{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import matplotlib.pylab as plt \n",
    "import sketch\n",
    "#plt.style.use('ggplot')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Import\n",
    "\n",
    "What we are doing here is importing some organizational data that we can compare to Heap's data. We need items like the Customer's Unique ID, the Instance ID, some ICP and use-case information, and the customer's customer name. \n",
    "\n",
    "Currently, I am pulling from [this look](https://northpasshq.looker.com/looks/125). Use this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Customers Customer ID</th>\n",
       "      <th>Mal Actuals School ID</th>\n",
       "      <th>Mal Actuals School Name</th>\n",
       "      <th>Chris Bookings ICP</th>\n",
       "      <th>Customers Organization</th>\n",
       "      <th>Customers Use Case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30799651</td>\n",
       "      <td>5365.0</td>\n",
       "      <td>CORE College</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>The CORE Group</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30801577</td>\n",
       "      <td>5454.0</td>\n",
       "      <td>Serge</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Serge</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30857025</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>Team Trudeau Training</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>The Federal Liberal Agency of Canada</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30857025</td>\n",
       "      <td>5265.0</td>\n",
       "      <td>Formation de l'Équipe Trudeau</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>The Federal Liberal Agency of Canada</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30861952</td>\n",
       "      <td>6097.0</td>\n",
       "      <td>ElderSource Internal</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Eldersource</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customers Customer ID  Mal Actuals School ID   \n",
       "0               30799651                 5365.0  \\\n",
       "1               30801577                 5454.0   \n",
       "2               30857025                 4581.0   \n",
       "3               30857025                 5265.0   \n",
       "4               30861952                 6097.0   \n",
       "\n",
       "         Mal Actuals School Name Chris Bookings ICP   \n",
       "0                   CORE College             Non-CE  \\\n",
       "1                          Serge             Non-CE   \n",
       "2          Team Trudeau Training         CE Non-ICP   \n",
       "3  Formation de l'Équipe Trudeau         CE Non-ICP   \n",
       "4           ElderSource Internal             Non-CE   \n",
       "\n",
       "                 Customers Organization Customers Use Case  \n",
       "0                        The CORE Group          Employees  \n",
       "1                                 Serge          Employees  \n",
       "2  The Federal Liberal Agency of Canada          Customers  \n",
       "3  The Federal Liberal Agency of Canada          Customers  \n",
       "4                           Eldersource          Employees  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids= pd.read_csv(\"/Users/normrasmussen/Downloads/school_names_ids.csv\")\n",
    "ids.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next, we need to rename the columns for better and easier access. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>school_id</th>\n",
       "      <th>school_name</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30799651</td>\n",
       "      <td>5365.0</td>\n",
       "      <td>CORE College</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>The CORE Group</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30801577</td>\n",
       "      <td>5454.0</td>\n",
       "      <td>Serge</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Serge</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30857025</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>Team Trudeau Training</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>The Federal Liberal Agency of Canada</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30857025</td>\n",
       "      <td>5265.0</td>\n",
       "      <td>Formation de l'Équipe Trudeau</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>The Federal Liberal Agency of Canada</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30861952</td>\n",
       "      <td>6097.0</td>\n",
       "      <td>ElderSource Internal</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Eldersource</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30861952</td>\n",
       "      <td>6099.0</td>\n",
       "      <td>ElderSource Ecommerce</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Eldersource</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30861952</td>\n",
       "      <td>6101.0</td>\n",
       "      <td>ElderSource Partner</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Eldersource</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30867042</td>\n",
       "      <td>4706.0</td>\n",
       "      <td>Givergy Academy</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Givergy</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30867043</td>\n",
       "      <td>3332.0</td>\n",
       "      <td>Access University</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Access E-Forms</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30867046</td>\n",
       "      <td>5667.0</td>\n",
       "      <td>School of Rokt</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Rokt Pte Ltd</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  school_id                    school_name icp_boolean   \n",
       "0     30799651     5365.0                   CORE College      Non-CE  \\\n",
       "1     30801577     5454.0                          Serge      Non-CE   \n",
       "2     30857025     4581.0          Team Trudeau Training  CE Non-ICP   \n",
       "3     30857025     5265.0  Formation de l'Équipe Trudeau  CE Non-ICP   \n",
       "4     30861952     6097.0           ElderSource Internal      Non-CE   \n",
       "5     30861952     6099.0          ElderSource Ecommerce      Non-CE   \n",
       "6     30861952     6101.0            ElderSource Partner      Non-CE   \n",
       "7     30867042     4706.0                Givergy Academy      Non-CE   \n",
       "8     30867043     3332.0              Access University  CE Non-ICP   \n",
       "9     30867046     5667.0                 School of Rokt      Non-CE   \n",
       "\n",
       "                          customer_name   use_case  \n",
       "0                        The CORE Group  Employees  \n",
       "1                                 Serge  Employees  \n",
       "2  The Federal Liberal Agency of Canada  Customers  \n",
       "3  The Federal Liberal Agency of Canada  Customers  \n",
       "4                           Eldersource  Employees  \n",
       "5                           Eldersource  Employees  \n",
       "6                           Eldersource  Employees  \n",
       "7                               Givergy  Employees  \n",
       "8                        Access E-Forms  Customers  \n",
       "9                          Rokt Pte Ltd  Employees  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = ids.rename(\n",
    "    columns={\n",
    "        'Customers Customer ID':'customer_id', \n",
    "        'Customers Organization':'customer_name', \n",
    "        'Mal Actuals School ID':'school_id', \n",
    "        'Mal Actuals School Name':'school_name',\n",
    "        'Chris Bookings ICP': 'icp_boolean',\n",
    "        'Customers Use Case':'use_case',\n",
    "        },\n",
    "    )\n",
    "ids.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>school_id</th>\n",
       "      <th>school_name</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>47167414</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>Walmart Suppliers Onboarding</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Volt Systems</td>\n",
       "      <td>Gig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7056.0</td>\n",
       "      <td>Spark Driver Resources Hub (MIGRATED TO AZURE)</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7540.0</td>\n",
       "      <td>Walmart Sandbox (MIGRATED TO AZURE)</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id  school_id                                     school_name   \n",
       "555     47167414     7684.0                    Walmart Suppliers Onboarding  \\\n",
       "585     47167440     7056.0  Spark Driver Resources Hub (MIGRATED TO AZURE)   \n",
       "586     47167440     7540.0             Walmart Sandbox (MIGRATED TO AZURE)   \n",
       "\n",
       "    icp_boolean customer_name       use_case  \n",
       "555      Non-CE  Volt Systems            Gig  \n",
       "585  CE Non-ICP  Walmart Inc.  Customers,Gig  \n",
       "586  CE Non-ICP  Walmart Inc.  Customers,Gig  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A quick little check to make sure Walmart is still on the list. Feel free to switch out different Academy names to find if a customer's school exists\n",
    "search_for = ['walmart', 'spark', 'luminate']\n",
    "ids.loc[ids.school_name.str.contains('|'.join(search_for), case=False, na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_47678/1163457763.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  ids.loc[ids.school_name.str.contains(\"(migrated to azure)\", case=False, na=False)]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>school_name</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7056.0</td>\n",
       "      <td>Spark Driver Resources Hub (MIGRATED TO AZURE)</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7540.0</td>\n",
       "      <td>Walmart Sandbox (MIGRATED TO AZURE)</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id  school_id                                     school_name   \n",
       "585     47167440     7056.0  Spark Driver Resources Hub (MIGRATED TO AZURE)  \\\n",
       "586     47167440     7540.0             Walmart Sandbox (MIGRATED TO AZURE)   \n",
       "\n",
       "    icp_boolean customer_name       use_case  \n",
       "585  CE Non-ICP  Walmart Inc.  Customers,Gig  \n",
       "586  CE Non-ICP  Walmart Inc.  Customers,Gig  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids.loc[ids.school_name.str.contains(\"(migrated to azure)\", case=False, na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>school_id</th>\n",
       "      <th>school_name</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>47167414</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>Walmart Suppliers Onboarding</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Volt Systems</td>\n",
       "      <td>Gig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7056.0</td>\n",
       "      <td>Spark Driver Resources Hub</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>47167440</td>\n",
       "      <td>7540.0</td>\n",
       "      <td>Walmart Sandbox</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>Customers,Gig</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id  school_id                   school_name icp_boolean   \n",
       "555     47167414     7684.0  Walmart Suppliers Onboarding      Non-CE  \\\n",
       "585     47167440     7056.0   Spark Driver Resources Hub   CE Non-ICP   \n",
       "586     47167440     7540.0              Walmart Sandbox   CE Non-ICP   \n",
       "\n",
       "    customer_name       use_case  \n",
       "555  Volt Systems            Gig  \n",
       "585  Walmart Inc.  Customers,Gig  \n",
       "586  Walmart Inc.  Customers,Gig  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Back to Walmart. If they aren't there, you need to go into the CSV and remove \"(MIGRATED TO AZURE)\" from the values. Uncomment the next line to find and remove.\n",
    "ids.school_name = ids.school_name.str.replace('(MIGRATED TO AZURE)', '')\n",
    "search_for = ['walmart', 'spark', 'luminate']\n",
    "ids.loc[ids.school_name.str.contains('|'.join(search_for), case=False, na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>School Name</th>\n",
       "      <th>Twotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'@properties</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Pillars</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASAPP</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Access</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ad Intel</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdCellerant</td>\n",
       "      <td>5270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adkins</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            School Name  Twotal\n",
       "0          '@properties     322\n",
       "1             4 Pillars     112\n",
       "2                 AIIM+     528\n",
       "3                 ASAPP     153\n",
       "4  Académie Évolupharma     253\n",
       "5                Access      11\n",
       "6         ActiveLearner    1907\n",
       "7              Ad Intel     767\n",
       "8           AdCellerant    5270\n",
       "9                Adkins     107"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%store -r pages\n",
    "pages = pages.reset_index()\n",
    "pages.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>school_name</th>\n",
       "      <th>total_views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'@properties</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Pillars</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASAPP</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Access</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ad Intel</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdCellerant</td>\n",
       "      <td>5270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adkins</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            school_name  total_views\n",
       "0          '@properties          322\n",
       "1             4 Pillars          112\n",
       "2                 AIIM+          528\n",
       "3                 ASAPP          153\n",
       "4  Académie Évolupharma          253\n",
       "5                Access           11\n",
       "6         ActiveLearner         1907\n",
       "7              Ad Intel          767\n",
       "8           AdCellerant         5270\n",
       "9                Adkins          107"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pages = pages.rename(columns={\n",
    "    'School Name':'school_name',\n",
    "    'Twotal':'total_views'})\n",
    "pages.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>school_name</th>\n",
       "      <th>total_views</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "      <td>46757198</td>\n",
       "      <td>7079.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AIIM</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "      <td>47167390</td>\n",
       "      <td>7326.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Évolupharma, Inc.</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "      <td>30867718</td>\n",
       "      <td>5301.0</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>BrandActive International Inc.</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akua Mind Body</td>\n",
       "      <td>578</td>\n",
       "      <td>37441607</td>\n",
       "      <td>6718.0</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Akua Mind &amp; Body</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Altima Dental</td>\n",
       "      <td>33</td>\n",
       "      <td>30867707</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Altima Dental Canada</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>American Concrete Pipe Association Learning Ce...</td>\n",
       "      <td>2864</td>\n",
       "      <td>47167457</td>\n",
       "      <td>7582.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>American Concrete Pipe Association</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andhealth Internal Training</td>\n",
       "      <td>17</td>\n",
       "      <td>47167421</td>\n",
       "      <td>7912.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AndHealth</td>\n",
       "      <td>Customers,Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Anthology Internal</td>\n",
       "      <td>104</td>\n",
       "      <td>47167502</td>\n",
       "      <td>7871.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Anthology Trial</td>\n",
       "      <td>418</td>\n",
       "      <td>47167502</td>\n",
       "      <td>7874.0</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ascend Training by Stenograph</td>\n",
       "      <td>9</td>\n",
       "      <td>30988476</td>\n",
       "      <td>7692.0</td>\n",
       "      <td>CE ICP</td>\n",
       "      <td>MarketScale</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         school_name  total_views   \n",
       "0                                              AIIM+          528  \\\n",
       "1                               Académie Évolupharma          253   \n",
       "2                                      ActiveLearner         1907   \n",
       "3                                     Akua Mind Body          578   \n",
       "4                                      Altima Dental           33   \n",
       "5  American Concrete Pipe Association Learning Ce...         2864   \n",
       "6                        Andhealth Internal Training           17   \n",
       "7                                 Anthology Internal          104   \n",
       "8                                    Anthology Trial          418   \n",
       "9                      Ascend Training by Stenograph            9   \n",
       "\n",
       "   customer_id  school_id icp_boolean                       customer_name   \n",
       "0     46757198     7079.0  CE Non-ICP                                AIIM  \\\n",
       "1     47167390     7326.0  CE Non-ICP                   Évolupharma, Inc.   \n",
       "2     30867718     5301.0      Non-CE      BrandActive International Inc.   \n",
       "3     37441607     6718.0      Non-CE                    Akua Mind & Body   \n",
       "4     30867707     5670.0      Non-CE                Altima Dental Canada   \n",
       "5     47167457     7582.0  CE Non-ICP  American Concrete Pipe Association   \n",
       "6     47167421     7912.0  CE Non-ICP                           AndHealth   \n",
       "7     47167502     7871.0  CE Non-ICP                     Blackboard Inc.   \n",
       "8     47167502     7874.0  CE Non-ICP                     Blackboard Inc.   \n",
       "9     30988476     7692.0      CE ICP                         MarketScale   \n",
       "\n",
       "              use_case  \n",
       "0            Customers  \n",
       "1            Customers  \n",
       "2            Employees  \n",
       "3            Employees  \n",
       "4            Employees  \n",
       "5            Customers  \n",
       "6  Customers,Employees  \n",
       "7            Customers  \n",
       "8            Customers  \n",
       "9            Customers  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd = pd.merge(pages, ids, on='school_name')\n",
    "newpd.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "school_name      object\n",
      "total_views       int64\n",
      "customer_id       int64\n",
      "school_id         int64\n",
      "icp_boolean      object\n",
      "customer_name    object\n",
      "use_case         object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_views</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "      <td>46757198</td>\n",
       "      <td>7079</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AIIM</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "      <td>47167390</td>\n",
       "      <td>7326</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Évolupharma, Inc.</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "      <td>30867718</td>\n",
       "      <td>5301</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>BrandActive International Inc.</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akua Mind Body</td>\n",
       "      <td>578</td>\n",
       "      <td>37441607</td>\n",
       "      <td>6718</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Akua Mind &amp; Body</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Altima Dental</td>\n",
       "      <td>33</td>\n",
       "      <td>30867707</td>\n",
       "      <td>5670</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Altima Dental Canada</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>American Concrete Pipe Association Learning Ce...</td>\n",
       "      <td>2864</td>\n",
       "      <td>47167457</td>\n",
       "      <td>7582</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>American Concrete Pipe Association</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andhealth Internal Training</td>\n",
       "      <td>17</td>\n",
       "      <td>47167421</td>\n",
       "      <td>7912</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AndHealth</td>\n",
       "      <td>Customers,Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Anthology Internal</td>\n",
       "      <td>104</td>\n",
       "      <td>47167502</td>\n",
       "      <td>7871</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         school_name  total_views   \n",
       "0                                              AIIM+          528  \\\n",
       "1                               Académie Évolupharma          253   \n",
       "2                                      ActiveLearner         1907   \n",
       "3                                     Akua Mind Body          578   \n",
       "4                                      Altima Dental           33   \n",
       "5  American Concrete Pipe Association Learning Ce...         2864   \n",
       "6                        Andhealth Internal Training           17   \n",
       "7                                 Anthology Internal          104   \n",
       "\n",
       "   customer_id  school_id icp_boolean                       customer_name   \n",
       "0     46757198       7079  CE Non-ICP                                AIIM  \\\n",
       "1     47167390       7326  CE Non-ICP                   Évolupharma, Inc.   \n",
       "2     30867718       5301      Non-CE      BrandActive International Inc.   \n",
       "3     37441607       6718      Non-CE                    Akua Mind & Body   \n",
       "4     30867707       5670      Non-CE                Altima Dental Canada   \n",
       "5     47167457       7582  CE Non-ICP  American Concrete Pipe Association   \n",
       "6     47167421       7912  CE Non-ICP                           AndHealth   \n",
       "7     47167502       7871  CE Non-ICP                     Blackboard Inc.   \n",
       "\n",
       "              use_case  \n",
       "0            Customers  \n",
       "1            Customers  \n",
       "2            Employees  \n",
       "3            Employees  \n",
       "4            Employees  \n",
       "5            Customers  \n",
       "6  Customers,Employees  \n",
       "7            Customers  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd['school_id'] =newpd['school_id'].astype(int)\n",
    "print(newpd.dtypes)\n",
    "newpd.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = pd.read_csv(\"/Users/normrasmussen/Downloads/ARR_OrgID.csv\")\n",
    "arr = arr.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30867043</td>\n",
       "      <td>Courtney Donargo</td>\n",
       "      <td>premium</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47167499</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>47167366</td>\n",
       "      <td>Support</td>\n",
       "      <td>essentials</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>47167483</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>premium</td>\n",
       "      <td>14438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>30867493</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>47167458</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>premium</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>41684806</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>46757198</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>premium</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>37441607</td>\n",
       "      <td>Support</td>\n",
       "      <td>essentials</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>30867644</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0  index  Unnamed: 0  customer_id               csm     product   \n",
       "0        0      0           1     30867043  Courtney Donargo     premium  \\\n",
       "1        1      1           2     47167499     Tracy McMahon     premium   \n",
       "2        2      2           3     47167366           Support  essentials   \n",
       "3        3      3           4     47167483     Nergis Tepeli     premium   \n",
       "4        4      4           5     30867493           Support     premium   \n",
       "5        5      5           6     47167458     Nergis Tepeli     premium   \n",
       "6        6      6           7     41684806     Tracy McMahon  enterprise   \n",
       "7        7      7           8     46757198    Norm Rasmussen     premium   \n",
       "8        8      8           9     37441607           Support  essentials   \n",
       "9        9      9          10     30867644           Support     premium   \n",
       "\n",
       "     arr  \n",
       "0   1872  \n",
       "1  20000  \n",
       "2   1500  \n",
       "3  14438  \n",
       "4   2160  \n",
       "5  26000  \n",
       "6  23000  \n",
       "7  22000  \n",
       "8   6000  \n",
       "9   3000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = arr.rename(\n",
    "    columns={\n",
    "        'Customer ID':'customer_id', \n",
    "        'CSM':'csm', \n",
    "        'Active ARR':'arr', \n",
    "        'Product':'product'\n",
    "    }).drop(\n",
    "        ['Account Executive', 'Mal Limit', 'Customer Since Date', \n",
    "        'Last Contacted Date', 'Actual Mal', 'Current Mal',\n",
    "        'Organization', 'Billing Interval'],\n",
    "        axis=1\n",
    "    ).reset_index()\n",
    "arr['arr'] = arr['arr'].str.replace(',', '').astype(int)\n",
    "arr.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "newpd = pd.merge(newpd, arr, on='customer_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>school_name</th>\n",
       "      <th>total_views</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "      <td>46757198</td>\n",
       "      <td>7079</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AIIM</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>premium</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "      <td>47167390</td>\n",
       "      <td>7326</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Évolupharma, Inc.</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "      <td>30867718</td>\n",
       "      <td>5301</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>BrandActive International Inc.</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akua Mind Body</td>\n",
       "      <td>578</td>\n",
       "      <td>37441607</td>\n",
       "      <td>6718</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Akua Mind &amp; Body</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Support</td>\n",
       "      <td>essentials</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Altima Dental</td>\n",
       "      <td>33</td>\n",
       "      <td>30867707</td>\n",
       "      <td>5670</td>\n",
       "      <td>Non-CE</td>\n",
       "      <td>Altima Dental Canada</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>7474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>American Concrete Pipe Association Learning Ce...</td>\n",
       "      <td>2864</td>\n",
       "      <td>47167457</td>\n",
       "      <td>7582</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>American Concrete Pipe Association</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>premium</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andhealth Internal Training</td>\n",
       "      <td>17</td>\n",
       "      <td>47167421</td>\n",
       "      <td>7912</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>AndHealth</td>\n",
       "      <td>Customers,Employees</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>31900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Anthology Internal</td>\n",
       "      <td>104</td>\n",
       "      <td>47167502</td>\n",
       "      <td>7871</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Anthology Trial</td>\n",
       "      <td>418</td>\n",
       "      <td>47167502</td>\n",
       "      <td>7874</td>\n",
       "      <td>CE Non-ICP</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ascend Training by Stenograph</td>\n",
       "      <td>9</td>\n",
       "      <td>30988476</td>\n",
       "      <td>7692</td>\n",
       "      <td>CE ICP</td>\n",
       "      <td>MarketScale</td>\n",
       "      <td>Customers</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         school_name  total_views   \n",
       "0                                              AIIM+          528  \\\n",
       "1                               Académie Évolupharma          253   \n",
       "2                                      ActiveLearner         1907   \n",
       "3                                     Akua Mind Body          578   \n",
       "4                                      Altima Dental           33   \n",
       "5  American Concrete Pipe Association Learning Ce...         2864   \n",
       "6                        Andhealth Internal Training           17   \n",
       "7                                 Anthology Internal          104   \n",
       "8                                    Anthology Trial          418   \n",
       "9                      Ascend Training by Stenograph            9   \n",
       "\n",
       "   customer_id  school_id icp_boolean                       customer_name   \n",
       "0     46757198       7079  CE Non-ICP                                AIIM  \\\n",
       "1     47167390       7326  CE Non-ICP                   Évolupharma, Inc.   \n",
       "2     30867718       5301      Non-CE      BrandActive International Inc.   \n",
       "3     37441607       6718      Non-CE                    Akua Mind & Body   \n",
       "4     30867707       5670      Non-CE                Altima Dental Canada   \n",
       "5     47167457       7582  CE Non-ICP  American Concrete Pipe Association   \n",
       "6     47167421       7912  CE Non-ICP                           AndHealth   \n",
       "7     47167502       7871  CE Non-ICP                     Blackboard Inc.   \n",
       "8     47167502       7874  CE Non-ICP                     Blackboard Inc.   \n",
       "9     30988476       7692      CE ICP                         MarketScale   \n",
       "\n",
       "              use_case             csm     product    arr  \n",
       "0            Customers  Norm Rasmussen     premium  22000  \n",
       "1            Customers   Tracy McMahon     premium  28000  \n",
       "2            Employees         Support     premium   6720  \n",
       "3            Employees         Support  essentials   6000  \n",
       "4            Employees         Support     premium   7474  \n",
       "5            Customers   Nergis Tepeli     premium  20000  \n",
       "6  Customers,Employees   Tracy McMahon     premium  31900  \n",
       "7            Customers  Norm Rasmussen  enterprise  95000  \n",
       "8            Customers  Norm Rasmussen  enterprise  95000  \n",
       "9            Customers   Nergis Tepeli  enterprise  68000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd = newpd.drop(['index', 'Unnamed: 0', 'level_0'], axis=1)\n",
    "newpd.head(10)"
   ]
  },
  {
   "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>customer_id</th>\n",
       "      <th>total_views</th>\n",
       "      <th>arr</th>\n",
       "      <th>use_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46757198</td>\n",
       "      <td>528</td>\n",
       "      <td>22000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47167390</td>\n",
       "      <td>253</td>\n",
       "      <td>28000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30867718</td>\n",
       "      <td>1907</td>\n",
       "      <td>6720</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37441607</td>\n",
       "      <td>578</td>\n",
       "      <td>6000</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30867707</td>\n",
       "      <td>33</td>\n",
       "      <td>7474</td>\n",
       "      <td>Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47167457</td>\n",
       "      <td>2864</td>\n",
       "      <td>20000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47167421</td>\n",
       "      <td>17</td>\n",
       "      <td>31900</td>\n",
       "      <td>Customers,Employees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47167502</td>\n",
       "      <td>104</td>\n",
       "      <td>95000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47167502</td>\n",
       "      <td>418</td>\n",
       "      <td>95000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30988476</td>\n",
       "      <td>9</td>\n",
       "      <td>68000</td>\n",
       "      <td>Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  total_views    arr             use_case\n",
       "0     46757198          528  22000            Customers\n",
       "1     47167390          253  28000            Customers\n",
       "2     30867718         1907   6720            Employees\n",
       "3     37441607          578   6000            Employees\n",
       "4     30867707           33   7474            Employees\n",
       "5     47167457         2864  20000            Customers\n",
       "6     47167421           17  31900  Customers,Employees\n",
       "7     47167502          104  95000            Customers\n",
       "8     47167502          418  95000            Customers\n",
       "9     30988476            9  68000            Customers"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usecases_split = newpd[['customer_id', 'total_views', 'arr', 'use_case']].copy()\n",
    "usecases_split.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from ast import literal_eval\n",
    "# #usecases_split = usecases_split.use_case.str.split('|')\n",
    "# # usecases_split = usecases_split.groupby('use_case').agg({'total_views':'sum','arr':'sum'})\n",
    "# usecases_split.use_case = usecases_split.use_case.apply(literal_eval)\n",
    "# usecases_split.use_case.explode(usecases_split.assign(use_case=usecases_split.use_case.str.split(',')), 'use_case')\n",
    "# usecases_split.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>total_views</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icp_boolean</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CE ICP</th>\n",
       "      <td>11139</td>\n",
       "      <td>1276680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CE Non-ICP</th>\n",
       "      <td>52362</td>\n",
       "      <td>1258130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-CE</th>\n",
       "      <td>48485</td>\n",
       "      <td>2407191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_views      arr\n",
       "icp_boolean                      \n",
       "CE ICP             11139  1276680\n",
       "CE Non-ICP         52362  1258130\n",
       "Non-CE             48485  2407191"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# icp_groups = pd.DataFrame(columns=['views_sum', 'arr_sum'])\n",
    "# icp_groups = newpd.groupby('icp_boolean')['total_views', 'arr'].sum()\n",
    "icp_groups = newpd.groupby('icp_boolean').agg({'total_views':'sum','arr':'sum'})\n",
    "icp_groups.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>total_views</th>\n",
       "      <th>arr</th>\n",
       "      <th>scaled_views_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHEK Institute</th>\n",
       "      <td>11138</td>\n",
       "      <td>20000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UX Design Institute</th>\n",
       "      <td>7167</td>\n",
       "      <td>37860</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anchor QEA, LLC</th>\n",
       "      <td>4853</td>\n",
       "      <td>10000</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOLITOR Formations</th>\n",
       "      <td>4009</td>\n",
       "      <td>9000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitetail Properties Real Estate</th>\n",
       "      <td>3604</td>\n",
       "      <td>18000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  total_views    arr  scaled_views_percent\n",
       "customer_name                                                             \n",
       "CHEK Institute                          11138  20000                   100\n",
       "UX Design Institute                      7167  37860                    64\n",
       "Anchor QEA, LLC                          4853  10000                    43\n",
       "MOLITOR Formations                       4009   9000                    35\n",
       "Whitetail Properties Real Estate         3604  18000                    32"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "cust_groups = newpd.groupby('customer_name').agg({'total_views':'sum','arr':'mean'})\n",
    "cust_groups.arr = cust_groups.arr.astype(int)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "cust_groups[[\"scaled_views\"]] = scaler.fit_transform(cust_groups[[\"total_views\"]])\n",
    "cust_groups['scaled_views'] = cust_groups['scaled_views'].multiply(100).astype(int)\n",
    "cust_groups['scaled_views_percent'] = cust_groups['scaled_views']\n",
    "cust_groups = cust_groups.drop(['scaled_views'], axis=1)\n",
    "\n",
    "cust_groups.nlargest(5, 'total_views')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>total_views</th>\n",
       "      <th>arr</th>\n",
       "      <th>scaled_views_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bolt Operations OÜ</th>\n",
       "      <td>1177</td>\n",
       "      <td>240480</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LJ Hooker Corporation Pty Ltd</th>\n",
       "      <td>1378</td>\n",
       "      <td>206250</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soham, Inc. (Zenoti)</th>\n",
       "      <td>116</td>\n",
       "      <td>120000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blackboard Inc.</th>\n",
       "      <td>522</td>\n",
       "      <td>95000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shipt</th>\n",
       "      <td>1034</td>\n",
       "      <td>88564</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               total_views     arr  scaled_views_percent\n",
       "customer_name                                                           \n",
       "Bolt Operations OÜ                    1177  240480                    10\n",
       "LJ Hooker Corporation Pty Ltd         1378  206250                    12\n",
       "Soham, Inc. (Zenoti)                   116  120000                     1\n",
       "Blackboard Inc.                        522   95000                     4\n",
       "Shipt                                 1034   88564                     9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_groups.nlargest(5, 'arr')"
   ]
  },
  {
   "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>total_views</th>\n",
       "      <th>arr</th>\n",
       "      <th>scaled_views_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Whitetail Properties Real Estate</th>\n",
       "      <td>3604</td>\n",
       "      <td>18000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luma Health, Inc.</th>\n",
       "      <td>3459</td>\n",
       "      <td>20000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPS Commerce</th>\n",
       "      <td>3205</td>\n",
       "      <td>51000</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Concrete Pipe Association</th>\n",
       "      <td>2864</td>\n",
       "      <td>20000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardskills Pte. Ltd</th>\n",
       "      <td>2780</td>\n",
       "      <td>12498</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    total_views    arr  scaled_views_percent\n",
       "customer_name                                                               \n",
       "Whitetail Properties Real Estate           3604  18000                    32\n",
       "Luma Health, Inc.                          3459  20000                    31\n",
       "SPS Commerce                               3205  51000                    28\n",
       "American Concrete Pipe Association         2864  20000                    25\n",
       "Hardskills Pte. Ltd                        2780  12498                    24"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_outliers = cust_groups.where((cust_groups.total_views < 4000) & (cust_groups.arr < 100000), 0)\n",
    "no_outliers.nlargest(5, 'total_views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_outliers.plot(kind='scatter', x='total_views', y='arr')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: It would be interesting to next compare the page views with the number of admins, or the number of MALs to admins, etc.  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following plot _contains_ outliers. \n",
    "The `cust_groups` dataframe has all the data. Look for `no_outliers` for the dataframe with outliers removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_groups.plot(kind='scatter', x='total_views', y='arr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "Yes, there appears to be a relationship between the arr column and total_views column. To further analyze this relationship, we can use a correlation coefficient to measure the strength of the relationship between the two variables. We can also use a scatter plot to visualize the relationship between the two variables. Additionally, we can use linear regression to determine if there is a linear relationship between the two variables. \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_groups.sketch.ask(\"Is there a relationships between arr column and total_views column? Conduct further analysis on the relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display:flex;flex-direction:row;justify-content:space-between;\">\n",
       "                <pre style=\"width: 100%; white-space: pre-wrap;\" id=\"ece29266-f5be-4128-9f90-0818706ac4c3\">\n",
       "import pandas as pd\n",
       "import numpy as np\n",
       "from sklearn.linear_model import LinearRegression\n",
       "\n",
       "# Create the X and y data sets\n",
       "X = cust_groups[['arr']]\n",
       "y = cust_groups['total_views']\n",
       "\n",
       "# Fit the linear regression model\n",
       "model = LinearRegression().fit(X, y)\n",
       "\n",
       "# Print the coefficients\n",
       "print('Coefficient: ', model.coef_)\n",
       "print('Intercept: ', model.intercept_)\n",
       "</pre>\n",
       "                <button style=\"height: fit-content;\" onclick=\"navigator.clipboard.writeText(JSON.parse(atob(`IlxuaW1wb3J0IHBhbmRhcyBhcyBwZFxuaW1wb3J0IG51bXB5IGFzIG5wXG5mcm9tIHNrbGVhcm4ubGluZWFyX21vZGVsIGltcG9ydCBMaW5lYXJSZWdyZXNzaW9uXG5cbiMgQ3JlYXRlIHRoZSBYIGFuZCB5IGRhdGEgc2V0c1xuWCA9IGN1c3RfZ3JvdXBzW1snYXJyJ11dXG55ID0gY3VzdF9ncm91cHNbJ3RvdGFsX3ZpZXdzJ11cblxuIyBGaXQgdGhlIGxpbmVhciByZWdyZXNzaW9uIG1vZGVsXG5tb2RlbCA9IExpbmVhclJlZ3Jlc3Npb24oKS5maXQoWCwgeSlcblxuIyBQcmludCB0aGUgY29lZmZpY2llbnRzXG5wcmludCgnQ29lZmZpY2llbnQ6ICcsIG1vZGVsLmNvZWZfKVxucHJpbnQoJ0ludGVyY2VwdDogJywgbW9kZWwuaW50ZXJjZXB0XylcbiI=`)))\">Copy</button>\n",
       "                </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_groups.sketch.howto(\"Apply linear regression to determine if there is a linear relationships between arr and total_views\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "Coefficient:  [0.00205744]\n",
      "Intercept:  788.6409091789558\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create the X and y data sets\n",
    "X = cust_groups[['arr']]\n",
    "y = cust_groups['total_views']\n",
    "\n",
    "# Fit the linear regression model\n",
    "model = LinearRegression().fit(X, y)\n",
    "\n",
    "# Print the coefficients\n",
    "print('Coefficient: ', model.coef_)\n",
    "print('Intercept: ', model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_views    int64\n",
       "arr            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 960,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe with only some columns. \n",
    "corrspd = cust_groups[['total_views', 'arr']]\n",
    "corrspd.plot.scatter(x=corrspd.total_views, y=corrspd.arr)\n",
    "\n",
    "\n",
    "# Correlation Matrix. Not useful here. \n",
    "# corrs = corrspd.corr()\n",
    "# fig = plt.figure(figsize=(10,10))\n",
    "# plt.matshow(corrs, cmap='RdBu')\n",
    "# plt.xticks(range(len(corrs.columns)), corrs.columns, rotation='vertical')\n",
    "# plt.yticks(range(len(corrs.columns)), corrs.columns)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_plot = cust_groups[['total_views']].copy()\n",
    "cust_plot = cust_plot.reset_index()\n",
    "cust_plot.plot.kde(ind=[1000, 5000, 10000, 15000, 20000])\n",
    "#cust_plot.plot(y=\"total_views\", x=\"customer_name\", figsize=(18,8))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_views</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use_case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Customers</th>\n",
       "      <td>62171</td>\n",
       "      <td>2360462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customers,Employees</th>\n",
       "      <td>1233</td>\n",
       "      <td>161484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customers,Gig</th>\n",
       "      <td>97</td>\n",
       "      <td>12864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employees</th>\n",
       "      <td>27020</td>\n",
       "      <td>690496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employees,Gig</th>\n",
       "      <td>3</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employees,Partner Training</th>\n",
       "      <td>1579</td>\n",
       "      <td>15225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gig</th>\n",
       "      <td>10252</td>\n",
       "      <td>1027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner Training</th>\n",
       "      <td>2046</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>7585</td>\n",
       "      <td>590938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            total_views      arr\n",
       "use_case                                        \n",
       "Customers                         62171  2360462\n",
       "Customers,Employees                1233   161484\n",
       "Customers,Gig                        97    12864\n",
       "Employees                         27020   690496\n",
       "Employees,Gig                         3     7800\n",
       "Employees,Partner Training         1579    15225\n",
       "Gig                               10252  1027732\n",
       "Partner Training                   2046    75000\n",
       "Real Estate                        7585   590938"
      ]
     },
     "execution_count": 861,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usecase_groups = newpd.groupby('use_case').agg({'total_views':'sum','arr':'sum'})\n",
    "usecase_groups.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_views</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>csm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Support</th>\n",
       "      <td>34553</td>\n",
       "      <td>616466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tracy McMahon</th>\n",
       "      <td>25921</td>\n",
       "      <td>961379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nergis Tepeli</th>\n",
       "      <td>18085</td>\n",
       "      <td>1571381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Courtney Donargo</th>\n",
       "      <td>17237</td>\n",
       "      <td>351893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm Rasmussen</th>\n",
       "      <td>16190</td>\n",
       "      <td>1440882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_views      arr\n",
       "csm                                   \n",
       "Support                 34553   616466\n",
       "Tracy McMahon           25921   961379\n",
       "Nergis Tepeli           18085  1571381\n",
       "Courtney Donargo        17237   351893\n",
       "Norm Rasmussen          16190  1440882"
      ]
     },
     "execution_count": 862,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csm_groups = newpd.groupby('csm').agg({'total_views':'sum','arr':'sum'})\n",
    "csm_groups.nlargest(25, 'total_views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a just a test. It's ugly formatting. \n",
    "\n",
    "# newpd[['total_views']].style.format('{:.3f}', na_rep=\"\") \\\n",
    "#     .bar(align=2, vmin=0, vmax=2000, cmap='bwr', height=60,\n",
    "#     width=100, props=\"width:1520px, border-right: 1px solid green;\") \\\n",
    "#         .text_gradient(cmap=\"bwr\", vmin=0, vmax=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'newpd' (DataFrame)\n"
     ]
    }
   ],
   "source": [
    "newpd.to_csv('/Users/normrasmussen/Downloads/total_views_with_acctdata.csv')\n",
    "%store newpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_24478/1696825021.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plotpd['school_name']=plotpd['school_name'].astype('category').cat.codes\n",
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_24478/1696825021.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plotpd['customer_name']=plotpd['customer_name'].astype('category').cat.codes\n",
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_24478/1696825021.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plotpd['use_case']=plotpd['use_case'].astype('category').cat.codes\n",
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_24478/1696825021.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plotpd['icp_boolean']=plotpd['icp_boolean'].astype('category').cat.codes\n",
      "/var/folders/5l/fmyj8l8n74v0m3zfncrycj7m0000gn/T/ipykernel_24478/1696825021.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plotpd['product']=plotpd['product'].astype('category').cat.codes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create new dataframe with only some columns. \n",
    "plotpd = newpd[['school_name', 'total_views', 'icp_boolean', 'customer_name', 'use_case', 'product', 'arr']]\n",
    "\n",
    "# First, we have to change strings to categorical data. So each column. \n",
    "plotpd['school_name']=plotpd['school_name'].astype('category').cat.codes\n",
    "plotpd['customer_name']=plotpd['customer_name'].astype('category').cat.codes\n",
    "plotpd['use_case']=plotpd['use_case'].astype('category').cat.codes\n",
    "plotpd['icp_boolean']=plotpd['icp_boolean'].astype('category').cat.codes\n",
    "plotpd['product']=plotpd['product'].astype('category').cat.codes\n",
    "corrs = plotpd.corr()\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "plt.matshow(corrs, cmap='RdBu')\n",
    "plt.xticks(range(len(corrs.columns)), corrs.columns, rotation='vertical')\n",
    "plt.yticks(range(len(corrs.columns)), corrs.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_views</th>\n",
       "      <th>icp_boolean</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>use_case</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>528</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1907</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>578</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2864</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   school_name  total_views  icp_boolean  customer_name  use_case  product   \n",
       "0            0          528            1              0         0        2  \\\n",
       "1            1          253            1            132         0        2   \n",
       "2            2         1907            2             13         3        2   \n",
       "3            3          578            2              2         3        1   \n",
       "4            4           33            2              3         3        2   \n",
       "5            5         2864            1              4         0        2   \n",
       "6            6           17            1              6         1        2   \n",
       "7            7          104            1              9         0        0   \n",
       "8            8          418            1              9         0        0   \n",
       "9            9            9            0             66         0        0   \n",
       "\n",
       "     arr  \n",
       "0  22000  \n",
       "1  28000  \n",
       "2   6720  \n",
       "3   6000  \n",
       "4   7474  \n",
       "5  20000  \n",
       "6  31900  \n",
       "7  95000  \n",
       "8  95000  \n",
       "9  68000  "
      ]
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotpd.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>school_id</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47167440</th>\n",
       "      <td>405</td>\n",
       "      <td>405</td>\n",
       "      <td>Spark Driver Resources Hub</td>\n",
       "      <td>1093</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>7056</td>\n",
       "      <td>326</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>820435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             level_0  index                 school_name  total_page_views   \n",
       "customer_id                                                                 \n",
       "47167440         405    405  Spark Driver Resources Hub              1093  \\\n",
       "\n",
       "            customer_name  school_id  Unnamed: 0             csm     product   \n",
       "customer_id                                                                    \n",
       "47167440     Walmart Inc.       7056         326  Norm Rasmussen  enterprise  \\\n",
       "\n",
       "                arr  \n",
       "customer_id          \n",
       "47167440     820435  "
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd.set_index('customer_id', inplace=True)\n",
    "newpd.loc[newpd.customer_name == 'Walmart Inc.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>school_id</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>46757198</th>\n",
       "      <td>AIIM+</td>\n",
       "      <td>528</td>\n",
       "      <td>AIIM</td>\n",
       "      <td>7079</td>\n",
       "      <td>8</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>premium</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167390</th>\n",
       "      <td>Académie Évolupharma</td>\n",
       "      <td>253</td>\n",
       "      <td>Évolupharma, Inc.</td>\n",
       "      <td>7326</td>\n",
       "      <td>94</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867718</th>\n",
       "      <td>ActiveLearner</td>\n",
       "      <td>1907</td>\n",
       "      <td>BrandActive International Inc.</td>\n",
       "      <td>5301</td>\n",
       "      <td>36</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37441607</th>\n",
       "      <td>Akua Mind Body</td>\n",
       "      <td>578</td>\n",
       "      <td>Akua Mind &amp; Body</td>\n",
       "      <td>6718</td>\n",
       "      <td>9</td>\n",
       "      <td>Support</td>\n",
       "      <td>essentials</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867707</th>\n",
       "      <td>Altima Dental</td>\n",
       "      <td>33</td>\n",
       "      <td>Altima Dental Canada</td>\n",
       "      <td>5670</td>\n",
       "      <td>14</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>7474</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867710</th>\n",
       "      <td>Missouri State Employees Retirement System</td>\n",
       "      <td>1</td>\n",
       "      <td>Missouri State Employees Retirement System</td>\n",
       "      <td>5444</td>\n",
       "      <td>184</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>5220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867711</th>\n",
       "      <td>My Meeting Courses</td>\n",
       "      <td>767</td>\n",
       "      <td>International Care Ltd.</td>\n",
       "      <td>5012</td>\n",
       "      <td>144</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867711</th>\n",
       "      <td>MyAuPairCourses</td>\n",
       "      <td>103</td>\n",
       "      <td>International Care Ltd.</td>\n",
       "      <td>4638</td>\n",
       "      <td>144</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36347227</th>\n",
       "      <td>NEMO Safety Training</td>\n",
       "      <td>255</td>\n",
       "      <td>New England Mechanical Overlay (NEMO)</td>\n",
       "      <td>6577</td>\n",
       "      <td>194</td>\n",
       "      <td>Support</td>\n",
       "      <td>essentials</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37790045</th>\n",
       "      <td>Nest U</td>\n",
       "      <td>434</td>\n",
       "      <td>At World Properties, LLC</td>\n",
       "      <td>6942</td>\n",
       "      <td>25</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>87500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            school_name  total_page_views   \n",
       "customer_id                                                                 \n",
       "46757198                                          AIIM+               528  \\\n",
       "47167390                           Académie Évolupharma               253   \n",
       "30867718                                  ActiveLearner              1907   \n",
       "37441607                                 Akua Mind Body               578   \n",
       "30867707                                  Altima Dental                33   \n",
       "...                                                 ...               ...   \n",
       "30867710     Missouri State Employees Retirement System                 1   \n",
       "30867711                             My Meeting Courses               767   \n",
       "30867711                                MyAuPairCourses               103   \n",
       "36347227                           NEMO Safety Training               255   \n",
       "37790045                                         Nest U               434   \n",
       "\n",
       "                                          customer_name  school_id   \n",
       "customer_id                                                          \n",
       "46757198                                           AIIM       7079  \\\n",
       "47167390                              Évolupharma, Inc.       7326   \n",
       "30867718                 BrandActive International Inc.       5301   \n",
       "37441607                               Akua Mind & Body       6718   \n",
       "30867707                           Altima Dental Canada       5670   \n",
       "...                                                 ...        ...   \n",
       "30867710     Missouri State Employees Retirement System       5444   \n",
       "30867711                        International Care Ltd.       5012   \n",
       "30867711                        International Care Ltd.       4638   \n",
       "36347227          New England Mechanical Overlay (NEMO)       6577   \n",
       "37790045                       At World Properties, LLC       6942   \n",
       "\n",
       "             Unnamed: 0             csm     product    arr  \n",
       "customer_id                                                 \n",
       "46757198              8  Norm Rasmussen     premium  22000  \n",
       "47167390             94   Tracy McMahon     premium  28000  \n",
       "30867718             36         Support     premium   6720  \n",
       "37441607              9         Support  essentials   6000  \n",
       "30867707             14         Support     premium   7474  \n",
       "...                 ...             ...         ...    ...  \n",
       "30867710            184         Support     premium   5220  \n",
       "30867711            144         Support     premium   7200  \n",
       "30867711            144         Support     premium   7200  \n",
       "36347227            194         Support  essentials   5000  \n",
       "37790045             25   Tracy McMahon  enterprise  87500  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd = newpd.drop(['level_0', 'index'], axis=1)\n",
    "newpd.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>school_id</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>47167440</th>\n",
       "      <td>Spark Driver Resources Hub</td>\n",
       "      <td>1093</td>\n",
       "      <td>Walmart Inc.</td>\n",
       "      <td>7056</td>\n",
       "      <td>326</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>820435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867645</th>\n",
       "      <td>Bolt</td>\n",
       "      <td>1177</td>\n",
       "      <td>Bolt Operations OÜ</td>\n",
       "      <td>5388</td>\n",
       "      <td>32</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>240480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167369</th>\n",
       "      <td>Lighthouse by LJ Hooker Group</td>\n",
       "      <td>1378</td>\n",
       "      <td>LJ Hooker Corporation Pty Ltd</td>\n",
       "      <td>7066</td>\n",
       "      <td>164</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>206250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867741</th>\n",
       "      <td>Skin Laundry</td>\n",
       "      <td>52</td>\n",
       "      <td>Soham, Inc. (Zenoti)</td>\n",
       "      <td>6510</td>\n",
       "      <td>274</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867741</th>\n",
       "      <td>ZENOTI</td>\n",
       "      <td>42</td>\n",
       "      <td>Soham, Inc. (Zenoti)</td>\n",
       "      <td>5689</td>\n",
       "      <td>274</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867741</th>\n",
       "      <td>ZU EWC</td>\n",
       "      <td>14</td>\n",
       "      <td>Soham, Inc. (Zenoti)</td>\n",
       "      <td>6167</td>\n",
       "      <td>274</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867741</th>\n",
       "      <td>ZU Sorbet</td>\n",
       "      <td>8</td>\n",
       "      <td>Soham, Inc. (Zenoti)</td>\n",
       "      <td>7654</td>\n",
       "      <td>274</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167502</th>\n",
       "      <td>Anthology Internal</td>\n",
       "      <td>104</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>7871</td>\n",
       "      <td>30</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167502</th>\n",
       "      <td>Anthology Trial</td>\n",
       "      <td>418</td>\n",
       "      <td>Blackboard Inc.</td>\n",
       "      <td>7874</td>\n",
       "      <td>30</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867653</th>\n",
       "      <td>Shipt Passport</td>\n",
       "      <td>1034</td>\n",
       "      <td>Shipt</td>\n",
       "      <td>4912</td>\n",
       "      <td>259</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>premium</td>\n",
       "      <td>88564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               school_name  total_page_views   \n",
       "customer_id                                                    \n",
       "47167440        Spark Driver Resources Hub              1093  \\\n",
       "30867645                              Bolt              1177   \n",
       "47167369     Lighthouse by LJ Hooker Group              1378   \n",
       "30867741                      Skin Laundry                52   \n",
       "30867741                            ZENOTI                42   \n",
       "30867741                            ZU EWC                14   \n",
       "30867741                         ZU Sorbet                 8   \n",
       "47167502                Anthology Internal               104   \n",
       "47167502                   Anthology Trial               418   \n",
       "30867653                    Shipt Passport              1034   \n",
       "\n",
       "                             customer_name  school_id  Unnamed: 0   \n",
       "customer_id                                                         \n",
       "47167440                      Walmart Inc.       7056         326  \\\n",
       "30867645                Bolt Operations OÜ       5388          32   \n",
       "47167369     LJ Hooker Corporation Pty Ltd       7066         164   \n",
       "30867741              Soham, Inc. (Zenoti)       6510         274   \n",
       "30867741              Soham, Inc. (Zenoti)       5689         274   \n",
       "30867741              Soham, Inc. (Zenoti)       6167         274   \n",
       "30867741              Soham, Inc. (Zenoti)       7654         274   \n",
       "47167502                   Blackboard Inc.       7871          30   \n",
       "47167502                   Blackboard Inc.       7874          30   \n",
       "30867653                             Shipt       4912         259   \n",
       "\n",
       "                        csm     product     arr  \n",
       "customer_id                                      \n",
       "47167440     Norm Rasmussen  enterprise  820435  \n",
       "30867645     Norm Rasmussen  enterprise  240480  \n",
       "47167369      Tracy McMahon  enterprise  206250  \n",
       "30867741      Nergis Tepeli  enterprise  120000  \n",
       "30867741      Nergis Tepeli  enterprise  120000  \n",
       "30867741      Nergis Tepeli  enterprise  120000  \n",
       "30867741      Nergis Tepeli  enterprise  120000  \n",
       "47167502     Norm Rasmussen  enterprise   95000  \n",
       "47167502     Norm Rasmussen  enterprise   95000  \n",
       "30867653      Nergis Tepeli     premium   88564  "
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpd.nlargest(10, 'arr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_name</th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>school_id</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>csm</th>\n",
       "      <th>product</th>\n",
       "      <th>arr</th>\n",
       "      <th>scaled_views</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>47167356</th>\n",
       "      <td>CHEK Institute</td>\n",
       "      <td>11138</td>\n",
       "      <td>CHEK Institute</td>\n",
       "      <td>7112</td>\n",
       "      <td>51</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34511981</th>\n",
       "      <td>Compass</td>\n",
       "      <td>4853</td>\n",
       "      <td>Anchor QEA, LLC</td>\n",
       "      <td>6508</td>\n",
       "      <td>20</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.435665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167413</th>\n",
       "      <td>MASSÉNA Formations</td>\n",
       "      <td>4009</td>\n",
       "      <td>MOLITOR Formations</td>\n",
       "      <td>7369</td>\n",
       "      <td>186</td>\n",
       "      <td>Support</td>\n",
       "      <td>premium</td>\n",
       "      <td>9000</td>\n",
       "      <td>0.359881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167511</th>\n",
       "      <td>Whitetail Properties Education Platform</td>\n",
       "      <td>3604</td>\n",
       "      <td>Whitetail Properties Real Estate</td>\n",
       "      <td>7952</td>\n",
       "      <td>330</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>18000</td>\n",
       "      <td>0.323516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867585</th>\n",
       "      <td>UX Design Institute</td>\n",
       "      <td>3583</td>\n",
       "      <td>UX Design Institute</td>\n",
       "      <td>5283</td>\n",
       "      <td>317</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>37860</td>\n",
       "      <td>0.321631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867585</th>\n",
       "      <td>UX Design Institute</td>\n",
       "      <td>3583</td>\n",
       "      <td>UX Design Institute</td>\n",
       "      <td>7744</td>\n",
       "      <td>317</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>37860</td>\n",
       "      <td>0.321631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167487</th>\n",
       "      <td>Luma U</td>\n",
       "      <td>3459</td>\n",
       "      <td>Luma Health, Inc.</td>\n",
       "      <td>7847</td>\n",
       "      <td>168</td>\n",
       "      <td>Tracy McMahon</td>\n",
       "      <td>premium</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.310497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867654</th>\n",
       "      <td>Training Center</td>\n",
       "      <td>3205</td>\n",
       "      <td>SPS Commerce</td>\n",
       "      <td>3209</td>\n",
       "      <td>281</td>\n",
       "      <td>Norm Rasmussen</td>\n",
       "      <td>premium</td>\n",
       "      <td>51000</td>\n",
       "      <td>0.287690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47167457</th>\n",
       "      <td>American Concrete Pipe Association Learning Ce...</td>\n",
       "      <td>2864</td>\n",
       "      <td>American Concrete Pipe Association</td>\n",
       "      <td>7582</td>\n",
       "      <td>15</td>\n",
       "      <td>Nergis Tepeli</td>\n",
       "      <td>premium</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.257071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30867576</th>\n",
       "      <td>Viva Wallet</td>\n",
       "      <td>2365</td>\n",
       "      <td>Viva Online Services SA</td>\n",
       "      <td>5549</td>\n",
       "      <td>322</td>\n",
       "      <td>Courtney Donargo</td>\n",
       "      <td>premium</td>\n",
       "      <td>5837</td>\n",
       "      <td>0.212265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   school_name   \n",
       "customer_id                                                      \n",
       "47167356                                        CHEK Institute  \\\n",
       "34511981                                               Compass   \n",
       "47167413                                    MASSÉNA Formations   \n",
       "47167511               Whitetail Properties Education Platform   \n",
       "30867585                                   UX Design Institute   \n",
       "30867585                                   UX Design Institute   \n",
       "47167487                                                Luma U   \n",
       "30867654                                       Training Center   \n",
       "47167457     American Concrete Pipe Association Learning Ce...   \n",
       "30867576                                           Viva Wallet   \n",
       "\n",
       "             total_page_views                       customer_name  school_id   \n",
       "customer_id                                                                    \n",
       "47167356                11138                      CHEK Institute       7112  \\\n",
       "34511981                 4853                     Anchor QEA, LLC       6508   \n",
       "47167413                 4009                  MOLITOR Formations       7369   \n",
       "47167511                 3604    Whitetail Properties Real Estate       7952   \n",
       "30867585                 3583                 UX Design Institute       5283   \n",
       "30867585                 3583                 UX Design Institute       7744   \n",
       "47167487                 3459                   Luma Health, Inc.       7847   \n",
       "30867654                 3205                        SPS Commerce       3209   \n",
       "47167457                 2864  American Concrete Pipe Association       7582   \n",
       "30867576                 2365             Viva Online Services SA       5549   \n",
       "\n",
       "             Unnamed: 0               csm     product    arr  scaled_views  \n",
       "customer_id                                                                 \n",
       "47167356             51     Tracy McMahon     premium  20000      1.000000  \n",
       "34511981             20           Support     premium  10000      0.435665  \n",
       "47167413            186           Support     premium   9000      0.359881  \n",
       "47167511            330     Tracy McMahon     premium  18000      0.323516  \n",
       "30867585            317     Nergis Tepeli  enterprise  37860      0.321631  \n",
       "30867585            317     Nergis Tepeli  enterprise  37860      0.321631  \n",
       "47167487            168     Tracy McMahon     premium  20000      0.310497  \n",
       "30867654            281    Norm Rasmussen     premium  51000      0.287690  \n",
       "47167457             15     Nergis Tepeli     premium  20000      0.257071  \n",
       "30867576            322  Courtney Donargo     premium   5837      0.212265  "
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "newpd[[\"scaled_views\"]] = scaler.fit_transform(newpd[[\"total_page_views\"]])\n",
    "\n",
    "newpd.nlargest(10, 'scaled_views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jupyter",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}