Analysis_and_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "415a8b06",
   "metadata": {},
   "source": [
    "### **ESTABLISHING AND TESTING CONNECTION WITH MYSQL DATABASE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "872dc877",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f7ea7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = pymysql.connect(\n",
    "    host = 'localhost',\n",
    "    user = 'root',\n",
    "    password = 'DMAProject',\n",
    "    db = 're'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e974af7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62a78dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"select @@version\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee715313",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "150d155b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('8.0.31',),)\n"
     ]
    }
   ],
   "source": [
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8408343f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"select * from agent_details\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b1abfbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d69c0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1001, 'Company Agent', 'Allyn Jansie', 'Female', 'Queens', '737-596-4600', 'ajansie0@mediafire.com', '4 Eagan Way', 'Pantian', None)\n",
      "(1002, 'Company Agent', 'Demetri Grishakin', 'Male', 'Manhattan', '507-146-7596', 'dgrishakin1@ucoz.ru', '65 Westridge Circle', 'Shatian', None)\n",
      "(1003, 'Company Agent', \"Thekla O'Hanley\", 'Female', 'Staten Island', '187-663-8314', 'tohanley2@delicious.com', '8 Graceland Point', 'Bujaków', '43-356')\n",
      "(1004, 'Company Agent', 'Charles Vearncomb', None, 'Brooklyn', '748-588-2657', 'cvearncomb3@vinaora.com', '48013 Center Center', 'Kotri', '76000')\n",
      "(1005, 'Third-Party Agent', 'Torey Manntschke', None, 'Brooklyn', '920-427-5141', 'tmanntschke4@163.com', '498 Anthes Alley', 'Légua', '3830-247')\n",
      "(1006, 'Company Agent', 'Franklin Jorg', None, 'Brooklyn', '482-772-9815', 'fjorg5@bandcamp.com', '89571 Maywood Avenue', 'Adolfo Lopez Mateos', '56528')\n",
      "(1007, 'Third-Party Agent', 'Ashlee Trayte', 'Female', 'Staten Island', '656-428-0454', 'atrayte6@msn.com', '9191 Claremont Parkway', 'Hostos', '11403')\n",
      "(1008, 'Company Agent', 'Barnard Akitt', 'Male', 'Queens', '113-853-6034', 'bakitt7@mac.com', '531 3rd Circle', 'Taraco', None)\n",
      "(1009, 'Company Agent', 'Baillie Charpling', 'Male', 'Brooklyn', '457-852-1570', 'bcharpling8@bbc.co.uk', '80291 Lerdahl Trail', 'Ülken', None)\n",
      "(1010, 'Company Agent', 'Bari Semper', 'Female', 'Manhattan', '421-488-1491', 'bsemper9@rambler.ru', '8 Alpine Way', 'Anwen', None)\n",
      "(1011, 'Third-Party Agent', 'Jessika Caesman', 'Female', 'Brooklyn', '834-517-4436', 'jcaesmana@squidoo.com', '3117 Sloan Plaza', 'Tunjë', None)\n",
      "(1012, 'Third-Party Agent', 'Tallie Tesyro', 'Female', 'Brooklyn', '796-706-4959', 'ttesyrob@digg.com', '9 2nd Pass', 'El Corozal', None)\n",
      "(1013, 'Third-Party Agent', 'Normy Kerins', 'Male', 'Manhattan', '780-624-8193', 'nkerinsc@hexun.com', '812 Thackeray Avenue', 'Rosthern', 'B0L')\n",
      "(1014, 'Third-Party Agent', 'Randi Parkhouse', 'Male', 'Manhattan', '982-585-1137', 'rparkhoused@trellian.com', '688 Starling Avenue', 'Isu', None)\n"
     ]
    }
   ],
   "source": [
    "for i in output:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263971b5",
   "metadata": {},
   "source": [
    "### QUERIES TO GET REQUIRED METRICS\n",
    "##### 1. Get the total properties sold, total sale value for every agent ID\n",
    "##### 2. Get average rating against every agent ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c2f35d6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9ac27476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute('''select a.agentID, \n",
    "                sum(pl.rate) as total_sale_value, \n",
    "                count(distinct pl.propID) as properties_sold\n",
    "                from\n",
    "                re.property_listing as pl\n",
    "                left join re.creates as c on c.propID = pl.propID\n",
    "                left join re.agent_details as a on a.agentID = c.agentID\n",
    "                group by a.agentID\n",
    "                order by total_sale_value desc, properties_sold desc''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8d065441",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance1 = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "895c397c",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance1 = pd.DataFrame(agent_performance1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e9abf1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance1.columns = ['Agent ID', 'Total Sale Value', 'Properties Sold']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0387ed0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute('''select agentID, format(avg(rating), 2) as avg_rating\n",
    "               from re.feedback\n",
    "               group by agentID\n",
    "               order by avg_rating desc''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c3b5ee8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance2 = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b020317f",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance2 = pd.DataFrame(agent_performance2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "15f72ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance2.columns = ['Agent ID', 'Average Rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2426a7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(agent_performance2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "55e94268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Agent ID Average Rating\n",
      "0       1007           4.38\n",
      "1       1009           4.00\n",
      "2       1012           4.00\n",
      "3       1010           3.38\n",
      "4       1004           3.30\n",
      "5       1002           3.13\n",
      "6       1003           3.00\n",
      "7       1008           2.88\n",
      "8       1013           2.83\n",
      "9       1006           2.75\n",
      "10      1001           2.67\n",
      "11      1005           2.63\n",
      "12      1014           2.25\n",
      "13      1011           1.75\n"
     ]
    }
   ],
   "source": [
    "print(agent_performance2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "10578530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Agent ID Total Sale Value  Properties Sold\n",
      "0       1012         12499030               57\n",
      "1       1009          8847876               34\n",
      "2       1010          7915462               36\n",
      "3       1007          6505882               29\n",
      "4       1003          6129063               27\n",
      "5       1008          5147289               24\n",
      "6       1002          5043268               21\n",
      "7       1013          4825781               19\n",
      "8       1006          3036858               11\n",
      "9       1004          2426154               10\n",
      "10      1005          2397649                8\n",
      "11      1011          2257104                9\n",
      "12      1001          1795025                8\n",
      "13      1014          1497290                7\n"
     ]
    }
   ],
   "source": [
    "print(agent_performance1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b8dafd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance3 = agent_performance1[['Agent ID', 'Total Sale Value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0d04d069",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance1 = agent_performance1[['Agent ID','Properties Sold']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68d03e2",
   "metadata": {},
   "source": [
    "### **DATA PREPROCESSING FOR VISUALIZATION**\n",
    "##### 1. Bucketing agents based on best rated, average rated and lowest rated\n",
    "##### 2. Convert it to a dataframe & combine with property sales values\n",
    "##### 3. Change data types for some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d27ef0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_performance2['Agent ID'] = agent_performance2['Agent ID'].astype(str)\n",
    "agent_performance2['Average Rating'] = agent_performance2['Average Rating'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "ba1eaca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = agent_performance2.iloc[0:6]\n",
    "above_avg_rating = [i for i in df['Agent ID']]\n",
    "category_name = ['Top 6','Top 6','Top 6',\n",
    "                 'Top 6','Top 6','Top 6']\n",
    "list1 =[above_avg_rating,category_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "983a53db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = agent_performance2.iloc[6:10]\n",
    "above_avg_rating = [i for i in df1['Agent ID']]\n",
    "category_name = ['Middle 4','Middle 4','Middle 4', 'Middle 4']\n",
    "list2 =[above_avg_rating,category_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "073a8ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = agent_performance2.iloc[10:14]\n",
    "above_avg_rating = [i for i in df2['Agent ID']]\n",
    "category_name = ['Bottom 4','Bottom 4','Bottom 4', 'Bottom 4']\n",
    "list3 =[above_avg_rating,category_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "290a309a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = pd.DataFrame (list1).transpose()\n",
    "dfm = pd.DataFrame (list2).transpose()\n",
    "dfb = pd.DataFrame (list3).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c4f11219",
   "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>Agent ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1009</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1012</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1010</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1002</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1008</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1013</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1006</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1005</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1014</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1011</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Agent ID  Category\n",
       "0     1007     Top 6\n",
       "1     1009     Top 6\n",
       "2     1012     Top 6\n",
       "3     1010     Top 6\n",
       "4     1004     Top 6\n",
       "5     1002     Top 6\n",
       "0     1003  Middle 4\n",
       "1     1008  Middle 4\n",
       "2     1013  Middle 4\n",
       "3     1006  Middle 4\n",
       "0     1001  Bottom 4\n",
       "1     1005  Bottom 4\n",
       "2     1014  Bottom 4\n",
       "3     1011  Bottom 4"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "union_df = pd.concat([dft, dfm, dfb])\n",
    "union_df.columns = ['Agent ID','Category']\n",
    "union_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c656fb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "union_df = union_df.dropna(how='any',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "976fc628",
   "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>Agent ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1009</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1012</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1010</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1002</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1008</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1013</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1006</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1005</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1014</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1011</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Agent ID  Category\n",
       "0     1007     Top 6\n",
       "1     1009     Top 6\n",
       "2     1012     Top 6\n",
       "3     1010     Top 6\n",
       "4     1004     Top 6\n",
       "5     1002     Top 6\n",
       "0     1003  Middle 4\n",
       "1     1008  Middle 4\n",
       "2     1013  Middle 4\n",
       "3     1006  Middle 4\n",
       "0     1001  Bottom 4\n",
       "1     1005  Bottom 4\n",
       "2     1014  Bottom 4\n",
       "3     1011  Bottom 4"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "union_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "62b8548d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agent ID</th>\n",
       "      <th>Properties Sold</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1009</td>\n",
       "      <td>34</td>\n",
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       "      <th>2</th>\n",
       "      <td>1010</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>29</td>\n",
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       "      <th>4</th>\n",
       "      <td>1003</td>\n",
       "      <td>27</td>\n",
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       "      <th>5</th>\n",
       "      <td>1008</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1002</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1013</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1006</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1005</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1011</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Agent ID  Properties Sold\n",
       "0       1012               57\n",
       "1       1009               34\n",
       "2       1010               36\n",
       "3       1007               29\n",
       "4       1003               27\n",
       "5       1008               24\n",
       "6       1002               21\n",
       "7       1013               19\n",
       "8       1006               11\n",
       "9       1004               10\n",
       "10      1005                8\n",
       "11      1011                9\n",
       "12      1001                8\n",
       "13      1014                7"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_performance1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "4ea4c677",
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution = pd.merge(agent_performance3, union_df, on=['Agent ID','Agent ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "2c4aeb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agent ID</th>\n",
       "      <th>Total Sale Value</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1012</td>\n",
       "      <td>12499030</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1009</td>\n",
       "      <td>8847876</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1010</td>\n",
       "      <td>7915462</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>6505882</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003</td>\n",
       "      <td>6129063</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1008</td>\n",
       "      <td>5147289</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1002</td>\n",
       "      <td>5043268</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1013</td>\n",
       "      <td>4825781</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1006</td>\n",
       "      <td>3036858</td>\n",
       "      <td>Middle 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1004</td>\n",
       "      <td>2426154</td>\n",
       "      <td>Top 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1005</td>\n",
       "      <td>2397649</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1011</td>\n",
       "      <td>2257104</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1001</td>\n",
       "      <td>1795025</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1014</td>\n",
       "      <td>1497290</td>\n",
       "      <td>Bottom 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Agent ID Total Sale Value  Category\n",
       "0      1012         12499030     Top 6\n",
       "1      1009          8847876     Top 6\n",
       "2      1010          7915462     Top 6\n",
       "3      1007          6505882     Top 6\n",
       "4      1003          6129063  Middle 4\n",
       "5      1008          5147289  Middle 4\n",
       "6      1002          5043268     Top 6\n",
       "7      1013          4825781  Middle 4\n",
       "8      1006          3036858  Middle 4\n",
       "9      1004          2426154     Top 6\n",
       "10     1005          2397649  Bottom 4\n",
       "11     1011          2257104  Bottom 4\n",
       "12     1001          1795025  Bottom 4\n",
       "13     1014          1497290  Bottom 4"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0884719b",
   "metadata": {},
   "source": [
    "### ** VISUALIZATIONS**\n",
    "##### 1. Agent wise properties sold\n",
    "##### 2. Agent wise total sales\n",
    "##### 3. Agent wise average rating\n",
    "##### 4. Contribution of agents to revenue - (categorized based on average ratings) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c9d10812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('light:b')[0:6]\n",
    "sns.set_style(\"darkgrid\")\n",
    "g1 = sns.barplot(data=agent_performance1, x=\"Agent ID\", y=\"Properties Sold\", \n",
    "                 order=agent_performance1.sort_values('Properties Sold')['Agent ID']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "6457a2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g2 = sns.barplot(data=agent_performance3, x=\"Agent ID\", y=\"Total Sale Value\",\n",
    "                 order=agent_performance3.sort_values('Total Sale Value')['Agent ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "ffc2073c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3 = sns.barplot(data=agent_performance2, x=\"Agent ID\", y=\"Average Rating\",\n",
    "                 order=agent_performance2.sort_values('Average Rating')['Agent ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "86f46c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution = contribution.groupby(['Category'])['Total Sale Value'].sum().reset_index(name='Total Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "0da8666e",
   "metadata": {},
   "outputs": [],
   "source": [
    "total = contribution['Total Sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9cc043c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution['Sales Contribution %'] = contribution['Total Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "0a69b456",
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution['Sales Contribution %'] = contribution['Sales Contribution %'] / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1decffd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution['Sales Contribution %'] = contribution['Sales Contribution %'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f7007b48",
   "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>Category</th>\n",
       "      <th>Total Sales</th>\n",
       "      <th>Sales Contribution %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bottom 4</td>\n",
       "      <td>7947068</td>\n",
       "      <td>11.30069165414445942863867675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Middle 4</td>\n",
       "      <td>19138991</td>\n",
       "      <td>27.21555117716948209132988123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Top 6</td>\n",
       "      <td>43237672</td>\n",
       "      <td>61.48375716868605848003144202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category Total Sales           Sales Contribution %\n",
       "0  Bottom 4     7947068  11.30069165414445942863867675\n",
       "1  Middle 4    19138991  27.21555117716948209132988123\n",
       "2     Top 6    43237672  61.48375716868605848003144202"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6802e599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('pastel')[0:10]\n",
    "g4 = plt.pie(contribution['Sales Contribution %'], labels = contribution['Category'], colors = colors, autopct='%.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5381b484",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}