <td>14.3</td>\n",
       "      <td>111</td>\n",
       "      <td>1495.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>513</td>\n",
       "      <td>ARON LEVES</td>\n",
       "      <td>155787</td>\n",
       "      <td>133117</td>\n",
       "      <td>22669.61</td>\n",
       "      <td>14.55</td>\n",
       "      <td>114</td>\n",
       "      <td>1366.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>513</td>\n",
       "      <td>ARON LEVES</td>\n",
       "      <td>153317</td>\n",
       "      <td>130234</td>\n",
       "      <td>23083.05</td>\n",
       "      <td>15.06</td>\n",
       "      <td>116</td>\n",
       "      <td>1321.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Period  SLS  SLSRP NAME   SALES    COST    PROFIT  PFT % ORDER  \\\n",
       "553         1       8  513  ARON LEVES  181778  155779  25999.27   14.3   115   \n",
       "476         1       7  513  ARON LEVES  168883  144593  24290.19  14.38   120   \n",
       "630         1       9  513  ARON LEVES  165990  142261  23729.26   14.3   111   \n",
       "400         1       6  513  ARON LEVES  155787  133117  22669.61  14.55   114   \n",
       "324         1       5  513  ARON LEVES  153317  130234  23083.05  15.06   116   \n",
       "\n",
       "       AVGOR  \n",
       "553  1580.68  \n",
       "476  1407.36  \n",
       "630  1495.41  \n",
       "400  1366.56  \n",
       "324  1321.70  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aron.nlargest(5,\"SALES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "maxSales = repGroup.max()[[\"Location\", \"SLS\", \"Period\", \"SALES\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "SR = list(maxSales.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Location</th>\n",
       "      <th>SLS</th>\n",
       "      <th>Period</th>\n",
       "      <th>SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLSRP NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARON LEVES</th>\n",
       "      <td>1</td>\n",
       "      <td>513</td>\n",
       "      <td>13</td>\n",
       "      <td>181778.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BO DING</th>\n",
       "      <td>3</td>\n",
       "      <td>581</td>\n",
       "      <td>5</td>\n",
       "      <td>4568.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRIAN GRAH</th>\n",
       "      <td>3</td>\n",
       "      <td>599</td>\n",
       "      <td>5</td>\n",
       "      <td>49982.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHANNING N</th>\n",
       "      <td>3</td>\n",
       "      <td>575</td>\n",
       "      <td>13</td>\n",
       "      <td>374035.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURVE LAKE</th>\n",
       "      <td>3</td>\n",
       "      <td>599</td>\n",
       "      <td>8</td>\n",
       "      <td>1234.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Location  SLS  Period      SALES\n",
       "SLSRP NAME                                  \n",
       "ARON LEVES         1  513      13  181778.74\n",
       "BO DING            3  581       5    4568.77\n",
       "BRIAN GRAH         3  599       5   49982.69\n",
       "CHANNING N         3  575      13  374035.62\n",
       "CURVE LAKE         3  599       8    1234.56"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxSales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxSales.columns = maxSales.columns.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#maxSales[\"Salesreps\"] = maxSales[\"Salesreps\"].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2448b54eb38>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Period\", y=\"Sales\", data=maxSales, hue = \"Location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mam = repGroup.get_group(\"MAMDOUH AB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AVGOR       21\n",
       "COST        21\n",
       "Location     2\n",
       "ORDER       16\n",
       "PFT %       21\n",
       "PROFIT      21\n",
       "Period      13\n",
       "SALES       21\n",
       "SLS          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mam.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2448b69be10>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Period\", y=\"SALES\", data=mam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "newReps.columns = newReps.columns.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminco\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "newReps[\"Slsrp Name\"] = newReps[\"Slsrp Name\"].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>Sls</th>\n",
       "      <th>Slsrp Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Pft %</th>\n",
       "      <th>Order</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "      <td>Ryan Irwin</td>\n",
       "      <td>56971.7</td>\n",
       "      <td>44910.1</td>\n",
       "      <td>12061.62</td>\n",
       "      <td>21.17</td>\n",
       "      <td>51</td>\n",
       "      <td>1117.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>508</td>\n",
       "      <td>Michael Da</td>\n",
       "      <td>77551.7</td>\n",
       "      <td>66643.7</td>\n",
       "      <td>10908.07</td>\n",
       "      <td>14.07</td>\n",
       "      <td>65</td>\n",
       "      <td>1193.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>Rhys Lloyd</td>\n",
       "      <td>99699</td>\n",
       "      <td>84334.3</td>\n",
       "      <td>15364.66</td>\n",
       "      <td>15.41</td>\n",
       "      <td>102</td>\n",
       "      <td>977.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period  Sls  Slsrp Name    Sales     Cost    Profit  Pft %  \\\n",
       "28         1       1  506  Ryan Irwin  56971.7  44910.1  12061.62  21.17   \n",
       "29         1       1  508  Michael Da  77551.7  66643.7  10908.07  14.07   \n",
       "30         1       1  511  Rhys Lloyd    99699  84334.3  15364.66  15.41   \n",
       "\n",
       "   Order    Avgor  \n",
       "28    51  1117.09  \n",
       "29    65  1193.10  \n",
       "30   102   977.44  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location        3\n",
       "Period         13\n",
       "Sls            41\n",
       "Slsrp Name     48\n",
       "Sales         521\n",
       "Cost          521\n",
       "Profit        521\n",
       "Pft %         402\n",
       "Order         162\n",
       "Avgor         520\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "locGroup = newReps.groupby([\"Location\", \"Period\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location  Period\n",
       "1         1         17\n",
       "          2         16\n",
       "          3         17\n",
       "          4         16\n",
       "          5         15\n",
       "          6         14\n",
       "          7         12\n",
       "          8         12\n",
       "          9         14\n",
       "          10        15\n",
       "          11        14\n",
       "          12        14\n",
       "          13        14\n",
       "2         1         15\n",
       "          2         14\n",
       "          3         14\n",
       "          4         13\n",
       "          5         11\n",
       "          6         10\n",
       "          7         11\n",
       "          8         11\n",
       "          9         10\n",
       "          10        10\n",
       "          11        10\n",
       "          12        11\n",
       "          13        12\n",
       "3         1         13\n",
       "          2         19\n",
       "          3         19\n",
       "          4         17\n",
       "          5         18\n",
       "          6         13\n",
       "          7         12\n",
       "          8         12\n",
       "          9         10\n",
       "          10        13\n",
       "          11        11\n",
       "          12        11\n",
       "          13        11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locGroup.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc1P1 = locGroup.get_group((1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>Sls</th>\n",
       "      <th>Slsrp Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Pft %</th>\n",
       "      <th>Order</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "      <td>Ryan Irwin</td>\n",
       "      <td>56971.7</td>\n",
       "      <td>44910.1</td>\n",
       "      <td>12061.62</td>\n",
       "      <td>21.17</td>\n",
       "      <td>51</td>\n",
       "      <td>1117.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>508</td>\n",
       "      <td>Michael Da</td>\n",
       "      <td>77551.7</td>\n",
       "      <td>66643.7</td>\n",
       "      <td>10908.07</td>\n",
       "      <td>14.07</td>\n",
       "      <td>65</td>\n",
       "      <td>1193.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>Rhys Lloyd</td>\n",
       "      <td>99699</td>\n",
       "      <td>84334.3</td>\n",
       "      <td>15364.66</td>\n",
       "      <td>15.41</td>\n",
       "      <td>102</td>\n",
       "      <td>977.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>513</td>\n",
       "      <td>Aron Leves</td>\n",
       "      <td>96556.7</td>\n",
       "      <td>80697</td>\n",
       "      <td>15859.70</td>\n",
       "      <td>16.43</td>\n",
       "      <td>68</td>\n",
       "      <td>1419.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>514</td>\n",
       "      <td>Paul Crell</td>\n",
       "      <td>146157</td>\n",
       "      <td>122457</td>\n",
       "      <td>23699.95</td>\n",
       "      <td>16.22</td>\n",
       "      <td>97</td>\n",
       "      <td>1506.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period  Sls  Slsrp Name    Sales     Cost    Profit  Pft %  \\\n",
       "28         1       1  506  Ryan Irwin  56971.7  44910.1  12061.62  21.17   \n",
       "29         1       1  508  Michael Da  77551.7  66643.7  10908.07  14.07   \n",
       "30         1       1  511  Rhys Lloyd    99699  84334.3  15364.66  15.41   \n",
       "31         1       1  513  Aron Leves  96556.7    80697  15859.70  16.43   \n",
       "32         1       1  514  Paul Crell   146157   122457  23699.95  16.22   \n",
       "\n",
       "   Order    Avgor  \n",
       "28    51  1117.09  \n",
       "29    65  1193.10  \n",
       "30   102   977.44  \n",
       "31    68  1419.95  \n",
       "32    97  1506.77  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc1P1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,9))\n",
    "ax = sns.barplot(x=\"Slsrp Name\", y=\"Sales\", data=loc1P1)\n",
    "plt.xticks(rotation=-25)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(521, 10)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit = locGroup.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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></th>\n",
       "      <th>Sls</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>9109</td>\n",
       "      <td>214332.70</td>\n",
       "      <td>22869.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8521</td>\n",
       "      <td>227745.62</td>\n",
       "      <td>21629.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9109</td>\n",
       "      <td>235181.51</td>\n",
       "      <td>25716.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Sls     Profit     Avgor\n",
       "Location Period                           \n",
       "1        1       9109  214332.70  22869.21\n",
       "         2       8521  227745.62  21629.91\n",
       "         3       9109  235181.51  25716.67"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit.reset_index(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "#fig.add_axes([0,0,1,1])\n",
    "sns.barplot(x=\"Period\", y=\"Profit\", data=profit, hue=\"Location\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "1    190\n",
       "2    152\n",
       "3    179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.groupby(\"Location\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminco\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2448b8b3080>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.distplot(profit[\"Profit\"],hist=True,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "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>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>Sls</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>6836</td>\n",
       "      <td>252592.41</td>\n",
       "      <td>17723.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6288</td>\n",
       "      <td>248477.97</td>\n",
       "      <td>16883.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6288</td>\n",
       "      <td>245173.28</td>\n",
       "      <td>16319.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6356</td>\n",
       "      <td>240420.81</td>\n",
       "      <td>17983.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8000</td>\n",
       "      <td>239708.75</td>\n",
       "      <td>21177.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7470</td>\n",
       "      <td>235838.52</td>\n",
       "      <td>27801.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6356</td>\n",
       "      <td>235774.03</td>\n",
       "      <td>17464.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7486</td>\n",
       "      <td>235232.07</td>\n",
       "      <td>22085.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9109</td>\n",
       "      <td>235181.51</td>\n",
       "      <td>25716.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8054</td>\n",
       "      <td>231665.28</td>\n",
       "      <td>21583.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7480</td>\n",
       "      <td>228127.38</td>\n",
       "      <td>21275.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8521</td>\n",
       "      <td>227745.62</td>\n",
       "      <td>21629.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5729</td>\n",
       "      <td>220413.09</td>\n",
       "      <td>11894.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6288</td>\n",
       "      <td>219667.59</td>\n",
       "      <td>13397.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8527</td>\n",
       "      <td>217893.05</td>\n",
       "      <td>19337.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5701</td>\n",
       "      <td>217753.99</td>\n",
       "      <td>12416.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9109</td>\n",
       "      <td>214332.70</td>\n",
       "      <td>22869.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7480</td>\n",
       "      <td>206226.78</td>\n",
       "      <td>20481.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>6260</td>\n",
       "      <td>203757.12</td>\n",
       "      <td>14280.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5701</td>\n",
       "      <td>201781.30</td>\n",
       "      <td>12428.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7480</td>\n",
       "      <td>193797.48</td>\n",
       "      <td>20968.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8064</td>\n",
       "      <td>182475.51</td>\n",
       "      <td>15482.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7475</td>\n",
       "      <td>178232.97</td>\n",
       "      <td>14389.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8064</td>\n",
       "      <td>176601.97</td>\n",
       "      <td>14766.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5701</td>\n",
       "      <td>172160.88</td>\n",
       "      <td>12150.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8606</td>\n",
       "      <td>163029.90</td>\n",
       "      <td>15507.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6675</td>\n",
       "      <td>108995.74</td>\n",
       "      <td>13470.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7248</td>\n",
       "      <td>105937.65</td>\n",
       "      <td>25677.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>10208</td>\n",
       "      <td>103106.25</td>\n",
       "      <td>18823.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6683</td>\n",
       "      <td>99349.95</td>\n",
       "      <td>12198.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7299</td>\n",
       "      <td>96621.38</td>\n",
       "      <td>15699.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5559</td>\n",
       "      <td>88126.21</td>\n",
       "      <td>10509.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6136</td>\n",
       "      <td>81909.70</td>\n",
       "      <td>12955.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>6136</td>\n",
       "      <td>78795.07</td>\n",
       "      <td>12407.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6136</td>\n",
       "      <td>76247.12</td>\n",
       "      <td>13027.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9701</td>\n",
       "      <td>69256.14</td>\n",
       "      <td>15411.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10925</td>\n",
       "      <td>66847.30</td>\n",
       "      <td>19373.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10925</td>\n",
       "      <td>61954.45</td>\n",
       "      <td>16830.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7415</td>\n",
       "      <td>56363.26</td>\n",
       "      <td>11591.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period    Sls     Profit     Avgor\n",
       "25         2      13   6836  252592.41  17723.43\n",
       "20         2       8   6288  248477.97  16883.25\n",
       "19         2       7   6288  245173.28  16319.20\n",
       "6          1       7   6356  240420.81  17983.20\n",
       "4          1       5   8000  239708.75  21177.43\n",
       "8          1       9   7470  235838.52  27801.89\n",
       "7          1       8   6356  235774.03  17464.45\n",
       "5          1       6   7486  235232.07  22085.67\n",
       "2          1       3   9109  235181.51  25716.67\n",
       "9          1      10   8054  231665.28  21583.76\n",
       "12         1      13   7480  228127.38  21275.13\n",
       "1          1       2   8521  227745.62  21629.91\n",
       "18         2       6   5729  220413.09  11894.02\n",
       "17         2       5   6288  219667.59  13397.72\n",
       "3          1       4   8527  217893.05  19337.89\n",
       "21         2       9   5701  217753.99  12416.77\n",
       "0          1       1   9109  214332.70  22869.21\n",
       "11         1      12   7480  206226.78  20481.99\n",
       "24         2      12   6260  203757.12  14280.42\n",
       "22         2      10   5701  201781.30  12428.67\n",
       "10         1      11   7480  193797.48  20968.74\n",
       "14         2       2   8064  182475.51  15482.79\n",
       "16         2       4   7475  178232.97  14389.72\n",
       "15         2       3   8064  176601.97  14766.68\n",
       "23         2      11   5701  172160.88  12150.88\n",
       "13         2       1   8606  163029.90  15507.38\n",
       "32         3       7   6675  108995.74  13470.38\n",
       "31         3       6   7248  105937.65  25677.64\n",
       "30         3       5  10208  103106.25  18823.57\n",
       "33         3       8   6683   99349.95  12198.08\n",
       "35         3      10   7299   96621.38  15699.95\n",
       "34         3       9   5559   88126.21  10509.75\n",
       "38         3      13   6136   81909.70  12955.70\n",
       "37         3      12   6136   78795.07  12407.03\n",
       "36         3      11   6136   76247.12  13027.06\n",
       "29         3       4   9701   69256.14  15411.45\n",
       "28         3       3  10925   66847.30  19373.51\n",
       "27         3       2  10925   61954.45  16830.31\n",
       "26         3       1   7415   56363.26  11591.88"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.sort_values(\"Profit\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Last Name  Gender  Company                     Occupation\n",
       "ID                                                                     \n",
       "1      Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "2    Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer\n",
       "3       Roger     Black    Male  Tagfeed              Account Executive"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID Food Item  Price\n",
       "0        1     Sushi   3.99\n",
       "1        2   Burrito   9.99\n",
       "2        3      Taco   2.99"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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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>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Steven</td>\n",
       "      <td>Evans</td>\n",
       "      <td>Male</td>\n",
       "      <td>Fatz</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>4</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Judy</td>\n",
       "      <td>Morrison</td>\n",
       "      <td>Female</td>\n",
       "      <td>Demivee</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>5</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name Last Name  Gender  Company                     Occupation  \\\n",
       "0     Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist   \n",
       "1   Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer   \n",
       "2      Roger     Black    Male  Tagfeed              Account Executive   \n",
       "3     Steven     Evans    Male     Fatz               Registered Nurse   \n",
       "4       Judy  Morrison  Female  Demivee                Legal Assistant   \n",
       "\n",
       "   Food ID   Food Item  Price  \n",
       "0        1       Sushi   3.99  \n",
       "1        2     Burrito   9.99  \n",
       "2        3        Taco   2.99  \n",
       "3        4  Quesadilla   4.25  \n",
       "4        5       Pizza   2.49  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoods = customers.merge(foods,how=\"left\",left_index=True,right_on=\"Food ID\")\n",
    "custFoods.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 1 to 1000\n",
      "Data columns (total 5 columns):\n",
      "First Name    1000 non-null object\n",
      "Last Name     1000 non-null object\n",
      "Gender        1000 non-null object\n",
      "Company       1000 non-null object\n",
      "Occupation    1000 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 46.9+ KB\n"
     ]
    }
   ],
   "source": [
    "customers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      "Food ID      10 non-null int64\n",
      "Food Item    10 non-null object\n",
      "Price        10 non-null float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "foods.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steven</td>\n",
       "      <td>Evans</td>\n",
       "      <td>Male</td>\n",
       "      <td>Fatz</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Judy</td>\n",
       "      <td>Morrison</td>\n",
       "      <td>Female</td>\n",
       "      <td>Demivee</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Last Name  Gender  Company                     Occupation\n",
       "ID                                                                     \n",
       "1      Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "2    Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer\n",
       "3       Roger     Black    Male  Tagfeed              Account Executive\n",
       "4      Steven     Evans    Male     Fatz               Registered Nurse\n",
       "5        Judy  Morrison  Female  Demivee                Legal Assistant"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Steak</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Salad</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Drink</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID   Food Item  Price\n",
       "0        1       Sushi   3.99\n",
       "1        2     Burrito   9.99\n",
       "2        3        Taco   2.99\n",
       "3        4  Quesadilla   4.25\n",
       "4        5       Pizza   2.49\n",
       "5        6       Pasta  13.99\n",
       "6        7       Steak  24.99\n",
       "7        8       Salad  11.25\n",
       "8        9       Donut   0.99\n",
       "9       10       Drink   1.75"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9\n",
       "1           97        4"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Last Name  Gender  Company                     Occupation\n",
       "ID                                                                     \n",
       "1      Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "2    Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "customersW1 = week1.merge(customers, how=\"left\",left_on=\"Customer ID\", right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name Last Name  Gender   Company  \\\n",
       "0          537        9     Cheryl   Carroll  Female  Zoombeat   \n",
       "1           97        4     Amanda   Watkins  Female       Ozu   \n",
       "\n",
       "            Occupation  \n",
       "0     Registered Nurse  \n",
       "1  Account Coordinator  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customersW1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID Food Item  Price\n",
       "0        1     Sushi   3.99\n",
       "1        2   Burrito   9.99"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "custFoodsW1 = customersW1.merge(foods, how=\"left\", on=\"Food ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</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>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name Last Name  Gender   Company  \\\n",
       "0          537        9     Cheryl   Carroll  Female  Zoombeat   \n",
       "1           97        4     Amanda   Watkins  Female       Ozu   \n",
       "\n",
       "            Occupation   Food Item  Price  \n",
       "0     Registered Nurse       Donut   0.99  \n",
       "1  Account Coordinator  Quesadilla   4.25  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 9)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID    221\n",
       "Food ID         10\n",
       "First Name     136\n",
       "Last Name      141\n",
       "Gender           2\n",
       "Company        173\n",
       "Occupation     115\n",
       "Food Item       10\n",
       "Price           10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "sexGroup = custFoods.groupby(\"Gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=1, ncols=2, figsize = (15,8))\n",
    "sns.countplot(x=\"Food Item\", data=custFoodsW1, hue=\"Gender\", ax=axes[0])\n",
    "sns.barplot(x=\"Food Item\", y=\"Price\", data=custFoodsW1, ax=axes[1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodItemGroup = custFoodsW1.groupby(\"Food Item\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food Item\n",
       "Burrito       26\n",
       "Donut         35\n",
       "Drink         24\n",
       "Pasta         28\n",
       "Pizza         19\n",
       "Quesadilla    21\n",
       "Salad         23\n",
       "Steak         27\n",
       "Sushi         24\n",
       "Taco          23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodItemGroup.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recruiter                       2\n",
       "Marketing Manager               2\n",
       "Administrative Officer          2\n",
       "Geological Engineer             2\n",
       "Actuary                         1\n",
       "Structural Analysis Engineer    1\n",
       "Librarian                       1\n",
       "Financial Advisor               1\n",
       "Developer I                     1\n",
       "Mechanical Systems Engineer     1\n",
       "Occupational Therapist          1\n",
       "Data Coordiator                 1\n",
       "Chief Design Engineer           1\n",
       "Automation Specialist III       1\n",
       "Editor                          1\n",
       "Desktop Support Technician      1\n",
       "Senior Financial Analyst        1\n",
       "Senior Developer                1\n",
       "Safety Technician IV            1\n",
       "Name: Occupation, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodItemGroup.get_group(\"Taco\")[\"Occupation\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name Last Name  Gender    Company  \\\n",
       "0          537        9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1           97        4     Amanda   Watkins  Female        Ozu   \n",
       "2          658        1    Patrick      Webb    Male  Browsebug   \n",
       "3          202        2      Louis  Campbell    Male  Rhynoodle   \n",
       "4          155        9    Carolyn      Diaz  Female   Gigazoom   \n",
       "\n",
       "                      Occupation   Food Item  Price  \n",
       "0               Registered Nurse       Donut   0.99  \n",
       "1            Account Coordinator  Quesadilla   4.25  \n",
       "2  Community Outreach Specialist       Sushi   3.99  \n",
       "3     Account Representative III     Burrito   9.99  \n",
       "4     Database Administrator III       Donut   0.99  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminco\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2448c506630>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(custFoodsW1[\"Price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99     35\n",
       "13.99    28\n",
       "24.99    27\n",
       "9.99     26\n",
       "1.75     24\n",
       "3.99     24\n",
       "11.25    23\n",
       "2.99     23\n",
       "4.25     21\n",
       "2.49     19\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1[\"Price\"].value_counts().sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "OccupationGroup = custFoodsW1.groupby(\"Occupation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "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>Food ID</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Speech Pathologist</th>\n",
       "      <td>1812</td>\n",
       "      <td>29</td>\n",
       "      <td>64.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compensation Analyst</th>\n",
       "      <td>3855</td>\n",
       "      <td>28</td>\n",
       "      <td>59.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recruiter</th>\n",
       "      <td>2761</td>\n",
       "      <td>30</td>\n",
       "      <td>57.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Manager</th>\n",
       "      <td>2089</td>\n",
       "      <td>25</td>\n",
       "      <td>55.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Outreach Specialist</th>\n",
       "      <td>2408</td>\n",
       "      <td>25</td>\n",
       "      <td>53.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>1701</td>\n",
       "      <td>36</td>\n",
       "      <td>52.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dental Hygienist</th>\n",
       "      <td>651</td>\n",
       "      <td>14</td>\n",
       "      <td>49.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Analyst Programmer</th>\n",
       "      <td>739</td>\n",
       "      <td>15</td>\n",
       "      <td>48.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account Executive</th>\n",
       "      <td>1375</td>\n",
       "      <td>11</td>\n",
       "      <td>44.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assistant Media Planner</th>\n",
       "      <td>2848</td>\n",
       "      <td>35</td>\n",
       "      <td>42.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Therapy Assistant</th>\n",
       "      <td>2667</td>\n",
       "      <td>21</td>\n",
       "      <td>37.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VP Quality Control</th>\n",
       "      <td>1336</td>\n",
       "      <td>22</td>\n",
       "      <td>36.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost Accountant</th>\n",
       "      <td>995</td>\n",
       "      <td>20</td>\n",
       "      <td>35.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Statistician III</th>\n",
       "      <td>1314</td>\n",
       "      <td>9</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Systems Administrator IV</th>\n",
       "      <td>658</td>\n",
       "      <td>22</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Quality Engineer</th>\n",
       "      <td>2200</td>\n",
       "      <td>21</td>\n",
       "      <td>34.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Associate</th>\n",
       "      <td>1179</td>\n",
       "      <td>12</td>\n",
       "      <td>33.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment Adjustment Coordinator</th>\n",
       "      <td>2334</td>\n",
       "      <td>13</td>\n",
       "      <td>31.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Associate Professor</th>\n",
       "      <td>761</td>\n",
       "      <td>12</td>\n",
       "      <td>31.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Associate</th>\n",
       "      <td>2072</td>\n",
       "      <td>31</td>\n",
       "      <td>30.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax Accountant</th>\n",
       "      <td>413</td>\n",
       "      <td>11</td>\n",
       "      <td>29.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemical Engineer</th>\n",
       "      <td>822</td>\n",
       "      <td>22</td>\n",
       "      <td>29.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technical Writer</th>\n",
       "      <td>1373</td>\n",
       "      <td>21</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Desktop Support Technician</th>\n",
       "      <td>1727</td>\n",
       "      <td>19</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legal Assistant</th>\n",
       "      <td>1621</td>\n",
       "      <td>19</td>\n",
       "      <td>27.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quality Control Specialist</th>\n",
       "      <td>1618</td>\n",
       "      <td>17</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administrative Assistant III</th>\n",
       "      <td>1544</td>\n",
       "      <td>17</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VP Sales</th>\n",
       "      <td>1385</td>\n",
       "      <td>17</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software Engineer IV</th>\n",
       "      <td>837</td>\n",
       "      <td>16</td>\n",
       "      <td>25.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web Developer IV</th>\n",
       "      <td>862</td>\n",
       "      <td>16</td>\n",
       "      <td>25.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Systems Administrator I</th>\n",
       "      <td>819</td>\n",
       "      <td>4</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Manager</th>\n",
       "      <td>745</td>\n",
       "      <td>4</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget/Accounting Analyst IV</th>\n",
       "      <td>968</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineer II</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Programmer IV</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assistant Manager</th>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Developer I</th>\n",
       "      <td>433</td>\n",
       "      <td>12</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Environmental Tech</th>\n",
       "      <td>1009</td>\n",
       "      <td>14</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chief Design Engineer</th>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Advisor</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actuary</th>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mechanical Systems Engineer</th>\n",
       "      <td>462</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff Scientist</th>\n",
       "      <td>114</td>\n",
       "      <td>5</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Director of Sales</th>\n",
       "      <td>945</td>\n",
       "      <td>5</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account Representative IV</th>\n",
       "      <td>966</td>\n",
       "      <td>5</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinical Specialist</th>\n",
       "      <td>351</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web Designer IV</th>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VP Product Management</th>\n",
       "      <td>682</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive Secretary</th>\n",
       "      <td>274</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Editor</th>\n",
       "      <td>574</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nuclear Power Engineer</th>\n",
       "      <td>450</td>\n",
       "      <td>10</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accountant I</th>\n",
       "      <td>961</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Database Administrator II</th>\n",
       "      <td>680</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical Engineer</th>\n",
       "      <td>307</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Programmer Analyst I</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automation Specialist II</th>\n",
       "      <td>271</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web Designer I</th>\n",
       "      <td>319</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources Manager</th>\n",
       "      <td>867</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered Nurse</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Coach I</th>\n",
       "      <td>249</td>\n",
       "      <td>9</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Customer ID  Food ID  Price\n",
       "Occupation                                                 \n",
       "Speech Pathologist                     1812       29  64.96\n",
       "Compensation Analyst                   3855       28  59.71\n",
       "Recruiter                              2761       30  57.71\n",
       "Marketing Manager                      2089       25  55.19\n",
       "Community Outreach Specialist          2408       25  53.95\n",
       "Sales Representative                   1701       36  52.20\n",
       "Dental Hygienist                        651       14  49.98\n",
       "Analyst Programmer                      739       15  48.97\n",
       "Account Executive                      1375       11  44.97\n",
       "Assistant Media Planner                2848       35  42.97\n",
       "Physical Therapy Assistant             2667       21  37.72\n",
       "VP Quality Control                     1336       22  36.49\n",
       "Cost Accountant                         995       20  35.72\n",
       "Statistician III                       1314        9  34.98\n",
       "Systems Administrator IV                658       22  34.98\n",
       "Senior Quality Engineer                2200       21  34.22\n",
       "Sales Associate                        1179       12  33.23\n",
       "Payment Adjustment Coordinator         2334       13  31.97\n",
       "Associate Professor                     761       12  31.23\n",
       "Research Associate                     2072       31  30.72\n",
       "Tax Accountant                          413       11  29.24\n",
       "Chemical Engineer                       822       22  29.23\n",
       "Technical Writer                       1373       21  28.97\n",
       "Desktop Support Technician             1727       19  28.97\n",
       "Legal Assistant                        1621       19  27.73\n",
       "Quality Control Specialist             1618       17  26.74\n",
       "Administrative Assistant III           1544       17  26.74\n",
       "VP Sales                               1385       17  26.74\n",
       "Software Engineer IV                    837       16  25.98\n",
       "Web Developer IV                        862       16  25.98\n",
       "...                                     ...      ...    ...\n",
       "Systems Administrator I                 819        4   4.25\n",
       "General Manager                         745        4   4.25\n",
       "Budget/Accounting Analyst IV            968        1   3.99\n",
       "Engineer II                             321        1   3.99\n",
       "Programmer IV                            20        1   3.99\n",
       "Assistant Manager                        92        1   3.99\n",
       "Developer I                             433       12   3.98\n",
       "Environmental Tech                     1009       14   3.48\n",
       "Chief Design Engineer                   121        3   2.99\n",
       "Financial Advisor                       134        3   2.99\n",
       "Actuary                                 108        3   2.99\n",
       "Mechanical Systems Engineer             462        3   2.99\n",
       "Staff Scientist                         114        5   2.49\n",
       "Director of Sales                       945        5   2.49\n",
       "Account Representative IV               966        5   2.49\n",
       "Clinical Specialist                     351       10   1.75\n",
       "Web Designer IV                          64       10   1.75\n",
       "VP Product Management                   682       10   1.75\n",
       "Executive Secretary                     274       10   1.75\n",
       "Senior Editor                           574       10   1.75\n",
       "Nuclear Power Engineer                  450       10   1.75\n",
       "Accountant I                            961        9   0.99\n",
       "Database Administrator II               680        9   0.99\n",
       "Electrical Engineer                     307        9   0.99\n",
       "Programmer Analyst I                     26        9   0.99\n",
       "Automation Specialist II                271        9   0.99\n",
       "Web Designer I                          319        9   0.99\n",
       "Human Resources Manager                 867        9   0.99\n",
       "Registered Nurse                        537        9   0.99\n",
       "Health Coach I                          249        9   0.99\n",
       "\n",
       "[115 rows x 3 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OccupationGroup.sum().sort_values(\"Price\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1962.6800000000003"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custFoodsW1[\"Price\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>Sls</th>\n",
       "      <th>Slsrp Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Pft %</th>\n",
       "      <th>Order</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "      <td>Ryan Irwin</td>\n",
       "      <td>56971.7</td>\n",
       "      <td>44910.1</td>\n",
       "      <td>12061.62</td>\n",
       "      <td>21.17</td>\n",
       "      <td>51</td>\n",
       "      <td>1117.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>508</td>\n",
       "      <td>Michael Da</td>\n",
       "      <td>77551.7</td>\n",
       "      <td>66643.7</td>\n",
       "      <td>10908.07</td>\n",
       "      <td>14.07</td>\n",
       "      <td>65</td>\n",
       "      <td>1193.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>Rhys Lloyd</td>\n",
       "      <td>99699</td>\n",
       "      <td>84334.3</td>\n",
       "      <td>15364.66</td>\n",
       "      <td>15.41</td>\n",
       "      <td>102</td>\n",
       "      <td>977.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>513</td>\n",
       "      <td>Aron Leves</td>\n",
       "      <td>96556.7</td>\n",
       "      <td>80697</td>\n",
       "      <td>15859.70</td>\n",
       "      <td>16.43</td>\n",
       "      <td>68</td>\n",
       "      <td>1419.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>514</td>\n",
       "      <td>Paul Crell</td>\n",
       "      <td>146157</td>\n",
       "      <td>122457</td>\n",
       "      <td>23699.95</td>\n",
       "      <td>16.22</td>\n",
       "      <td>97</td>\n",
       "      <td>1506.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period  Sls  Slsrp Name    Sales     Cost    Profit  Pft %  \\\n",
       "28         1       1  506  Ryan Irwin  56971.7  44910.1  12061.62  21.17   \n",
       "29         1       1  508  Michael Da  77551.7  66643.7  10908.07  14.07   \n",
       "30         1       1  511  Rhys Lloyd    99699  84334.3  15364.66  15.41   \n",
       "31         1       1  513  Aron Leves  96556.7    80697  15859.70  16.43   \n",
       "32         1       1  514  Paul Crell   146157   122457  23699.95  16.22   \n",
       "\n",
       "   Order    Avgor  \n",
       "28    51  1117.09  \n",
       "29    65  1193.10  \n",
       "30   102   977.44  \n",
       "31    68  1419.95  \n",
       "32    97  1506.77  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(521, 10)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnet = pd.read_excel(\"Pnet_New.xlsx\",parse_dates=[\"Last Logon Date\",\"Last Logon Time\",\"Last Order Date\",\"Last Order Time\",\"Account Expires\"])\n",
    "client = pd.read_excel(\"clients.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Username</th>\n",
       "      <th>Password</th>\n",
       "      <th>Company</th>\n",
       "      <th>Division</th>\n",
       "      <th>Location</th>\n",
       "      <th>Customer#</th>\n",
       "      <th>Status</th>\n",
       "      <th>Last Logon Date</th>\n",
       "      <th>Last Logon Time</th>\n",
       "      <th>Last Order Date</th>\n",
       "      <th>Last Order Time</th>\n",
       "      <th>Logons</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Account Expires</th>\n",
       "      <th>Customer Email Address</th>\n",
       "      <th>Group Profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46940</td>\n",
       "      <td>PN2114</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>46940</td>\n",
       "      <td>A</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75159</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75409</td>\n",
       "      <td>2306</td>\n",
       "      <td>SCOUTS CANADA CAMP OPEMIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA</td>\n",
       "      <td>Profile00013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11899</td>\n",
       "      <td>PN1899</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11899</td>\n",
       "      <td>D</td>\n",
       "      <td>20130325</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>WOODSTOCK GEN HOSPITAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>TGOODALL@WGH.ON.CA</td>\n",
       "      <td>Profile00001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Username Password  Company  Division  Location  Customer# Status  \\\n",
       "0    46940   PN2114        1         1         2      46940      A   \n",
       "1    11899   PN1899        1         1         1      11899      D   \n",
       "\n",
       "   Last Logon Date  Last Logon Time  Last Order Date  Last Order Time  Logons  \\\n",
       "0         20190221            75159         20190221            75409    2306   \n",
       "1         20130325                0                0                0      39   \n",
       "\n",
       "               Customer Name Phone Number Account Expires  \\\n",
       "0  SCOUTS CANADA CAMP OPEMIK          NaN      2019-12-31   \n",
       "1     WOODSTOCK GEN HOSPITAL          NaN      2019-12-31   \n",
       "\n",
       "                       Customer Email Address Group Profile  \n",
       "0  JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA  Profile00013  \n",
       "1                          TGOODALL@WGH.ON.CA  Profile00001  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnet.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Username', 'Password', 'Company', 'Division', 'Location', 'Customer#',\n",
       "       'Status', 'Last Logon Date', 'Last Logon Time', 'Last Order Date',\n",
       "       'Last Order Time', 'Logons', 'Customer Name', 'Phone Number',\n",
       "       'Account Expires', 'Customer Email Address', 'Group Profile'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnet.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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#</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>SalesRep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50415</td>\n",
       "      <td>DORCHESTER</td>\n",
       "      <td>ON</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126130</td>\n",
       "      <td>MISSISSAUGA</td>\n",
       "      <td>ON</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126000</td>\n",
       "      <td>OAKVILLE</td>\n",
       "      <td>ON</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer#         City Province  SalesRep\n",
       "0      50415   DORCHESTER       ON       511\n",
       "1     126130  MISSISSAUGA       ON       600\n",
       "2     126000     OAKVILLE       ON       600"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12484 entries, 0 to 12483\n",
      "Data columns (total 4 columns):\n",
      "Customer#    12484 non-null int64\n",
      "City         12348 non-null object\n",
      "Province     12409 non-null object\n",
      "SalesRep     12484 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 390.2+ KB\n"
     ]
    }
   ],
   "source": [
    "client.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5337 entries, 0 to 5336\n",
      "Data columns (total 17 columns):\n",
      "Username                  5337 non-null object\n",
      "Password                  5337 non-null object\n",
      "Company                   5337 non-null int64\n",
      "Division                  5337 non-null int64\n",
      "Location                  5337 non-null int64\n",
      "Customer#                 5337 non-null int64\n",
      "Status                    5337 non-null object\n",
      "Last Logon Date           5337 non-null int64\n",
      "Last Logon Time           5337 non-null int64\n",
      "Last Order Date           5337 non-null int64\n",
      "Last Order Time           5337 non-null int64\n",
      "Logons                    5337 non-null int64\n",
      "Customer Name             2363 non-null object\n",
      "Phone Number              555 non-null object\n",
      "Account Expires           5337 non-null datetime64[ns]\n",
      "Customer Email Address    4718 non-null object\n",
      "Group Profile             2239 non-null object\n",
      "dtypes: datetime64[ns](1), int64(9), object(7)\n",
      "memory usage: 708.9+ KB\n"
     ]
    }
   ],
   "source": [
    "pnet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    2338\n",
       "1    1462\n",
       "2     990\n",
       "8     547\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnet[\"Location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "maskA = pnet[\"Location\"] == 2\n",
    "maskB = pnet[\"Status\"] != \"D\"\n",
    "pnetLoc2 = pnet[maskA & maskB]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnetSls = pnet.merge(client,how=\"inner\",on=\"Customer#\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "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>Username</th>\n",
       "      <th>Password</th>\n",
       "      <th>Company</th>\n",
       "      <th>Division</th>\n",
       "      <th>Location</th>\n",
       "      <th>Customer#</th>\n",
       "      <th>Status</th>\n",
       "      <th>Last Logon Date</th>\n",
       "      <th>Last Logon Time</th>\n",
       "      <th>Last Order Date</th>\n",
       "      <th>Last Order Time</th>\n",
       "      <th>Logons</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Account Expires</th>\n",
       "      <th>Customer Email Address</th>\n",
       "      <th>Group Profile</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>SalesRep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46940</td>\n",
       "      <td>PN2114</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>46940</td>\n",
       "      <td>A</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75159</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75409</td>\n",
       "      <td>2306</td>\n",
       "      <td>SCOUTS CANADA CAMP OPEMIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA</td>\n",
       "      <td>Profile00013</td>\n",
       "      <td>MABERLY</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WW100</td>\n",
       "      <td>SAMIND</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>701000</td>\n",
       "      <td>A</td>\n",
       "      <td>20190304</td>\n",
       "      <td>115432</td>\n",
       "      <td>20190304</td>\n",
       "      <td>122227</td>\n",
       "      <td>1362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>ww100@bestchickenwings.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WW100</td>\n",
       "      <td>SAMIND</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>701000</td>\n",
       "      <td>A</td>\n",
       "      <td>20190304</td>\n",
       "      <td>115432</td>\n",
       "      <td>20190304</td>\n",
       "      <td>122227</td>\n",
       "      <td>1362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>ww100@bestchickenwings.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70392M</td>\n",
       "      <td>DONTGIVE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70392</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Reporting</td>\n",
       "      <td>123456789</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>helpdesk@summit.colabor.com</td>\n",
       "      <td>Import</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70392</td>\n",
       "      <td>PN3104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>70392</td>\n",
       "      <td>A</td>\n",
       "      <td>20140604</td>\n",
       "      <td>75835</td>\n",
       "      <td>20140602</td>\n",
       "      <td>154901</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>collegest@coffeeculturecafe.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Username  Password  Company  Division  Location  Customer# Status  \\\n",
       "0    46940    PN2114        1         1         2      46940      A   \n",
       "1    WW100    SAMIND        1         1         3     701000      A   \n",
       "2    WW100    SAMIND        1         1         3     701000      A   \n",
       "3   70392M  DONTGIVE        1         1         3      70392      D   \n",
       "4    70392    PN3104        1         1         8      70392      A   \n",
       "\n",
       "   Last Logon Date  Last Logon Time  Last Order Date  Last Order Time  Logons  \\\n",
       "0         20190221            75159         20190221            75409    2306   \n",
       "1         20190304           115432         20190304           122227    1362   \n",
       "2         20190304           115432         20190304           122227    1362   \n",
       "3                0                0                0                0       0   \n",
       "4         20140604            75835         20140602           154901       2   \n",
       "\n",
       "               Customer Name Phone Number Account Expires  \\\n",
       "0  SCOUTS CANADA CAMP OPEMIK          NaN      2019-12-31   \n",
       "1                        NaN          NaN      2019-12-31   \n",
       "2                        NaN          NaN      2019-12-31   \n",
       "3                  Reporting    123456789      2019-12-31   \n",
       "4                        NaN          NaN      2019-12-31   \n",
       "\n",
       "                       Customer Email Address Group Profile     City Province  \\\n",
       "0  JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA  Profile00013  MABERLY       ON   \n",
       "1                  ww100@bestchickenwings.com           NaN  TORONTO       ON   \n",
       "2                  ww100@bestchickenwings.com           NaN  TORONTO       ON   \n",
       "3                 helpdesk@summit.colabor.com        Import  TORONTO       ON   \n",
       "4             collegest@coffeeculturecafe.com           NaN  TORONTO       ON   \n",
       "\n",
       "   SalesRep  \n",
       "0       564  \n",
       "1       740  \n",
       "2       740  \n",
       "3       493  \n",
       "4       493  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnetSls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "maskA = pnetSls[\"Location\"] == 2\n",
    "maskB = pnetSls[\"Status\"] != \"D\"\n",
    "maskC = pnetSls[\"SalesRep\"] == 588\n",
    "maskD = pnetSls[\"SalesRep\"] == 584\n",
    "pnet588 = pnetSls[maskC | maskD]\n",
    "pnetLoc2 = pnetSls[maskA & maskB]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "588    115\n",
       "584     34\n",
       "Name: SalesRep, dtype: int64"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnet588[\"SalesRep\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Username</th>\n",
       "      <th>Password</th>\n",
       "      <th>Company</th>\n",
       "      <th>Division</th>\n",
       "      <th>Location</th>\n",
       "      <th>Customer#</th>\n",
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       "      <th>Account Expires</th>\n",
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       "      <th>Group Profile</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>SalesRep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>31656</td>\n",
       "      <td>PN7672</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31656</td>\n",
       "      <td>D</td>\n",
       "      <td>20141030</td>\n",
       "      <td>152852</td>\n",
       "      <td>20141030</td>\n",
       "      <td>155145</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>lynee.smith@bluedoorshelters.ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEWMARKET</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>31135</td>\n",
       "      <td>PN6322</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31135</td>\n",
       "      <td>D</td>\n",
       "      <td>20140116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>chow.jd@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Username Password  Company  Division  Location  Customer# Status  \\\n",
       "41    31656   PN7672        1         1         8      31656      D   \n",
       "49    31135   PN6322        1         1         8      31135      D   \n",
       "\n",
       "    Last Logon Date  Last Logon Time  Last Order Date  Last Order Time  \\\n",
       "41         20141030           152852         20141030           155145   \n",
       "49         20140116                0                0                0   \n",
       "\n",
       "    Logons Customer Name Phone Number Account Expires  \\\n",
       "41       4           NaN          NaN      2019-12-31   \n",
       "49       2           NaN          NaN      2019-12-31   \n",
       "\n",
       "             Customer Email Address Group Profile       City Province  \\\n",
       "41  lynee.smith@bluedoorshelters.ca           NaN  NEWMARKET       ON   \n",
       "49                chow.jd@gmail.com           NaN  BEAVERTON       ON   \n",
       "\n",
       "    SalesRep  \n",
       "41       588  \n",
       "49       588  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnet588.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2306</td>\n",
       "      <td>SCOUTS CANADA CAMP OPEMIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA</td>\n",
       "      <td>Profile00013</td>\n",
       "      <td>MABERLY</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>WW107</td>\n",
       "      <td>123456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>701070</td>\n",
       "      <td>A</td>\n",
       "      <td>20190304</td>\n",
       "      <td>95437</td>\n",
       "      <td>20190304</td>\n",
       "      <td>95922</td>\n",
       "      <td>1122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>wildwingottawa@rogers.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ORLEANS</td>\n",
       "      <td>ON</td>\n",
       "      <td>740</td>\n",
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       "</table>\n",
       "</div>"
      ],
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       "   Username Password  Company  Division  Location  Customer# Status  \\\n",
       "0     46940   PN2114        1         1         2      46940      A   \n",
       "36    WW107   123456        1         1         2     701070      A   \n",
       "\n",
       "    Last Logon Date  Last Logon Time  Last Order Date  Last Order Time  \\\n",
       "0          20190221            75159         20190221            75409   \n",
       "36         20190304            95437         20190304            95922   \n",
       "\n",
       "    Logons              Customer Name Phone Number Account Expires  \\\n",
       "0     2306  SCOUTS CANADA CAMP OPEMIK          NaN      2019-12-31   \n",
       "36    1122                        NaN          NaN      2019-12-31   \n",
       "\n",
       "                        Customer Email Address Group Profile     City  \\\n",
       "0   JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA  Profile00013  MABERLY   \n",
       "36                   wildwingottawa@rogers.com           NaN  ORLEANS   \n",
       "\n",
       "   Province  SalesRep  \n",
       "0        ON       564  \n",
       "36       ON       740  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnetLoc2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(837, 20)"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = pnetLoc2.append(pnet588,ignore_index=True)\n",
    "report.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1444</td>\n",
       "      <td>BYTOWN LES TRAITEURS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>STEVE@BYTOWNCATERING.COM</td>\n",
       "      <td>Profile00013</td>\n",
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       "      <td>1355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2099-12-31</td>\n",
       "      <td>wildwingottawa@rogers.com</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>318</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>43300</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2099-12-31</td>\n",
       "      <td>augustin@northontariofoodsales.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH BAY</td>\n",
       "      <td>ON</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>40481</td>\n",
       "      <td>PN1840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40481</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>94138</td>\n",
       "      <td>20190303</td>\n",
       "      <td>93906</td>\n",
       "      <td>1249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>chef_james@live.ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHELSEA</td>\n",
       "      <td>QC</td>\n",
       "      <td>578</td>\n",
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       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>686430</td>\n",
       "      <td>PN8643</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>686430</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>101804</td>\n",
       "      <td>20190305</td>\n",
       "      <td>101911</td>\n",
       "      <td>1125</td>\n",
       "      <td>EAST SIDE MARIOS 8643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>fleurdelys@eastsidemarios.com</td>\n",
       "      <td>ESMFRENCH</td>\n",
       "      <td>QUEBEC CITY</td>\n",
       "      <td>QC</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>40257</td>\n",
       "      <td>PN8163</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40257</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>93458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>winonabennett1@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COBDEN</td>\n",
       "      <td>ON</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>686340</td>\n",
       "      <td>PN8634</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>686340</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>115736</td>\n",
       "      <td>20190305</td>\n",
       "      <td>120436</td>\n",
       "      <td>1050</td>\n",
       "      <td>EAST SIDE MARIOS 8634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>cornwall@eastsidemarios.com</td>\n",
       "      <td>PRIME</td>\n",
       "      <td>CORNWALL</td>\n",
       "      <td>ON</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>43207</td>\n",
       "      <td>PN8413</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43207</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>121635</td>\n",
       "      <td>20190304</td>\n",
       "      <td>104027</td>\n",
       "      <td>1010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>nektar_m@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LAVAL</td>\n",
       "      <td>QC</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>687580</td>\n",
       "      <td>PN8758</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>687580</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>142115</td>\n",
       "      <td>20190303</td>\n",
       "      <td>103835</td>\n",
       "      <td>998</td>\n",
       "      <td>EAST SIDE MARIOS 8758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Ottawahuntclub@eastsidemarios.com</td>\n",
       "      <td>PRIME</td>\n",
       "      <td>NEPEAN</td>\n",
       "      <td>ON</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>686590</td>\n",
       "      <td>PN7041</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>686590</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>142633</td>\n",
       "      <td>20190305</td>\n",
       "      <td>95453</td>\n",
       "      <td>911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>esm8659@cara.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>158371</td>\n",
       "      <td>PN6283</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>158371</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>84122</td>\n",
       "      <td>20190305</td>\n",
       "      <td>91926</td>\n",
       "      <td>624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>pubs5837@cara.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KANATA</td>\n",
       "      <td>ON</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>37055</td>\n",
       "      <td>BOBINO</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37055</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>114136</td>\n",
       "      <td>20180320</td>\n",
       "      <td>110506</td>\n",
       "      <td>522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>info@gainsbourg.ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GATINEAU</td>\n",
       "      <td>QC</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>686820</td>\n",
       "      <td>PN8682</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>686820</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>100314</td>\n",
       "      <td>20190305</td>\n",
       "      <td>101604</td>\n",
       "      <td>464</td>\n",
       "      <td>EAST SIDE MARIOS 8682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Kanata@eastsidemarios.com</td>\n",
       "      <td>PRIME</td>\n",
       "      <td>KANATA</td>\n",
       "      <td>ON</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>43721</td>\n",
       "      <td>PN2438</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43721</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>102550</td>\n",
       "      <td>20190304</td>\n",
       "      <td>175420</td>\n",
       "      <td>418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Nick.Launderville@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>241PIZ197</td>\n",
       "      <td>PN2280</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>301972</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>122124</td>\n",
       "      <td>20190305</td>\n",
       "      <td>122548</td>\n",
       "      <td>416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>james_restoule@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STURGEON FALLS</td>\n",
       "      <td>ON</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>241PIZ197</td>\n",
       "      <td>PN2280</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>301972</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>122124</td>\n",
       "      <td>20190305</td>\n",
       "      <td>122548</td>\n",
       "      <td>416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>james_restoule@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STURGEON FALLS</td>\n",
       "      <td>ON</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>43731</td>\n",
       "      <td>PN0226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43731</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>122044</td>\n",
       "      <td>20190304</td>\n",
       "      <td>175733</td>\n",
       "      <td>336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>nick.launderville@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>43712</td>\n",
       "      <td>PN3428</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43712</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>133027</td>\n",
       "      <td>20190305</td>\n",
       "      <td>133500</td>\n",
       "      <td>321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Nick.Launderville@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>50630</td>\n",
       "      <td>PN1917</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50630</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>153357</td>\n",
       "      <td>20190204</td>\n",
       "      <td>153156</td>\n",
       "      <td>312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>chef@tudorhall.net</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>43717</td>\n",
       "      <td>PN8428</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43717</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>124040</td>\n",
       "      <td>20190305</td>\n",
       "      <td>131042</td>\n",
       "      <td>294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Nick.Launderville@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>43720</td>\n",
       "      <td>PN1438</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43720</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>141853</td>\n",
       "      <td>20190305</td>\n",
       "      <td>144713</td>\n",
       "      <td>290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Nick.Launderville@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>30533</td>\n",
       "      <td>PN1740</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30533</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>121829</td>\n",
       "      <td>20190305</td>\n",
       "      <td>125753</td>\n",
       "      <td>119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Edith.Chabot@summit.colabor.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KANATA</td>\n",
       "      <td>ON</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>127510</td>\n",
       "      <td>PN1111</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>127510</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>124410</td>\n",
       "      <td>20190305</td>\n",
       "      <td>130537</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>harveys2751@cara.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHAMBLY</td>\n",
       "      <td>QC</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>43923</td>\n",
       "      <td>PN1212</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43923</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>142229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>lolabingo@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTTAWA</td>\n",
       "      <td>ON</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>EGGPRESSHO</td>\n",
       "      <td>PN0077</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43217</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>144802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>tsatsa93@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DORVAL</td>\n",
       "      <td>QC</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>30999</td>\n",
       "      <td>PN1942</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30999</td>\n",
       "      <td>A</td>\n",
       "      <td>20190305</td>\n",
       "      <td>112737</td>\n",
       "      <td>20190301</td>\n",
       "      <td>74015</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>batonrougevaudreuil@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VAUDREUIL</td>\n",
       "      <td>QC</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>47435</td>\n",
       "      <td>PN2148</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47435</td>\n",
       "      <td>A</td>\n",
       "      <td>20190304</td>\n",
       "      <td>134059</td>\n",
       "      <td>20190304</td>\n",
       "      <td>134816</td>\n",
       "      <td>3941</td>\n",
       "      <td>SKI FORTUNE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>CHEFFLOWERS@CAMPFORTUNE.COM</td>\n",
       "      <td>Profile00013</td>\n",
       "      <td>CHELSEA</td>\n",
       "      <td>QC</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>38317</td>\n",
       "      <td>SUMMIT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38317</td>\n",
       "      <td>A</td>\n",
       "      <td>20190304</td>\n",
       "      <td>105059</td>\n",
       "      <td>20190304</td>\n",
       "      <td>105434</td>\n",
       "      <td>2766</td>\n",
       "      <td>Francisco Chavez</td>\n",
       "      <td>5147396440</td>\n",
       "      <td>2099-12-31</td>\n",
       "      <td>fchavez@rosdevhotels.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VILLE ST-LAUREN</td>\n",
       "      <td>QC</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>476731</td>\n",
       "      <td>RESETRQST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>476731</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>orderconfirmation@cara.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BROCKVILLE</td>\n",
       "      <td>ON</td>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>301510</td>\n",
       "      <td>19820207MM</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>301510</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Manoocher</td>\n",
       "      <td>7054987259</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>mrazaiy@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH BAY</td>\n",
       "      <td>ON</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>301510</td>\n",
       "      <td>19820207MM</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>301510</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Manoocher</td>\n",
       "      <td>7054987259</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>mrazaiy@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH BAY</td>\n",
       "      <td>ON</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>127530</td>\n",
       "      <td>PN1111</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>127530</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>harveys2753@recipeunlimited.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VERDUN</td>\n",
       "      <td>QC</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>45275</td>\n",
       "      <td>PN6836</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45275</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>p.springer@rogers.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KESWICK</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>45275</td>\n",
       "      <td>PN6836</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45275</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>p.springer@rogers.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KESWICK</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>32134</td>\n",
       "      <td>PN4312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>32134</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>karen.dee.lyncroft@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>43710</td>\n",
       "      <td>PN0173</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43710</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Terry.bev.smith@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PORT SYDNEY</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>30970</td>\n",
       "      <td>PN2180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30970</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>saritrsll@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEWMARKET</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>30970</td>\n",
       "      <td>PN2180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30970</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>saritrsll@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEWMARKET</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>30972</td>\n",
       "      <td>PN3180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30972</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>nerry.bagelworld@yahoo.ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>30972</td>\n",
       "      <td>PN3180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30972</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>nerry.bagelworld@yahoo.ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>31107</td>\n",
       "      <td>DONTGIVE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31107</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASAGA BEACH</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>45080</td>\n",
       "      <td>PN4580</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45080</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Franca@preciousbeginning.info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WOODBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>45080</td>\n",
       "      <td>PN4580</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45080</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Franca@preciousbeginning.info</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WOODBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>49651</td>\n",
       "      <td>PN2670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>49651</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>janetkorrey@live.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KESWICK</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>30062</td>\n",
       "      <td>INQUIRY</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30062</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AHMIC HARBOUR</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>34392</td>\n",
       "      <td>PN2943</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>34392</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>robgwynne@rogers.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEWMARKET</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>30973</td>\n",
       "      <td>PN9308</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30973</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>deliadoria@ymail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>THORNHILL</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>30973</td>\n",
       "      <td>PN9308</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30973</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>deliadoria@ymail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>THORNHILL</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>30055</td>\n",
       "      <td>PN5500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30055</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>amin4547@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>30055</td>\n",
       "      <td>PN5500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30055</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>amin4547@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TORONTO</td>\n",
       "      <td>ON</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>44301</td>\n",
       "      <td>PN2514</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>44301</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>cheffriesen@bell.net</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BRACEBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>32745</td>\n",
       "      <td>PN6358</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>32745</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>Jen@campcouchiching.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LONGFORD MILLS</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>46435</td>\n",
       "      <td>PN8967</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>46435</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>abwbeatty@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UXBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>46435</td>\n",
       "      <td>PN8967</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>46435</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>abwbeatty@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UXBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>30977</td>\n",
       "      <td>PN3467</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30977</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>abhikpatel@yahoo.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MARKHAM</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>30977</td>\n",
       "      <td>PN3467</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30977</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>abhikpatel@yahoo.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MARKHAM</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>51288</td>\n",
       "      <td>PN2297</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>51288</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>louisethorn78@gmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WYEBRIDGE</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>42825</td>\n",
       "      <td>PN6437</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>42825</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>muskokanorth@hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GRAVENHURST</td>\n",
       "      <td>ON</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>837 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Username    Password  Company  Division  Location  Customer# Status  \\\n",
       "377       34105      PN4105        1         1         2      34105      A   \n",
       "35        40640      PN2028        1         1         2      40640      A   \n",
       "317        WW16    LAURA101        1         1         2     700160      A   \n",
       "318        WW16    LAURA101        1         1         2     700160      A   \n",
       "533       43300      PN5138        1         1         2      43300      A   \n",
       "27        40481      PN1840        1         1         2      40481      A   \n",
       "327      686430      PN8643        1         1         2     686430      A   \n",
       "489       40257      PN8163        1         1         2      40257      A   \n",
       "257      686340      PN8634        1         1         2     686340      A   \n",
       "438       43207      PN8413        1         1         2      43207      A   \n",
       "315      687580      PN8758        1         1         2     687580      A   \n",
       "597      686590      PN7041        1         1         2     686590      A   \n",
       "543      158371      PN6283        1         1         2     158371      A   \n",
       "375       37055      BOBINO        1         1         2      37055      A   \n",
       "319      686820      PN8682        1         1         2     686820      A   \n",
       "448       43721      PN2438        1         1         2      43721      A   \n",
       "291   241PIZ197      PN2280        1         1         2     301972      A   \n",
       "292   241PIZ197      PN2280        1         1         2     301972      A   \n",
       "229       43731      PN0226        1         1         2      43731      A   \n",
       "207       43712      PN3428        1         1         2      43712      A   \n",
       "572       50630      PN1917        1         1         2      50630      A   \n",
       "451       43717      PN8428        1         1         2      43717      A   \n",
       "446       43720      PN1438        1         1         2      43720      A   \n",
       "594       30533      PN1740        1         1         2      30533      A   \n",
       "675      127510      PN1111        1         1         2     127510      A   \n",
       "661       43923      PN1212        1         1         2      43923      A   \n",
       "647  EGGPRESSHO      PN0077        1         1         2      43217      A   \n",
       "623       30999      PN1942        1         1         2      30999      A   \n",
       "84        47435      PN2148        1         1         2      47435      A   \n",
       "255       38317      SUMMIT        1         1         2      38317      A   \n",
       "..          ...         ...      ...       ...       ...        ...    ...   \n",
       "677      476731   RESETRQST        1         1         2     476731      A   \n",
       "680      301510  19820207MM        1         1         2     301510      A   \n",
       "681      301510  19820207MM        1         1         2     301510      A   \n",
       "687      127530      PN1111        1         1         2     127530      A   \n",
       "700       45275      PN6836        1         1         3      45275      D   \n",
       "701       45275      PN6836        1         1         3      45275      D   \n",
       "706       32134      PN4312        1         1         3      32134      D   \n",
       "707       43710      PN0173        1         1         2      43710      D   \n",
       "716       30970      PN2180        1         1         3      30970      A   \n",
       "717       30970      PN2180        1         1         3      30970      A   \n",
       "718       30972      PN3180        1         1         3      30972      A   \n",
       "719       30972      PN3180        1         1         3      30972      A   \n",
       "722       31107    DONTGIVE        1         1         3      31107      A   \n",
       "723       45080      PN4580        1         1         3      45080      A   \n",
       "724       45080      PN4580        1         1         3      45080      A   \n",
       "733       49651      PN2670        1         1         8      49651      D   \n",
       "735       30062     INQUIRY        1         1         3      30062      A   \n",
       "755       34392      PN2943        1         1         8      34392      D   \n",
       "763       30973      PN9308        1         1         3      30973      A   \n",
       "764       30973      PN9308        1         1         3      30973      A   \n",
       "765       30055      PN5500        1         1         3      30055      A   \n",
       "766       30055      PN5500        1         1         3      30055      A   \n",
       "771       44301      PN2514        1         1         3      44301      A   \n",
       "778       32745      PN6358        1         1         3      32745      A   \n",
       "783       46435      PN8967        1         1         3      46435      A   \n",
       "784       46435      PN8967        1         1         3      46435      A   \n",
       "787       30977      PN3467        1         1         3      30977      A   \n",
       "788       30977      PN3467        1         1         3      30977      A   \n",
       "792       51288      PN2297        1         1         3      51288      A   \n",
       "806       42825      PN6437        1         1         3      42825      A   \n",
       "\n",
       "     Last Logon Date  Last Logon Time  Last Order Date  Last Order Time  \\\n",
       "377         20190305           153153         20190305           153559   \n",
       "35          20190305           144733         20190304           170608   \n",
       "317         20190305           104127         20190304            91448   \n",
       "318         20190305           104127         20190304            91448   \n",
       "533         20190305           142222         20190305           133200   \n",
       "27          20190305            94138         20190303            93906   \n",
       "327         20190305           101804         20190305           101911   \n",
       "489         20190305            93458                0                0   \n",
       "257         20190305           115736         20190305           120436   \n",
       "438         20190305           121635         20190304           104027   \n",
       "315         20190305           142115         20190303           103835   \n",
       "597         20190305           142633         20190305            95453   \n",
       "543         20190305            84122         20190305            91926   \n",
       "375         20190305           114136         20180320           110506   \n",
       "319         20190305           100314         20190305           101604   \n",
       "448         20190305           102550         20190304           175420   \n",
       "291         20190305           122124         20190305           122548   \n",
       "292         20190305           122124         20190305           122548   \n",
       "229         20190305           122044         20190304           175733   \n",
       "207         20190305           133027         20190305           133500   \n",
       "572         20190305           153357         20190204           153156   \n",
       "451         20190305           124040         20190305           131042   \n",
       "446         20190305           141853         20190305           144713   \n",
       "594         20190305           121829         20190305           125753   \n",
       "675         20190305           124410         20190305           130537   \n",
       "661         20190305           142229                0                0   \n",
       "647         20190305           144802                0                0   \n",
       "623         20190305           112737         20190301            74015   \n",
       "84          20190304           134059         20190304           134816   \n",
       "255         20190304           105059         20190304           105434   \n",
       "..               ...              ...              ...              ...   \n",
       "677                0                0                0                0   \n",
       "680                0                0                0                0   \n",
       "681                0                0                0                0   \n",
       "687                0                0                0                0   \n",
       "700                0                0                0                0   \n",
       "701                0                0                0                0   \n",
       "706                0                0                0                0   \n",
       "707                0                0                0                0   \n",
       "716                0                0                0                0   \n",
       "717                0                0                0                0   \n",
       "718                0                0                0                0   \n",
       "719                0                0                0                0   \n",
       "722                0                0                0                0   \n",
       "723                0                0                0                0   \n",
       "724                0                0                0                0   \n",
       "733                0                0                0                0   \n",
       "735                0                0                0                0   \n",
       "755                0                0                0                0   \n",
       "763                0                0                0                0   \n",
       "764                0                0                0                0   \n",
       "765                0                0                0                0   \n",
       "766                0                0                0                0   \n",
       "771                0                0                0                0   \n",
       "778                0                0                0                0   \n",
       "783                0                0                0                0   \n",
       "784                0                0                0                0   \n",
       "787                0                0                0                0   \n",
       "788                0                0                0                0   \n",
       "792                0                0                0                0   \n",
       "806                0                0                0                0   \n",
       "\n",
       "     Logons          Customer Name Phone Number Account Expires  \\\n",
       "377    1802     SKI MONT STE-MARIE          NaN      2019-12-31   \n",
       "35     1444   BYTOWN LES TRAITEURS          NaN      2019-12-31   \n",
       "317    1355                    NaN          NaN      2099-12-31   \n",
       "318    1355                    NaN          NaN      2099-12-31   \n",
       "533    1320                    NaN          NaN      2099-12-31   \n",
       "27     1249                    NaN          NaN      2019-12-31   \n",
       "327    1125  EAST SIDE MARIOS 8643          NaN      2019-12-31   \n",
       "489    1075                    NaN          NaN      2019-12-31   \n",
       "257    1050  EAST SIDE MARIOS 8634          NaN      2019-12-31   \n",
       "438    1010                    NaN          NaN      2019-12-31   \n",
       "315     998  EAST SIDE MARIOS 8758          NaN      2019-12-31   \n",
       "597     911                    NaN          NaN      2019-12-31   \n",
       "543     624                    NaN          NaN      2019-12-31   \n",
       "375     522                    NaN          NaN      2019-12-31   \n",
       "319     464  EAST SIDE MARIOS 8682          NaN      2019-12-31   \n",
       "448     418                    NaN          NaN      2019-12-31   \n",
       "291     416                    NaN          NaN      2019-12-31   \n",
       "292     416                    NaN          NaN      2019-12-31   \n",
       "229     336                    NaN          NaN      2019-12-31   \n",
       "207     321                    NaN          NaN      2019-12-31   \n",
       "572     312                    NaN          NaN      2019-12-31   \n",
       "451     294                    NaN          NaN      2019-12-31   \n",
       "446     290                    NaN          NaN      2019-12-31   \n",
       "594     119                    NaN          NaN      2019-12-31   \n",
       "675      72                    NaN          NaN      2019-12-31   \n",
       "661      52                    NaN          NaN      2019-12-31   \n",
       "647      45                    NaN          NaN      2019-12-31   \n",
       "623      30                    NaN          NaN      2019-12-31   \n",
       "84     3941            SKI FORTUNE          NaN      2019-12-31   \n",
       "255    2766       Francisco Chavez   5147396440      2099-12-31   \n",
       "..      ...                    ...          ...             ...   \n",
       "677       0                    NaN          NaN      2019-12-31   \n",
       "680       0              Manoocher   7054987259      2019-12-31   \n",
       "681       0              Manoocher   7054987259      2019-12-31   \n",
       "687       0                    NaN          NaN      2019-12-31   \n",
       "700       0                    NaN          NaN      2019-12-31   \n",
       "701       0                    NaN          NaN      2019-12-31   \n",
       "706       0                    NaN          NaN      2019-12-31   \n",
       "707       0                    NaN          NaN      2019-12-31   \n",
       "716       0                    NaN          NaN      2019-12-31   \n",
       "717       0                    NaN          NaN      2019-12-31   \n",
       "718       0                    NaN          NaN      2019-12-31   \n",
       "719       0                    NaN          NaN      2019-12-31   \n",
       "722       0                    NaN          NaN      2019-12-31   \n",
       "723       0                    NaN          NaN      2019-12-31   \n",
       "724       0                    NaN          NaN      2019-12-31   \n",
       "733       0                    NaN          NaN      2019-12-31   \n",
       "735       0                    NaN          NaN      2019-12-31   \n",
       "755       0                    NaN          NaN      2019-12-31   \n",
       "763       0                    NaN          NaN      2019-12-31   \n",
       "764       0                    NaN          NaN      2019-12-31   \n",
       "765       0                    NaN          NaN      2019-12-31   \n",
       "766       0                    NaN          NaN      2019-12-31   \n",
       "771       0                    NaN          NaN      2019-12-31   \n",
       "778       0                    NaN          NaN      2019-12-31   \n",
       "783       0                    NaN          NaN      2019-12-31   \n",
       "784       0                    NaN          NaN      2019-12-31   \n",
       "787       0                    NaN          NaN      2019-12-31   \n",
       "788       0                    NaN          NaN      2019-12-31   \n",
       "792       0                    NaN          NaN      2019-12-31   \n",
       "806       0                    NaN          NaN      2019-12-31   \n",
       "\n",
       "                   Customer Email Address Group Profile             City  \\\n",
       "377              CUISINE@MONTSTEMARIE.COM  Profile00004    LAC STE MARIE   \n",
       "35               STEVE@BYTOWNCATERING.COM  Profile00013           OTTAWA   \n",
       "317             wildwingottawa@rogers.com           NaN           OTTAWA   \n",
       "318             wildwingottawa@rogers.com           NaN           OTTAWA   \n",
       "533    augustin@northontariofoodsales.com           NaN        NORTH BAY   \n",
       "27                     chef_james@live.ca           NaN          CHELSEA   \n",
       "327         fleurdelys@eastsidemarios.com     ESMFRENCH      QUEBEC CITY   \n",
       "489              winonabennett1@gmail.com           NaN           COBDEN   \n",
       "257           cornwall@eastsidemarios.com         PRIME         CORNWALL   \n",
       "438                  nektar_m@hotmail.com           NaN            LAVAL   \n",
       "315     Ottawahuntclub@eastsidemarios.com         PRIME           NEPEAN   \n",
       "597                      esm8659@cara.com           NaN           OTTAWA   \n",
       "543                     pubs5837@cara.com           NaN           KANATA   \n",
       "375                    info@gainsbourg.ca           NaN         GATINEAU   \n",
       "319             Kanata@eastsidemarios.com         PRIME           KANATA   \n",
       "448  Nick.Launderville@summit.colabor.com           NaN           OTTAWA   \n",
       "291            james_restoule@hotmail.com           NaN   STURGEON FALLS   \n",
       "292            james_restoule@hotmail.com           NaN   STURGEON FALLS   \n",
       "229  nick.launderville@summit.colabor.com           NaN           OTTAWA   \n",
       "207  Nick.Launderville@summit.colabor.com           NaN           OTTAWA   \n",
       "572                    chef@tudorhall.net           NaN           OTTAWA   \n",
       "451  Nick.Launderville@summit.colabor.com           NaN           OTTAWA   \n",
       "446  Nick.Launderville@summit.colabor.com           NaN           OTTAWA   \n",
       "594       Edith.Chabot@summit.colabor.com           NaN           KANATA   \n",
       "675                  harveys2751@cara.com           NaN          CHAMBLY   \n",
       "661                 lolabingo@hotmail.com           NaN           OTTAWA   \n",
       "647                  tsatsa93@hotmail.com           NaN           DORVAL   \n",
       "623         batonrougevaudreuil@gmail.com           NaN        VAUDREUIL   \n",
       "84            CHEFFLOWERS@CAMPFORTUNE.COM  Profile00013          CHELSEA   \n",
       "255              fchavez@rosdevhotels.com           NaN  VILLE ST-LAUREN   \n",
       "..                                    ...           ...              ...   \n",
       "677            orderconfirmation@cara.com           NaN       BROCKVILLE   \n",
       "680                   mrazaiy@hotmail.com           NaN        NORTH BAY   \n",
       "681                   mrazaiy@hotmail.com           NaN        NORTH BAY   \n",
       "687       harveys2753@recipeunlimited.com           NaN           VERDUN   \n",
       "700                 p.springer@rogers.com           NaN          KESWICK   \n",
       "701                 p.springer@rogers.com           NaN          KESWICK   \n",
       "706          karen.dee.lyncroft@gmail.com           NaN          TORONTO   \n",
       "707             Terry.bev.smith@gmail.com           NaN      PORT SYDNEY   \n",
       "716                   saritrsll@gmail.com           NaN        NEWMARKET   \n",
       "717                   saritrsll@gmail.com           NaN        NEWMARKET   \n",
       "718             nerry.bagelworld@yahoo.ca           NaN          TORONTO   \n",
       "719             nerry.bagelworld@yahoo.ca           NaN          TORONTO   \n",
       "722                                   NaN           NaN     WASAGA BEACH   \n",
       "723         Franca@preciousbeginning.info           NaN       WOODBRIDGE   \n",
       "724         Franca@preciousbeginning.info           NaN       WOODBRIDGE   \n",
       "733                  janetkorrey@live.com           NaN          KESWICK   \n",
       "735                                   NaN           NaN    AHMIC HARBOUR   \n",
       "755                  robgwynne@rogers.com           NaN        NEWMARKET   \n",
       "763                  deliadoria@ymail.com           NaN        THORNHILL   \n",
       "764                  deliadoria@ymail.com           NaN        THORNHILL   \n",
       "765                    amin4547@gmail.com           NaN          TORONTO   \n",
       "766                    amin4547@gmail.com           NaN          TORONTO   \n",
       "771                  cheffriesen@bell.net           NaN      BRACEBRIDGE   \n",
       "778               Jen@campcouchiching.com           NaN   LONGFORD MILLS   \n",
       "783                   abwbeatty@gmail.com           NaN         UXBRIDGE   \n",
       "784                   abwbeatty@gmail.com           NaN         UXBRIDGE   \n",
       "787                  abhikpatel@yahoo.com           NaN          MARKHAM   \n",
       "788                  abhikpatel@yahoo.com           NaN          MARKHAM   \n",
       "792               louisethorn78@gmail.com           NaN        WYEBRIDGE   \n",
       "806              muskokanorth@hotmail.com           NaN      GRAVENHURST   \n",
       "\n",
       "    Province  SalesRep  \n",
       "377       QC       559  \n",
       "35        ON       575  \n",
       "317       ON       740  \n",
       "318       ON       740  \n",
       "533       ON       566  \n",
       "27        QC       578  \n",
       "327       QC       635  \n",
       "489       ON       566  \n",
       "257       ON       635  \n",
       "438       QC       562  \n",
       "315       ON       635  \n",
       "597       ON       635  \n",
       "543       ON       650  \n",
       "375       QC       578  \n",
       "319       ON       635  \n",
       "448       ON       575  \n",
       "291       ON       760  \n",
       "292       ON       760  \n",
       "229       ON       575  \n",
       "207       ON       575  \n",
       "572       ON       574  \n",
       "451       ON       575  \n",
       "446       ON       575  \n",
       "594       ON       559  \n",
       "675       QC       600  \n",
       "661       ON       574  \n",
       "647       QC       562  \n",
       "623       QC       562  \n",
       "84        QC       559  \n",
       "255       QC       576  \n",
       "..       ...       ...  \n",
       "677       ON       860  \n",
       "680       ON       760  \n",
       "681       ON       760  \n",
       "687       QC       600  \n",
       "700       ON       588  \n",
       "701       ON       588  \n",
       "706       ON       584  \n",
       "707       ON       588  \n",
       "716       ON       588  \n",
       "717       ON       588  \n",
       "718       ON       588  \n",
       "719       ON       588  \n",
       "722       ON       588  \n",
       "723       ON       588  \n",
       "724       ON       588  \n",
       "733       ON       588  \n",
       "735       ON       588  \n",
       "755       ON       588  \n",
       "763       ON       588  \n",
       "764       ON       588  \n",
       "765       ON       584  \n",
       "766       ON       584  \n",
       "771       ON       588  \n",
       "778       ON       588  \n",
       "783       ON       588  \n",
       "784       ON       588  \n",
       "787       ON       588  \n",
       "788       ON       588  \n",
       "792       ON       588  \n",
       "806       ON       588  \n",
       "\n",
       "[837 rows x 20 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.sort_values(by=[\"Last Logon Date\",\"Logons\"], ascending=[False,False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Period</th>\n",
       "      <th>Sls</th>\n",
       "      <th>Slsrp Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Pft %</th>\n",
       "      <th>Order</th>\n",
       "      <th>Avgor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "      <td>Ryan Irwin</td>\n",
       "      <td>56971.7</td>\n",
       "      <td>44910.1</td>\n",
       "      <td>12061.62</td>\n",
       "      <td>21.17</td>\n",
       "      <td>51</td>\n",
       "      <td>1117.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>508</td>\n",
       "      <td>Michael Da</td>\n",
       "      <td>77551.7</td>\n",
       "      <td>66643.7</td>\n",
       "      <td>10908.07</td>\n",
       "      <td>14.07</td>\n",
       "      <td>65</td>\n",
       "      <td>1193.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>Rhys Lloyd</td>\n",
       "      <td>99699</td>\n",
       "      <td>84334.3</td>\n",
       "      <td>15364.66</td>\n",
       "      <td>15.41</td>\n",
       "      <td>102</td>\n",
       "      <td>977.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>513</td>\n",
       "      <td>Aron Leves</td>\n",
       "      <td>96556.7</td>\n",
       "      <td>80697</td>\n",
       "      <td>15859.70</td>\n",
       "      <td>16.43</td>\n",
       "      <td>68</td>\n",
       "      <td>1419.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>514</td>\n",
       "      <td>Paul Crell</td>\n",
       "      <td>146157</td>\n",
       "      <td>122457</td>\n",
       "      <td>23699.95</td>\n",
       "      <td>16.22</td>\n",
       "      <td>97</td>\n",
       "      <td>1506.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location  Period  Sls  Slsrp Name    Sales     Cost    Profit  Pft %  \\\n",
       "28         1       1  506  Ryan Irwin  56971.7  44910.1  12061.62  21.17   \n",
       "29         1       1  508  Michael Da  77551.7  66643.7  10908.07  14.07   \n",
       "30         1       1  511  Rhys Lloyd    99699  84334.3  15364.66  15.41   \n",
       "31         1       1  513  Aron Leves  96556.7    80697  15859.70  16.43   \n",
       "32         1       1  514  Paul Crell   146157   122457  23699.95  16.22   \n",
       "\n",
       "   Order    Avgor  \n",
       "28    51  1117.09  \n",
       "29    65  1193.10  \n",
       "30   102   977.44  \n",
       "31    68  1419.95  \n",
       "32    97  1506.77  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newReps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "per1 = newReps.groupby(\"Period\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "per1 = per1.get_group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 10)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pnetReport = report.merge(per1, left_on=\"SalesRep\", right_on=\"Sls\",how=\"outer\", indicator= True).drop([ 'Location_x', 'Period',\n",
    "       'Sls','Sales', 'Cost', 'Profit', 'Pft %', 'Order',\n",
    "       'Avgor'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Username', 'Password', 'Company', 'Division', 'Customer#', 'Status',\n",
       "       'Last Logon Date', 'Last Logon Time', 'Last Order Date',\n",
       "       'Last Order Time', 'Logons', 'Customer Name', 'Phone Number',\n",
       "       'Account Expires', 'Customer Email Address', 'Group Profile', 'City',\n",
       "       'Province', 'SalesRep', 'Location_y', 'Slsrp Name', '_merge'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnetReport.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "notin = pnetReport[\"_merge\"] == \"right_only\"\n",
    "pnetReport = pnetReport[~notin]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnetReport.drop([\"Company\",\"Division\",\"_merge\",\"Group Profile\"], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Username', 'Password', 'Customer#', 'Status', 'Last Logon Date',\n",
       "       'Last Logon Time', 'Last Order Date', 'Last Order Time', 'Logons',\n",
       "       'Customer Name', 'Phone Number', 'Account Expires',\n",
       "       'Customer Email Address', 'City', 'Province', 'SalesRep', 'Location_y',\n",
       "       'Slsrp Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnetReport.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1094 entries, 0 to 1093\n",
      "Data columns (total 18 columns):\n",
      "Username                  1094 non-null object\n",
      "Password                  1094 non-null object\n",
      "Customer#                 1094 non-null float64\n",
      "Status                    1094 non-null object\n",
      "Last Logon Date           1094 non-null float64\n",
      "Last Logon Time           1094 non-null float64\n",
      "Last Order Date           1094 non-null float64\n",
      "Last Order Time           1094 non-null float64\n",
      "Logons                    1094 non-null float64\n",
      "Customer Name             310 non-null object\n",
      "Phone Number              53 non-null object\n",
      "Account Expires           1094 non-null datetime64[ns]\n",
      "Customer Email Address    1057 non-null object\n",
      "City                      1080 non-null object\n",
      "Province                  1086 non-null object\n",
      "SalesRep                  1094 non-null float64\n",
      "Location_y                636 non-null float64\n",
      "Slsrp Name                636 non-null object\n",
      "dtypes: datetime64[ns](1), float64(8), object(9)\n",
      "memory usage: 162.4+ KB\n"
     ]
    }
   ],
   "source": [
    "pnetReport.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnetReport[\"Customer#\"] = pnetReport[\"Customer#\"].astype(int)\n",
    "pnetReport[\"Last Logon Date\"] = pnetReport[\"Last Logon Date\"].astype(\"int\")\n",
    "pnetReport[\"Last Logon Time\"] = pnetReport[\"Last Logon Time\"].astype(\"int\")\n",
    "pnetReport[\"Last Order Date\"] = pnetReport[\"Last Order Date\"].astype(\"int\")\n",
    "pnetReport[\"Last Order Time\"] = pnetReport[\"Last Order Time\"].astype(\"int\")\n",
    "pnetReport[\"Logons\"] = pnetReport[\"Logons\"].astype(int)\n",
    "pnetReport[\"SalesRep\"] = pnetReport[\"SalesRep\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "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>Username</th>\n",
       "      <th>Password</th>\n",
       "      <th>Customer#</th>\n",
       "      <th>Status</th>\n",
       "      <th>Last Logon Date</th>\n",
       "      <th>Last Logon Time</th>\n",
       "      <th>Last Order Date</th>\n",
       "      <th>Last Order Time</th>\n",
       "      <th>Logons</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Account Expires</th>\n",
       "      <th>Customer Email Address</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>SalesRep</th>\n",
       "      <th>Location_y</th>\n",
       "      <th>Slsrp Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46940</td>\n",
       "      <td>PN2114</td>\n",
       "      <td>46940</td>\n",
       "      <td>A</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75159</td>\n",
       "      <td>20190221</td>\n",
       "      <td>75409</td>\n",
       "      <td>2306</td>\n",
       "      <td>SCOUTS CANADA CAMP OPEMIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA</td>\n",
       "      <td>MABERLY</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Lysa Harve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49505</td>\n",
       "      <td>PN6016</td>\n",
       "      <td>49505</td>\n",
       "      <td>A</td>\n",
       "      <td>20171124</td>\n",
       "      <td>202603</td>\n",
       "      <td>20170516</td>\n",
       "      <td>153721</td>\n",
       "      <td>325</td>\n",
       "      <td>David Potts</td>\n",
       "      <td>6139859788</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>d.b.potts@hotmail.com</td>\n",
       "      <td>SHARBOT LAKE</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Lysa Harve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41030</td>\n",
       "      <td>PN0301</td>\n",
       "      <td>41030</td>\n",
       "      <td>A</td>\n",
       "      <td>20180209</td>\n",
       "      <td>182824</td>\n",
       "      <td>20160913</td>\n",
       "      <td>124054</td>\n",
       "      <td>879</td>\n",
       "      <td>Ed Saikaly</td>\n",
       "      <td>6132830265</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>edsaikaly@gmail.com</td>\n",
       "      <td>LOMBARDY</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Lysa Harve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32720</td>\n",
       "      <td>PN2388</td>\n",
       "      <td>32720</td>\n",
       "      <td>A</td>\n",
       "      <td>20180816</td>\n",
       "      <td>120927</td>\n",
       "      <td>20180814</td>\n",
       "      <td>104417</td>\n",
       "      <td>56</td>\n",
       "      <td>Cindy Byker</td>\n",
       "      <td>6133411411</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>cbyker@ripnet.com</td>\n",
       "      <td>MABERLY</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Lysa Harve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30615</td>\n",
       "      <td>PN1634</td>\n",
       "      <td>30615</td>\n",
       "      <td>A</td>\n",
       "      <td>20190111</td>\n",
       "      <td>124730</td>\n",
       "      <td>20180423</td>\n",
       "      <td>111654</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>tiffany@antrimtruckstop.com</td>\n",
       "      <td>ARNPRIOR</td>\n",
       "      <td>ON</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Lysa Harve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Username Password  Customer# Status  Last Logon Date  Last Logon Time  \\\n",
       "0    46940   PN2114      46940      A         20190221            75159   \n",
       "1    49505   PN6016      49505      A         20171124           202603   \n",
       "2    41030   PN0301      41030      A         20180209           182824   \n",
       "3    32720   PN2388      32720      A         20180816           120927   \n",
       "4    30615   PN1634      30615      A         20190111           124730   \n",
       "\n",
       "   Last Order Date  Last Order Time  Logons              Customer Name  \\\n",
       "0         20190221            75409    2306  SCOUTS CANADA CAMP OPEMIK   \n",
       "1         20170516           153721     325                David Potts   \n",
       "2         20160913           124054     879                 Ed Saikaly   \n",
       "3         20180814           104417      56                Cindy Byker   \n",
       "4         20180423           111654      10                        NaN   \n",
       "\n",
       "  Phone Number Account Expires                      Customer Email Address  \\\n",
       "0          NaN      2019-12-31  JOHN.RATHIER@OTTAWA.CA; MJOHNSON@SCOUTS.CA   \n",
       "1   6139859788      2019-12-31                       d.b.potts@hotmail.com   \n",
       "2   6132830265      2019-12-31                         edsaikaly@gmail.com   \n",
       "3   6133411411      2019-12-31                           cbyker@ripnet.com   \n",
       "4          NaN      2019-12-31                 tiffany@antrimtruckstop.com   \n",
       "\n",
       "           City Province  SalesRep  Location_y  Slsrp Name  \n",
       "0       MABERLY       ON       564         2.0  Lysa Harve  \n",
       "1  SHARBOT LAKE       ON       564         2.0  Lysa Harve  \n",
       "2      LOMBARDY       ON       564         2.0  Lysa Harve  \n",
       "3       MABERLY       ON       564         2.0  Lysa Harve  \n",
       "4      ARNPRIOR       ON       564         2.0  Lysa Harve  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnetReport.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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  }
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