sw_baseball_connect.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install the following packages from the anaconda prompt/terminal\n",
    "- sqlalchemy     \n",
    "    - `pip install sqlalchemy`\n",
    "- psycopg2       \n",
    "    - `pip install psycopg2-binary`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import psycopg2 \n",
    "import sqlalchemy as sa\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use sqlalchemy to create an `engine`; the `engine` will be used by pandas to connect to postgreSQL\n",
    "- the engine needs a connection defined in this format: `postgresql://user:password@server:port/database_name`\n",
    "- everything after the `//` is specific to your database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = sa.create_engine('postgresql://postgres:postgres@localhost:5432/lahman_baseball')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now you can use the pandas `read_sql( )` method\n",
    "- set `sql=` the query you want to run\n",
    "- set `con = engine` to execute a query on any table in the connected database  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerid</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>birthmonth</th>\n",
       "      <th>birthday</th>\n",
       "      <th>birthcountry</th>\n",
       "      <th>birthstate</th>\n",
       "      <th>birthcity</th>\n",
       "      <th>deathyear</th>\n",
       "      <th>deathmonth</th>\n",
       "      <th>deathday</th>\n",
       "      <th>...</th>\n",
       "      <th>namelast</th>\n",
       "      <th>namegiven</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalgame</th>\n",
       "      <th>retroid</th>\n",
       "      <th>bbrefid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>David Allan</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>aardd001</td>\n",
       "      <td>aardsda01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AL</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Henry Louis</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1954-04-13</td>\n",
       "      <td>1976-10-03</td>\n",
       "      <td>aaroh101</td>\n",
       "      <td>aaronha01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AL</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Tommie Lee</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1962-04-10</td>\n",
       "      <td>1971-09-26</td>\n",
       "      <td>aarot101</td>\n",
       "      <td>aaronto01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Orange</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Aase</td>\n",
       "      <td>Donald William</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1977-07-26</td>\n",
       "      <td>1990-10-03</td>\n",
       "      <td>aased001</td>\n",
       "      <td>aasedo01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Abad</td>\n",
       "      <td>Fausto Andres</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "      <td>abada001</td>\n",
       "      <td>abadan01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerid  birthyear  birthmonth  birthday birthcountry birthstate  \\\n",
       "0  aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "1  aaronha01     1934.0         2.0       5.0          USA         AL   \n",
       "2  aaronto01     1939.0         8.0       5.0          USA         AL   \n",
       "3   aasedo01     1954.0         9.0       8.0          USA         CA   \n",
       "4   abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "\n",
       "    birthcity  deathyear  deathmonth  deathday  ... namelast       namegiven  \\\n",
       "0      Denver        NaN         NaN       NaN  ...  Aardsma     David Allan   \n",
       "1      Mobile        NaN         NaN       NaN  ...    Aaron     Henry Louis   \n",
       "2      Mobile     1984.0         8.0      16.0  ...    Aaron      Tommie Lee   \n",
       "3      Orange        NaN         NaN       NaN  ...     Aase  Donald William   \n",
       "4  Palm Beach        NaN         NaN       NaN  ...     Abad   Fausto Andres   \n",
       "\n",
       "  weight height bats throws       debut   finalgame   retroid    bbrefid  \n",
       "0  215.0   75.0    R      R  2004-04-06  2015-08-23  aardd001  aardsda01  \n",
       "1  180.0   72.0    R      R  1954-04-13  1976-10-03  aaroh101  aaronha01  \n",
       "2  190.0   75.0    R      R  1962-04-10  1971-09-26  aarot101  aaronto01  \n",
       "3  190.0   75.0    R      R  1977-07-26  1990-10-03  aased001   aasedo01  \n",
       "4  184.0   73.0    L      L  2001-09-10  2006-04-13  abada001   abadan01  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = pd.read_sql(sql = 'SELECT * FROM people;', con = engine)\n",
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['playerid', 'birthyear', 'birthmonth', 'birthday', 'birthcountry',\n",
       "       'birthstate', 'birthcity', 'deathyear', 'deathmonth', 'deathday',\n",
       "       'deathcountry', 'deathstate', 'deathcity', 'namefirst', 'namelast',\n",
       "       'namegiven', 'weight', 'height', 'bats', 'throws', 'debut',\n",
       "       'finalgame', 'retroid', 'bbrefid'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze all the colleges in the state of Tennessee. Which college has had the most success in the major leagues. Use whatever metric for success you like - number of players, number of games, salaries, world series wins, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>schoolid</th>\n",
       "      <th>schoolname</th>\n",
       "      <th>schoolcity</th>\n",
       "      <th>schoolstate</th>\n",
       "      <th>schoolnick</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abilchrist</td>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>TX</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adelphi</td>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Garden City</td>\n",
       "      <td>NY</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     schoolid                    schoolname   schoolcity schoolstate  \\\n",
       "0  abilchrist  Abilene Christian University      Abilene          TX   \n",
       "1     adelphi            Adelphi University  Garden City          NY   \n",
       "\n",
       "  schoolnick  \n",
       "0        USA  \n",
       "1        USA  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools = pd.read_sql(sql = 'SELECT * FROM schools;', con = engine)\n",
    "schools.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>playerid</th>\n",
       "      <th>schoolid</th>\n",
       "      <th>yearid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>pennst</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>rice</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    playerid schoolid  yearid\n",
       "0  aardsda01   pennst    2001\n",
       "1  aardsda01     rice    2002"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_play = pd.read_sql(sql = 'SELECT * FROM collegeplaying;', con = engine)\n",
    "college_play.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn_schools = schools.loc[schools.schoolstate == 'TN']\n",
    "tn_schools.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(396, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn = pd.merge(tn_schools, college_play, on = 'schoolid')\n",
    "tn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>schoolid</th>\n",
       "      <th>schoolname</th>\n",
       "      <th>schoolcity</th>\n",
       "      <th>schoolstate</th>\n",
       "      <th>schoolnick</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>kellesh01</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     schoolid                    schoolname   schoolcity schoolstate  \\\n",
       "0  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "1  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "2  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "3  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "4  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "\n",
       "  schoolnick   playerid  yearid  \n",
       "0        USA  ellisaj01    2000  \n",
       "1        USA  ellisaj01    2001  \n",
       "2        USA  ellisaj01    2002  \n",
       "3        USA  ellisaj01    2003  \n",
       "4        USA  kellesh01    2003  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(396, 30)\n"
     ]
    },
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       "      <th>schoolid</th>\n",
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       "      <th>schoolcity</th>\n",
       "      <th>schoolstate</th>\n",
       "      <th>schoolnick</th>\n",
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       "      <th>birthday</th>\n",
       "      <th>...</th>\n",
       "      <th>namelast</th>\n",
       "      <th>namegiven</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bats</th>\n",
       "      <th>throws</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalgame</th>\n",
       "      <th>retroid</th>\n",
       "      <th>bbrefid</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2000</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Ellis</td>\n",
       "      <td>Andrew James</td>\n",
       "      <td>225.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2008-09-15</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>ellia001</td>\n",
       "      <td>ellisaj01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>austinpeay</td>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>Clarksville</td>\n",
       "      <td>TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>ellisaj01</td>\n",
       "      <td>2001</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Ellis</td>\n",
       "      <td>Andrew James</td>\n",
       "      <td>225.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>2008-09-15</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>ellia001</td>\n",
       "      <td>ellisaj01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     schoolid                    schoolname   schoolcity schoolstate  \\\n",
       "0  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "1  austinpeay  Austin Peay State University  Clarksville          TN   \n",
       "\n",
       "  schoolnick   playerid  yearid  birthyear  birthmonth  birthday  ...  \\\n",
       "0        USA  ellisaj01    2000     1981.0         4.0       9.0  ...   \n",
       "1        USA  ellisaj01    2001     1981.0         4.0       9.0  ...   \n",
       "\n",
       "  namelast     namegiven weight  height  bats  throws       debut   finalgame  \\\n",
       "0    Ellis  Andrew James  225.0    74.0     R       R  2008-09-15  2016-09-29   \n",
       "1    Ellis  Andrew James  225.0    74.0     R       R  2008-09-15  2016-09-29   \n",
       "\n",
       "    retroid    bbrefid  \n",
       "0  ellia001  ellisaj01  \n",
       "1  ellia001  ellisaj01  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn_players = pd.merge(tn, players, on = 'playerid')\n",
    "print(tn_players.shape)\n",
    "tn_players.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For starters, let's look at total salary and find the top 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>lgid</th>\n",
       "      <th>playerid</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>barkele01</td>\n",
       "      <td>870000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>ATL</td>\n",
       "      <td>NL</td>\n",
       "      <td>bedrost01</td>\n",
       "      <td>550000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearid teamid lgid   playerid    salary\n",
       "0    1985    ATL   NL  barkele01  870000.0\n",
       "1    1985    ATL   NL  bedrost01  550000.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries = pd.read_sql(sql = 'SELECT * FROM salaries;', con = engine)\n",
    "salaries.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn_salaries = pd.merge(tn_players, salaries, on = 'playerid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playerid</th>\n",
       "      <th>namefirst</th>\n",
       "      <th>namelast</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>heltoto01</th>\n",
       "      <th>Todd</th>\n",
       "      <th>Helton</th>\n",
       "      <td>491520000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>priceda01</th>\n",
       "      <th>David</th>\n",
       "      <th>Price</th>\n",
       "      <td>245553888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ugglada01</th>\n",
       "      <th>Dan</th>\n",
       "      <th>Uggla</th>\n",
       "      <td>189702978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dickera01</th>\n",
       "      <th>R. A.</th>\n",
       "      <th>Dickey</th>\n",
       "      <td>150345000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>headlch01</th>\n",
       "      <th>Chase</th>\n",
       "      <th>Headley</th>\n",
       "      <td>103478400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   salary\n",
       "playerid  namefirst namelast             \n",
       "heltoto01 Todd      Helton    491520000.0\n",
       "priceda01 David     Price     245553888.0\n",
       "ugglada01 Dan       Uggla     189702978.0\n",
       "dickera01 R. A.     Dickey    150345000.0\n",
       "headlch01 Chase     Headley   103478400.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn_salaries.groupby(['playerid', 'namefirst', 'namelast'])['salary'] \\\n",
    "          .agg('sum') \\\n",
    "          .to_frame() \\\n",
    "          .sort_values('salary', ascending = False) \\\n",
    "          .head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What about All Star game appearances?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>playerid</th>\n",
       "      <th>yearid</th>\n",
       "      <th>gamenum</th>\n",
       "      <th>gameid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>lgid</th>\n",
       "      <th>gp</th>\n",
       "      <th>startingpos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gomezle01</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>ALS193307060</td>\n",
       "      <td>NYA</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ferreri01</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>ALS193307060</td>\n",
       "      <td>BOS</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerid  yearid  gamenum        gameid teamid lgid   gp  startingpos\n",
       "0  gomezle01    1933        0  ALS193307060    NYA   AL  1.0          1.0\n",
       "1  ferreri01    1933        0  ALS193307060    BOS   AL  1.0          2.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allstar = pd.read_sql(sql = 'SELECT * FROM allstarfull;', con = engine)\n",
    "allstar.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn_allstars = pd.merge(tn_players, allstar, on = 'playerid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>playerid</th>\n",
       "      <th>namefirst</th>\n",
       "      <th>namelast</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bridgto01</td>\n",
       "      <td>Tommy</td>\n",
       "      <td>Bridges</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>heltoto01</td>\n",
       "      <td>Todd</td>\n",
       "      <td>Helton</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>priceda01</td>\n",
       "      <td>David</td>\n",
       "      <td>Price</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>altmage01</td>\n",
       "      <td>George</td>\n",
       "      <td>Altman</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ugglada01</td>\n",
       "      <td>Dan</td>\n",
       "      <td>Uggla</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>garneph01</td>\n",
       "      <td>Phil</td>\n",
       "      <td>Garner</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sewelri01</td>\n",
       "      <td>Rip</td>\n",
       "      <td>Sewell</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>honeyri01</td>\n",
       "      <td>Rick</td>\n",
       "      <td>Honeycutt</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baileed01</td>\n",
       "      <td>Ed</td>\n",
       "      <td>Bailey</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>leach01</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>Lea</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hammaat01</td>\n",
       "      <td>Atlee</td>\n",
       "      <td>Hammaker</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alvarpe01</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>grayso01</td>\n",
       "      <td>Sonny</td>\n",
       "      <td>Gray</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dickera01</td>\n",
       "      <td>R. A.</td>\n",
       "      <td>Dickey</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>corajo01</td>\n",
       "      <td>Joey</td>\n",
       "      <td>Cora</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>wrighcl01</td>\n",
       "      <td>Clyde</td>\n",
       "      <td>Wright</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sandesc01</td>\n",
       "      <td>Scott</td>\n",
       "      <td>Sanderson</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sherrge01</td>\n",
       "      <td>George</td>\n",
       "      <td>Sherrill</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>grimsro02</td>\n",
       "      <td>Ross</td>\n",
       "      <td>Grimsley</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>priorma01</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Prior</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>brownto05</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Browning</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerid namefirst   namelast  count\n",
       "3   bridgto01     Tommy    Bridges     18\n",
       "11  heltoto01      Todd     Helton     15\n",
       "14  priceda01     David      Price     15\n",
       "0   altmage01    George     Altman      9\n",
       "19  ugglada01       Dan      Uggla      9\n",
       "7   garneph01      Phil     Garner      9\n",
       "17  sewelri01       Rip     Sewell      8\n",
       "12  honeyri01      Rick  Honeycutt      8\n",
       "2   baileed01        Ed     Bailey      6\n",
       "13    leach01   Charlie        Lea      3\n",
       "10  hammaat01     Atlee   Hammaker      3\n",
       "1   alvarpe01     Pedro    Alvarez      3\n",
       "8    grayso01     Sonny       Gray      3\n",
       "6   dickera01     R. A.     Dickey      3\n",
       "5    corajo01      Joey       Cora      3\n",
       "20  wrighcl01     Clyde     Wright      3\n",
       "16  sandesc01     Scott  Sanderson      2\n",
       "18  sherrge01    George   Sherrill      2\n",
       "9   grimsro02      Ross   Grimsley      1\n",
       "15  priorma01      Mark      Prior      1\n",
       "4   brownto05       Tom   Browning      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn_allstars.groupby(['playerid', 'namefirst', 'namelast'])['schoolid']\\\n",
    "          .agg('count') \\\n",
    "          .reset_index() \\\n",
    "          .sort_values('schoolid', ascending = False) \\\n",
    ".rename(columns = {'schoolid': 'count'})\n",
    "          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Todd Helton earned the most (nearly half a billion dollars) but Tommy Bridges appeared in more all star games! Let's see what their college affiliation is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>playerid</th>\n",
       "      <th>schoolid</th>\n",
       "      <th>yearid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6723</th>\n",
       "      <td>heltoto01</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6724</th>\n",
       "      <td>heltoto01</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6725</th>\n",
       "      <td>heltoto01</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerid   schoolid  yearid\n",
       "6723  heltoto01  tennessee    1993\n",
       "6724  heltoto01  tennessee    1994\n",
       "6725  heltoto01  tennessee    1995"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_play.loc[college_play.playerid.isin(['bridgto', 'heltoto01'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looks like UT may be the winner here...depends on the metric we choose. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there any correlation between number of wins and team salary? Use data from 2000 and later to answer this question. As you do this analysis, keep in mind that salaries across the whole league tend to increase together, so you may want to look on a year-by-year basis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- first we'll get salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>lgid</th>\n",
       "      <th>playerid</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12263</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>anderga01</td>\n",
       "      <td>3250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12264</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>belchti01</td>\n",
       "      <td>4600000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       yearid teamid lgid   playerid     salary\n",
       "12263    2000    ANA   AL  anderga01  3250000.0\n",
       "12264    2000    ANA   AL  belchti01  4600000.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries2000 = salaries.loc[salaries.yearid >= 2000]\n",
    "salaries2000.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>51464167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>ARI</td>\n",
       "      <td>81027833.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearid teamid      salary\n",
       "0    2000    ANA  51464167.0\n",
       "1    2000    ARI  81027833.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_salaries = salaries2000.groupby(['yearid','teamid']) \\\n",
    "                            .sum() \\\n",
    "                            .reset_index() \n",
    "team_salaries.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- next we'll get number of wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearid</th>\n",
       "      <th>lgid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>franchid</th>\n",
       "      <th>divid</th>\n",
       "      <th>rank</th>\n",
       "      <th>g</th>\n",
       "      <th>ghome</th>\n",
       "      <th>w</th>\n",
       "      <th>l</th>\n",
       "      <th>...</th>\n",
       "      <th>dp</th>\n",
       "      <th>fp</th>\n",
       "      <th>name</th>\n",
       "      <th>park</th>\n",
       "      <th>attendance</th>\n",
       "      <th>bpf</th>\n",
       "      <th>ppf</th>\n",
       "      <th>teamidbr</th>\n",
       "      <th>teamidlahman45</th>\n",
       "      <th>teamidretro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871</td>\n",
       "      <td>NA</td>\n",
       "      <td>BS1</td>\n",
       "      <td>BNA</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.838</td>\n",
       "      <td>Boston Red Stockings</td>\n",
       "      <td>South End Grounds I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103</td>\n",
       "      <td>98</td>\n",
       "      <td>BOS</td>\n",
       "      <td>BS1</td>\n",
       "      <td>BS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871</td>\n",
       "      <td>NA</td>\n",
       "      <td>CH1</td>\n",
       "      <td>CNA</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.829</td>\n",
       "      <td>Chicago White Stockings</td>\n",
       "      <td>Union Base-Ball Grounds</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>102</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CH1</td>\n",
       "      <td>CH1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearid lgid teamid franchid divid  rank   g  ghome   w   l  ...  dp     fp  \\\n",
       "0    1871   NA    BS1      BNA  None     3  31    NaN  20  10  ... NaN  0.838   \n",
       "1    1871   NA    CH1      CNA  None     2  28    NaN  19   9  ... NaN  0.829   \n",
       "\n",
       "                      name                     park  attendance  bpf  ppf  \\\n",
       "0     Boston Red Stockings      South End Grounds I         NaN  103   98   \n",
       "1  Chicago White Stockings  Union Base-Ball Grounds         NaN  104  102   \n",
       "\n",
       "   teamidbr  teamidlahman45  teamidretro  \n",
       "0       BOS             BS1          BS1  \n",
       "1       CHI             CH1          CH1  \n",
       "\n",
       "[2 rows x 48 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = pd.read_sql(sql = 'SELECT * FROM teams;', con = engine)\n",
    "teams.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>2000</td>\n",
       "      <td>ANA</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>2000</td>\n",
       "      <td>ARI</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      yearid teamid   w\n",
       "2325    2000    ANA  82\n",
       "2326    2000    ARI  85"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_wins = teams[['yearid', 'teamid', 'w']]\n",
    "team_wins = team_wins.loc[team_wins.yearid >= 2000]\n",
    "team_wins.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>teamid</th>\n",
       "      <th>salary</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
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       "      <td>82</td>\n",
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       "      <th>1</th>\n",
       "      <td>2000</td>\n",
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       "      <td>85</td>\n",
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      ],
      "text/plain": [
       "   yearid teamid      salary   w\n",
       "0    2000    ANA  51464167.0  82\n",
       "1    2000    ARI  81027833.0  85"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries_and_wins = pd.merge(team_salaries, team_wins, on = ['yearid', 'teamid'])\n",
    "salaries_and_wins.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>salary</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">2000</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.331090</td>\n",
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       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.331090</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2001</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.320648</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2002</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.442416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.442416</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2003</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.418704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.418704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.541015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.541015</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.491070</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2006</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.536276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.536276</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2007</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.491055</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.326973</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.476310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.476310</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.368869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.368869</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2011</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.409203</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2012</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.190057</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.299579</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.322433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.322433</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.238783</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016</th>\n",
       "      <th>salary</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.636288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.636288</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 salary         w\n",
       "yearid                           \n",
       "2000   salary  1.000000  0.331090\n",
       "       w       0.331090  1.000000\n",
       "2001   salary  1.000000  0.320648\n",
       "       w       0.320648  1.000000\n",
       "2002   salary  1.000000  0.442416\n",
       "       w       0.442416  1.000000\n",
       "2003   salary  1.000000  0.418704\n",
       "       w       0.418704  1.000000\n",
       "2004   salary  1.000000  0.541015\n",
       "       w       0.541015  1.000000\n",
       "2005   salary  1.000000  0.491070\n",
       "       w       0.491070  1.000000\n",
       "2006   salary  1.000000  0.536276\n",
       "       w       0.536276  1.000000\n",
       "2007   salary  1.000000  0.491055\n",
       "       w       0.491055  1.000000\n",
       "2008   salary  1.000000  0.326973\n",
       "       w       0.326973  1.000000\n",
       "2009   salary  1.000000  0.476310\n",
       "       w       0.476310  1.000000\n",
       "2010   salary  1.000000  0.368869\n",
       "       w       0.368869  1.000000\n",
       "2011   salary  1.000000  0.409203\n",
       "       w       0.409203  1.000000\n",
       "2012   salary  1.000000  0.190057\n",
       "       w       0.190057  1.000000\n",
       "2013   salary  1.000000  0.299579\n",
       "       w       0.299579  1.000000\n",
       "2014   salary  1.000000  0.322433\n",
       "       w       0.322433  1.000000\n",
       "2015   salary  1.000000  0.238783\n",
       "       w       0.238783  1.000000\n",
       "2016   salary  1.000000  0.636288\n",
       "       w       0.636288  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries_and_wins.groupby('yearid').corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(salaries_and_wins, col = 'yearid', col_wrap = 5, height = 2)\n",
    "g.map(plt.scatter, 'salary', 'w')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this question, you will explore the connection between number of wins and attendance.\n",
    "\n",
    " - Does there appear to be any correlation between attendance at home games and number of wins?\n",
    " - Do teams that win the world series see a boost in attendance the following year? What about teams that made the playoffs? Making the playoffs means either being a division winner or a wild card winner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871</td>\n",
       "      <td>CH1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1871</td>\n",
       "      <td>CL1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1871</td>\n",
       "      <td>FW1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1871</td>\n",
       "      <td>NY2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearid teamid   w\n",
       "0    1871    BS1  20\n",
       "1    1871    CH1  19\n",
       "2    1871    CL1  10\n",
       "3    1871    FW1   7\n",
       "4    1871    NY2  16"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_wins_all = teams[['yearid', 'teamid', 'w']]\n",
    "team_wins_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>year</th>\n",
       "      <th>league</th>\n",
       "      <th>team</th>\n",
       "      <th>park</th>\n",
       "      <th>span_first</th>\n",
       "      <th>span_last</th>\n",
       "      <th>games</th>\n",
       "      <th>openings</th>\n",
       "      <th>attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871</td>\n",
       "      <td>NA</td>\n",
       "      <td>BS1</td>\n",
       "      <td>BOS01</td>\n",
       "      <td>1871-05-16</td>\n",
       "      <td>1871-10-07</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>32600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871</td>\n",
       "      <td>NA</td>\n",
       "      <td>BS1</td>\n",
       "      <td>NYC01</td>\n",
       "      <td>1871-05-27</td>\n",
       "      <td>1871-05-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year league team   park  span_first   span_last  games  openings  \\\n",
       "0  1871     NA  BS1  BOS01  1871-05-16  1871-10-07     16        16   \n",
       "1  1871     NA  BS1  NYC01  1871-05-27  1871-05-27      1         1   \n",
       "\n",
       "   attendance  \n",
       "0       32600  \n",
       "1        3000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homegames = pd.read_sql(sql = 'SELECT * FROM homegames;', con = engine)\n",
    "homegames.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>year</th>\n",
       "      <th>team</th>\n",
       "      <th>games</th>\n",
       "      <th>attendance</th>\n",
       "      <th>avg_attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>16</td>\n",
       "      <td>32600</td>\n",
       "      <td>2037.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year team  games  attendance  avg_attendance\n",
       "0  1871  BS1     16       32600          2037.5\n",
       "1  1871  BS1      1        3000          3000.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homegames = homegames[['year', 'team', 'games', 'attendance']]\n",
    "homegames['avg_attendance'] = homegames.attendance / homegames.games\n",
    "homegames.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>yearid</th>\n",
       "      <th>teamid</th>\n",
       "      <th>w</th>\n",
       "      <th>year</th>\n",
       "      <th>team</th>\n",
       "      <th>games</th>\n",
       "      <th>attendance</th>\n",
       "      <th>avg_attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>20</td>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>16</td>\n",
       "      <td>32600</td>\n",
       "      <td>2037.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>20</td>\n",
       "      <td>1871</td>\n",
       "      <td>BS1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1871</td>\n",
       "      <td>CH1</td>\n",
       "      <td>19</td>\n",
       "      <td>1871</td>\n",
       "      <td>CH1</td>\n",
       "      <td>16</td>\n",
       "      <td>85500</td>\n",
       "      <td>5343.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1871</td>\n",
       "      <td>CH1</td>\n",
       "      <td>19</td>\n",
       "      <td>1871</td>\n",
       "      <td>CH1</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1871</td>\n",
       "      <td>CL1</td>\n",
       "      <td>10</td>\n",
       "      <td>1871</td>\n",
       "      <td>CL1</td>\n",
       "      <td>2</td>\n",
       "      <td>2200</td>\n",
       "      <td>1100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearid teamid   w  year team  games  attendance  avg_attendance\n",
       "0    1871    BS1  20  1871  BS1     16       32600         2037.50\n",
       "1    1871    BS1  20  1871  BS1      1        3000         3000.00\n",
       "2    1871    CH1  19  1871  CH1     16       85500         5343.75\n",
       "3    1871    CH1  19  1871  CH1      1         600          600.00\n",
       "4    1871    CL1  10  1871  CL1      2        2200         1100.00"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins_and_attendance = pd.merge(team_wins_all, homegames,\n",
    "                               left_on = ['yearid', 'teamid'],\n",
    "                               right_on = ['year', 'team'])\n",
    "wins_and_attendance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter('w', 'attendance', data = wins_and_attendance)\n",
    "plt.xlabel('wins')\n",
    "plt.ylabel('attendance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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