wrangle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation for the Bachelorette Predictor\n",
    "### Kwame V. Taylor\n",
    "\n",
    "I need to join the data from ```bachelorette_edit-to-join.csv``` with the data from ```bachelorette-contestants.csv```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up env and load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('bachelorette-contestants.csv')\n",
    "join = pd.read_csv('bachelorette_edit-to-join.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>CONTESTANT</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>ELIMINATION-4</th>\n",
       "      <th>ELIMINATION-5</th>\n",
       "      <th>ELIMINATION-6</th>\n",
       "      <th>ELIMINATION-7</th>\n",
       "      <th>ELIMINATION-8</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>12</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D6</td>\n",
       "      <td>D12</td>\n",
       "      <td>D11</td>\n",
       "      <td>D1</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>12_ROBBY_H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D3</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>12_CHASE_M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D1</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>12_LUKE_P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D1</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12_JAMES_T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D6</td>\n",
       "      <td>D1</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEASON   CONTESTANT ELIMINATION-1 ELIMINATION-2 ELIMINATION-3  \\\n",
       "0      12  12_JORDAN_R            R1           NaN           NaN   \n",
       "1      12   12_ROBBY_H           NaN           NaN           NaN   \n",
       "2      12   12_CHASE_M           NaN           NaN             R   \n",
       "3      12    12_LUKE_P           NaN           NaN           NaN   \n",
       "4      12   12_JAMES_T           NaN             R             R   \n",
       "\n",
       "  ELIMINATION-4 ELIMINATION-5 ELIMINATION-6 ELIMINATION-7 ELIMINATION-8  ...  \\\n",
       "0             R             R           NaN           NaN           NaN  ...   \n",
       "1           NaN             R           NaN             R           NaN  ...   \n",
       "2           NaN           NaN             R           NaN           NaN  ...   \n",
       "3             R           NaN             R           NaN             E  ...   \n",
       "4           NaN           NaN           NaN             E           NaN  ...   \n",
       "\n",
       "  DATES-1 DATES-2  DATES-3 DATES-4 DATES-5 DATES-6 DATES-7 DATES-8 DATES-9  \\\n",
       "0     NaN      D6      D12     D11      D1      D5      D1      D1      D1   \n",
       "1     NaN     D10      NaN     D11      D9      D5      D3      D1      D1   \n",
       "2     NaN     NaN       D1     D11      D9      D2      D3      D1      D1   \n",
       "3     NaN     D10      NaN      D1      D9      D5      D1      D1     NaN   \n",
       "4     NaN      D6       D1     D11      D9      D5      D3     NaN     NaN   \n",
       "\n",
       "  DATES-10  \n",
       "0       D1  \n",
       "1       D1  \n",
       "2      NaN  \n",
       "3      NaN  \n",
       "4      NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up contestant names on both DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**```join``` df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the matching strings \n",
    "#join.CONTESTANT = join.CONTESTANT.replace(to_replace ='\\d+(_)', value = '', regex = True) \n",
    "  \n",
    "# Print the updated dataframe \n",
    "#join.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**```df``` df**"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Ryan Sutter\n",
    "Charlie Maher\n",
    "Russ\n",
    "Greg T.\n",
    "Bob Guiney"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Turn last name into just the first letter (if there is a last name)\n",
    "2. Add '_X' if there is no last name\n",
    "3. Remove periods\n",
    "4. Replace the space with a '_'\n",
    "5. Uppercase all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
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       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>T</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
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      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season last_initial  \n",
       "0       1            S  \n",
       "1       1            M  \n",
       "2       1          NaN  \n",
       "3       1            T  \n",
       "4       1            G  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bachelorette-contestants.csv')\n",
    "join = pd.read_csv('bachelorette_edit-to-join.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
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       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
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      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season last_initial  \n",
       "0       1            S  \n",
       "1       1            M  \n",
       "2       1            X  \n",
       "3       1            T  \n",
       "4       1            G  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.last_initial = df.last_initial.fillna('X')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>last_initial</th>\n",
       "      <th>first_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Ryan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>Charlie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Russ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>Greg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season last_initial first_name  \n",
       "0       1            S       Ryan  \n",
       "1       1            M    Charlie  \n",
       "2       1            X       Russ  \n",
       "3       1            T       Greg  \n",
       "4       1            G        Bob  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex1 = '( )\\w+.'\n",
    "\n",
    "# Truncate last names / get first names\n",
    "df['first_name'] = df.Name.replace(to_replace = regex1, value = '', regex = True)\n",
    "\n",
    "# Print the updated dataframe \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>last_initial</th>\n",
       "      <th>first_name</th>\n",
       "      <th>CONTESTANT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>RYAN_ S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>CHARLIE_ M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Russ</td>\n",
       "      <td>RUSS_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>Greg</td>\n",
       "      <td>GREG_ T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>Bob</td>\n",
       "      <td>BOB_ G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season last_initial first_name  CONTESTANT  \n",
       "0       1            S       Ryan     RYAN_ S  \n",
       "1       1            M    Charlie  CHARLIE_ M  \n",
       "2       1            X       Russ      RUSS_X  \n",
       "3       1            T       Greg     GREG_ T  \n",
       "4       1            G        Bob      BOB_ G  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat newly formatted first and last names with a '_' in between\n",
    "\n",
    "df['CONTESTANT'] = df['first_name'].str.upper() + '_' + df['last_initial'].str.upper()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>last_initial</th>\n",
       "      <th>first_name</th>\n",
       "      <th>CONTESTANT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>RYAN_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>CHARLIE_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Russ</td>\n",
       "      <td>RUSS_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>Greg</td>\n",
       "      <td>GREG_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>Bob</td>\n",
       "      <td>BOB_G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age         Occupation                   Hometown  ElimWeek  \\\n",
       "0    Ryan Sutter   29        Firefighter             Vail, Colorado       NaN   \n",
       "1  Charlie Maher   28  Financial Analyst  Hermosa Beach, California       6.0   \n",
       "2           Russ   30             Writer     San Rafael, California       5.0   \n",
       "3        Greg T.   28           Importer        Manhattan, New York       4.0   \n",
       "4     Bob Guiney   31    Mortgage Broker         Ferndale, Michigan       3.0   \n",
       "\n",
       "   Season last_initial first_name CONTESTANT  \n",
       "0       1            S       Ryan     RYAN_S  \n",
       "1       1            M    Charlie  CHARLIE_M  \n",
       "2       1            X       Russ     RUSS_X  \n",
       "3       1            T       Greg     GREG_T  \n",
       "4       1            G        Bob      BOB_G  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take out white spaces\n",
    "\n",
    "df.CONTESTANT = df.CONTESTANT.str.replace(\" \", \"\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate Rows based on CONTESTANT :\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Name</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Chris</td>\n",
       "      <td>31</td>\n",
       "      <td>Accountant</td>\n",
       "      <td>Boston, Massachusetts</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>X</td>\n",
       "      <td>Chris</td>\n",
       "      <td>CHRIS_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Brian Winchester</td>\n",
       "      <td>29</td>\n",
       "      <td>Computer network consultant</td>\n",
       "      <td>New Castle, Indiana</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "      <td>Brian</td>\n",
       "      <td>BRIAN_W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Ryan Miller</td>\n",
       "      <td>27</td>\n",
       "      <td>Constructor Estimator</td>\n",
       "      <td>Novi, Michigan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>M</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>RYAN_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Mike</td>\n",
       "      <td>28</td>\n",
       "      <td>Dental Student</td>\n",
       "      <td>London, England</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>X</td>\n",
       "      <td>Mike</td>\n",
       "      <td>MIKE_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Chris Soules</td>\n",
       "      <td>32</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Lamont, Iowa</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10</td>\n",
       "      <td>S</td>\n",
       "      <td>Chris</td>\n",
       "      <td>CHRIS_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Craig Muhlbauer</td>\n",
       "      <td>29</td>\n",
       "      <td>Tax Accountant</td>\n",
       "      <td>Defiance, Iowa</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>M</td>\n",
       "      <td>Craig</td>\n",
       "      <td>CRAIG_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Nick Viall</td>\n",
       "      <td>34</td>\n",
       "      <td>Software Sales Executive</td>\n",
       "      <td>Waukesha, Wisconsin</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11</td>\n",
       "      <td>V</td>\n",
       "      <td>Nick</td>\n",
       "      <td>NICK_V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Chris Strandburg</td>\n",
       "      <td>28</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>Granite Bay, California</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11</td>\n",
       "      <td>S</td>\n",
       "      <td>Chris</td>\n",
       "      <td>CHRIS_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Justin Reich</td>\n",
       "      <td>28</td>\n",
       "      <td>Fitness Trainer</td>\n",
       "      <td>Elgin, Illinois</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>R</td>\n",
       "      <td>Justin</td>\n",
       "      <td>JUSTIN_R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Ryan Beckett</td>\n",
       "      <td>32</td>\n",
       "      <td>Realtor</td>\n",
       "      <td>Wellington, Florida</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>RYAN_B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Ryan McDill</td>\n",
       "      <td>28</td>\n",
       "      <td>Junkyard Specialist</td>\n",
       "      <td>Kansas City, Missouri</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>M</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>RYAN_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Jake Patton</td>\n",
       "      <td>26</td>\n",
       "      <td>Landscape Architect</td>\n",
       "      <td>Playa Vista, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>P</td>\n",
       "      <td>Jake</td>\n",
       "      <td>JAKE_P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Jonathan Hamilton</td>\n",
       "      <td>29</td>\n",
       "      <td>Technical Sales Rep</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>H</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>JONATHAN_H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Nick Sharp</td>\n",
       "      <td>26</td>\n",
       "      <td>Software Salesman</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>S</td>\n",
       "      <td>Nick</td>\n",
       "      <td>NICK_S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Age                   Occupation  \\\n",
       "44               Chris   31                   Accountant   \n",
       "65    Brian Winchester   29  Computer network consultant   \n",
       "146        Ryan Miller   27        Constructor Estimator   \n",
       "202               Mike   28               Dental Student   \n",
       "207       Chris Soules   32                       Farmer   \n",
       "222    Craig Muhlbauer   29               Tax Accountant   \n",
       "231         Nick Viall   34     Software Sales Executive   \n",
       "235   Chris Strandburg   28                      Dentist   \n",
       "240       Justin Reich   28              Fitness Trainer   \n",
       "244       Ryan Beckett   32                      Realtor   \n",
       "255        Ryan McDill   28          Junkyard Specialist   \n",
       "277        Jake Patton   26          Landscape Architect   \n",
       "278  Jonathan Hamilton   29          Technical Sales Rep   \n",
       "279         Nick Sharp   26            Software Salesman   \n",
       "\n",
       "                        Hometown  ElimWeek  Season last_initial first_name  \\\n",
       "44         Boston, Massachusetts       1.0       2            X      Chris   \n",
       "65           New Castle, Indiana       1.0       4            W      Brian   \n",
       "146               Novi, Michigan       2.0       7            M       Ryan   \n",
       "202              London, England       1.0       9            X       Mike   \n",
       "207                 Lamont, Iowa       9.0      10            S      Chris   \n",
       "222               Defiance, Iowa       2.0      10            M      Craig   \n",
       "231          Waukesha, Wisconsin       9.0      11            V       Nick   \n",
       "235      Granite Bay, California       7.0      11            S      Chris   \n",
       "240              Elgin, Illinois       5.0      11            R     Justin   \n",
       "244          Wellington, Florida       4.0      11            B       Ryan   \n",
       "255        Kansas City, Missouri       1.0      11            M       Ryan   \n",
       "277      Playa Vista, California       1.0      12            P       Jake   \n",
       "278  Vancouver, British Columbia       1.0      12            H   Jonathan   \n",
       "279    San Francisco, California       1.0      12            S       Nick   \n",
       "\n",
       "     CONTESTANT  \n",
       "44      CHRIS_X  \n",
       "65      BRIAN_W  \n",
       "146      RYAN_M  \n",
       "202      MIKE_X  \n",
       "207     CHRIS_S  \n",
       "222     CRAIG_M  \n",
       "231      NICK_V  \n",
       "235     CHRIS_S  \n",
       "240    JUSTIN_R  \n",
       "244      RYAN_B  \n",
       "255      RYAN_M  \n",
       "277      JAKE_P  \n",
       "278  JONATHAN_H  \n",
       "279      NICK_S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting duplicate rows based \n",
    "# on 'CONTESTANT' column \n",
    "duplicate = df[df.duplicated('CONTESTANT')] \n",
    "  \n",
    "print(\"Duplicate Rows based on CONTESTANT :\") \n",
    "  \n",
    "# Print the resultant Dataframe \n",
    "duplicate "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are duplicates, I am going to revert the CONTESTANT format back to the original format with the season appended to the front of each name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             object\n",
       "Age               int64\n",
       "Occupation       object\n",
       "Hometown         object\n",
       "ElimWeek        float64\n",
       "Season            int64\n",
       "last_initial     object\n",
       "first_name       object\n",
       "CONTESTANT       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>last_initial</th>\n",
       "      <th>first_name</th>\n",
       "      <th>CONTESTANT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>1_RYAN_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>1_CHARLIE_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Russ</td>\n",
       "      <td>1_RUSS_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>Greg</td>\n",
       "      <td>1_GREG_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>Bob</td>\n",
       "      <td>1_BOB_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jamie Blyth</td>\n",
       "      <td>27</td>\n",
       "      <td>Professional Basketball Player</td>\n",
       "      <td>Chicago, Illinois</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>Jamie</td>\n",
       "      <td>1_JAMIE_B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mike</td>\n",
       "      <td>24</td>\n",
       "      <td>Sales and Consulting</td>\n",
       "      <td>Cincinnati, Ohio</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Mike</td>\n",
       "      <td>1_MIKE_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rob</td>\n",
       "      <td>29</td>\n",
       "      <td>Computer Programmer</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Rob</td>\n",
       "      <td>1_ROB_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brian C.</td>\n",
       "      <td>28</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1_BRIAN_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brian S.</td>\n",
       "      <td>28</td>\n",
       "      <td>Sales Engineer</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1_BRIAN_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Brook</td>\n",
       "      <td>29</td>\n",
       "      <td>Rodeo Cowboy</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Brook</td>\n",
       "      <td>1_BROOK_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jack</td>\n",
       "      <td>27</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Pepper Pike, Ohio</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Jack</td>\n",
       "      <td>1_JACK_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jeff</td>\n",
       "      <td>25</td>\n",
       "      <td>Professional Football Player</td>\n",
       "      <td>San Diego, California</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>1_JEFF_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Josh</td>\n",
       "      <td>31</td>\n",
       "      <td>Radio Advertising Executive</td>\n",
       "      <td>Boca Raton, Florida</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Josh</td>\n",
       "      <td>1_JOSH_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brian H.</td>\n",
       "      <td>30</td>\n",
       "      <td>National Sales Manager</td>\n",
       "      <td>Cleveland, Ohio</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1_BRIAN_H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Billy</td>\n",
       "      <td>28</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Billy</td>\n",
       "      <td>1_BILLY_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brian K.</td>\n",
       "      <td>31</td>\n",
       "      <td>Breast Implant Sales</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>K</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1_BRIAN_K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chris</td>\n",
       "      <td>35</td>\n",
       "      <td>V.P. of Auto Parts Company</td>\n",
       "      <td>Sugarloaf, Pennsylvania</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Chris</td>\n",
       "      <td>1_CHRIS_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Duane</td>\n",
       "      <td>28</td>\n",
       "      <td>Flight Instructor</td>\n",
       "      <td>Chisago City, Minnesota</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Duane</td>\n",
       "      <td>1_DUANE_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Eric</td>\n",
       "      <td>33</td>\n",
       "      <td>Commercial Pilot</td>\n",
       "      <td>St. Louis, Missouri</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Eric</td>\n",
       "      <td>1_ERIC_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Gregg H.</td>\n",
       "      <td>34</td>\n",
       "      <td>Marble Company Owner</td>\n",
       "      <td>Freehold, New Jersey</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>Gregg</td>\n",
       "      <td>1_GREGG_H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Matt</td>\n",
       "      <td>42</td>\n",
       "      <td>Gym Owner</td>\n",
       "      <td>Marina Del Rey, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Matt</td>\n",
       "      <td>1_MATT_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Paul</td>\n",
       "      <td>26</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Honolulu, Hawaii</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Paul</td>\n",
       "      <td>1_PAUL_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Peter</td>\n",
       "      <td>25</td>\n",
       "      <td>Construction Business Owner</td>\n",
       "      <td>Long Island, New York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Peter</td>\n",
       "      <td>1_PETER_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Wayne</td>\n",
       "      <td>37</td>\n",
       "      <td>Commercial Pilot</td>\n",
       "      <td>Sacramento, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>1_WAYNE_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ian McKee</td>\n",
       "      <td>29</td>\n",
       "      <td>Equity Research Sales</td>\n",
       "      <td>New York, New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>Ian</td>\n",
       "      <td>2_IAN_M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Matthew Hickl</td>\n",
       "      <td>28</td>\n",
       "      <td>Pharmaceutical Sales Rep</td>\n",
       "      <td>Friendswood, Texas</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>2_MATTHEW_H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Chad</td>\n",
       "      <td>31</td>\n",
       "      <td>Pharmaceutical Sales Rep</td>\n",
       "      <td>Buffalo, New York</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>X</td>\n",
       "      <td>Chad</td>\n",
       "      <td>2_CHAD_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lanny</td>\n",
       "      <td>26</td>\n",
       "      <td>Stallion and Breeding Manager</td>\n",
       "      <td>Aubrey, Texas</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>X</td>\n",
       "      <td>Lanny</td>\n",
       "      <td>2_LANNY_X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brad</td>\n",
       "      <td>29</td>\n",
       "      <td>Pharmaceutical Sales Rep</td>\n",
       "      <td>Grand Rapids, Michigan</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>X</td>\n",
       "      <td>Brad</td>\n",
       "      <td>2_BRAD_X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Age                      Occupation  \\\n",
       "0     Ryan Sutter   29                     Firefighter   \n",
       "1   Charlie Maher   28               Financial Analyst   \n",
       "2            Russ   30                          Writer   \n",
       "3         Greg T.   28                        Importer   \n",
       "4      Bob Guiney   31                 Mortgage Broker   \n",
       "5     Jamie Blyth   27  Professional Basketball Player   \n",
       "6            Mike   24            Sales and Consulting   \n",
       "7             Rob   29             Computer Programmer   \n",
       "8        Brian C.   28                 Mortgage Broker   \n",
       "9        Brian S.   28                  Sales Engineer   \n",
       "10          Brook   29                    Rodeo Cowboy   \n",
       "11           Jack   27                     Firefighter   \n",
       "12           Jeff   25    Professional Football Player   \n",
       "13           Josh   31     Radio Advertising Executive   \n",
       "14       Brian H.   30          National Sales Manager   \n",
       "15          Billy   28                     Firefighter   \n",
       "16       Brian K.   31            Breast Implant Sales   \n",
       "17          Chris   35      V.P. of Auto Parts Company   \n",
       "18          Duane   28               Flight Instructor   \n",
       "19           Eric   33                Commercial Pilot   \n",
       "20       Gregg H.   34            Marble Company Owner   \n",
       "21           Matt   42                       Gym Owner   \n",
       "22           Paul   26              Telecommunications   \n",
       "23          Peter   25     Construction Business Owner   \n",
       "24          Wayne   37                Commercial Pilot   \n",
       "25      Ian McKee   29           Equity Research Sales   \n",
       "26  Matthew Hickl   28        Pharmaceutical Sales Rep   \n",
       "27           Chad   31        Pharmaceutical Sales Rep   \n",
       "28          Lanny   26   Stallion and Breeding Manager   \n",
       "29           Brad   29        Pharmaceutical Sales Rep   \n",
       "\n",
       "                      Hometown  ElimWeek  Season last_initial first_name  \\\n",
       "0               Vail, Colorado       NaN       1            S       Ryan   \n",
       "1    Hermosa Beach, California       6.0       1            M    Charlie   \n",
       "2       San Rafael, California       5.0       1            X       Russ   \n",
       "3          Manhattan, New York       4.0       1            T       Greg   \n",
       "4           Ferndale, Michigan       3.0       1            G        Bob   \n",
       "5            Chicago, Illinois       3.0       1            B      Jamie   \n",
       "6             Cincinnati, Ohio       3.0       1            X       Mike   \n",
       "7                Dallas, Texas       3.0       1            X        Rob   \n",
       "8                Dallas, Texas       2.0       1            C      Brian   \n",
       "9                Dallas, Texas       2.0       1            S      Brian   \n",
       "10               Dallas, Texas       2.0       1            X      Brook   \n",
       "11           Pepper Pike, Ohio       2.0       1            X       Jack   \n",
       "12       San Diego, California       2.0       1            X       Jeff   \n",
       "13         Boca Raton, Florida       2.0       1            X       Josh   \n",
       "14             Cleveland, Ohio       2.0       1            H      Brian   \n",
       "15   Hermosa Beach, California       1.0       1            X      Billy   \n",
       "16   San Francisco, California       1.0       1            K      Brian   \n",
       "17     Sugarloaf, Pennsylvania       1.0       1            X      Chris   \n",
       "18     Chisago City, Minnesota       1.0       1            X      Duane   \n",
       "19         St. Louis, Missouri       1.0       1            X       Eric   \n",
       "20        Freehold, New Jersey       1.0       1            H      Gregg   \n",
       "21  Marina Del Rey, California       1.0       1            X       Matt   \n",
       "22            Honolulu, Hawaii       1.0       1            X       Paul   \n",
       "23       Long Island, New York       1.0       1            X      Peter   \n",
       "24      Sacramento, California       1.0       1            X      Wayne   \n",
       "25          New York, New York       NaN       2            M        Ian   \n",
       "26          Friendswood, Texas       8.0       2            H    Matthew   \n",
       "27           Buffalo, New York       6.0       2            X       Chad   \n",
       "28               Aubrey, Texas       5.0       2            X      Lanny   \n",
       "29      Grand Rapids, Michigan       4.0       2            X       Brad   \n",
       "\n",
       "     CONTESTANT  \n",
       "0      1_RYAN_S  \n",
       "1   1_CHARLIE_M  \n",
       "2      1_RUSS_X  \n",
       "3      1_GREG_T  \n",
       "4       1_BOB_G  \n",
       "5     1_JAMIE_B  \n",
       "6      1_MIKE_X  \n",
       "7       1_ROB_X  \n",
       "8     1_BRIAN_C  \n",
       "9     1_BRIAN_S  \n",
       "10    1_BROOK_X  \n",
       "11     1_JACK_X  \n",
       "12     1_JEFF_X  \n",
       "13     1_JOSH_X  \n",
       "14    1_BRIAN_H  \n",
       "15    1_BILLY_X  \n",
       "16    1_BRIAN_K  \n",
       "17    1_CHRIS_X  \n",
       "18    1_DUANE_X  \n",
       "19     1_ERIC_X  \n",
       "20    1_GREGG_H  \n",
       "21     1_MATT_X  \n",
       "22     1_PAUL_X  \n",
       "23    1_PETER_X  \n",
       "24    1_WAYNE_X  \n",
       "25      2_IAN_M  \n",
       "26  2_MATTHEW_H  \n",
       "27     2_CHAD_X  \n",
       "28    2_LANNY_X  \n",
       "29     2_BRAD_X  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add season number to front of each contestant name\n",
    "\n",
    "df['CONTESTANT'] = df['Season'].map(str) + '_' + df['CONTESTANT']\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>CONTESTANT</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>ELIMINATION-4</th>\n",
       "      <th>ELIMINATION-5</th>\n",
       "      <th>ELIMINATION-6</th>\n",
       "      <th>ELIMINATION-7</th>\n",
       "      <th>ELIMINATION-8</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1</td>\n",
       "      <td>01_RYAN_S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1</td>\n",
       "      <td>01_CHARLIE_M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1</td>\n",
       "      <td>01_RUSS_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1</td>\n",
       "      <td>01_GREG_T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BOB_G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1</td>\n",
       "      <td>01_JAMIE_B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1</td>\n",
       "      <td>01_MIKE_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1</td>\n",
       "      <td>01_ROB_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BRIAN_C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BRIAN_S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BROOK_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1</td>\n",
       "      <td>01_JACK_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>1</td>\n",
       "      <td>01_JEFF_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>01_JOSH_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BRIAN_H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BILLY_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1</td>\n",
       "      <td>01_BRIAN_K</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1</td>\n",
       "      <td>01_CHRIS_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1</td>\n",
       "      <td>01_DUANE_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1</td>\n",
       "      <td>01_ERIC_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1</td>\n",
       "      <td>01_GREGG_H</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1</td>\n",
       "      <td>01_MATT_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>1</td>\n",
       "      <td>01_PAUL_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1</td>\n",
       "      <td>01_PETER_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1</td>\n",
       "      <td>01_WAYNE_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEASON    CONTESTANT ELIMINATION-1 ELIMINATION-2 ELIMINATION-3  \\\n",
       "257       1     01_RYAN_S           NaN           NaN           NaN   \n",
       "258       1  01_CHARLIE_M           NaN           NaN           NaN   \n",
       "259       1     01_RUSS_X           NaN           NaN           NaN   \n",
       "260       1     01_GREG_T           NaN           NaN           NaN   \n",
       "261       1      01_BOB_G           NaN           NaN             E   \n",
       "262       1    01_JAMIE_B           NaN           NaN             E   \n",
       "263       1     01_MIKE_X           NaN           NaN             E   \n",
       "264       1      01_ROB_X           NaN           NaN             E   \n",
       "265       1    01_BRIAN_C           NaN             E           NaN   \n",
       "266       1    01_BRIAN_S           NaN             E           NaN   \n",
       "267       1    01_BROOK_X           NaN             E           NaN   \n",
       "268       1     01_JACK_X           NaN             E           NaN   \n",
       "269       1     01_JEFF_X           NaN             E           NaN   \n",
       "270       1     01_JOSH_X           NaN             E           NaN   \n",
       "271       1    01_BRIAN_H           NaN            EQ           NaN   \n",
       "272       1    01_BILLY_X             E           NaN           NaN   \n",
       "273       1    01_BRIAN_K             E           NaN           NaN   \n",
       "274       1    01_CHRIS_X             E           NaN           NaN   \n",
       "275       1    01_DUANE_X             E           NaN           NaN   \n",
       "276       1     01_ERIC_X             E           NaN           NaN   \n",
       "277       1    01_GREGG_H             E           NaN           NaN   \n",
       "278       1     01_MATT_X             E           NaN           NaN   \n",
       "279       1     01_PAUL_X             E           NaN           NaN   \n",
       "280       1    01_PETER_X             E           NaN           NaN   \n",
       "281       1    01_WAYNE_X             E           NaN           NaN   \n",
       "\n",
       "    ELIMINATION-4 ELIMINATION-5 ELIMINATION-6 ELIMINATION-7 ELIMINATION-8  \\\n",
       "257           NaN           NaN             W           NaN           NaN   \n",
       "258           NaN           NaN             E           NaN           NaN   \n",
       "259           NaN             E           NaN           NaN           NaN   \n",
       "260             E           NaN           NaN           NaN           NaN   \n",
       "261           NaN           NaN           NaN           NaN           NaN   \n",
       "262           NaN           NaN           NaN           NaN           NaN   \n",
       "263           NaN           NaN           NaN           NaN           NaN   \n",
       "264           NaN           NaN           NaN           NaN           NaN   \n",
       "265           NaN           NaN           NaN           NaN           NaN   \n",
       "266           NaN           NaN           NaN           NaN           NaN   \n",
       "267           NaN           NaN           NaN           NaN           NaN   \n",
       "268           NaN           NaN           NaN           NaN           NaN   \n",
       "269           NaN           NaN           NaN           NaN           NaN   \n",
       "270           NaN           NaN           NaN           NaN           NaN   \n",
       "271           NaN           NaN           NaN           NaN           NaN   \n",
       "272           NaN           NaN           NaN           NaN           NaN   \n",
       "273           NaN           NaN           NaN           NaN           NaN   \n",
       "274           NaN           NaN           NaN           NaN           NaN   \n",
       "275           NaN           NaN           NaN           NaN           NaN   \n",
       "276           NaN           NaN           NaN           NaN           NaN   \n",
       "277           NaN           NaN           NaN           NaN           NaN   \n",
       "278           NaN           NaN           NaN           NaN           NaN   \n",
       "279           NaN           NaN           NaN           NaN           NaN   \n",
       "280           NaN           NaN           NaN           NaN           NaN   \n",
       "281           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "     ... DATES-1 DATES-2  DATES-3 DATES-4 DATES-5 DATES-6 DATES-7 DATES-8  \\\n",
       "257  ...     NaN     D14       D1      D1      D1      D1     NaN     NaN   \n",
       "258  ...     NaN     D14       D1      D1      D1      D1     NaN     NaN   \n",
       "259  ...     NaN     D14       D1      D1      D1     NaN     NaN     NaN   \n",
       "260  ...     NaN     D14       D5      D1     NaN     NaN     NaN     NaN   \n",
       "261  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "262  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "263  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "264  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "265  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "266  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "267  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "268  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "269  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "270  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "271  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "272  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "273  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "274  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "275  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "276  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "277  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "278  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "279  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "280  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "281  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "    DATES-9 DATES-10  \n",
       "257     NaN      NaN  \n",
       "258     NaN      NaN  \n",
       "259     NaN      NaN  \n",
       "260     NaN      NaN  \n",
       "261     NaN      NaN  \n",
       "262     NaN      NaN  \n",
       "263     NaN      NaN  \n",
       "264     NaN      NaN  \n",
       "265     NaN      NaN  \n",
       "266     NaN      NaN  \n",
       "267     NaN      NaN  \n",
       "268     NaN      NaN  \n",
       "269     NaN      NaN  \n",
       "270     NaN      NaN  \n",
       "271     NaN      NaN  \n",
       "272     NaN      NaN  \n",
       "273     NaN      NaN  \n",
       "274     NaN      NaN  \n",
       "275     NaN      NaN  \n",
       "276     NaN      NaN  \n",
       "277     NaN      NaN  \n",
       "278     NaN      NaN  \n",
       "279     NaN      NaN  \n",
       "280     NaN      NaN  \n",
       "281     NaN      NaN  \n",
       "\n",
       "[25 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join[join['SEASON'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need to take the '0's out of join.CONTESTANT to make both dataframes match before I join them."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "04_JONATHAN_K\n",
    "04_LUKE_H\n",
    "04_PATRICK_C\n",
    "04_SPERO_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>CONTESTANT</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>ELIMINATION-4</th>\n",
       "      <th>ELIMINATION-5</th>\n",
       "      <th>ELIMINATION-6</th>\n",
       "      <th>ELIMINATION-7</th>\n",
       "      <th>ELIMINATION-8</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>12_JORDAN_R</td>\n",
       "      <td>R1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D6</td>\n",
       "      <td>D12</td>\n",
       "      <td>D11</td>\n",
       "      <td>D1</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>12_ROBBY_H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D3</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>12_CHASE_M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D1</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>12_LUKE_P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D1</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12_JAMES_T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D6</td>\n",
       "      <td>D1</td>\n",
       "      <td>D11</td>\n",
       "      <td>D9</td>\n",
       "      <td>D5</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEASON   CONTESTANT ELIMINATION-1 ELIMINATION-2 ELIMINATION-3  \\\n",
       "0      12  12_JORDAN_R            R1           NaN           NaN   \n",
       "1      12   12_ROBBY_H           NaN           NaN           NaN   \n",
       "2      12   12_CHASE_M           NaN           NaN             R   \n",
       "3      12    12_LUKE_P           NaN           NaN           NaN   \n",
       "4      12   12_JAMES_T           NaN             R             R   \n",
       "\n",
       "  ELIMINATION-4 ELIMINATION-5 ELIMINATION-6 ELIMINATION-7 ELIMINATION-8  ...  \\\n",
       "0             R             R           NaN           NaN           NaN  ...   \n",
       "1           NaN             R           NaN             R           NaN  ...   \n",
       "2           NaN           NaN             R           NaN           NaN  ...   \n",
       "3             R           NaN             R           NaN             E  ...   \n",
       "4           NaN           NaN           NaN             E           NaN  ...   \n",
       "\n",
       "  DATES-1 DATES-2  DATES-3 DATES-4 DATES-5 DATES-6 DATES-7 DATES-8 DATES-9  \\\n",
       "0     NaN      D6      D12     D11      D1      D5      D1      D1      D1   \n",
       "1     NaN     D10      NaN     D11      D9      D5      D3      D1      D1   \n",
       "2     NaN     NaN       D1     D11      D9      D2      D3      D1      D1   \n",
       "3     NaN     D10      NaN      D1      D9      D5      D1      D1     NaN   \n",
       "4     NaN      D6       D1     D11      D9      D5      D3     NaN     NaN   \n",
       "\n",
       "  DATES-10  \n",
       "0       D1  \n",
       "1       D1  \n",
       "2      NaN  \n",
       "3      NaN  \n",
       "4      NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace the matching strings \n",
    "join.CONTESTANT = join.CONTESTANT.replace(to_replace ='^0', value = '', regex = True) \n",
    "  \n",
    "# Print the updated dataframe \n",
    "join.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>CONTESTANT</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>ELIMINATION-4</th>\n",
       "      <th>ELIMINATION-5</th>\n",
       "      <th>ELIMINATION-6</th>\n",
       "      <th>ELIMINATION-7</th>\n",
       "      <th>ELIMINATION-8</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1</td>\n",
       "      <td>1_RYAN_S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1</td>\n",
       "      <td>1_CHARLIE_M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1</td>\n",
       "      <td>1_RUSS_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1</td>\n",
       "      <td>1_GREG_T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BOB_G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1</td>\n",
       "      <td>1_JAMIE_B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1</td>\n",
       "      <td>1_MIKE_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1</td>\n",
       "      <td>1_ROB_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BRIAN_C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BRIAN_S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BROOK_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1</td>\n",
       "      <td>1_JACK_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>1</td>\n",
       "      <td>1_JEFF_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>1_JOSH_X</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BRIAN_H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BILLY_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1</td>\n",
       "      <td>1_BRIAN_K</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1</td>\n",
       "      <td>1_CHRIS_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1</td>\n",
       "      <td>1_DUANE_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1</td>\n",
       "      <td>1_ERIC_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1</td>\n",
       "      <td>1_GREGG_H</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1</td>\n",
       "      <td>1_MATT_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>1</td>\n",
       "      <td>1_PAUL_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1</td>\n",
       "      <td>1_PETER_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1</td>\n",
       "      <td>1_WAYNE_X</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEASON   CONTESTANT ELIMINATION-1 ELIMINATION-2 ELIMINATION-3  \\\n",
       "257       1     1_RYAN_S           NaN           NaN           NaN   \n",
       "258       1  1_CHARLIE_M           NaN           NaN           NaN   \n",
       "259       1     1_RUSS_X           NaN           NaN           NaN   \n",
       "260       1     1_GREG_T           NaN           NaN           NaN   \n",
       "261       1      1_BOB_G           NaN           NaN             E   \n",
       "262       1    1_JAMIE_B           NaN           NaN             E   \n",
       "263       1     1_MIKE_X           NaN           NaN             E   \n",
       "264       1      1_ROB_X           NaN           NaN             E   \n",
       "265       1    1_BRIAN_C           NaN             E           NaN   \n",
       "266       1    1_BRIAN_S           NaN             E           NaN   \n",
       "267       1    1_BROOK_X           NaN             E           NaN   \n",
       "268       1     1_JACK_X           NaN             E           NaN   \n",
       "269       1     1_JEFF_X           NaN             E           NaN   \n",
       "270       1     1_JOSH_X           NaN             E           NaN   \n",
       "271       1    1_BRIAN_H           NaN            EQ           NaN   \n",
       "272       1    1_BILLY_X             E           NaN           NaN   \n",
       "273       1    1_BRIAN_K             E           NaN           NaN   \n",
       "274       1    1_CHRIS_X             E           NaN           NaN   \n",
       "275       1    1_DUANE_X             E           NaN           NaN   \n",
       "276       1     1_ERIC_X             E           NaN           NaN   \n",
       "277       1    1_GREGG_H             E           NaN           NaN   \n",
       "278       1     1_MATT_X             E           NaN           NaN   \n",
       "279       1     1_PAUL_X             E           NaN           NaN   \n",
       "280       1    1_PETER_X             E           NaN           NaN   \n",
       "281       1    1_WAYNE_X             E           NaN           NaN   \n",
       "\n",
       "    ELIMINATION-4 ELIMINATION-5 ELIMINATION-6 ELIMINATION-7 ELIMINATION-8  \\\n",
       "257           NaN           NaN             W           NaN           NaN   \n",
       "258           NaN           NaN             E           NaN           NaN   \n",
       "259           NaN             E           NaN           NaN           NaN   \n",
       "260             E           NaN           NaN           NaN           NaN   \n",
       "261           NaN           NaN           NaN           NaN           NaN   \n",
       "262           NaN           NaN           NaN           NaN           NaN   \n",
       "263           NaN           NaN           NaN           NaN           NaN   \n",
       "264           NaN           NaN           NaN           NaN           NaN   \n",
       "265           NaN           NaN           NaN           NaN           NaN   \n",
       "266           NaN           NaN           NaN           NaN           NaN   \n",
       "267           NaN           NaN           NaN           NaN           NaN   \n",
       "268           NaN           NaN           NaN           NaN           NaN   \n",
       "269           NaN           NaN           NaN           NaN           NaN   \n",
       "270           NaN           NaN           NaN           NaN           NaN   \n",
       "271           NaN           NaN           NaN           NaN           NaN   \n",
       "272           NaN           NaN           NaN           NaN           NaN   \n",
       "273           NaN           NaN           NaN           NaN           NaN   \n",
       "274           NaN           NaN           NaN           NaN           NaN   \n",
       "275           NaN           NaN           NaN           NaN           NaN   \n",
       "276           NaN           NaN           NaN           NaN           NaN   \n",
       "277           NaN           NaN           NaN           NaN           NaN   \n",
       "278           NaN           NaN           NaN           NaN           NaN   \n",
       "279           NaN           NaN           NaN           NaN           NaN   \n",
       "280           NaN           NaN           NaN           NaN           NaN   \n",
       "281           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "     ... DATES-1 DATES-2  DATES-3 DATES-4 DATES-5 DATES-6 DATES-7 DATES-8  \\\n",
       "257  ...     NaN     D14       D1      D1      D1      D1     NaN     NaN   \n",
       "258  ...     NaN     D14       D1      D1      D1      D1     NaN     NaN   \n",
       "259  ...     NaN     D14       D1      D1      D1     NaN     NaN     NaN   \n",
       "260  ...     NaN     D14       D5      D1     NaN     NaN     NaN     NaN   \n",
       "261  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "262  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "263  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "264  ...     NaN     D14       D5     NaN     NaN     NaN     NaN     NaN   \n",
       "265  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "266  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "267  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "268  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "269  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "270  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "271  ...     NaN     D14      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "272  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "273  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "274  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "275  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "276  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "277  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "278  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "279  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "280  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "281  ...     NaN     NaN      NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "    DATES-9 DATES-10  \n",
       "257     NaN      NaN  \n",
       "258     NaN      NaN  \n",
       "259     NaN      NaN  \n",
       "260     NaN      NaN  \n",
       "261     NaN      NaN  \n",
       "262     NaN      NaN  \n",
       "263     NaN      NaN  \n",
       "264     NaN      NaN  \n",
       "265     NaN      NaN  \n",
       "266     NaN      NaN  \n",
       "267     NaN      NaN  \n",
       "268     NaN      NaN  \n",
       "269     NaN      NaN  \n",
       "270     NaN      NaN  \n",
       "271     NaN      NaN  \n",
       "272     NaN      NaN  \n",
       "273     NaN      NaN  \n",
       "274     NaN      NaN  \n",
       "275     NaN      NaN  \n",
       "276     NaN      NaN  \n",
       "277     NaN      NaN  \n",
       "278     NaN      NaN  \n",
       "279     NaN      NaN  \n",
       "280     NaN      NaN  \n",
       "281     NaN      NaN  \n",
       "\n",
       "[25 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join[join['SEASON'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join the two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>last_initial</th>\n",
       "      <th>first_name</th>\n",
       "      <th>CONTESTANT</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>1_RYAN_S</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>1_CHARLIE_M</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>Russ</td>\n",
       "      <td>1_RUSS_X</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>Greg</td>\n",
       "      <td>1_GREG_T</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>Bob</td>\n",
       "      <td>1_BOB_G</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Jake Patton</td>\n",
       "      <td>26</td>\n",
       "      <td>Landscape Architect</td>\n",
       "      <td>Playa Vista, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>P</td>\n",
       "      <td>Jake</td>\n",
       "      <td>12_JAKE_P</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Jonathan Hamilton</td>\n",
       "      <td>29</td>\n",
       "      <td>Technical Sales Rep</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>H</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>12_JONATHAN_H</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Nick Sharp</td>\n",
       "      <td>26</td>\n",
       "      <td>Software Salesman</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>S</td>\n",
       "      <td>Nick</td>\n",
       "      <td>12_NICK_S</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Peter Medina</td>\n",
       "      <td>26</td>\n",
       "      <td>Staffing Agency Manager</td>\n",
       "      <td>Rockdale, Illinois</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>Peter</td>\n",
       "      <td>12_PETER_M</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Sal DeJulio</td>\n",
       "      <td>28</td>\n",
       "      <td>Operations Manager</td>\n",
       "      <td>Hubbard, Ohio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>D</td>\n",
       "      <td>Sal</td>\n",
       "      <td>12_SAL_D</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Age               Occupation  \\\n",
       "0          Ryan Sutter   29              Firefighter   \n",
       "1        Charlie Maher   28        Financial Analyst   \n",
       "2                 Russ   30                   Writer   \n",
       "3              Greg T.   28                 Importer   \n",
       "4           Bob Guiney   31          Mortgage Broker   \n",
       "..                 ...  ...                      ...   \n",
       "277        Jake Patton   26      Landscape Architect   \n",
       "278  Jonathan Hamilton   29      Technical Sales Rep   \n",
       "279         Nick Sharp   26        Software Salesman   \n",
       "280       Peter Medina   26  Staffing Agency Manager   \n",
       "281        Sal DeJulio   28       Operations Manager   \n",
       "\n",
       "                        Hometown  ElimWeek  Season last_initial first_name  \\\n",
       "0                 Vail, Colorado       NaN       1            S       Ryan   \n",
       "1      Hermosa Beach, California       6.0       1            M    Charlie   \n",
       "2         San Rafael, California       5.0       1            X       Russ   \n",
       "3            Manhattan, New York       4.0       1            T       Greg   \n",
       "4             Ferndale, Michigan       3.0       1            G        Bob   \n",
       "..                           ...       ...     ...          ...        ...   \n",
       "277      Playa Vista, California       1.0      12            P       Jake   \n",
       "278  Vancouver, British Columbia       1.0      12            H   Jonathan   \n",
       "279    San Francisco, California       1.0      12            S       Nick   \n",
       "280           Rockdale, Illinois       1.0      12            M      Peter   \n",
       "281                Hubbard, Ohio       1.0      12            D        Sal   \n",
       "\n",
       "        CONTESTANT  SEASON  ... DATES-1 DATES-2 DATES-3 DATES-4 DATES-5  \\\n",
       "0         1_RYAN_S       1  ...     NaN     D14      D1      D1      D1   \n",
       "1      1_CHARLIE_M       1  ...     NaN     D14      D1      D1      D1   \n",
       "2         1_RUSS_X       1  ...     NaN     D14      D1      D1      D1   \n",
       "3         1_GREG_T       1  ...     NaN     D14      D5      D1     NaN   \n",
       "4          1_BOB_G       1  ...     NaN     D14      D5     NaN     NaN   \n",
       "..             ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "277      12_JAKE_P      12  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "278  12_JONATHAN_H      12  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "279      12_NICK_S      12  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "280     12_PETER_M      12  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "281       12_SAL_D      12  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "    DATES-6 DATES-7 DATES-8 DATES-9 DATES-10  \n",
       "0        D1     NaN     NaN     NaN      NaN  \n",
       "1        D1     NaN     NaN     NaN      NaN  \n",
       "2       NaN     NaN     NaN     NaN      NaN  \n",
       "3       NaN     NaN     NaN     NaN      NaN  \n",
       "4       NaN     NaN     NaN     NaN      NaN  \n",
       "..      ...     ...     ...     ...      ...  \n",
       "277     NaN     NaN     NaN     NaN      NaN  \n",
       "278     NaN     NaN     NaN     NaN      NaN  \n",
       "279     NaN     NaN     NaN     NaN      NaN  \n",
       "280     NaN     NaN     NaN     NaN      NaN  \n",
       "281     NaN     NaN     NaN     NaN      NaN  \n",
       "\n",
       "[264 rows x 30 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.join(join.set_index('CONTESTANT'), on='CONTESTANT', how='inner')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(282, 9)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(282, 22)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(264, 30)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I only lost 18 rows on the inner join, so I feel good enough to move on through the pipeline with this data. First I will tidy the data a bit further so it's ready to explore and model with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop unnecessary features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Occupation', 'Hometown', 'ElimWeek', 'Season',\n",
       "       'last_initial', 'first_name', 'CONTESTANT', 'SEASON', 'ELIMINATION-1',\n",
       "       'ELIMINATION-2', 'ELIMINATION-3', 'ELIMINATION-4', 'ELIMINATION-5',\n",
       "       'ELIMINATION-6', 'ELIMINATION-7', 'ELIMINATION-8', 'ELIMINATION-9',\n",
       "       'ELIMINATION-10', 'DATES-1', 'DATES-2', 'DATES-3', 'DATES-4', 'DATES-5',\n",
       "       'DATES-6', 'DATES-7', 'DATES-8', 'DATES-9', 'DATES-10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.drop(columns=['last_initial', 'first_name', 'SEASON'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.set_index('CONTESTANT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>ELIMINATION-4</th>\n",
       "      <th>...</th>\n",
       "      <th>DATES-1</th>\n",
       "      <th>DATES-2</th>\n",
       "      <th>DATES-3</th>\n",
       "      <th>DATES-4</th>\n",
       "      <th>DATES-5</th>\n",
       "      <th>DATES-6</th>\n",
       "      <th>DATES-7</th>\n",
       "      <th>DATES-8</th>\n",
       "      <th>DATES-9</th>\n",
       "      <th>DATES-10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTESTANT</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1_RYAN_S</th>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_CHARLIE_M</th>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_RUSS_X</th>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_GREG_T</th>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_BOB_G</th>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  Age         Occupation                   Hometown  \\\n",
       "CONTESTANT                                                                      \n",
       "1_RYAN_S       Ryan Sutter   29        Firefighter             Vail, Colorado   \n",
       "1_CHARLIE_M  Charlie Maher   28  Financial Analyst  Hermosa Beach, California   \n",
       "1_RUSS_X              Russ   30             Writer     San Rafael, California   \n",
       "1_GREG_T           Greg T.   28           Importer        Manhattan, New York   \n",
       "1_BOB_G         Bob Guiney   31    Mortgage Broker         Ferndale, Michigan   \n",
       "\n",
       "             ElimWeek  Season ELIMINATION-1 ELIMINATION-2 ELIMINATION-3  \\\n",
       "CONTESTANT                                                                \n",
       "1_RYAN_S          NaN       1           NaN           NaN           NaN   \n",
       "1_CHARLIE_M       6.0       1           NaN           NaN           NaN   \n",
       "1_RUSS_X          5.0       1           NaN           NaN           NaN   \n",
       "1_GREG_T          4.0       1           NaN           NaN           NaN   \n",
       "1_BOB_G           3.0       1           NaN           NaN             E   \n",
       "\n",
       "            ELIMINATION-4  ... DATES-1 DATES-2 DATES-3 DATES-4 DATES-5  \\\n",
       "CONTESTANT                 ...                                           \n",
       "1_RYAN_S              NaN  ...     NaN     D14      D1      D1      D1   \n",
       "1_CHARLIE_M           NaN  ...     NaN     D14      D1      D1      D1   \n",
       "1_RUSS_X              NaN  ...     NaN     D14      D1      D1      D1   \n",
       "1_GREG_T                E  ...     NaN     D14      D5      D1     NaN   \n",
       "1_BOB_G               NaN  ...     NaN     D14      D5     NaN     NaN   \n",
       "\n",
       "            DATES-6  DATES-7 DATES-8 DATES-9 DATES-10  \n",
       "CONTESTANT                                             \n",
       "1_RYAN_S         D1      NaN     NaN     NaN      NaN  \n",
       "1_CHARLIE_M      D1      NaN     NaN     NaN      NaN  \n",
       "1_RUSS_X        NaN      NaN     NaN     NaN      NaN  \n",
       "1_GREG_T        NaN      NaN     NaN     NaN      NaN  \n",
       "1_BOB_G         NaN      NaN     NaN     NaN      NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will write this df to disk, put the code from this notebook into a prepare.py file, and move on to the preprocessing stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.to_csv('joined_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>CONTESTANT</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>ElimWeek</th>\n",
       "      <th>Season</th>\n",
       "      <th>ELIMINATION-1</th>\n",
       "      <th>ELIMINATION-2</th>\n",
       "      <th>ELIMINATION-3</th>\n",
       "      <th>...</th>\n",
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       "      <th>DATES-6</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_RYAN_S</td>\n",
       "      <td>Ryan Sutter</td>\n",
       "      <td>29</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Vail, Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_CHARLIE_M</td>\n",
       "      <td>Charlie Maher</td>\n",
       "      <td>28</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Hermosa Beach, California</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_RUSS_X</td>\n",
       "      <td>Russ</td>\n",
       "      <td>30</td>\n",
       "      <td>Writer</td>\n",
       "      <td>San Rafael, California</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_GREG_T</td>\n",
       "      <td>Greg T.</td>\n",
       "      <td>28</td>\n",
       "      <td>Importer</td>\n",
       "      <td>Manhattan, New York</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1_BOB_G</td>\n",
       "      <td>Bob Guiney</td>\n",
       "      <td>31</td>\n",
       "      <td>Mortgage Broker</td>\n",
       "      <td>Ferndale, Michigan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D14</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>12_JAKE_P</td>\n",
       "      <td>Jake Patton</td>\n",
       "      <td>26</td>\n",
       "      <td>Landscape Architect</td>\n",
       "      <td>Playa Vista, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>12_JONATHAN_H</td>\n",
       "      <td>Jonathan Hamilton</td>\n",
       "      <td>29</td>\n",
       "      <td>Technical Sales Rep</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>12_NICK_S</td>\n",
       "      <td>Nick Sharp</td>\n",
       "      <td>26</td>\n",
       "      <td>Software Salesman</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>12_PETER_M</td>\n",
       "      <td>Peter Medina</td>\n",
       "      <td>26</td>\n",
       "      <td>Staffing Agency Manager</td>\n",
       "      <td>Rockdale, Illinois</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>12_SAL_D</td>\n",
       "      <td>Sal DeJulio</td>\n",
       "      <td>28</td>\n",
       "      <td>Operations Manager</td>\n",
       "      <td>Hubbard, Ohio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CONTESTANT               Name  Age               Occupation  \\\n",
       "0         1_RYAN_S        Ryan Sutter   29              Firefighter   \n",
       "1      1_CHARLIE_M      Charlie Maher   28        Financial Analyst   \n",
       "2         1_RUSS_X               Russ   30                   Writer   \n",
       "3         1_GREG_T            Greg T.   28                 Importer   \n",
       "4          1_BOB_G         Bob Guiney   31          Mortgage Broker   \n",
       "..             ...                ...  ...                      ...   \n",
       "259      12_JAKE_P        Jake Patton   26      Landscape Architect   \n",
       "260  12_JONATHAN_H  Jonathan Hamilton   29      Technical Sales Rep   \n",
       "261      12_NICK_S         Nick Sharp   26        Software Salesman   \n",
       "262     12_PETER_M       Peter Medina   26  Staffing Agency Manager   \n",
       "263       12_SAL_D        Sal DeJulio   28       Operations Manager   \n",
       "\n",
       "                        Hometown  ElimWeek  Season ELIMINATION-1  \\\n",
       "0                 Vail, Colorado       NaN       1           NaN   \n",
       "1      Hermosa Beach, California       6.0       1           NaN   \n",
       "2         San Rafael, California       5.0       1           NaN   \n",
       "3            Manhattan, New York       4.0       1           NaN   \n",
       "4             Ferndale, Michigan       3.0       1           NaN   \n",
       "..                           ...       ...     ...           ...   \n",
       "259      Playa Vista, California       1.0      12             E   \n",
       "260  Vancouver, British Columbia       1.0      12             E   \n",
       "261    San Francisco, California       1.0      12             E   \n",
       "262           Rockdale, Illinois       1.0      12             E   \n",
       "263                Hubbard, Ohio       1.0      12             E   \n",
       "\n",
       "    ELIMINATION-2 ELIMINATION-3  ... DATES-1 DATES-2 DATES-3 DATES-4 DATES-5  \\\n",
       "0             NaN           NaN  ...     NaN     D14      D1      D1      D1   \n",
       "1             NaN           NaN  ...     NaN     D14      D1      D1      D1   \n",
       "2             NaN           NaN  ...     NaN     D14      D1      D1      D1   \n",
       "3             NaN           NaN  ...     NaN     D14      D5      D1     NaN   \n",
       "4             NaN             E  ...     NaN     D14      D5     NaN     NaN   \n",
       "..            ...           ...  ...     ...     ...     ...     ...     ...   \n",
       "259           NaN           NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "260           NaN           NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "261           NaN           NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "262           NaN           NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "263           NaN           NaN  ...     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "    DATES-6 DATES-7  DATES-8 DATES-9 DATES-10  \n",
       "0        D1     NaN      NaN     NaN      NaN  \n",
       "1        D1     NaN      NaN     NaN      NaN  \n",
       "2       NaN     NaN      NaN     NaN      NaN  \n",
       "3       NaN     NaN      NaN     NaN      NaN  \n",
       "4       NaN     NaN      NaN     NaN      NaN  \n",
       "..      ...     ...      ...     ...      ...  \n",
       "259     NaN     NaN      NaN     NaN      NaN  \n",
       "260     NaN     NaN      NaN     NaN      NaN  \n",
       "261     NaN     NaN      NaN     NaN      NaN  \n",
       "262     NaN     NaN      NaN     NaN      NaN  \n",
       "263     NaN     NaN      NaN     NaN      NaN  \n",
       "\n",
       "[264 rows x 27 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doublecheck = pd.read_csv('joined_df.csv')\n",
    "doublecheck"
   ]
  }
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