NBA Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='players.csv' mode='r' encoding='UTF-8'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open('players.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Shake Milton</td>\n",
       "      <td>SG</td>\n",
       "      <td>Philadelphia Sixers</td>\n",
       "      <td>24</td>\n",
       "      <td>6' 5\"</td>\n",
       "      <td>6.5</td>\n",
       "      <td>205</td>\n",
       "      <td>SMU</td>\n",
       "      <td>1846738.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Position                 Team  Age Height  Height_i  Weight  \\\n",
       "76  Shake Milton       SG  Philadelphia Sixers   24  6' 5\"       6.5     205   \n",
       "\n",
       "   College     Salary  Points  Rebounds  Assists  \n",
       "76     SMU  1846738.0    13.0       2.3      3.1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv('players.csv', encoding=\"UTF-8\")\n",
    "nba.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(558, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning: From the 558 samples, and the 12 columns of information, some of it is unnecesary, redundant or not correctly well expressed. The first  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Bruno Fernando\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"LaMarcus Aldridge\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Evan Fournier\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Aaron Henry\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Georges Niang\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Grant Riller\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Precious Achiuwa\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Isaac Bonga\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Sam Dekker\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Lamar Stevens\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Brodric Thomas\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Andre Iguodala\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Kendrick Nunn\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"JaQuori McLaughlin\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Eugene Omoruyi\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Matthew Hurt\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Jarrett Culver\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Juancho Hernangomez\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Yves Pons\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Devonte' Graham\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Nathan Knight\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Charlie Brown Jr.\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Vit Krejci\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Trendon Watford\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Eric Paschall\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Ricky Rubio\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Chris Smith\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"DeJon Jarreau\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"George Hill\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Marcus Garrett\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"D.J. Stewart Jr.\"]\n",
    "row.at[row.index[0], \"Position\"] = \"SG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Jeff Dowtin\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Hassani Gravett\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Jordan Bell\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PF\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = nba[nba['Name'] == \"Chris Chiozza\"]\n",
    "row.at[row.index[0], \"Position\"] = \"PG\"\n",
    "nba.update(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SG    138\n",
       "SF    114\n",
       "PG    111\n",
       "PF    107\n",
       "C      88\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.Position.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the Position column is cleared and based in just 5 values, I dropped the Height column (it is repeated in a worse format than Height_i as it is not numeric) and checked if there is any Player Name that is repeated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba = nba.drop(columns=[\"Height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "People found with the same name\n"
     ]
    }
   ],
   "source": [
    "\n",
    "duplicates = nba[nba.duplicated(subset=[\"Name\"], keep=False)]\n",
    "\n",
    "if duplicates.empty:\n",
    "    print(\"No duplicate names in the nba DataFrame!\")\n",
    "else:\n",
    "    print(\"People found with the same name\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Charlie Brown Jr.</td>\n",
       "      <td>SF</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>Saint Joseph's</td>\n",
       "      <td>1701593.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Charlie Brown Jr.</td>\n",
       "      <td>SF</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>Saint Joseph's</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Position                   Team   Age  Height_i  \\\n",
       "502  Charlie Brown Jr.       SF  Oklahoma City Thunder  24.0       6.6   \n",
       "503  Charlie Brown Jr.       SF  Oklahoma City Thunder  24.0       6.6   \n",
       "\n",
       "     Weight         College     Salary  Points  Rebounds  Assists  \n",
       "502   199.0  Saint Joseph's  1701593.0     4.4       1.9      1.0  \n",
       "503   199.0  Saint Joseph's        NaN     4.4       1.9      1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicates = nba[nba.duplicated(subset='Name', keep=False)]\n",
    "duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba = nba.drop(index=503)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No duplicate names in the nba DataFrame!\n"
     ]
    }
   ],
   "source": [
    "duplicates = nba[nba.duplicated(subset=[\"Name\"], keep=False)]\n",
    "\n",
    "if duplicates.empty:\n",
    "    print(\"No duplicate names in the nba DataFrame!\")\n",
    "else:\n",
    "    print(\"People found with the same name\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to add a new column with numeric values related with position in the court"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>NumPosition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Josh Green</td>\n",
       "      <td>SG</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>2957520.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Cameron Payne</td>\n",
       "      <td>PG</td>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Murray State</td>\n",
       "      <td>6500000.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Daniel Gafford</td>\n",
       "      <td>C</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1782621.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Nate Darling</td>\n",
       "      <td>SG</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Isaiah Stewart</td>\n",
       "      <td>C</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>3277080.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name Position                Team   Age  Height_i  Weight  \\\n",
       "380      Josh Green       SG    Dallas Mavericks  20.0       6.5   200.0   \n",
       "349   Cameron Payne       PG        Phoenix Suns  27.0       6.1   183.0   \n",
       "272  Daniel Gafford        C  Washington Wizards  22.0       6.1   234.0   \n",
       "212    Nate Darling       SG   Charlotte Hornets  23.0       6.6   200.0   \n",
       "151  Isaiah Stewart        C     Detroit Pistons  20.0       6.8   250.0   \n",
       "\n",
       "          College     Salary  Points  Rebounds  Assists  NumPosition  \n",
       "380       Arizona  2957520.0     2.6       2.0      0.7            2  \n",
       "349  Murray State  6500000.0     8.4       2.4      3.6            1  \n",
       "272      Arkansas  1782621.0     7.0       4.3      0.5            5  \n",
       "212      Delaware        NaN     1.3       0.1      0.1            2  \n",
       "151    Washington  3277080.0     7.9       6.7      0.9            5  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position = {\"PG\": 1, \"SG\": 2, \"SF\": 3, \"PF\": 4, \"C\": 5}\n",
    "nba[\"NumPosition\"] = nba[\"Position\"].map(position)\n",
    "nba.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(557, 12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HYPOTHESIS 0: Playing with the CSV: Can simple stats solve anything?\n",
    "0.1 Scavenger Hunt: Will it be possible to find the candidates of that year of the MVP award just looking at the basic stats? The final candidates were the players Joel Embiid, Nikola Jokic and Giannis Antetokounmpo\n",
    "0.2 Let's find the wealth correlation: Who wins more money in the NBA?\n",
    "0.3 Are the tall players the best playing basketball?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the first group of hypothesis, we must drop the nan values of the columns with quantitative values, as they will be needed for the correlations and else. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(394, 12)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbastats=nba.copy()\n",
    "nbastats.dropna(subset=[\"Salary\", \"Points\", \"Rebounds\", \"Assists\", \"Position\"], inplace=True)\n",
    "nbastats.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scavenger Hunt! Is there possible to find the MVP candidates with the maximum of any of the big stat categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Stephen Curry'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_point = nbastats[\"Points\"].max()\n",
    "shooter = nbastats[nbastats[\"Points\"] == max_point][\"Name\"].values[0]\n",
    "shooter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Russell Westbrook'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_assists = nbastats[\"Assists\"].max()\n",
    "assister = nbastats[nbastats[\"Assists\"] == max_assists][\"Name\"].values[0]\n",
    "assister"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Clint Capela'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_rebounds = nbastats[\"Rebounds\"].max()\n",
    "rebounder = nbastats[nbastats[\"Rebounds\"] == max_rebounds][\"Name\"].values[0]\n",
    "rebounder"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just looking at the total stats we were not lucky, as not a single of the MVP candidates were present in the Top categories! But maybe we will be more lucky if we divide the data by Position of the Field: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cstats = nbastats[nbastats['Position'] == 'C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Nikola Jokic'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_assists = cstats[\"Assists\"].max()\n",
    "cassister = cstats[cstats[\"Assists\"] == c_assists][\"Name\"].values[0]\n",
    "cassister"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Joel Embiid'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_points = cstats[\"Points\"].max()\n",
    "cpointer = cstats[cstats[\"Points\"] == c_points][\"Name\"].values[0]\n",
    "cpointer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pfstats = nbastats[nbastats['Position'] == 'PF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Giannis Antetokounmpo'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_points = pfstats[\"Points\"].max()\n",
    "pfpointer = pfstats[pfstats[\"Points\"] == pf_points][\"Name\"].values[0]\n",
    "pfpointer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Giannis Antetokounmpo'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_rebounds = pfstats[\"Rebounds\"].max()\n",
    "pfrebounder = pfstats[pfstats[\"Rebounds\"] == pf_rebounds][\"Name\"].values[0]\n",
    "pfrebounder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Draymond Green'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_assists = pfstats[\"Assists\"].max()\n",
    "pfassister = pfstats[pfstats[\"Assists\"] == pf_assists][\"Name\"].values[0]\n",
    "pfassister"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the Dataset by the column \"Position\", we were able to find \"absolute\" data that support the candidates to win the MVP Trophy! Nikola Jokic was the Center with most assists, Joel Embiid was the Center with most points and Giannis Antetokounmpo was the Power Forward with both most points and rebounds!\n",
    "Now is time to find the correlation between the salary and the rest of the quantitative variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>NumPosition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.768042</td>\n",
       "      <td>0.470484</td>\n",
       "      <td>0.649531</td>\n",
       "      <td>-0.060419</td>\n",
       "      <td>-0.095712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Points</th>\n",
       "      <td>0.768042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522986</td>\n",
       "      <td>0.705846</td>\n",
       "      <td>-0.100150</td>\n",
       "      <td>-0.165456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rebounds</th>\n",
       "      <td>0.470484</td>\n",
       "      <td>0.522986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297267</td>\n",
       "      <td>0.085435</td>\n",
       "      <td>0.451573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>0.649531</td>\n",
       "      <td>0.705846</td>\n",
       "      <td>0.297267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.234700</td>\n",
       "      <td>-0.430952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height_i</th>\n",
       "      <td>-0.060419</td>\n",
       "      <td>-0.100150</td>\n",
       "      <td>0.085435</td>\n",
       "      <td>-0.234700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.367892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumPosition</th>\n",
       "      <td>-0.095712</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>0.451573</td>\n",
       "      <td>-0.430952</td>\n",
       "      <td>0.367892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Salary    Points  Rebounds   Assists  Height_i  NumPosition\n",
       "Salary       1.000000  0.768042  0.470484  0.649531 -0.060419    -0.095712\n",
       "Points       0.768042  1.000000  0.522986  0.705846 -0.100150    -0.165456\n",
       "Rebounds     0.470484  0.522986  1.000000  0.297267  0.085435     0.451573\n",
       "Assists      0.649531  0.705846  0.297267  1.000000 -0.234700    -0.430952\n",
       "Height_i    -0.060419 -0.100150  0.085435 -0.234700  1.000000     0.367892\n",
       "NumPosition -0.095712 -0.165456  0.451573 -0.430952  0.367892     1.000000"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_table = nbastats[[\"Salary\", \"Points\", \"Rebounds\", \"Assists\",\"Height_i\", \"NumPosition\"]].corr()\n",
    "corr_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.triu(np.ones_like(corr_table, dtype=bool))\n",
    "color_map = sns.diverging_palette(0, 10, as_cmap=True)\n",
    "sns.heatmap(corr_table,  \n",
    "            mask = mask,\n",
    "            cmap=\"viridis\",\n",
    "            square=True, \n",
    "            linewidth=0.5,\n",
    "            vmax=1,\n",
    "            cbar_kws={\"shrink\": .5}\n",
    ");\n",
    "plt.savefig(\"Images/Correlations.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the table above, there are some correlations that must be pointed out: The salary has an obvious positive correlation with Points, Rebounds and Assists (as if you play better, you win more money) but looking at this 3 stats it seems that the less valued stat is the Rebound, as it is the less correlated. On the other hand, both Height and Position (strongly correlated between them) have a negative correlation: That means that the shortest players (Point Guards and Shooting Guards) that are top scorers and priorize assisting before rebounding will have the best NBA contracts!\n",
    "\n",
    "Lastly, let's take a look at the tall players; as we have already seen, the tall players use to play in a position close to the rim (Centers or Power Forwards). I will sort by position and find the mean of the top stats to compare each position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_9240\\341753194.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped = nbastats.groupby(\"Position\").mean()[[\"Points\", \"Assists\", \"Rebounds\"]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Points</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Rebounds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Position</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>9.865714</td>\n",
       "      <td>1.441429</td>\n",
       "      <td>6.482857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF</th>\n",
       "      <td>9.253521</td>\n",
       "      <td>1.726761</td>\n",
       "      <td>4.595775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>12.456410</td>\n",
       "      <td>4.126923</td>\n",
       "      <td>3.292308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>10.445349</td>\n",
       "      <td>1.879070</td>\n",
       "      <td>3.889535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG</th>\n",
       "      <td>11.696629</td>\n",
       "      <td>2.501124</td>\n",
       "      <td>3.232584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Points   Assists  Rebounds\n",
       "Position                               \n",
       "C          9.865714  1.441429  6.482857\n",
       "PF         9.253521  1.726761  4.595775\n",
       "PG        12.456410  4.126923  3.292308\n",
       "SF        10.445349  1.879070  3.889535\n",
       "SG        11.696629  2.501124  3.232584"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = nbastats.groupby(\"Position\").mean()[[\"Points\", \"Assists\", \"Rebounds\"]]\n",
    "grouped = grouped.sort_values(\"Position\")\n",
    "grouped\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as in the last correlation table we found out that the salaries of the \"big men\" were less than the small guys; that might be related with data, as apart from getting more rebounds, both Centers and Power Forwards are the Position with less scoring points and less assists, the other two categories. \n",
    "Although the easy way of taking down this data will be to consider that more \"role players\" are big men, just consider that the Centers are the Position with less players in the season, and Power Forward were the 3rd! So if there is a lot of big men role players, for sure there will be much more that cover the rest of the positions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SG    138\n",
       "SF    113\n",
       "PG    111\n",
       "PF    107\n",
       "C      88\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.Position.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped.plot(kind=\"bar\")\n",
    "plt.xticks(rotation=0.5)\n",
    "plt.title('Mean Stats per Position')\n",
    "plt.savefig(\"Images/PositionStats.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HYPOTHESIS 1: Playing Basketball in colleges with NCAA Trophies is the best way of arriving to the NBA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(557, 12)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbacol = nba.copy()\n",
    "nbacol.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I did some web scrapping to find a Wikipedia Page about College Basketball Stats; my first guess is that the more Trophies the college, more nba players will be active in the NBA. So first of all I must drop all the NAN values of the column \"College\", as i am not interested in players that came by another way to the NBA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(484, 12)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbacol.dropna(subset=[\"College\"], inplace=True)\n",
    "nbacol.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://es.wikipedia.org/wiki/Campeonato_de_la_Divisi%C3%B3n_I_de_Baloncesto_Masculino_de_la_NCAA#Trofeos_y_premios\"\n",
    "response = requests.get(url)\n",
    "soup = BeautifulSoup(response.content, \"html.parser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = soup.find_all(\"table\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next table is related with the number of NCAA titles per college"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unico=nbacol.College.unique()\n",
    "#unico=set(unico)\n",
    "#unico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>11</td>\n",
       "      <td>1964, 1965, 1967, 1968, 1969, 1970, 1971, 1972...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>1948, 1949, 1951, 1958, 1978, 1996, 1998, 2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Carolina del Norte</td>\n",
       "      <td>6</td>\n",
       "      <td>1957, 1982,  1993, 2005, 2009, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>5</td>\n",
       "      <td>1991, 1992, 2001, 2010, 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>5</td>\n",
       "      <td>1940, 1953, 1976, 1981, 1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>4</td>\n",
       "      <td>1952, 1988, 2008, 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>4</td>\n",
       "      <td>1999, 2004, 2011, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>3</td>\n",
       "      <td>1980, 1986, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>3</td>\n",
       "      <td>1985, 2016, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2</td>\n",
       "      <td>1945, 1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2</td>\n",
       "      <td>1955, 1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>2</td>\n",
       "      <td>1961, 1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2</td>\n",
       "      <td>1974, 1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>2</td>\n",
       "      <td>1979, 2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2</td>\n",
       "      <td>2006, 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>1</td>\n",
       "      <td>1942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>CC New York</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>La Salle</td>\n",
       "      <td>1</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>1</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>1</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>1</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º           Universidad  Títulos  \\\n",
       "0     1                  UCLA       11   \n",
       "1     2              Kentucky        8   \n",
       "2     3    Carolina del Norte        6   \n",
       "3     4                  Duke        5   \n",
       "4     4               Indiana        5   \n",
       "5     6                Kansas        4   \n",
       "6     6           Connecticut        4   \n",
       "7     8            Louisville        3   \n",
       "8     8             Villanova        3   \n",
       "9    10        Oklahoma State        2   \n",
       "10   10         San Francisco        2   \n",
       "11   10            Cincinnati        2   \n",
       "12   10  North Carolina State        2   \n",
       "13   10        Michigan State        2   \n",
       "14   10               Florida        2   \n",
       "15   16                Oregon        1   \n",
       "16   16             Wisconsin        1   \n",
       "17   16              Stanford        1   \n",
       "18   16               Wyoming        1   \n",
       "19   16                  Utah        1   \n",
       "20   16            Holy Cross        1   \n",
       "21   16           CC New York        1   \n",
       "22   16              La Salle        1   \n",
       "23   16            California        1   \n",
       "24   16            Ohio State        1   \n",
       "25   16        Loyola Chicago        1   \n",
       "26   16                  UTEP        1   \n",
       "27   16             Marquette        1   \n",
       "28   16            Georgetown        1   \n",
       "29   16              Michigan        1   \n",
       "30   16                  UNLV        1   \n",
       "31   16              Arkansas        1   \n",
       "32   16               Arizona        1   \n",
       "33   16              Maryland        1   \n",
       "34   16              Syracuse        1   \n",
       "35   16              Virginia        1   \n",
       "36   16                Baylor        1   \n",
       "\n",
       "                                                 Años  \n",
       "0   1964, 1965, 1967, 1968, 1969, 1970, 1971, 1972...  \n",
       "1      1948, 1949, 1951, 1958, 1978, 1996, 1998, 2012  \n",
       "2                 1957, 1982,  1993, 2005, 2009, 2017  \n",
       "3                        1991, 1992, 2001, 2010, 2015  \n",
       "4                        1940, 1953, 1976, 1981, 1987  \n",
       "5                              1952, 1988, 2008, 2022  \n",
       "6                              1999, 2004, 2011, 2014  \n",
       "7                                    1980, 1986, 2013  \n",
       "8                                    1985, 2016, 2018  \n",
       "9                                          1945, 1946  \n",
       "10                                         1955, 1956  \n",
       "11                                         1961, 1962  \n",
       "12                                         1974, 1983  \n",
       "13                                         1979, 2000  \n",
       "14                                         2006, 2007  \n",
       "15                                               1939  \n",
       "16                                               1941  \n",
       "17                                               1942  \n",
       "18                                               1943  \n",
       "19                                               1944  \n",
       "20                                               1947  \n",
       "21                                               1950  \n",
       "22                                               1954  \n",
       "23                                               1959  \n",
       "24                                               1960  \n",
       "25                                               1963  \n",
       "26                                               1966  \n",
       "27                                               1977  \n",
       "28                                               1984  \n",
       "29                                               1989  \n",
       "30                                               1990  \n",
       "31                                               1994  \n",
       "32                                               1997  \n",
       "33                                               2002  \n",
       "34                                               2003  \n",
       "35                                               2019  \n",
       "36                                               2021  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table = pd.read_html(tags[7].prettify())\n",
    "my_table=my_table[0]\n",
    "type(my_table)\n",
    "my_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_table['Universidad'] = my_table['Universidad'].replace(\"Carolina del Norte\", \"North Carolina\")\n",
    "#A little cleaning here of the web scrapping, it was the only university translated to Spanish from the web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbacol['College_in_my_table'] = nbacol['College'].isin(my_table['Universidad'])\n",
    "counts = nbacol[nbacol['College_in_my_table']].groupby('College').size().reset_index(name='Jugadores')\n",
    "#Boolean to know if the College is in the web scrapping, and Groupby with counts to finally merge in my_table the number of players per university\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Años</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>11</td>\n",
       "      <td>1964, 1965, 1967, 1968, 1969, 1970, 1971, 1972...</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>1948, 1949, 1951, 1958, 1978, 1996, 1998, 2012</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6</td>\n",
       "      <td>1957, 1982,  1993, 2005, 2009, 2017</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>5</td>\n",
       "      <td>1991, 1992, 2001, 2010, 2015</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>5</td>\n",
       "      <td>1940, 1953, 1976, 1981, 1987</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>4</td>\n",
       "      <td>1952, 1988, 2008, 2022</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>4</td>\n",
       "      <td>1999, 2004, 2011, 2014</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>3</td>\n",
       "      <td>1980, 1986, 2013</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>3</td>\n",
       "      <td>1985, 2016, 2018</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2</td>\n",
       "      <td>1945, 1946</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2</td>\n",
       "      <td>1955, 1956</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>2</td>\n",
       "      <td>1961, 1962</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2</td>\n",
       "      <td>1974, 1983</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>2</td>\n",
       "      <td>1979, 2000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2</td>\n",
       "      <td>2006, 2007</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>1939</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>1941</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>1</td>\n",
       "      <td>1942</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>1943</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>1944</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>CC New York</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>La Salle</td>\n",
       "      <td>1</td>\n",
       "      <td>1954</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>1</td>\n",
       "      <td>1960</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>1</td>\n",
       "      <td>1963</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>1</td>\n",
       "      <td>1977</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>1989</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º           Universidad  Títulos  \\\n",
       "0     1                  UCLA       11   \n",
       "1     2              Kentucky        8   \n",
       "2     3        North Carolina        6   \n",
       "3     4                  Duke        5   \n",
       "4     4               Indiana        5   \n",
       "5     6                Kansas        4   \n",
       "6     6           Connecticut        4   \n",
       "7     8            Louisville        3   \n",
       "8     8             Villanova        3   \n",
       "9    10        Oklahoma State        2   \n",
       "10   10         San Francisco        2   \n",
       "11   10            Cincinnati        2   \n",
       "12   10  North Carolina State        2   \n",
       "13   10        Michigan State        2   \n",
       "14   10               Florida        2   \n",
       "15   16                Oregon        1   \n",
       "16   16             Wisconsin        1   \n",
       "17   16              Stanford        1   \n",
       "18   16               Wyoming        1   \n",
       "19   16                  Utah        1   \n",
       "20   16            Holy Cross        1   \n",
       "21   16           CC New York        1   \n",
       "22   16              La Salle        1   \n",
       "23   16            California        1   \n",
       "24   16            Ohio State        1   \n",
       "25   16        Loyola Chicago        1   \n",
       "26   16                  UTEP        1   \n",
       "27   16             Marquette        1   \n",
       "28   16            Georgetown        1   \n",
       "29   16              Michigan        1   \n",
       "30   16                  UNLV        1   \n",
       "31   16              Arkansas        1   \n",
       "32   16               Arizona        1   \n",
       "33   16              Maryland        1   \n",
       "34   16              Syracuse        1   \n",
       "35   16              Virginia        1   \n",
       "36   16                Baylor        1   \n",
       "\n",
       "                                                 Años  Jugadores  \n",
       "0   1964, 1965, 1967, 1968, 1969, 1970, 1971, 1972...       13.0  \n",
       "1      1948, 1949, 1951, 1958, 1978, 1996, 1998, 2012       30.0  \n",
       "2                 1957, 1982,  1993, 2005, 2009, 2017       13.0  \n",
       "3                        1991, 1992, 2001, 2010, 2015       27.0  \n",
       "4                        1940, 1953, 1976, 1981, 1987        8.0  \n",
       "5                              1952, 1988, 2008, 2022       12.0  \n",
       "6                              1999, 2004, 2011, 2014        5.0  \n",
       "7                                    1980, 1986, 2013        6.0  \n",
       "8                                    1985, 2016, 2018        9.0  \n",
       "9                                          1945, 1946        2.0  \n",
       "10                                         1955, 1956        NaN  \n",
       "11                                         1961, 1962        1.0  \n",
       "12                                         1974, 1983        2.0  \n",
       "13                                         1979, 2000        8.0  \n",
       "14                                         2006, 2007        7.0  \n",
       "15                                               1939        8.0  \n",
       "16                                               1941        2.0  \n",
       "17                                               1942        7.0  \n",
       "18                                               1943        2.0  \n",
       "19                                               1944        3.0  \n",
       "20                                               1947        NaN  \n",
       "21                                               1950        NaN  \n",
       "22                                               1954        NaN  \n",
       "23                                               1959        1.0  \n",
       "24                                               1960        4.0  \n",
       "25                                               1963        NaN  \n",
       "26                                               1966        NaN  \n",
       "27                                               1977        5.0  \n",
       "28                                               1984        3.0  \n",
       "29                                               1989       11.0  \n",
       "30                                               1990        3.0  \n",
       "31                                               1994        5.0  \n",
       "32                                               1997       11.0  \n",
       "33                                               2002        6.0  \n",
       "34                                               2003        5.0  \n",
       "35                                               2019        8.0  \n",
       "36                                               2021        5.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table = my_table.merge(counts, left_on='Universidad', right_on='College', how='left')\n",
    "my_table = my_table.drop(columns=['College'])\n",
    "my_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UCLA</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Duke</td>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Louisville</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Villanova</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Michigan State</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Florida</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CC New York</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>La Salle</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ohio State</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Marquette</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Baylor</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Universidad  Títulos  Jugadores\n",
       "0                   UCLA       11       13.0\n",
       "1               Kentucky        8       30.0\n",
       "2         North Carolina        6       13.0\n",
       "3                   Duke        5       27.0\n",
       "4                Indiana        5        8.0\n",
       "5                 Kansas        4       12.0\n",
       "6            Connecticut        4        5.0\n",
       "7             Louisville        3        6.0\n",
       "8              Villanova        3        9.0\n",
       "9         Oklahoma State        2        2.0\n",
       "10         San Francisco        2        NaN\n",
       "11            Cincinnati        2        1.0\n",
       "12  North Carolina State        2        2.0\n",
       "13        Michigan State        2        8.0\n",
       "14               Florida        2        7.0\n",
       "15                Oregon        1        8.0\n",
       "16             Wisconsin        1        2.0\n",
       "17              Stanford        1        7.0\n",
       "18               Wyoming        1        2.0\n",
       "19                  Utah        1        3.0\n",
       "20            Holy Cross        1        NaN\n",
       "21           CC New York        1        NaN\n",
       "22              La Salle        1        NaN\n",
       "23            California        1        1.0\n",
       "24            Ohio State        1        4.0\n",
       "25        Loyola Chicago        1        NaN\n",
       "26                  UTEP        1        NaN\n",
       "27             Marquette        1        5.0\n",
       "28            Georgetown        1        3.0\n",
       "29              Michigan        1       11.0\n",
       "30                  UNLV        1        3.0\n",
       "31              Arkansas        1        5.0\n",
       "32               Arizona        1       11.0\n",
       "33              Maryland        1        6.0\n",
       "34              Syracuse        1        5.0\n",
       "35              Virginia        1        8.0\n",
       "36                Baylor        1        5.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytablesub=my_table[[\"Universidad\", \"Títulos\", \"Jugadores\"]]\n",
    "mytablesub\n",
    "#Here I create a new df just for the graph, using the only 2 variables that I wanted to include; Títulos and Jugadores, to try to stablish a relation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_9240\\3108557039.py:2: UserWarning: The palette list has more values (3) than needed (2), which may not be intended.\n",
      "  sns.lineplot(x='Universidad', y=\"value\", hue='variable',\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.melt(mytablesub, ['Universidad'])\n",
    "sns.lineplot(x='Universidad', y=\"value\", hue='variable', \n",
    "             data=data, \n",
    "             palette=['red', 'blue', 'purple']);\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('College')\n",
    "plt.ylabel('Amount')\n",
    "plt.title('Is this comparison clear?')\n",
    "plt.savefig(\"Images/clearcomparison.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph is a total mess: Although it seems pretty correlated that the \"Top\" Universities has more active players than the Colleges with less Trophies, the variability is really high. In addition, we are just analyzing the 36 colleges with at least 1 trophie... but in the NBA there are active players from more than 130 colleges! It is practically impossible and really confusing to try to put it in one big graph and observing the 2 lines converge or diverge... So I changed a bit my point of view. What if I compare the players/college rate depending on boolean factors? In that way, I can stablish the type of college that has an attribute that, in case another college do not have it, will be a big added value if you want to make it to the NBA. \n",
    "\n",
    "I set 5 different boolean factors: \n",
    "\n",
    "1. The college has won at least a Trophy vs NO\n",
    "2. The college has appeared a lot in the Final Four vs NO\n",
    "3. The college has played in a lot of Tournaments vs NO\n",
    "4. The college has gained a lot of matches in Tournaments vs NO\n",
    "5. The college has won at least a Trophy in XXI Century vs NO\n",
    "\n",
    "Let's find the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "first_value = my_table['Jugadores'].sum() / my_table.shape[0]\n",
    "\n",
    "second_value = (nbacol.shape[0] - my_table['Jugadores'].sum()) / (len(np.unique(nbacol['College'])) - my_table.shape[0])\n",
    "\n",
    "values = pd.DataFrame({'Rate': [first_value, second_value],\n",
    "                       'Difference': ['Players per NCAA Champ', 'Players per not NCAA Champ']})\n",
    "\n",
    "\n",
    "sns.barplot(x='Difference', y='Rate', data=values)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Final Four Played: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Final Four Participations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Final Four Participations\n",
       "0       1  North Carolina                         21\n",
       "1       2        Kentucky                         17\n",
       "2       2            UCLA                         17\n",
       "3       4            Duke                         17\n",
       "4       5          Kansas                         15\n",
       "5       6      Ohio State                         10\n",
       "6       6      Louisville                         10\n",
       "7       8  Michigan State                          9\n",
       "8       9         Indiana                          8"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table2 = pd.read_html(tags[8].prettify())\n",
    "my_table2=my_table2[0]\n",
    "my_table2.columns = [col if col != \"#\" else \"Final Four Participations\" for col in my_table2.columns]\n",
    "my_table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Final Four Participations</th>\n",
       "      <th>College</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>21</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>17</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>17</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>17</td>\n",
       "      <td>Duke</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>15</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>10</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>10</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Final Four Participations         College  \\\n",
       "0       1  North Carolina                         21  North Carolina   \n",
       "1       2        Kentucky                         17        Kentucky   \n",
       "2       2            UCLA                         17            UCLA   \n",
       "3       4            Duke                         17            Duke   \n",
       "4       5          Kansas                         15          Kansas   \n",
       "5       6      Ohio State                         10      Ohio State   \n",
       "6       6      Louisville                         10      Louisville   \n",
       "7       8  Michigan State                          9  Michigan State   \n",
       "8       9         Indiana                          8         Indiana   \n",
       "\n",
       "   Jugadores  \n",
       "0         13  \n",
       "1         30  \n",
       "2         13  \n",
       "3         27  \n",
       "4         12  \n",
       "5          4  \n",
       "6          6  \n",
       "7          8  \n",
       "8          8  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table2 = my_table2.merge(counts, left_on='Universidad', right_on='College', how='left')\n",
    "my_table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Final Four Participations</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Final Four Participations  Jugadores\n",
       "0       1  North Carolina                         21         13\n",
       "1       2        Kentucky                         17         30\n",
       "2       2            UCLA                         17         13\n",
       "3       4            Duke                         17         27\n",
       "4       5          Kansas                         15         12\n",
       "5       6      Ohio State                         10          4\n",
       "6       6      Louisville                         10          6\n",
       "7       8  Michigan State                          9          8\n",
       "8       9         Indiana                          8          8"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table2 = my_table2.drop(columns=['College'])\n",
    "my_table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_value2 = my_table2['Jugadores'].sum() / my_table2.shape[0]\n",
    "\n",
    "second_value2 = (nbacol.shape[0] - my_table2['Jugadores'].sum()) / (len(np.unique(nbacol['College'])) - my_table2.shape[0])\n",
    "\n",
    "values2 = pd.DataFrame({'Rate': [first_value2, second_value2],\n",
    "                       'Difference': ['Players per most F4', 'Players per not most F4']})\n",
    "\n",
    "sns.barplot(x='Difference', y='Rate', data=values2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More NCAA Tournaments Played: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Participations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Duke</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Participations\n",
       "0       1        Kentucky              56\n",
       "1       2  North Carolina              48\n",
       "2       3            UCLA              46\n",
       "3       3          Kansas              46\n",
       "4       5      Louisville              42\n",
       "5       6            Duke              41\n",
       "6       7         Indiana              39"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table3 = pd.read_html(tags[9].prettify())\n",
    "my_table3=my_table3[0]\n",
    "my_table3.columns = [col if col != \"#\" else \"Participations\" for col in my_table3.columns]\n",
    "my_table3['Universidad'] = my_table3['Universidad'].replace(\"Carolina del Norte\", \"North Carolina\")\n",
    "my_table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Participations</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>48</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>46</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Duke</td>\n",
       "      <td>41</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Participations  Jugadores\n",
       "0       1        Kentucky              56         30\n",
       "1       2  North Carolina              48         13\n",
       "2       3            UCLA              46         13\n",
       "3       3          Kansas              46         12\n",
       "4       5      Louisville              42          6\n",
       "5       6            Duke              41         27\n",
       "6       7         Indiana              39          8"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table3 = my_table3.merge(counts, left_on='Universidad', right_on='College', how='left')\n",
    "my_table3 = my_table3.drop(columns=['College'])\n",
    "\n",
    "my_table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1yzM1zPdc/wbwkYHHvcu49zP3wR9tr4AX9svvGdjGXwFH9suvBf5yoP/D59nWrjxwZuJvAn/ULx8PXA5s3z9f3wIe2dd2F7AP3WvgvJlts/Br480Dz/0FwI797f8J/P7Aekf3y/8NOLlffjfwvlk1z7md/nd3/cBj+onfx6b68u04bJ/ksn75QuCjs+4P8L+TPJPuRbMHsHtV3ZzkX5I8EdgduLSq/iXJIXQvvEv7n9+J7gVzC/DNqvpq334TsE+SPwH+Gjh3nvr+oqruB25IchPwc/32H5/k8H6dXfo+7ga+VlXfmGdbL0k3B9I2wCPo3kJf0d93xsD3P16k/28AJyV5AnAfsN/A+oP9L1bneoD++V8F3A48Djgv3RuArYFbkzwE2KOqPgtQVXf1Pzffc30h8EdJ3k33R+zCeZ6PzUVL++DddH+IANYCv9wvPw349X75E3R/FBazJ/Dn6d4Vbke3X874XFX9APhBki/RTQ55e1/bTQBJzqAbcJzJwq+NP++/P7Vv//t+/9yObtAy46yBxzXzWJ5Dd5EqAFX13f5d01zbuYPuD9NHk5wz8DxtcgZ/f3x1gftfDkwAB1bVPUluphvxAJwMvBr4WeCUvi3A/6mqDw9uJMkqupEQ8KMd4ADgV+iO6b6EbqQz2+wLLarv4+iq+sKsPg4a7GPWfXsDvws8ue/71IHHMbuf+ZZnbr8R+GfgALqR4F0D9w/2v1CdPxxouo9uXwxwdVU9bdb6D5nrMTHPc93/zJOA5wF/mOT8qnrnPNvYHDSxD/buqX44ywO/9+X6E+C9VXV23+/xs2pkjts/0b6E18bM4wndO6Qj5qlnZp9e7HHNu50kTwEOBg4HjgKevcB2ls1j/IvbBdjQv+CexY/PdvdZ4FDgyXRXItN/f+3AMbs9kjx89kaT7AZsVVWfAd4GPGme/l+cZKv++OY+dG8FvwC8Psm2/bb2S7LjIo9jZ7od+I7+WOhzZ93/0oHvg6OYufrfBbi1HwW+km5kPpeNrfN6YCLJ0/r1t03y2Kr6PrA+yYv69gelO/Nlzuc6ySOBf6uq04ATmP+53VKslH1wIV/hgZHxy+ne+QB8H5jvD/8uPDDP1+xpVg9L8uAkP0N3iOeSvv0p6aaL2YpuX/87Fn9tzPgq8Iw88BnYjkn2m2fdGecB/33mRpJd59tO//vapar+hm5wdcAi2142R/yLOx34qyRX0h3HvG7mjqq6u38beXtV3de3nZvk54GL+rdxdwKvoBsFDNoD+FgeOLPiuHn6vwX4Gt3O+dtVdVeSk+mP76brZJru7Id5VdXlSS7t6/8W8PezVtk1yRV0o5bBkchc/f8p8Jkkr6I7/jvfCG+j6uyfz8OB9yfZhW7/fB9wNd0fmA8neSdwD/DiBZ7rx9B9IHh/v+7rF3putgArYh9cxNF9Lcf223pN3/4p4CNJ3kB3PP4fBn7meODTSb4LfJHuM4YZVwBfojue/gdV9Y99SF8CnES3j3wJ+GxV3b/IawOAqppO8mrgjCQP6pvfBnx9gcf1h8AHklxF9/y/o6rOmmc73wc+l+TBdO8K/scC2/2pOGXDT6F/wayjC6EbhrD9U+mOUZ+5qbc9q5+bgcmqum1W+0j61/KtlH1wU0pyPN0H4yfOaj8I+N2qesEYytqseKhnmdJdVHIjcP4wXnDSYtwHtVyO+CWpMY74JakxBr8kNcbgl6TGGPxasfLArJdXp5sb5k15YG6WySTv75cflG6ul8uSvDTJL/U/c1mS7cf7KKRNz/P4tZL96IrY/gKmT9Kdi/72qpqiOyce4IkAA+t+iO7K19OW0kl/Hnv6C9qkzZ4jfjWhqjbQTSx2VDoHJTmn/4NwGvDkfoT/W3RTF/xBktNho2a6nG+9a5N8pH8Xce7Mu4hsxIyj0qZk8KsZ/eRcWwMPH2jbQDez44XVzYn/YeBs4Niqenm6Cc/2pZvk6wnAgekmS6Nv/9Oqeiyw/yLrfaBf73a62UOhuyL3A1V1APB0ugnpFupP2iQ81CMtbKkzXS603jeq6rK+fS2wKhs/4+gFw3hwapPBr2Yk2YduvpQNwM8v9cdYwkyXi6w3eybShT4wnnfGUWlT8VCPmpBkAvgQcFJt3OXqS5rpciPWA2BjZxzdiHqlRTni10o28w9OtgXupfsHH+/dmA0sdabLjZgRc9DGzDi6YWPqlhbiXD2S1BgP9UhSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1Jh/B8HGlbEzqju2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_value3 = my_table3['Jugadores'].sum() / my_table3.shape[0]\n",
    "\n",
    "second_value3 = (nbacol.shape[0] - my_table3['Jugadores'].sum()) / (len(np.unique(nbacol['College'])) - my_table3.shape[0])\n",
    "\n",
    "values3 = pd.DataFrame({'Rate': [first_value3, second_value3],\n",
    "                       'Difference': ['Players per appareneces', 'Players per not apparences']})\n",
    "\n",
    "sns.barplot(x='Difference', y='Rate', data=values3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More NCAA Tournament Victories: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Victories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Duke</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Victories\n",
       "0       1        Kentucky        124\n",
       "1       2  North Carolina        123\n",
       "2       3            Duke        108\n",
       "3       4          Kansas        103\n",
       "4       5            UCLA        100\n",
       "5       6      Louisville         76\n",
       "6       7         Indiana         66"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table4 = pd.read_html(tags[10].prettify())\n",
    "my_table4=my_table4[0]\n",
    "my_table4.columns = [col if col != \"#\" else \"Victories\" for col in my_table4.columns]\n",
    "my_table4['Universidad'] = my_table4['Universidad'].replace(\"Carolina del Norte\", \"North Carolina\")\n",
    "my_table4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Puesto</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Victories</th>\n",
       "      <th>Jugadores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>124</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>123</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Duke</td>\n",
       "      <td>108</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>103</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>100</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>66</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Puesto     Universidad  Victories  Jugadores\n",
       "0       1        Kentucky        124         30\n",
       "1       2  North Carolina        123         13\n",
       "2       3            Duke        108         27\n",
       "3       4          Kansas        103         12\n",
       "4       5            UCLA        100         13\n",
       "5       6      Louisville         76          6\n",
       "6       7         Indiana         66          8"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table4 = my_table4.merge(counts, left_on='Universidad', right_on='College', how='left')\n",
    "my_table4 = my_table4.drop(columns=['College'])\n",
    "my_table4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Difference', ylabel='Rate'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_value4 = my_table4['Jugadores'].sum() / my_table4.shape[0]\n",
    "\n",
    "second_value4 = (nbacol.shape[0] - my_table4['Jugadores'].sum()) / (len(np.unique(nbacol['College'])) - my_table4.shape[0])\n",
    "\n",
    "values4 = pd.DataFrame({'Rate': [first_value4, second_value4],\n",
    "                       'Difference': ['Players per victories', 'Players per not victories']})\n",
    "\n",
    "sns.barplot(x='Difference', y='Rate', data=values4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is it more related with the Trophies accomplished during the XXI Century?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_table5=my_table.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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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       "        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>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Años</th>\n",
       "      <th>Jugadores</th>\n",
       "      <th>XXI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>11</td>\n",
       "      <td>[1964,  1965,  1967,  1968,  1969,  1970,  197...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>[1948,  1949,  1951,  1958,  1978,  1996,  199...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[2012]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6</td>\n",
       "      <td>[1957,  1982,   1993,  2005,  2009,  2017]</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[2005, 2009, 2017]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>5</td>\n",
       "      <td>[1991,  1992,  2001,  2010,  2015]</td>\n",
       "      <td>27.0</td>\n",
       "      <td>[2001, 2010, 2015]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>5</td>\n",
       "      <td>[1940,  1953,  1976,  1981,  1987]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>4</td>\n",
       "      <td>[1952,  1988,  2008,  2022]</td>\n",
       "      <td>12.0</td>\n",
       "      <td>[2008, 2022]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>4</td>\n",
       "      <td>[1999,  2004,  2011,  2014]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2004, 2011, 2014]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>3</td>\n",
       "      <td>[1980,  1986,  2013]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2013]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>3</td>\n",
       "      <td>[1985,  2016,  2018]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2016, 2018]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1945,  1946]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2</td>\n",
       "      <td>[1955,  1956]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>2</td>\n",
       "      <td>[1961,  1962]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1974,  1983]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1979,  2000]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2</td>\n",
       "      <td>[2006,  2007]</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[2006, 2007]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>[1939]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>[1941]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>1</td>\n",
       "      <td>[1942]</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>[1943]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>[1944]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>1</td>\n",
       "      <td>[1947]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>CC New York</td>\n",
       "      <td>1</td>\n",
       "      <td>[1950]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>La Salle</td>\n",
       "      <td>1</td>\n",
       "      <td>[1954]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>[1959]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>1</td>\n",
       "      <td>[1960]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>1</td>\n",
       "      <td>[1963]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "      <td>[1966]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>1</td>\n",
       "      <td>[1977]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1</td>\n",
       "      <td>[1984]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>[1989]</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "      <td>[1990]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>[1994]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>[1997]</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>[2002]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2002]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "      <td>[2003]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2003]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>[2019]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2019]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>1</td>\n",
       "      <td>[2021]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2021]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º           Universidad  Títulos  \\\n",
       "0     1                  UCLA       11   \n",
       "1     2              Kentucky        8   \n",
       "2     3        North Carolina        6   \n",
       "3     4                  Duke        5   \n",
       "4     4               Indiana        5   \n",
       "5     6                Kansas        4   \n",
       "6     6           Connecticut        4   \n",
       "7     8            Louisville        3   \n",
       "8     8             Villanova        3   \n",
       "9    10        Oklahoma State        2   \n",
       "10   10         San Francisco        2   \n",
       "11   10            Cincinnati        2   \n",
       "12   10  North Carolina State        2   \n",
       "13   10        Michigan State        2   \n",
       "14   10               Florida        2   \n",
       "15   16                Oregon        1   \n",
       "16   16             Wisconsin        1   \n",
       "17   16              Stanford        1   \n",
       "18   16               Wyoming        1   \n",
       "19   16                  Utah        1   \n",
       "20   16            Holy Cross        1   \n",
       "21   16           CC New York        1   \n",
       "22   16              La Salle        1   \n",
       "23   16            California        1   \n",
       "24   16            Ohio State        1   \n",
       "25   16        Loyola Chicago        1   \n",
       "26   16                  UTEP        1   \n",
       "27   16             Marquette        1   \n",
       "28   16            Georgetown        1   \n",
       "29   16              Michigan        1   \n",
       "30   16                  UNLV        1   \n",
       "31   16              Arkansas        1   \n",
       "32   16               Arizona        1   \n",
       "33   16              Maryland        1   \n",
       "34   16              Syracuse        1   \n",
       "35   16              Virginia        1   \n",
       "36   16                Baylor        1   \n",
       "\n",
       "                                                 Años  Jugadores  \\\n",
       "0   [1964,  1965,  1967,  1968,  1969,  1970,  197...       13.0   \n",
       "1   [1948,  1949,  1951,  1958,  1978,  1996,  199...       30.0   \n",
       "2          [1957,  1982,   1993,  2005,  2009,  2017]       13.0   \n",
       "3                  [1991,  1992,  2001,  2010,  2015]       27.0   \n",
       "4                  [1940,  1953,  1976,  1981,  1987]        8.0   \n",
       "5                         [1952,  1988,  2008,  2022]       12.0   \n",
       "6                         [1999,  2004,  2011,  2014]        5.0   \n",
       "7                                [1980,  1986,  2013]        6.0   \n",
       "8                                [1985,  2016,  2018]        9.0   \n",
       "9                                       [1945,  1946]        2.0   \n",
       "10                                      [1955,  1956]        NaN   \n",
       "11                                      [1961,  1962]        1.0   \n",
       "12                                      [1974,  1983]        2.0   \n",
       "13                                      [1979,  2000]        8.0   \n",
       "14                                      [2006,  2007]        7.0   \n",
       "15                                             [1939]        8.0   \n",
       "16                                             [1941]        2.0   \n",
       "17                                             [1942]        7.0   \n",
       "18                                             [1943]        2.0   \n",
       "19                                             [1944]        3.0   \n",
       "20                                             [1947]        NaN   \n",
       "21                                             [1950]        NaN   \n",
       "22                                             [1954]        NaN   \n",
       "23                                             [1959]        1.0   \n",
       "24                                             [1960]        4.0   \n",
       "25                                             [1963]        NaN   \n",
       "26                                             [1966]        NaN   \n",
       "27                                             [1977]        5.0   \n",
       "28                                             [1984]        3.0   \n",
       "29                                             [1989]       11.0   \n",
       "30                                             [1990]        3.0   \n",
       "31                                             [1994]        5.0   \n",
       "32                                             [1997]       11.0   \n",
       "33                                             [2002]        6.0   \n",
       "34                                             [2003]        5.0   \n",
       "35                                             [2019]        8.0   \n",
       "36                                             [2021]        5.0   \n",
       "\n",
       "                   XXI  \n",
       "0                   []  \n",
       "1               [2012]  \n",
       "2   [2005, 2009, 2017]  \n",
       "3   [2001, 2010, 2015]  \n",
       "4                   []  \n",
       "5         [2008, 2022]  \n",
       "6   [2004, 2011, 2014]  \n",
       "7               [2013]  \n",
       "8         [2016, 2018]  \n",
       "9                   []  \n",
       "10                  []  \n",
       "11                  []  \n",
       "12                  []  \n",
       "13              [2000]  \n",
       "14        [2006, 2007]  \n",
       "15                  []  \n",
       "16                  []  \n",
       "17                  []  \n",
       "18                  []  \n",
       "19                  []  \n",
       "20                  []  \n",
       "21                  []  \n",
       "22                  []  \n",
       "23                  []  \n",
       "24                  []  \n",
       "25                  []  \n",
       "26                  []  \n",
       "27                  []  \n",
       "28                  []  \n",
       "29                  []  \n",
       "30                  []  \n",
       "31                  []  \n",
       "32                  []  \n",
       "33              [2002]  \n",
       "34              [2003]  \n",
       "35              [2019]  \n",
       "36              [2021]  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table5['Años'] = my_table5['Años'].str.split(',')\n",
    "my_table5['XXI'] = [[int(year) for year in years if int(year) >= 2000] for years in my_table5['Años']]\n",
    "my_table5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Años</th>\n",
       "      <th>Jugadores</th>\n",
       "      <th>XXI</th>\n",
       "      <th>XXITitles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>11</td>\n",
       "      <td>[1964,  1965,  1967,  1968,  1969,  1970,  197...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>[1948,  1949,  1951,  1958,  1978,  1996,  199...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[2012]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6</td>\n",
       "      <td>[1957,  1982,   1993,  2005,  2009,  2017]</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[2005, 2009, 2017]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>5</td>\n",
       "      <td>[1991,  1992,  2001,  2010,  2015]</td>\n",
       "      <td>27.0</td>\n",
       "      <td>[2001, 2010, 2015]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>5</td>\n",
       "      <td>[1940,  1953,  1976,  1981,  1987]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>4</td>\n",
       "      <td>[1952,  1988,  2008,  2022]</td>\n",
       "      <td>12.0</td>\n",
       "      <td>[2008, 2022]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>4</td>\n",
       "      <td>[1999,  2004,  2011,  2014]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2004, 2011, 2014]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>3</td>\n",
       "      <td>[1980,  1986,  2013]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2013]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>3</td>\n",
       "      <td>[1985,  2016,  2018]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2016, 2018]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1945,  1946]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2</td>\n",
       "      <td>[1955,  1956]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>2</td>\n",
       "      <td>[1961,  1962]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1974,  1983]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>2</td>\n",
       "      <td>[1979,  2000]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2000]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2</td>\n",
       "      <td>[2006,  2007]</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[2006, 2007]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>[1939]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>[1941]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>1</td>\n",
       "      <td>[1942]</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>[1943]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>[1944]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>1</td>\n",
       "      <td>[1947]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>CC New York</td>\n",
       "      <td>1</td>\n",
       "      <td>[1950]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>La Salle</td>\n",
       "      <td>1</td>\n",
       "      <td>[1954]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>[1959]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>1</td>\n",
       "      <td>[1960]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>1</td>\n",
       "      <td>[1963]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "      <td>[1966]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>1</td>\n",
       "      <td>[1977]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1</td>\n",
       "      <td>[1984]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>[1989]</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "      <td>[1990]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>[1994]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>[1997]</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>[2002]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2002]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "      <td>[2003]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2003]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>[2019]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2019]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>1</td>\n",
       "      <td>[2021]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2021]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º           Universidad  Títulos  \\\n",
       "0     1                  UCLA       11   \n",
       "1     2              Kentucky        8   \n",
       "2     3        North Carolina        6   \n",
       "3     4                  Duke        5   \n",
       "4     4               Indiana        5   \n",
       "5     6                Kansas        4   \n",
       "6     6           Connecticut        4   \n",
       "7     8            Louisville        3   \n",
       "8     8             Villanova        3   \n",
       "9    10        Oklahoma State        2   \n",
       "10   10         San Francisco        2   \n",
       "11   10            Cincinnati        2   \n",
       "12   10  North Carolina State        2   \n",
       "13   10        Michigan State        2   \n",
       "14   10               Florida        2   \n",
       "15   16                Oregon        1   \n",
       "16   16             Wisconsin        1   \n",
       "17   16              Stanford        1   \n",
       "18   16               Wyoming        1   \n",
       "19   16                  Utah        1   \n",
       "20   16            Holy Cross        1   \n",
       "21   16           CC New York        1   \n",
       "22   16              La Salle        1   \n",
       "23   16            California        1   \n",
       "24   16            Ohio State        1   \n",
       "25   16        Loyola Chicago        1   \n",
       "26   16                  UTEP        1   \n",
       "27   16             Marquette        1   \n",
       "28   16            Georgetown        1   \n",
       "29   16              Michigan        1   \n",
       "30   16                  UNLV        1   \n",
       "31   16              Arkansas        1   \n",
       "32   16               Arizona        1   \n",
       "33   16              Maryland        1   \n",
       "34   16              Syracuse        1   \n",
       "35   16              Virginia        1   \n",
       "36   16                Baylor        1   \n",
       "\n",
       "                                                 Años  Jugadores  \\\n",
       "0   [1964,  1965,  1967,  1968,  1969,  1970,  197...       13.0   \n",
       "1   [1948,  1949,  1951,  1958,  1978,  1996,  199...       30.0   \n",
       "2          [1957,  1982,   1993,  2005,  2009,  2017]       13.0   \n",
       "3                  [1991,  1992,  2001,  2010,  2015]       27.0   \n",
       "4                  [1940,  1953,  1976,  1981,  1987]        8.0   \n",
       "5                         [1952,  1988,  2008,  2022]       12.0   \n",
       "6                         [1999,  2004,  2011,  2014]        5.0   \n",
       "7                                [1980,  1986,  2013]        6.0   \n",
       "8                                [1985,  2016,  2018]        9.0   \n",
       "9                                       [1945,  1946]        2.0   \n",
       "10                                      [1955,  1956]        NaN   \n",
       "11                                      [1961,  1962]        1.0   \n",
       "12                                      [1974,  1983]        2.0   \n",
       "13                                      [1979,  2000]        8.0   \n",
       "14                                      [2006,  2007]        7.0   \n",
       "15                                             [1939]        8.0   \n",
       "16                                             [1941]        2.0   \n",
       "17                                             [1942]        7.0   \n",
       "18                                             [1943]        2.0   \n",
       "19                                             [1944]        3.0   \n",
       "20                                             [1947]        NaN   \n",
       "21                                             [1950]        NaN   \n",
       "22                                             [1954]        NaN   \n",
       "23                                             [1959]        1.0   \n",
       "24                                             [1960]        4.0   \n",
       "25                                             [1963]        NaN   \n",
       "26                                             [1966]        NaN   \n",
       "27                                             [1977]        5.0   \n",
       "28                                             [1984]        3.0   \n",
       "29                                             [1989]       11.0   \n",
       "30                                             [1990]        3.0   \n",
       "31                                             [1994]        5.0   \n",
       "32                                             [1997]       11.0   \n",
       "33                                             [2002]        6.0   \n",
       "34                                             [2003]        5.0   \n",
       "35                                             [2019]        8.0   \n",
       "36                                             [2021]        5.0   \n",
       "\n",
       "                   XXI  XXITitles  \n",
       "0                   []          0  \n",
       "1               [2012]          1  \n",
       "2   [2005, 2009, 2017]          3  \n",
       "3   [2001, 2010, 2015]          3  \n",
       "4                   []          0  \n",
       "5         [2008, 2022]          2  \n",
       "6   [2004, 2011, 2014]          3  \n",
       "7               [2013]          1  \n",
       "8         [2016, 2018]          2  \n",
       "9                   []          0  \n",
       "10                  []          0  \n",
       "11                  []          0  \n",
       "12                  []          0  \n",
       "13              [2000]          1  \n",
       "14        [2006, 2007]          2  \n",
       "15                  []          0  \n",
       "16                  []          0  \n",
       "17                  []          0  \n",
       "18                  []          0  \n",
       "19                  []          0  \n",
       "20                  []          0  \n",
       "21                  []          0  \n",
       "22                  []          0  \n",
       "23                  []          0  \n",
       "24                  []          0  \n",
       "25                  []          0  \n",
       "26                  []          0  \n",
       "27                  []          0  \n",
       "28                  []          0  \n",
       "29                  []          0  \n",
       "30                  []          0  \n",
       "31                  []          0  \n",
       "32                  []          0  \n",
       "33              [2002]          1  \n",
       "34              [2003]          1  \n",
       "35              [2019]          1  \n",
       "36              [2021]          1  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "my_table5['XXITitles'] = my_table5['XXI'].map(len)\n",
    "my_table5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Jugadores</th>\n",
       "      <th>XXI</th>\n",
       "      <th>XXITitles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[2012]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[2005, 2009, 2017]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>27.0</td>\n",
       "      <td>[2001, 2010, 2015]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>12.0</td>\n",
       "      <td>[2008, 2022]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2004, 2011, 2014]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2013]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2016, 2018]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2000]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[2006, 2007]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Utah</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Holy Cross</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>CC New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>La Salle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>4.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>Loyola Chicago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2002]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2003]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2019]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2021]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º           Universidad  Jugadores                 XXI  XXITitles\n",
       "0     1                  UCLA       13.0                  []          0\n",
       "1     2              Kentucky       30.0              [2012]          1\n",
       "2     3        North Carolina       13.0  [2005, 2009, 2017]          3\n",
       "3     4                  Duke       27.0  [2001, 2010, 2015]          3\n",
       "4     4               Indiana        8.0                  []          0\n",
       "5     6                Kansas       12.0        [2008, 2022]          2\n",
       "6     6           Connecticut        5.0  [2004, 2011, 2014]          3\n",
       "7     8            Louisville        6.0              [2013]          1\n",
       "8     8             Villanova        9.0        [2016, 2018]          2\n",
       "9    10        Oklahoma State        2.0                  []          0\n",
       "10   10         San Francisco        NaN                  []          0\n",
       "11   10            Cincinnati        1.0                  []          0\n",
       "12   10  North Carolina State        2.0                  []          0\n",
       "13   10        Michigan State        8.0              [2000]          1\n",
       "14   10               Florida        7.0        [2006, 2007]          2\n",
       "15   16                Oregon        8.0                  []          0\n",
       "16   16             Wisconsin        2.0                  []          0\n",
       "17   16              Stanford        7.0                  []          0\n",
       "18   16               Wyoming        2.0                  []          0\n",
       "19   16                  Utah        3.0                  []          0\n",
       "20   16            Holy Cross        NaN                  []          0\n",
       "21   16           CC New York        NaN                  []          0\n",
       "22   16              La Salle        NaN                  []          0\n",
       "23   16            California        1.0                  []          0\n",
       "24   16            Ohio State        4.0                  []          0\n",
       "25   16        Loyola Chicago        NaN                  []          0\n",
       "26   16                  UTEP        NaN                  []          0\n",
       "27   16             Marquette        5.0                  []          0\n",
       "28   16            Georgetown        3.0                  []          0\n",
       "29   16              Michigan       11.0                  []          0\n",
       "30   16                  UNLV        3.0                  []          0\n",
       "31   16              Arkansas        5.0                  []          0\n",
       "32   16               Arizona       11.0                  []          0\n",
       "33   16              Maryland        6.0              [2002]          1\n",
       "34   16              Syracuse        5.0              [2003]          1\n",
       "35   16              Virginia        8.0              [2019]          1\n",
       "36   16                Baylor        5.0              [2021]          1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table5.drop(columns=['Títulos', \"Años\"], inplace=True)\n",
    "my_table5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>N.º</th>\n",
       "      <th>Universidad</th>\n",
       "      <th>Jugadores</th>\n",
       "      <th>XXI</th>\n",
       "      <th>XXITitles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[2012]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[2005, 2009, 2017]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Duke</td>\n",
       "      <td>27.0</td>\n",
       "      <td>[2001, 2010, 2015]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>12.0</td>\n",
       "      <td>[2008, 2022]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2004, 2011, 2014]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2013]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2016, 2018]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2000]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[2006, 2007]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[2002]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2003]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2019]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[2021]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N.º     Universidad  Jugadores                 XXI  XXITitles\n",
       "1     2        Kentucky       30.0              [2012]          1\n",
       "2     3  North Carolina       13.0  [2005, 2009, 2017]          3\n",
       "3     4            Duke       27.0  [2001, 2010, 2015]          3\n",
       "5     6          Kansas       12.0        [2008, 2022]          2\n",
       "6     6     Connecticut        5.0  [2004, 2011, 2014]          3\n",
       "7     8      Louisville        6.0              [2013]          1\n",
       "8     8       Villanova        9.0        [2016, 2018]          2\n",
       "13   10  Michigan State        8.0              [2000]          1\n",
       "14   10         Florida        7.0        [2006, 2007]          2\n",
       "33   16        Maryland        6.0              [2002]          1\n",
       "34   16        Syracuse        5.0              [2003]          1\n",
       "35   16        Virginia        8.0              [2019]          1\n",
       "36   16          Baylor        5.0              [2021]          1"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table5 = my_table5[my_table5['XXITitles'] != 0]\n",
    "my_table5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Difference', ylabel='Rate'>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_value5 = my_table5['Jugadores'].sum() / my_table5.shape[0]\n",
    "\n",
    "second_value5 = (nbacol.shape[0] - my_table5['Jugadores'].sum()) / (len(np.unique(nbacol['College'])) - my_table5.shape[0])\n",
    "\n",
    "values5 = pd.DataFrame({'Rate': [first_value5, second_value5],\n",
    "                       'Difference': ['Players per XXI Titles', 'Players per XXI not Titles']})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.barplot(x='Difference', y='Rate', data=values5, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "name=(\"Trophies\", \"Final Fours\", \"Tournaments\", \"Victories\", \"XXI Trophies\")\n",
    "rate= (first_value-second_value, first_value2-second_value2, first_value3-second_value3, first_value4-second_value4, first_value5-second_value5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(name, rate)\n",
    "plt.xlabel(\"Rates\", fontsize=15);\n",
    "plt.ylabel(\"Differences\", fontsize=15);\n",
    "\n",
    "plt.title(\"The different rate conditions of the NBA players that came from college\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(20,15))\n",
    "sns.barplot(x='Difference', y='Rate', data=values, ax=axs[0,0])\n",
    "sns.barplot(x='Difference', y='Rate', data=values2, ax=axs[0,1])\n",
    "sns.barplot(x='Difference', y='Rate', data=values3, ax=axs[0,2])\n",
    "sns.barplot(x='Difference', y='Rate', data=values4, ax=axs[1,0])\n",
    "sns.barplot(x='Difference', y='Rate', data=values5, ax=axs[1,1])\n",
    "plt.sca(axs[1,2])\n",
    "plt.plot(name, rate)\n",
    "\n",
    "fig.savefig(\"Images/Comparison.png\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not as I predicted, the colleges with more College Tournaments played and more victories has the highest active player rate. This is because the national repercussion of the NCAA, even if you don't win, can open a lot of doors to play in the big league!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HYPOTHESIS 2: Calculating the IMC depending on the city and the Salaries per capacity of the stadium"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also with web scrapping, I saw that in my original database there were both Weight and Height, perfect for calculating the IMC! so I found a database with connects each team with a city, and searched the cities with most obesity rate to try to stablish a comparison between this cities and the players that live in it. On the other hand, as the new table had a \"Seating Capacity\" column, I tried to observe if there is a link between the capacity of the stadium and the salaries of the players (more spectators means more money)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "url2= \"https://geojango.com/pages/list-of-nba-teams\"\n",
    "response2 = requests.get(url2)\n",
    "soup2 = BeautifulSoup(response2.content, \"html.parser\")\n",
    "tags2 = soup2.find_all(\"table\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Team Name</th>\n",
       "      <th>Arena Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Opening Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>Atlanta, Georgia</td>\n",
       "      <td>18118</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>TD Garden</td>\n",
       "      <td>Boston, Massachusetts</td>\n",
       "      <td>18624</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Barclays Center</td>\n",
       "      <td>Brooklyn, New York</td>\n",
       "      <td>17732</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Spectrum Center</td>\n",
       "      <td>Charlotte, North Carolina</td>\n",
       "      <td>19077</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>United Center</td>\n",
       "      <td>Chicago, Illinois</td>\n",
       "      <td>20917</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Rocket Mortgage Fieldhouse</td>\n",
       "      <td>Cleveland, Ohio</td>\n",
       "      <td>19432</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>American Airlines Center</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>19200</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Pepsi Center</td>\n",
       "      <td>Denver, Colorado</td>\n",
       "      <td>19520</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Little Caesars Arena</td>\n",
       "      <td>Detroit, Michigan</td>\n",
       "      <td>20491</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>Chase Center</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>18064</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Toyota Center</td>\n",
       "      <td>Houston, Texas</td>\n",
       "      <td>18055</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Bankers Life Fieldhouse</td>\n",
       "      <td>Indianapolis, Indiana</td>\n",
       "      <td>17923</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles, California</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles, California</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>FedEx Forum</td>\n",
       "      <td>Memphis, Tennessee</td>\n",
       "      <td>17794</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>American Airlines Arena</td>\n",
       "      <td>Miami, Florida</td>\n",
       "      <td>19600</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Fiserv Forum</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>17500</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Target Center</td>\n",
       "      <td>Minneapolis, Minnesota</td>\n",
       "      <td>18978</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>Smoothie King Center</td>\n",
       "      <td>New Orleans, Louisiana</td>\n",
       "      <td>16867</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>Madison Square Garden</td>\n",
       "      <td>New York City, New York</td>\n",
       "      <td>19812</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Chesapeake Energy Arena</td>\n",
       "      <td>Oklahoma City, Oklahoma</td>\n",
       "      <td>18203</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Amway Center</td>\n",
       "      <td>Orlando, Florida</td>\n",
       "      <td>18846</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Wells Fargo Center</td>\n",
       "      <td>Philadelphia, Pennsylvania</td>\n",
       "      <td>20478</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Talking Stick Resort Arena</td>\n",
       "      <td>Phoenix, Arizona</td>\n",
       "      <td>18055</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Moda Center</td>\n",
       "      <td>Portland, Oregon</td>\n",
       "      <td>19441</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Golden 1 Center</td>\n",
       "      <td>Sacramento, California</td>\n",
       "      <td>17583</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>AT&amp;T Center</td>\n",
       "      <td>San Antonio, Texas</td>\n",
       "      <td>18418</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Scotiabank Arena</td>\n",
       "      <td>Toronto, Ontario</td>\n",
       "      <td>19800</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Vivint Smart Home Arena</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>18306</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Capital One Arena</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>20356</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name                  Arena Name  \\\n",
       "0            Atlanta Hawks            State Farm Arena   \n",
       "1           Boston Celtics                   TD Garden   \n",
       "2            Brooklyn Nets             Barclays Center   \n",
       "3        Charlotte Hornets             Spectrum Center   \n",
       "4            Chicago Bulls               United Center   \n",
       "5      Cleveland Cavaliers  Rocket Mortgage Fieldhouse   \n",
       "6         Dallas Mavericks    American Airlines Center   \n",
       "7           Denver Nuggets                Pepsi Center   \n",
       "8          Detroit Pistons        Little Caesars Arena   \n",
       "9    Golden State Warriors                Chase Center   \n",
       "10         Houston Rockets               Toyota Center   \n",
       "11          Indiana Pacers     Bankers Life Fieldhouse   \n",
       "12    Los Angeles Clippers              Staples Center   \n",
       "13      Los Angeles Lakers              Staples Center   \n",
       "14       Memphis Grizzlies                 FedEx Forum   \n",
       "15              Miami Heat     American Airlines Arena   \n",
       "16         Milwaukee Bucks                Fiserv Forum   \n",
       "17  Minnesota Timberwolves               Target Center   \n",
       "18    New Orleans Pelicans        Smoothie King Center   \n",
       "19         New York Knicks       Madison Square Garden   \n",
       "20   Oklahoma City Thunder     Chesapeake Energy Arena   \n",
       "21           Orlando Magic                Amway Center   \n",
       "22      Philadelphia 76ers          Wells Fargo Center   \n",
       "23            Phoenix Suns  Talking Stick Resort Arena   \n",
       "24  Portland Trail Blazers                 Moda Center   \n",
       "25        Sacramento Kings             Golden 1 Center   \n",
       "26       San Antonio Spurs                 AT&T Center   \n",
       "27         Toronto Raptors            Scotiabank Arena   \n",
       "28               Utah Jazz     Vivint Smart Home Arena   \n",
       "29      Washington Wizards           Capital One Arena   \n",
       "\n",
       "                Arena Location  Seating Capacity  Opening Year  \n",
       "0             Atlanta, Georgia             18118          1999  \n",
       "1        Boston, Massachusetts             18624          1995  \n",
       "2           Brooklyn, New York             17732          2012  \n",
       "3    Charlotte, North Carolina             19077          2005  \n",
       "4            Chicago, Illinois             20917          1994  \n",
       "5              Cleveland, Ohio             19432          1994  \n",
       "6                Dallas, Texas             19200          2001  \n",
       "7             Denver, Colorado             19520          1999  \n",
       "8            Detroit, Michigan             20491          2017  \n",
       "9    San Francisco, California             18064          2019  \n",
       "10              Houston, Texas             18055          2003  \n",
       "11       Indianapolis, Indiana             17923          1999  \n",
       "12     Los Angeles, California             19068          1999  \n",
       "13     Los Angeles, California             19068          1999  \n",
       "14          Memphis, Tennessee             17794          2004  \n",
       "15              Miami, Florida             19600          1999  \n",
       "16        Milwaukee, Wisconsin             17500          2018  \n",
       "17      Minneapolis, Minnesota             18978          1990  \n",
       "18      New Orleans, Louisiana             16867          1999  \n",
       "19     New York City, New York             19812          1968  \n",
       "20     Oklahoma City, Oklahoma             18203          2002  \n",
       "21            Orlando, Florida             18846          2010  \n",
       "22  Philadelphia, Pennsylvania             20478          1996  \n",
       "23            Phoenix, Arizona             18055          1992  \n",
       "24            Portland, Oregon             19441          1995  \n",
       "25      Sacramento, California             17583          2016  \n",
       "26          San Antonio, Texas             18418          2002  \n",
       "27            Toronto, Ontario             19800          1999  \n",
       "28        Salt Lake City, Utah             18306          1991  \n",
       "29            Washington, D.C.             20356          1997  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table10 = pd.read_html(tags2[0].prettify())\n",
    "my_table10=my_table10[0]\n",
    "type(my_table10)\n",
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obesity=[\"Memphis\", \"New Orleans\", \"Oklahoma City\", \"San Antonio\", \"Dallas\"]\n",
    "type(obesity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Team Name</th>\n",
       "      <th>Arena Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Opening Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>18118</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>TD Garden</td>\n",
       "      <td>Boston</td>\n",
       "      <td>18624</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Barclays Center</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>17732</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Spectrum Center</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>19077</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>United Center</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>20917</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Rocket Mortgage Fieldhouse</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>19432</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>American Airlines Center</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>19200</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Pepsi Center</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19520</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Little Caesars Arena</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>20491</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>Chase Center</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>18064</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Toyota Center</td>\n",
       "      <td>Houston</td>\n",
       "      <td>18055</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Bankers Life Fieldhouse</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>17923</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>FedEx Forum</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>17794</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>American Airlines Arena</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19600</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Fiserv Forum</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>17500</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Target Center</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>18978</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>Smoothie King Center</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>16867</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>Madison Square Garden</td>\n",
       "      <td>New York City</td>\n",
       "      <td>19812</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Chesapeake Energy Arena</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>18203</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Amway Center</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>18846</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Wells Fargo Center</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>20478</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Talking Stick Resort Arena</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>18055</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Moda Center</td>\n",
       "      <td>Portland</td>\n",
       "      <td>19441</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Golden 1 Center</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>17583</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>AT&amp;T Center</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>18418</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Scotiabank Arena</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>19800</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Vivint Smart Home Arena</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>18306</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Capital One Arena</td>\n",
       "      <td>Washington</td>\n",
       "      <td>20356</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name                  Arena Name  Arena Location  \\\n",
       "0            Atlanta Hawks            State Farm Arena         Atlanta   \n",
       "1           Boston Celtics                   TD Garden          Boston   \n",
       "2            Brooklyn Nets             Barclays Center        Brooklyn   \n",
       "3        Charlotte Hornets             Spectrum Center       Charlotte   \n",
       "4            Chicago Bulls               United Center         Chicago   \n",
       "5      Cleveland Cavaliers  Rocket Mortgage Fieldhouse       Cleveland   \n",
       "6         Dallas Mavericks    American Airlines Center          Dallas   \n",
       "7           Denver Nuggets                Pepsi Center          Denver   \n",
       "8          Detroit Pistons        Little Caesars Arena         Detroit   \n",
       "9    Golden State Warriors                Chase Center   San Francisco   \n",
       "10         Houston Rockets               Toyota Center         Houston   \n",
       "11          Indiana Pacers     Bankers Life Fieldhouse    Indianapolis   \n",
       "12    Los Angeles Clippers              Staples Center     Los Angeles   \n",
       "13      Los Angeles Lakers              Staples Center     Los Angeles   \n",
       "14       Memphis Grizzlies                 FedEx Forum         Memphis   \n",
       "15              Miami Heat     American Airlines Arena           Miami   \n",
       "16         Milwaukee Bucks                Fiserv Forum       Milwaukee   \n",
       "17  Minnesota Timberwolves               Target Center     Minneapolis   \n",
       "18    New Orleans Pelicans        Smoothie King Center     New Orleans   \n",
       "19         New York Knicks       Madison Square Garden   New York City   \n",
       "20   Oklahoma City Thunder     Chesapeake Energy Arena   Oklahoma City   \n",
       "21           Orlando Magic                Amway Center         Orlando   \n",
       "22      Philadelphia 76ers          Wells Fargo Center    Philadelphia   \n",
       "23            Phoenix Suns  Talking Stick Resort Arena         Phoenix   \n",
       "24  Portland Trail Blazers                 Moda Center        Portland   \n",
       "25        Sacramento Kings             Golden 1 Center      Sacramento   \n",
       "26       San Antonio Spurs                 AT&T Center     San Antonio   \n",
       "27         Toronto Raptors            Scotiabank Arena         Toronto   \n",
       "28               Utah Jazz     Vivint Smart Home Arena  Salt Lake City   \n",
       "29      Washington Wizards           Capital One Arena      Washington   \n",
       "\n",
       "    Seating Capacity  Opening Year  \n",
       "0              18118          1999  \n",
       "1              18624          1995  \n",
       "2              17732          2012  \n",
       "3              19077          2005  \n",
       "4              20917          1994  \n",
       "5              19432          1994  \n",
       "6              19200          2001  \n",
       "7              19520          1999  \n",
       "8              20491          2017  \n",
       "9              18064          2019  \n",
       "10             18055          2003  \n",
       "11             17923          1999  \n",
       "12             19068          1999  \n",
       "13             19068          1999  \n",
       "14             17794          2004  \n",
       "15             19600          1999  \n",
       "16             17500          2018  \n",
       "17             18978          1990  \n",
       "18             16867          1999  \n",
       "19             19812          1968  \n",
       "20             18203          2002  \n",
       "21             18846          2010  \n",
       "22             20478          1996  \n",
       "23             18055          1992  \n",
       "24             19441          1995  \n",
       "25             17583          2016  \n",
       "26             18418          2002  \n",
       "27             19800          1999  \n",
       "28             18306          1991  \n",
       "29             20356          1997  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table10[\"Arena Location\"]=my_table10[\"Arena Location\"].str.split(\",\", expand=True)[0]\n",
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(445, 12)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbawg=nba.copy()\n",
    "nbawg.dropna(subset=[\"Height_i\", \"Weight\", \"Salary\"], inplace=True)\n",
    "nbawg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>NumPosition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Bojan Bogdanovic</td>\n",
       "      <td>SF</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18700000.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name Position       Team   Age  Height_i  Weight College  \\\n",
       "541  Bojan Bogdanovic       SF  Utah Jazz  32.0       6.7   226.0     NaN   \n",
       "\n",
       "         Salary  Points  Rebounds  Assists  NumPosition  \n",
       "541  18700000.0    17.0       3.9      1.9            3  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbawg.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>NumPosition</th>\n",
       "      <th>Inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Jayson Tatum</td>\n",
       "      <td>SF</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>28103500.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Position            Team   Age Height_i  Weight College  \\\n",
       "16  Jayson Tatum       SF  Boston Celtics  23.0      6.8   210.0    Duke   \n",
       "\n",
       "        Salary  Points  Rebounds  Assists  NumPosition  Inches  \n",
       "16  28103500.0    26.4       7.4      4.3            3      80  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "nbawg['Height_i'] = nbawg['Height_i'].astype(str)\n",
    "nbawg['Inches'] = nbawg['Height_i'].str.split(\".\", expand=True)[0].astype(int) * 12 + nbawg['Height_i'].str.split(\".\", expand=True)[1].astype(int)\n",
    "nbawg.sample()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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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       "\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>Position</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_i</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Points</th>\n",
       "      <th>Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>NumPosition</th>\n",
       "      <th>Inches</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>Jusuf Nurkic</td>\n",
       "      <td>C</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.11</td>\n",
       "      <td>290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5</td>\n",
       "      <td>83</td>\n",
       "      <td>29.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>PF</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>21780000.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>27.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Jaylen Nowell</td>\n",
       "      <td>SG</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>201.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1782621.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>24.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>Brandon Ingram</td>\n",
       "      <td>SF</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>29467800.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>20.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Devin Booker</td>\n",
       "      <td>SG</td>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>31650600.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>24.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Kyle Kuzma</td>\n",
       "      <td>SF</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>221.0</td>\n",
       "      <td>Utah</td>\n",
       "      <td>13000000.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>29.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Ish Smith</td>\n",
       "      <td>PG</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Wake Forest</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>23.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>De'Anthony Melton</td>\n",
       "      <td>SG</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>200.0</td>\n",
       "      <td>USC</td>\n",
       "      <td>8805976.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>25.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>SF</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>215.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>1669178.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>23.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Derrick White</td>\n",
       "      <td>PG</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>15178571.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>23.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Position                    Team   Age Height_i  \\\n",
       "534       Jusuf Nurkic        C  Portland Trail Blazers  27.0     6.11   \n",
       "58       Julius Randle       PF         New York Knicks  26.0      6.8   \n",
       "494      Jaylen Nowell       SG  Minnesota Timberwolves  22.0      6.4   \n",
       "439     Brandon Ingram       SF    New Orleans Pelicans  24.0      6.8   \n",
       "338       Devin Booker       SG            Phoenix Suns  24.0      6.5   \n",
       "279         Kyle Kuzma       SF      Washington Wizards  26.0      6.1   \n",
       "224          Ish Smith       PG       Charlotte Hornets  33.0      6.0   \n",
       "424  De'Anthony Melton       SG       Memphis Grizzlies  23.0      6.2   \n",
       "323       Trevor Ariza       SF      Los Angeles Lakers  36.0      6.8   \n",
       "466      Derrick White       PG       San Antonio Spurs  27.0      6.4   \n",
       "\n",
       "     Weight      College      Salary  Points  Rebounds  Assists  NumPosition  \\\n",
       "534   290.0          NaN  12000000.0    11.5       9.0      3.4            5   \n",
       "58    250.0     Kentucky  21780000.0    24.1      10.2      6.0            4   \n",
       "494   201.0   Washington   1782621.0     9.0       2.3      1.5            2   \n",
       "439   190.0         Duke  29467800.0    23.8       4.9      4.9            3   \n",
       "338   206.0     Kentucky  31650600.0    25.6       4.2      4.3            2   \n",
       "279   221.0         Utah  13000000.0    12.9       6.1      1.9            3   \n",
       "224   175.0  Wake Forest   4500000.0     6.7       3.4      3.9            1   \n",
       "424   200.0          USC   8805976.0     9.1       3.1      2.5            2   \n",
       "323   215.0         UCLA   1669178.0     9.4       4.8      1.8            3   \n",
       "466   190.0     Colorado  15178571.0    15.4       3.0      3.5            1   \n",
       "\n",
       "     Inches     IMC  \n",
       "534      83  29.594  \n",
       "58       80  27.461  \n",
       "494      76  24.464  \n",
       "439      80  20.870  \n",
       "338      77  24.425  \n",
       "279      73  29.154  \n",
       "224      72  23.732  \n",
       "424      74  25.676  \n",
       "323      80  23.616  \n",
       "466      76  23.125  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbawg[\"IMC\"]= round((nbawg[\"Weight\"]/(nbawg[\"Inches\"]**2))*703, 3)\n",
    "nbawg.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbawg['Team'] = nbawg['Team'].replace(\"Sacremento Kings\", \"Sacramento Kings\").replace(\"Philadelphia Sixers\", \"Philadelphia 76ers\")\n",
    "#A little bit more of cleaning, there were a couple of NBA Teams with the name misspelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Team Name</th>\n",
       "      <th>Arena Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Opening Year</th>\n",
       "      <th>Obesity Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>State Farm Arena</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>18118</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>TD Garden</td>\n",
       "      <td>Boston</td>\n",
       "      <td>18624</td>\n",
       "      <td>1995</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Barclays Center</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>17732</td>\n",
       "      <td>2012</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Spectrum Center</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>19077</td>\n",
       "      <td>2005</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>United Center</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>20917</td>\n",
       "      <td>1994</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Rocket Mortgage Fieldhouse</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>19432</td>\n",
       "      <td>1994</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>American Airlines Center</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>19200</td>\n",
       "      <td>2001</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Pepsi Center</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19520</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Little Caesars Arena</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>20491</td>\n",
       "      <td>2017</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>Chase Center</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>18064</td>\n",
       "      <td>2019</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Toyota Center</td>\n",
       "      <td>Houston</td>\n",
       "      <td>18055</td>\n",
       "      <td>2003</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Bankers Life Fieldhouse</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>17923</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Staples Center</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>FedEx Forum</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>17794</td>\n",
       "      <td>2004</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>American Airlines Arena</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19600</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Fiserv Forum</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>17500</td>\n",
       "      <td>2018</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Target Center</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>18978</td>\n",
       "      <td>1990</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>Smoothie King Center</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>16867</td>\n",
       "      <td>1999</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>Madison Square Garden</td>\n",
       "      <td>New York City</td>\n",
       "      <td>19812</td>\n",
       "      <td>1968</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Chesapeake Energy Arena</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>18203</td>\n",
       "      <td>2002</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Amway Center</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>18846</td>\n",
       "      <td>2010</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Wells Fargo Center</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>20478</td>\n",
       "      <td>1996</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Talking Stick Resort Arena</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>18055</td>\n",
       "      <td>1992</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Moda Center</td>\n",
       "      <td>Portland</td>\n",
       "      <td>19441</td>\n",
       "      <td>1995</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Golden 1 Center</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>17583</td>\n",
       "      <td>2016</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>AT&amp;T Center</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>18418</td>\n",
       "      <td>2002</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Scotiabank Arena</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>19800</td>\n",
       "      <td>1999</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Vivint Smart Home Arena</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>18306</td>\n",
       "      <td>1991</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Capital One Arena</td>\n",
       "      <td>Washington</td>\n",
       "      <td>20356</td>\n",
       "      <td>1997</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name                  Arena Name  Arena Location  \\\n",
       "0            Atlanta Hawks            State Farm Arena         Atlanta   \n",
       "1           Boston Celtics                   TD Garden          Boston   \n",
       "2            Brooklyn Nets             Barclays Center        Brooklyn   \n",
       "3        Charlotte Hornets             Spectrum Center       Charlotte   \n",
       "4            Chicago Bulls               United Center         Chicago   \n",
       "5      Cleveland Cavaliers  Rocket Mortgage Fieldhouse       Cleveland   \n",
       "6         Dallas Mavericks    American Airlines Center          Dallas   \n",
       "7           Denver Nuggets                Pepsi Center          Denver   \n",
       "8          Detroit Pistons        Little Caesars Arena         Detroit   \n",
       "9    Golden State Warriors                Chase Center   San Francisco   \n",
       "10         Houston Rockets               Toyota Center         Houston   \n",
       "11          Indiana Pacers     Bankers Life Fieldhouse    Indianapolis   \n",
       "12    Los Angeles Clippers              Staples Center     Los Angeles   \n",
       "13      Los Angeles Lakers              Staples Center     Los Angeles   \n",
       "14       Memphis Grizzlies                 FedEx Forum         Memphis   \n",
       "15              Miami Heat     American Airlines Arena           Miami   \n",
       "16         Milwaukee Bucks                Fiserv Forum       Milwaukee   \n",
       "17  Minnesota Timberwolves               Target Center     Minneapolis   \n",
       "18    New Orleans Pelicans        Smoothie King Center     New Orleans   \n",
       "19         New York Knicks       Madison Square Garden   New York City   \n",
       "20   Oklahoma City Thunder     Chesapeake Energy Arena   Oklahoma City   \n",
       "21           Orlando Magic                Amway Center         Orlando   \n",
       "22      Philadelphia 76ers          Wells Fargo Center    Philadelphia   \n",
       "23            Phoenix Suns  Talking Stick Resort Arena         Phoenix   \n",
       "24  Portland Trail Blazers                 Moda Center        Portland   \n",
       "25        Sacramento Kings             Golden 1 Center      Sacramento   \n",
       "26       San Antonio Spurs                 AT&T Center     San Antonio   \n",
       "27         Toronto Raptors            Scotiabank Arena         Toronto   \n",
       "28               Utah Jazz     Vivint Smart Home Arena  Salt Lake City   \n",
       "29      Washington Wizards           Capital One Arena      Washington   \n",
       "\n",
       "    Seating Capacity  Opening Year Obesity Risk  \n",
       "0              18118          1999     Low Risk  \n",
       "1              18624          1995     Low Risk  \n",
       "2              17732          2012     Low Risk  \n",
       "3              19077          2005     Low Risk  \n",
       "4              20917          1994     Low Risk  \n",
       "5              19432          1994     Low Risk  \n",
       "6              19200          2001    High Risk  \n",
       "7              19520          1999     Low Risk  \n",
       "8              20491          2017     Low Risk  \n",
       "9              18064          2019     Low Risk  \n",
       "10             18055          2003     Low Risk  \n",
       "11             17923          1999     Low Risk  \n",
       "12             19068          1999     Low Risk  \n",
       "13             19068          1999     Low Risk  \n",
       "14             17794          2004    High Risk  \n",
       "15             19600          1999     Low Risk  \n",
       "16             17500          2018     Low Risk  \n",
       "17             18978          1990     Low Risk  \n",
       "18             16867          1999    High Risk  \n",
       "19             19812          1968     Low Risk  \n",
       "20             18203          2002    High Risk  \n",
       "21             18846          2010     Low Risk  \n",
       "22             20478          1996     Low Risk  \n",
       "23             18055          1992     Low Risk  \n",
       "24             19441          1995     Low Risk  \n",
       "25             17583          2016     Low Risk  \n",
       "26             18418          2002    High Risk  \n",
       "27             19800          1999     Low Risk  \n",
       "28             18306          1991     Low Risk  \n",
       "29             20356          1997     Low Risk  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table10[\"Obesity Risk\"]=np.where(my_table10[\"Arena Location\"].isin(obesity), \"High Risk\", \"Low Risk\")\n",
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Team Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Obesity Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>18118</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Boston</td>\n",
       "      <td>18624</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>17732</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>19077</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>20917</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>19432</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>19200</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19520</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>20491</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>18064</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Houston</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>17923</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>17794</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19600</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>17500</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>18978</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>16867</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>New York City</td>\n",
       "      <td>19812</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>18203</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>18846</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>20478</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Portland</td>\n",
       "      <td>19441</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>17583</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>18418</td>\n",
       "      <td>High Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>19800</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>18306</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Washington</td>\n",
       "      <td>20356</td>\n",
       "      <td>Low Risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name  Arena Location  Seating Capacity Obesity Risk\n",
       "0            Atlanta Hawks         Atlanta             18118     Low Risk\n",
       "1           Boston Celtics          Boston             18624     Low Risk\n",
       "2            Brooklyn Nets        Brooklyn             17732     Low Risk\n",
       "3        Charlotte Hornets       Charlotte             19077     Low Risk\n",
       "4            Chicago Bulls         Chicago             20917     Low Risk\n",
       "5      Cleveland Cavaliers       Cleveland             19432     Low Risk\n",
       "6         Dallas Mavericks          Dallas             19200    High Risk\n",
       "7           Denver Nuggets          Denver             19520     Low Risk\n",
       "8          Detroit Pistons         Detroit             20491     Low Risk\n",
       "9    Golden State Warriors   San Francisco             18064     Low Risk\n",
       "10         Houston Rockets         Houston             18055     Low Risk\n",
       "11          Indiana Pacers    Indianapolis             17923     Low Risk\n",
       "12    Los Angeles Clippers     Los Angeles             19068     Low Risk\n",
       "13      Los Angeles Lakers     Los Angeles             19068     Low Risk\n",
       "14       Memphis Grizzlies         Memphis             17794    High Risk\n",
       "15              Miami Heat           Miami             19600     Low Risk\n",
       "16         Milwaukee Bucks       Milwaukee             17500     Low Risk\n",
       "17  Minnesota Timberwolves     Minneapolis             18978     Low Risk\n",
       "18    New Orleans Pelicans     New Orleans             16867    High Risk\n",
       "19         New York Knicks   New York City             19812     Low Risk\n",
       "20   Oklahoma City Thunder   Oklahoma City             18203    High Risk\n",
       "21           Orlando Magic         Orlando             18846     Low Risk\n",
       "22      Philadelphia 76ers    Philadelphia             20478     Low Risk\n",
       "23            Phoenix Suns         Phoenix             18055     Low Risk\n",
       "24  Portland Trail Blazers        Portland             19441     Low Risk\n",
       "25        Sacramento Kings      Sacramento             17583     Low Risk\n",
       "26       San Antonio Spurs     San Antonio             18418    High Risk\n",
       "27         Toronto Raptors         Toronto             19800     Low Risk\n",
       "28               Utah Jazz  Salt Lake City             18306     Low Risk\n",
       "29      Washington Wizards      Washington             20356     Low Risk"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table10.drop(columns=[\"Arena Name\", \"Opening Year\"], inplace=True)\n",
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_9240\\3526918679.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  mean_by_team = nbawg.groupby('Team').mean()\n"
     ]
    }
   ],
   "source": [
    "mean_by_team = nbawg.groupby('Team').mean()\n",
    "mean_by_team = mean_by_team.reset_index()\n",
    "\n",
    "merged_df = pd.merge(my_table10, mean_by_team, left_on='Team Name', right_on='Team')\n",
    "my_table10['IMCmean'] = merged_df['IMC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Team Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Obesity Risk</th>\n",
       "      <th>IMCmean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>18118</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.755214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Boston</td>\n",
       "      <td>18624</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.073875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>17732</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.308812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>19077</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.349688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>20917</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.853308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>19432</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.983429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>19200</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>25.213200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19520</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.574600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>20491</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.626286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>18064</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.517625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Houston</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.697267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>17923</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.426733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.220643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.700538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>17794</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.454056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19600</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.721429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>17500</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.986929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>18978</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.427182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>16867</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.111937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>New York City</td>\n",
       "      <td>19812</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.541062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>18203</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.298867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>18846</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.224643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>20478</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.178714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.298533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Portland</td>\n",
       "      <td>19441</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.895231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>17583</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.039188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>18418</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>25.053471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>19800</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>23.879667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>18306</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.766067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Washington</td>\n",
       "      <td>20356</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.480250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name  Arena Location  Seating Capacity Obesity Risk  \\\n",
       "0            Atlanta Hawks         Atlanta             18118     Low Risk   \n",
       "1           Boston Celtics          Boston             18624     Low Risk   \n",
       "2            Brooklyn Nets        Brooklyn             17732     Low Risk   \n",
       "3        Charlotte Hornets       Charlotte             19077     Low Risk   \n",
       "4            Chicago Bulls         Chicago             20917     Low Risk   \n",
       "5      Cleveland Cavaliers       Cleveland             19432     Low Risk   \n",
       "6         Dallas Mavericks          Dallas             19200    High Risk   \n",
       "7           Denver Nuggets          Denver             19520     Low Risk   \n",
       "8          Detroit Pistons         Detroit             20491     Low Risk   \n",
       "9    Golden State Warriors   San Francisco             18064     Low Risk   \n",
       "10         Houston Rockets         Houston             18055     Low Risk   \n",
       "11          Indiana Pacers    Indianapolis             17923     Low Risk   \n",
       "12    Los Angeles Clippers     Los Angeles             19068     Low Risk   \n",
       "13      Los Angeles Lakers     Los Angeles             19068     Low Risk   \n",
       "14       Memphis Grizzlies         Memphis             17794    High Risk   \n",
       "15              Miami Heat           Miami             19600     Low Risk   \n",
       "16         Milwaukee Bucks       Milwaukee             17500     Low Risk   \n",
       "17  Minnesota Timberwolves     Minneapolis             18978     Low Risk   \n",
       "18    New Orleans Pelicans     New Orleans             16867    High Risk   \n",
       "19         New York Knicks   New York City             19812     Low Risk   \n",
       "20   Oklahoma City Thunder   Oklahoma City             18203    High Risk   \n",
       "21           Orlando Magic         Orlando             18846     Low Risk   \n",
       "22      Philadelphia 76ers    Philadelphia             20478     Low Risk   \n",
       "23            Phoenix Suns         Phoenix             18055     Low Risk   \n",
       "24  Portland Trail Blazers        Portland             19441     Low Risk   \n",
       "25        Sacramento Kings      Sacramento             17583     Low Risk   \n",
       "26       San Antonio Spurs     San Antonio             18418    High Risk   \n",
       "27         Toronto Raptors         Toronto             19800     Low Risk   \n",
       "28               Utah Jazz  Salt Lake City             18306     Low Risk   \n",
       "29      Washington Wizards      Washington             20356     Low Risk   \n",
       "\n",
       "      IMCmean  \n",
       "0   25.755214  \n",
       "1   26.073875  \n",
       "2   26.308812  \n",
       "3   24.349688  \n",
       "4   24.853308  \n",
       "5   24.983429  \n",
       "6   25.213200  \n",
       "7   25.574600  \n",
       "8   24.626286  \n",
       "9   24.517625  \n",
       "10  25.697267  \n",
       "11  24.426733  \n",
       "12  25.220643  \n",
       "13  26.700538  \n",
       "14  24.454056  \n",
       "15  25.721429  \n",
       "16  24.986929  \n",
       "17  24.427182  \n",
       "18  24.111937  \n",
       "19  24.541062  \n",
       "20  24.298867  \n",
       "21  25.224643  \n",
       "22  25.178714  \n",
       "23  25.298533  \n",
       "24  24.895231  \n",
       "25  25.039188  \n",
       "26  25.053471  \n",
       "27  23.879667  \n",
       "28  26.766067  \n",
       "29  26.480250  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = my_table10.groupby(\"Obesity Risk\")\n",
    "mean_values = grouped[\"IMCmean\"].mean()\n",
    "\n",
    "mean_values.plot.bar()\n",
    "plt.xlabel(\"Comparison between Obesity Risk Cities and not\")\n",
    "plt.ylabel(\"IMC\")\n",
    "plt.title(\"IMC by Obesity Risk\")\n",
    "plt.savefig(\"Images/HighLow.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So... Yeah, it has nothing to do the Obesity rate with the player's IMC. Moreover, in the cities with lest Obesity Rate, the IMC of the players is higher!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_9240\\61918642.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  mean_by_team = nbawg.groupby('Team').mean()\n"
     ]
    }
   ],
   "source": [
    "mean_by_team = nbawg.groupby('Team').mean()\n",
    "mean_by_team = mean_by_team.reset_index()\n",
    "\n",
    "merged_df = pd.merge(my_table10, mean_by_team, left_on='Team Name', right_on='Team')\n",
    "my_table10['Salarymean'] = merged_df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BSxBmA69Wu0+99qBW3mODxwnCbMC10h402Tak+YtH2fPky9niuB+w58mXu+2iT7mKyWzAtXLTm8ncKMcXSQ4OpVG3+8fIyEgsXLiw22GYWQ17nnx5aRfnmdOmctVxr+9CRAYgaVFEjDTzHpcgzIZcu693cAP34HAbhNkQq+J6BzdwDw4nCJuQGxwHVxXXO5Q1cAvYZ5sZLa/TusMJwuoaxCtqnfBWqaI66JCdZvLWXWaiwrQALlg0OtTfdT9ygrC6Bu2K2kFMeJNRVXXQFbc9yPjuL/283wwrJwirq5kzzH44Mx+0hDdZVY2Z5YbqweAEYXU1eobZL2fmPnCtrqoxs9xQPRjczdXqmrvv1qtd9ATlZ5j1zsx76eIoD0P+fFWMmdXofmO9zSUIq6vRM8x+OTP3MOSd4dF8B8OEJQhJewInAJvn5QVERLys2tCsVzRyhtkvZ+aTGULCmuPRfPtfI1VM3wA+DCwCVk6wrA2pfqpS8IHLrDGNJIhHI+LSyiOxvuYzc+tHvq1qfY0kiCsknQJcCDw1NjEirqssKutLPjO3fuJRZyfWSILYLf8tjgIYgIdlNLO+1S8977ppwgQREft0IhAzs07ql5533dTQdRCS3gxsB6w7Ni0iTqoqKDOzqvVLz7tumvA6CEmnAe8A/prUxfXtpC6vZmZ9y9fETKyRC+X2iIgjgUci4kRgd+Dl1YZlZlYtX8w3sUaqmMbKYE9K2hR4GNikupCsUb3WRa/X4jGbiHve1ddIgrhY0jTgFOA6Ug+mM6oMyibWa130ei0eM5u8CauYIuJTEbE8Ii4gtT1sExEfrz40q6fXhq3utXjMbPIaaaR+gaSPS/p6RDwFvFjSgR2IzerotS56vRaPmU1eI43U3yRdQb17fj0KfLqyiKwhvTbefq/FY2aT10iC2DIi/gl4BiAinoTVbjdbk6T9JC2VtEzScSXzZ0u6QtJiSTdIOqCp6IdYr3XR67V4zGzyGmmkflrSVFLjNJK2pDAmUy2SpgBfAf4MuAe4VtJFEXFLYbGPAedHxFclbQtcAsxp7iMMp14bHK/X4qmae2zZMGgkQXwSuAyYJek7wJ7AUQ28b1dgWUTcCSDpXOBgoJggAtggP38RcG9jYRv0Xhe9XounKu6xZcOikbGYfijpOuDVpKqlYyPioQbWPRO4u/D6HlYN/DfmBOC/Jf018ELgjWUrknQMcAzA7NmzG9i0WXU6PcibSyvWLTXbICTtPPYgdW+9j3SGPztPa4fDgXkRsRlwAPAfkp4XU0ScHhEjETEyY8aMNm3arDWd7LE1VloZXb6CYFVpZf7i0bZvy2y8eiWIhcBNwFhpodgw3chw36PArMLrzfK0ovcC+wFExC8lrQtMB343wbrNuqaTg7x5SGrrpnq9mP4WeIw01MY3gbdExD750ci9IK4FtpK0haS1gcOAi8Yt8z/AGwAkvYI0WuyDTX4Gs47qZI8tX19i3VQzQUTElyLiNaRRXGcBP5Z0vqQdG1lxRDwLfBBYANxK6q10s6STJB2UF/sI8H5J1wPnAEdFRLT+ccyq18lB3nx9iXWTGjkeS9qOVAJ4F/D3EXF+1YHVMjIyEgsXLuzW5s06anyPKUilFY86as2StCgiRiZecpWabRCSXkZKCgeTeiOdC3w2Ily2NeuQYbu+xHpLzRKEpD8CNwD/SWqLWG3BiPhi5dGVcAnCzKx5bS1BACexKims13JUZmbWl2omiIg4oYNxmJlZj2lksD4zMxtCThBmZlaqkRsGTZloGTMzGzyNlCB+LemUPBy3mZkNiUaG+96BdD3EGXkgvTOBcyPisUojayOPhmlm1rwJSxAR8XhEfD0i9gD+gXR/iPskfUvSn1Qe4SR5NEwzs9Y01AYh6SBJ3we+BHwBeBnwX6Q7wPW0eqNhmplZbY1UMf0auAI4JSJ+UZj+PUmvrSas9vFomGZmralbgsg9mOZFxHvHJQcAIuJDlUXWJh4N08ysNXUTRESsBA7sUCyV6OTY/WZmg6SRKqarJP0bcB7wh7GJEXFdZVG1kUfDNDNrTSMJYsf896TCtEZuOdozDtlpphOCmVmTJkwQEbFPJwIxM7Pe0kgJAklvBrYj3TMagIg4qfY7zMys3zVyHcRpwDtI96YW8HZg84rjMjOzLmtkLKY9IuJI4JGIOBHYHXh5tWGZmVm3NZIgxq4oe1LSpsAzwCbVhWRmZr2gkTaIiyVNA04BriP1YDqjyqDMhoUHkrRe1kgvpk/lpxdIuhhYNyIerTYss8E3NpDk2FhhYwNJAk4S1hNqJghJh9aZR0RcWE1IZsOh3kCSThDWC+qVIN5SZ14AThDWNYNQNeOBJK3X1UwQEXF0JwMxa9SgVM1sOm0qoyXJwANJWq9opBcTkt4s6e8lfWLsUXVgZrUMyj0+PJCk9boJG6nzhXIvAPYh9V56G/CriuMyq2lQqmY8kKT1uka6ue4REX8q6YaIOFHSF4BLqw7MrJZBqprxQJLWy3yhnPUdV82YdUarF8p9vcqgzOpx1YxZZygiGl9YWocuXyg3MjISCxcu7Nbmzcz6kqRFETHSzHtqVjFJepWklxZeHwmcD3xK0kath2lmZv2gXhvE14CnASS9FjgZ+DbwKHB69aGZmVk31WuDmBIRv8/P3wGcHhEXkMZkWlJ5ZGZm1lV1E4SkNSPiWeANwDENvs/MbKAMwtAurah3oD8H+Imkh0hdXX8GIOlPSNVMZmYDb1CGdmlFzTaIiPgM8BFgHvCaWNXdaQ3S7UfNzAbeoAzt0oq6VUURcXXJtNurC8c6aViLzWbNGJShXVrR0GB9rZK0n6SlkpZJOq7GMn8h6RZJN0s6u8p4bJWxYvPo8hUEq4rN8xePdjs0s55SawiXfhzapVmVJQhJU4CvAPsD2wKHS9p23DJbAccDe0bEdsDfVBWPrW6Yi81mzRjmoV2q7I20K7AsIu4EkHQucDBwS2GZ9wNfiYhHACLidxXGYwXDXGw2a8YwD+1SZYKYCdxdeH0PsNu4ZV4OIOkqYApwQkRcNn5Fko4hd7OdPXt2JcEOm0EaEdWsasM66m6lbRANWBPYCtgbOBz4eh4YcDURcXpEjETEyIwZMzob4YAa5mKzmTWmyhLEKDCr8HqzPK3oHuCaiHgG+I2k20kJ49oK4zKGu9hsZo2pMkFcC2wlaQtSYjgMOGLcMvNJJYdvSppOqnK6s8KYrGBYi81m1pjKqpjyEB0fBBYAtwLnR8TNkk6SdFBebAHwsKRbgCuAuRHxcFUxmZlZ45q6H0Qv8P0gzMya19b7QZiZ2XBzgjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMrVWmCkLSfpKWSlkk6rs5yb5UUkkaqjMfMzBpXWYKQNAX4CrA/sC1wuKRtS5ZbHzgWuKaqWMzMrHlVliB2BZZFxJ0R8TRwLnBwyXKfAj4P/G+FsZiZWZOqTBAzgbsLr+/J054jaWdgVkT8oN6KJB0jaaGkhQ8++GD7IzUzs+fpWiO1pDWALwIfmWjZiDg9IkYiYmTGjBnVB2dmZpUmiFFgVuH1ZnnamPWB7YErJf0WeDVwkRuqzcx6Q5UJ4lpgK0lbSFobOAy4aGxmRDwaEdMjYk5EzAGuBg6KiIUVxmRmZg2qLEFExLPAB4EFwK3A+RFxs6STJB1U1XbNzKw91qxy5RFxCXDJuGmfqLHs3lXGYmZmzfGV1GZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKxUpd1czWqZv3iUUxYs5d7lK9h02lTm7rs1h+w0c+I3mlnHOEFYx81fPMrxF97IimdWAjC6fAXHX3gjgJOEWQ9xFZN13CkLlj6XHMaseGYlpyxY2qWIzKyME4R13L3LVzQ13cy6wwnCOm7TaVObmm5m3eEEYR03d9+tmbrWlNWmTV1rCnP33bpLEZlZGTdSW8eNNUS7F5NZb3OCsK44ZKeZTghmPc5VTGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalFBHdjqEpkh4E7qp4M9OBhyreRiscV+N6MSbozbh6MSbozbh6MSZoLK7NI2JGMyvtuwTRCZIWRsRIt+MYz3E1rhdjgt6Mqxdjgt6MqxdjgurichWTmZmVcoIwM7NSThDlTu92ADU4rsb1YkzQm3H1YkzQm3H1YkxQUVxugzAzs1IuQZiZWSknCDMzKxcRA/sAzgR+B9xUmHYesCQ/fgssKcw7HlgGLAX2LUzfL09bBhxXmL4FcE2efh6wdjtjAuYAKwrzTiu8Zxfgxrztf2FVdeFGwA+BX+e/G07iu9oRuDpveyGwa56uvM1lwA3AzoX3vDtv+9fAuyeKt81x7Q08Wvi+PtHB33AH4Jf5M/4XsEEn96tm4+rUvgXMAq4AbgFuBo6tt65O7FstxNSp/apWXG/Pr/8IjIx7T6X7VtcP4lU+gNcCOxf/YcbN/8LYjw1sC1wPrJO/xDuAKflxB/AyYO28zLb5PecDh+XnpwEfaHNMc+os9yvg1fkf6lJg/zz9n8Z2COA44POtflfAfxfWewBwZeH5pXnbrwauKfyD3Zn/bpifb1gv3jbHtTdwcck6Kv8NgWuB1+Xn7wE+1cn9qoW4OrJvAZuQD/LA+sDt+TspXVcn9q0WYurUflUrrlcAWwNXUkgQndi3un4Qr/pR6x8h70x3A1vl18cDxxfmLwB2z48FhenH54dIVy6umaevtlybYqq13CbAbYXXhwNfy8+XApsUllva6neVv4N3FLZxdn7+NeDwwnJL87aei6O4XL142xzX3pT/I1f+G5LOMMfOtGcBt3R6v2oyro7uW4X1/SfwZ7XW1el9q8GYOrZflcVVeH0lqyeIyvetYW6D2At4ICJ+nV/PJB2cx9yTp9WavjGwPCKeHTe9nTEBbCFpsaSfSNqrEOs9JTEBvCQi7svP7wdeMol4/gY4RdLdwD+TdrKx7TfzXdWLt51xAewu6XpJl0raboJ42/kb3gwcnJ+/nXQwrrftTu1XteKCDu9bkuYAO5GqOGqtq6P7VoMxQYf3q3Fx1VL5vjXMCeJw4JxuBzHO+JjuA2ZHxE7A3wJnS9qg0ZVFOk2IScTzAeDDETEL+DDwjUmsq51qxXUdabyZHYB/BeZ3MKb3AP9P0iJS9cDTHdx2PbXi6ui+JWk94ALgbyLiscmsq12aiKmj+1W9uDptKBOEpDWBQ0mNNGNGWf3sarM8rdb0h4FpeV3F6W2LKSKeioiH8/NFpHrFl+ftbFYSE8ADkjbJ69yE1GjZqncDF+bn3wV2zc+b/a7qxdu2uCLisYh4Ij+/BFhL0vQ6cbXtN4yI2yLiTRGxCynJ35FndXW/qhVXJ/ctSWuRDnjfiYix363WujqybzUTUyf3qxpx1VL5vjWUCQJ4I6neslg0vQg4TNI6krYAtiI1fl0LbCVpC0lrA4cBF+UzjCuAt+X3v5tUZ9i2mCTNkDQlP39ZjunOXAx+TNKrJQk4srDti3Is7YjpXuB1+fnrSb07xrZxpJJXA4/mmBYAb5K0oaQNgTeR6jjrxdu2uCS9NK8fSbuS9u+H6cBvKOnF+e8awMdIDYDQ5f2qVlyd2rfyOr4B3BoRXyzMqrWuyvetZmPq1H5VJ65aqt+3mm046acH6YzpPuAZUn3be/P0ecBfliz/UdKZ1FIKPSFIPStuz/M+Wpj+svyDLCOdya7TzpiAt5LqkJeQirlvKcwbAW7KMf0bqxoiNwZ+TDpo/gjYqNXvCngNsIjUC+IaYJe8rICv5G3fyOoNZ+/J38cy4OiJ4m1zXB/M39f1pG6we3TqNwSOzeu/HTi5+Pk6sV81G1en9q38WwWpy+qS/Dig1ro6sW+1EFOn9qtacf15/j2fAh5g9QboSvctD7VhZmalhrWKyczMJuAEYWZmpZwgzMyslBOEmZmVcoIwM7NSThDWFyR9VNLNkm6QtETSbi2u5xBJ2xZenyTpjW2KcT1JX5N0h6RFkq5sNc4Wtv2L/HeOpCM6sU0bfGtOvIhZd0naHTiQNNLlU/kq1rVbXN0hwMWkIZWJiE+0JcjkDOA3pMEW/5gvXtp2gve0RUTskZ/OAY4Azu7Edm2wuQRh/WAT4KGIeAogIh6KiHsBJO2iNNjcIkkLCkMlvF/StUoDrF0g6QWS9gAOIg30t0TSlpLmSXpbfs9vJZ0o6TpJN0raJk+fIemHuQRzhqS7cpJ6jqQtgd2Aj0XEH3Ocv4mIH+T583OMN0s6pvC+JySdmqf/WNKMWvHn6S+R9P08/fr8mZD0RF7lycBe+fN9WNJPJe1Y2N7PJe3Qzh/HBpcThPWD/wZmSbpd0r9Leh08N27NvwJvizTW0JnAZ/J7LoyIV0UaYO1W0hXrvyANTzA3InaMiDuevykeioidga8Cf5enfRK4PCK2A74HzC5533akGz2trPEZ3pNjHAE+JGnjPP2FwMK87p/kbZXGn6f/C/CTPH1n0hW+RccBP8uf71TS0A1H5e/r5cC6EXF9jRjNVuMEYT0v0kBpuwDHAA8C50k6inQTle2BH0paQhpraGzgtu0l/UzSjcA7SQfwRowNkLaIVF0DaQiEc3MslwGPtPAxPiRpbKiGWaRxcyDdJWxsgMaz8rbqxf96UvIiIlZGxKMTbPe7wIE5mb6HNKSLWUPcBmF9IZ+ZXwlcmQ+a7yYdxG+OiN1L3jIPOCQirs/JZO8GN/VU/ruS5v4/bgZ2kDRlfClC0t6kwRh3j4gnJV0JrFtjPWNj38yjtfhXX1na3g9J94T4C1KiNWuISxDW8yRtLWmrwqQdgbtIA5TNyI3YSFpLq27msj5wXz5zfmfhvY/nec24inRwRdKbSLe8XE2urloInFgY+XOOpDcDLwIeyQfrbUi3xxyzBqtG1zwC+PkE8f+YdD8MJE2R9KJxoZR9vjNIVVPXRkQrpR8bUk4Q1g/WA74l6RZJN5B6Bp0QEU+TDq6fz9U3S4Cx3jwfJ430ehVwW2Fd5wJzle6ktmWD2z+RNNT0TaS7st1POhCP9z7SXciW5WXnke4pcBmwpqRbSY3IVxfe8wdg17z864GTJoj/WGCfXIpaxPN7Sd0ArMwN2B+G5+738BjwzQY/rxmAR3M1m4ikdYCVEfFsLq18NSJ2bNO6n4iI9dqxrjrb2JRUPbfNWA8rs0a4DcJsYrOB85VuuvM08P4ux9MwSUeSenb9rZODNcslCDMzK+U2CDMzK+UEYWZmpZwgzMyslBOEmZmVcoIwM7NS/x+bf5qXulL+DgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(my_table10[\"Seating Capacity\"], my_table10[\"Salarymean\"])\n",
    "plt.xlabel(\"Seating Capacity\")\n",
    "plt.ylabel(\"Salary Mean\")\n",
    "plt.title(\"Seating Capacity vs. Salary Mean\")\n",
    "plt.savefig(\"Images/MeanCapacity.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After observing this scatterplot, I just didn't see any relation between the mean of the Salaries per Team depending on the Seating Capacity of the Stadium. Then I realized that maybe there were some teams that hire more players with less money (10 days contract or dual contracts, or just keep firing players in the mid season), so the mean of this teams will change drastically and gets extremely lower that the teams that are with the same players during the whole season, so I decided to try to see if the relation between the Seating Capacity and the Total Salary (total Salaries of all the team members) exist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#merged = pd.merge(my_table10, nbawg, left_on=\"Team Name\", right_on=\"Team\", how=\"left\")\n",
    "#my_table10[\"TotalSalary\"] = merged.groupby(\"Team Name\")[\"SalaryM\"].sum()\n",
    "#my_table10[\"TotalSalary\"].fillna(0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_9240\\4008999327.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped_salaries = nbawg.groupby(\"Team\").sum()[\"Salary\"]\n"
     ]
    }
   ],
   "source": [
    "grouped_salaries = nbawg.groupby(\"Team\").sum()[\"Salary\"]\n",
    "my_table10 = pd.merge(my_table10, grouped_salaries, left_on=\"Team Name\", right_index=True)\n",
    "my_table10.rename(columns={\"Salary\": \"TotalSalary\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Team Name</th>\n",
       "      <th>Arena Location</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Obesity Risk</th>\n",
       "      <th>IMCmean</th>\n",
       "      <th>Salarymean</th>\n",
       "      <th>TotalSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>18118</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.755214</td>\n",
       "      <td>9.504514e+06</td>\n",
       "      <td>133063190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Boston</td>\n",
       "      <td>18624</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.073875</td>\n",
       "      <td>8.880164e+06</td>\n",
       "      <td>142082621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>17732</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.308812</td>\n",
       "      <td>1.072710e+07</td>\n",
       "      <td>171633659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>19077</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.349688</td>\n",
       "      <td>6.752946e+06</td>\n",
       "      <td>108047136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>20917</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.853308</td>\n",
       "      <td>1.016693e+07</td>\n",
       "      <td>132170140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>19432</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.983429</td>\n",
       "      <td>9.314309e+06</td>\n",
       "      <td>130400325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>19200</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>25.213200</td>\n",
       "      <td>7.896939e+06</td>\n",
       "      <td>118454083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19520</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.574600</td>\n",
       "      <td>8.987236e+06</td>\n",
       "      <td>134808541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>20491</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.626286</td>\n",
       "      <td>6.174477e+06</td>\n",
       "      <td>86442675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>18064</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.517625</td>\n",
       "      <td>1.108017e+07</td>\n",
       "      <td>177282779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>Houston</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.697267</td>\n",
       "      <td>8.228100e+06</td>\n",
       "      <td>123421495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>17923</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.426733</td>\n",
       "      <td>8.923113e+06</td>\n",
       "      <td>133846695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.220643</td>\n",
       "      <td>1.173848e+07</td>\n",
       "      <td>164338785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19068</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.700538</td>\n",
       "      <td>1.151002e+07</td>\n",
       "      <td>149630287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>17794</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.454056</td>\n",
       "      <td>6.017279e+06</td>\n",
       "      <td>108311021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19600</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.721429</td>\n",
       "      <td>9.353919e+06</td>\n",
       "      <td>130954862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>17500</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.986929</td>\n",
       "      <td>1.058471e+07</td>\n",
       "      <td>148185964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>18978</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.427182</td>\n",
       "      <td>1.157992e+07</td>\n",
       "      <td>127379143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>16867</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.111937</td>\n",
       "      <td>7.293240e+06</td>\n",
       "      <td>116691835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>New York City</td>\n",
       "      <td>19812</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.541062</td>\n",
       "      <td>8.907381e+06</td>\n",
       "      <td>142518097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>18203</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>24.298867</td>\n",
       "      <td>3.363586e+06</td>\n",
       "      <td>50453784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>18846</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.224643</td>\n",
       "      <td>7.997892e+06</td>\n",
       "      <td>111970491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>20478</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.178714</td>\n",
       "      <td>1.005251e+07</td>\n",
       "      <td>140735182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>18055</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.298533</td>\n",
       "      <td>8.772889e+06</td>\n",
       "      <td>131593332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>Portland</td>\n",
       "      <td>19441</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>24.895231</td>\n",
       "      <td>1.039220e+07</td>\n",
       "      <td>135098550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>17583</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>25.039188</td>\n",
       "      <td>8.239935e+06</td>\n",
       "      <td>131838955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>18418</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>25.053471</td>\n",
       "      <td>6.624914e+06</td>\n",
       "      <td>112623535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>19800</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>23.879667</td>\n",
       "      <td>9.143035e+06</td>\n",
       "      <td>137145522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>18306</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.766067</td>\n",
       "      <td>1.031449e+07</td>\n",
       "      <td>154717409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>Washington</td>\n",
       "      <td>20356</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>26.480250</td>\n",
       "      <td>8.515902e+06</td>\n",
       "      <td>136254438.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team Name  Arena Location  Seating Capacity Obesity Risk  \\\n",
       "0            Atlanta Hawks         Atlanta             18118     Low Risk   \n",
       "1           Boston Celtics          Boston             18624     Low Risk   \n",
       "2            Brooklyn Nets        Brooklyn             17732     Low Risk   \n",
       "3        Charlotte Hornets       Charlotte             19077     Low Risk   \n",
       "4            Chicago Bulls         Chicago             20917     Low Risk   \n",
       "5      Cleveland Cavaliers       Cleveland             19432     Low Risk   \n",
       "6         Dallas Mavericks          Dallas             19200    High Risk   \n",
       "7           Denver Nuggets          Denver             19520     Low Risk   \n",
       "8          Detroit Pistons         Detroit             20491     Low Risk   \n",
       "9    Golden State Warriors   San Francisco             18064     Low Risk   \n",
       "10         Houston Rockets         Houston             18055     Low Risk   \n",
       "11          Indiana Pacers    Indianapolis             17923     Low Risk   \n",
       "12    Los Angeles Clippers     Los Angeles             19068     Low Risk   \n",
       "13      Los Angeles Lakers     Los Angeles             19068     Low Risk   \n",
       "14       Memphis Grizzlies         Memphis             17794    High Risk   \n",
       "15              Miami Heat           Miami             19600     Low Risk   \n",
       "16         Milwaukee Bucks       Milwaukee             17500     Low Risk   \n",
       "17  Minnesota Timberwolves     Minneapolis             18978     Low Risk   \n",
       "18    New Orleans Pelicans     New Orleans             16867    High Risk   \n",
       "19         New York Knicks   New York City             19812     Low Risk   \n",
       "20   Oklahoma City Thunder   Oklahoma City             18203    High Risk   \n",
       "21           Orlando Magic         Orlando             18846     Low Risk   \n",
       "22      Philadelphia 76ers    Philadelphia             20478     Low Risk   \n",
       "23            Phoenix Suns         Phoenix             18055     Low Risk   \n",
       "24  Portland Trail Blazers        Portland             19441     Low Risk   \n",
       "25        Sacramento Kings      Sacramento             17583     Low Risk   \n",
       "26       San Antonio Spurs     San Antonio             18418    High Risk   \n",
       "27         Toronto Raptors         Toronto             19800     Low Risk   \n",
       "28               Utah Jazz  Salt Lake City             18306     Low Risk   \n",
       "29      Washington Wizards      Washington             20356     Low Risk   \n",
       "\n",
       "      IMCmean    Salarymean  TotalSalary  \n",
       "0   25.755214  9.504514e+06  133063190.0  \n",
       "1   26.073875  8.880164e+06  142082621.0  \n",
       "2   26.308812  1.072710e+07  171633659.0  \n",
       "3   24.349688  6.752946e+06  108047136.0  \n",
       "4   24.853308  1.016693e+07  132170140.0  \n",
       "5   24.983429  9.314309e+06  130400325.0  \n",
       "6   25.213200  7.896939e+06  118454083.0  \n",
       "7   25.574600  8.987236e+06  134808541.0  \n",
       "8   24.626286  6.174477e+06   86442675.0  \n",
       "9   24.517625  1.108017e+07  177282779.0  \n",
       "10  25.697267  8.228100e+06  123421495.0  \n",
       "11  24.426733  8.923113e+06  133846695.0  \n",
       "12  25.220643  1.173848e+07  164338785.0  \n",
       "13  26.700538  1.151002e+07  149630287.0  \n",
       "14  24.454056  6.017279e+06  108311021.0  \n",
       "15  25.721429  9.353919e+06  130954862.0  \n",
       "16  24.986929  1.058471e+07  148185964.0  \n",
       "17  24.427182  1.157992e+07  127379143.0  \n",
       "18  24.111937  7.293240e+06  116691835.0  \n",
       "19  24.541062  8.907381e+06  142518097.0  \n",
       "20  24.298867  3.363586e+06   50453784.0  \n",
       "21  25.224643  7.997892e+06  111970491.0  \n",
       "22  25.178714  1.005251e+07  140735182.0  \n",
       "23  25.298533  8.772889e+06  131593332.0  \n",
       "24  24.895231  1.039220e+07  135098550.0  \n",
       "25  25.039188  8.239935e+06  131838955.0  \n",
       "26  25.053471  6.624914e+06  112623535.0  \n",
       "27  23.879667  9.143035e+06  137145522.0  \n",
       "28  26.766067  1.031449e+07  154717409.0  \n",
       "29  26.480250  8.515902e+06  136254438.0  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "my_table10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(my_table10[\"Seating Capacity\"], my_table10[\"TotalSalary\"])\n",
    "plt.xlabel(\"Seating Capacity\")\n",
    "plt.ylabel(\"Total Salary\")\n",
    "plt.title(\"Seating Capacity vs. Total Salary\")\n",
    "plt.savefig(\"Images/TotalCapacity.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not exist. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ironhack",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "827cb459ef7dfa823b7fba71272161b29eeaf9e5af81f5ec193dda0aa6af3e3d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}