College_Football.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Table Scraper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import lxml.html as lh\n",
    "import pandas as pd\n",
    "\n",
    "def table_parser(url):\n",
    "    \n",
    "    #Create a handle, page, to handle the contents of the website\n",
    "    page = requests.get(url)\n",
    "    #Store the contents of the website under doc\n",
    "    doc = lh.fromstring(page.content)\n",
    "    #Parse data that are stored between <tr>..</tr> of HTML\n",
    "    tr_elements = doc.xpath('//tr')\n",
    "\n",
    "    #Create empty list\n",
    "    col=[]\n",
    "    i=0\n",
    "    #For each row, store each first element (header) and an empty list\n",
    "    for t in tr_elements[0]:\n",
    "        i+=1\n",
    "        name=t.text_content()\n",
    "        #print('%d:\"%s\"'%(i,name))\n",
    "        col.append((name,[]))\n",
    "\n",
    "    #Check the length of the first 12 rows\n",
    "    length = [len(T) for T in tr_elements[:12]]\n",
    "    length = length[0]\n",
    "    \n",
    "    for j in range(1,len(tr_elements)):\n",
    "    #T is our j'th row\n",
    "        T=tr_elements[j]\n",
    "    \n",
    "        #If row is not of size 10, the //tr data is not from our table \n",
    "        if len(T)!=length:\n",
    "            break\n",
    "    \n",
    "        #i is the index of our column\n",
    "        i=0\n",
    "    \n",
    "        #Iterate through each element of the row\n",
    "        for t in T.iterchildren():\n",
    "            data=t.text_content() \n",
    "            #Check if row is empty\n",
    "            if i>0:\n",
    "            #Convert any numerical value to integers\n",
    "                try:\n",
    "                    data=data\n",
    "                except:\n",
    "                    pass\n",
    "            #Append the data to the empty list of the i'th column\n",
    "            col[i][1].append(data)\n",
    "            #Increment i for the next column\n",
    "            i+=1\n",
    "            \n",
    "    Dict={title:column for (title,column) in col}\n",
    "    df=pd.DataFrame(Dict)\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "coach = table_parser('https://sports.usatoday.com/ncaa/salaries/football/coach')\n",
    "coach = coach[:-1]\n",
    "coach['Total Pay'] = [str(p) for p in coach['Total Pay']]\n",
    "coach = coach.replace('\\$','', regex=True)\n",
    "coach = coach.replace(',','', regex=True)\n",
    "coach = coach.replace(r'--', '0', regex=True)\n",
    "coach['Total Pay'] = [int(p) for p in coach['Total Pay']]\n",
    "coach = coach[coach['Total Pay']>0]\n",
    "coach['Type'] = \"Head Coach\"\n",
    "coach = coach.drop(['Bonuses paid (2018-19)'], axis=1)\n",
    "coach = coach.drop(['School Buyout As Of 12/1/19'], axis=1)\n",
    "\n",
    "\n",
    "college_info = table_parser(\"https://en.wikipedia.org/wiki/List_of_NCAA_Division_I_FBS_football_programs\")\n",
    "college_info = college_info.replace('\\n','', regex=True)\n",
    "college_info = college_info[['Team\\n', \"Nickname\\n\", \"City\\n\", \"State[1]\\n\", \"Enrollment\\n\", \"CurrentConference\\n\"]]\n",
    "college_info.columns = ['Team', \"Nickname\", \"City\", \"State\", \"Enrollment\", \"CurrentConference\"]\n",
    "\n",
    "assistant = table_parser(\"https://sports.usatoday.com/ncaa/salaries/football/assistant\")\n",
    "assistant = assistant[:-1]\n",
    "assistant['Total Pay'] = [str(p) for p in assistant['Total Pay']]\n",
    "assistant = assistant.replace('\\$','', regex=True)\n",
    "assistant = assistant.replace(',','', regex=True)\n",
    "assistant = assistant.replace(r'--', '0', regex=True)\n",
    "assistant['Total Pay'] = [int(p) for p in assistant['Total Pay']]\n",
    "assistant = assistant[assistant['Total Pay']>0]\n",
    "assistant[\"Type\"] = \"Assistant\"\n",
    "\n",
    "strength = table_parser(\"https://sports.usatoday.com/ncaa/salaries/football/strength\")\n",
    "strength = strength[:-1]\n",
    "strength['Total Pay'] = [str(p) for p in strength['Total Pay']]\n",
    "strength = strength.replace('\\$','', regex=True)\n",
    "strength = strength.replace(',','', regex=True)\n",
    "strength = strength.replace(r'--', '0', regex=True)\n",
    "strength['Total Pay'] = [int(p) for p in strength['Total Pay']]\n",
    "strength = strength[strength['Total Pay']>0]\n",
    "strength['Type'] = \"Strength\"\n",
    "\n",
    "coaches = pd.concat([coach, assistant, strength])\n",
    "coaches = coaches.drop(\"Rk\", axis=1)\n",
    "coaches = coaches.drop(\"Max Bonus\", axis=1)\n",
    "coaches = coaches.drop(\"Asst Pay Total\", axis=1)\n",
    "coaches = coaches.drop(\"School Pay\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate by School"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "school_names = {\n",
    "    'Mississippi': 'Ole Miss', 'Southern Mississippi': 'Southern Miss', \n",
    "    'Central Florida': 'UCF', 'Connecticut': 'UConn', 'Florida International': 'FIU', \n",
    "    \"Louisiana State\": 'LSU', 'Massachusetts': 'UMass', 'Miami (Ohio)': 'Miami (OH)', 'Southern California': 'USC', \n",
    "    'Texas Christian': 'TCU', 'Texas El Paso': 'UTEP', 'UL Lafayette': 'Louisiana', \n",
    "    'UL Monroe': 'Louisiana–Monroe', 'Texas-San Antonio': 'UTSA'}\n",
    "coaches = coaches.replace({\"School\": school_names})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "school_sum = coaches.groupby(\"School\").sum()\n",
    "football_totals = school_sum.merge(college_info, left_index=True, right_on=\"Team\")\n",
    "football_totals['Enrollment'] = football_totals['Enrollment'].replace(',','', regex=True)\n",
    "football_totals['Enrollment'] = football_totals['Enrollment'].astype(int)\n",
    "football_totals[\"Price Per Student\"] = football_totals['Total Pay']/football_totals['Enrollment']\n",
    "\n",
    "football_totals = football_totals[['Team', \"Nickname\", \"City\", \"State\", \"CurrentConference\", \"Price Per Student\", \"Total Pay\", 'Enrollment']]\n",
    "football_totals.columns = ['Team', \"Nickname\", \"City\", \"State\", \"Conference\", \"Price Per Student\", \"Total Pay\", 'Enrollment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schools without salary information:{'Air Force', 'Rice', 'Miami (FL)', 'Army', 'Liberty', 'SMU', 'Temple', 'BYU'}\n"
     ]
    }
   ],
   "source": [
    "x = set(school_sum.index)\n",
    "y = set(college_info[\"Team\"])\n",
    "no_salary = x-y\n",
    "print(\"Schools without salary information:\" + str(y-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total cost of LSU and Clemson Coaching Staffs Respectively: LSU=$11165000 Clemson=$17335600\n"
     ]
    }
   ],
   "source": [
    "clemson = coaches[coaches['School']==\"Clemson\"]\n",
    "Clemson_Total = clemson['Total Pay'].sum()\n",
    "\n",
    "LSU = coaches[coaches['School']==\"LSU\"]\n",
    "LSU_Total = LSU['Total Pay'].sum()\n",
    "\n",
    "print(\"The total cost of LSU and Clemson Coaching Staffs Respectively: LSU=$\"+str(LSU_Total)+\" Clemson=$\"+str(Clemson_Total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total cost of LSU and Clemson Coaching Staffs Respectively: LSU=$362.5 Clemson=$693.424\n"
     ]
    }
   ],
   "source": [
    "clemson = football_totals[football_totals['Team']==\"Clemson\"]\n",
    "Clemson_Total = float(clemson['Price Per Student'])\n",
    "\n",
    "LSU = football_totals[football_totals['Team']==\"LSU\"]\n",
    "LSU_Total = float(LSU['Price Per Student'])\n",
    "\n",
    "print(\"The total cost of LSU and Clemson Coaching Staffs Respectively: LSU=$\"+str(LSU_Total)+\" Clemson=$\"+str(Clemson_Total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Salary Information by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_state = football_totals.groupby(\"State\").sum()\n",
    "by_state[\"Price Per Student\"] = by_state['Total Pay']/by_state['Enrollment']\n",
    "by_state_schools = football_totals.groupby(\"State\").count()\n",
    "by_state_schools = by_state_schools[[\"Team\"]]\n",
    "by_state = by_state.merge(by_state_schools, left_index=True, right_index=True)\n",
    "\n",
    "\n",
    "state_codes = {\n",
    "    'District of Columbia' : 'dc','Mississippi': 'MS', 'Oklahoma': 'OK', \n",
    "    'Delaware': 'DE', 'Minnesota': 'MN', 'Illinois': 'IL', 'Arkansas': 'AR', \n",
    "    'New Mexico': 'NM', 'Indiana': 'IN', 'Maryland': 'MD', 'Louisiana': 'LA', \n",
    "    'Idaho': 'ID', 'Wyoming': 'WY', 'Tennessee': 'TN', 'Arizona': 'AZ', \n",
    "    'Iowa': 'IA', 'Michigan': 'MI', 'Kansas': 'KS', 'Utah': 'UT', \n",
    "    'Virginia': 'VA', 'Oregon': 'OR', 'Connecticut': 'CT', 'Montana': 'MT', \n",
    "    'California': 'CA', 'Massachusetts': 'MA', 'West Virginia': 'WV', \n",
    "    'South Carolina': 'SC', 'New Hampshire': 'NH', 'Wisconsin': 'WI',\n",
    "    'Vermont': 'VT', 'Georgia': 'GA', 'North Dakota': 'ND', \n",
    "    'Pennsylvania': 'PA', 'Florida': 'FL', 'Alaska': 'AK', 'Kentucky': 'KY', \n",
    "    \"Hawai'i\": 'HI', 'Nebraska': 'NE', 'Missouri': 'MO', 'Ohio': 'OH', \n",
    "    'Alabama': 'AL', 'Rhode Island': 'RI', 'South Dakota': 'SD', \n",
    "    'Colorado': 'CO', 'New Jersey': 'NJ', 'Washington': 'WA', \n",
    "    'North Carolina': 'NC', 'New York': 'NY', 'Texas': 'TX', \n",
    "    'Nevada': 'NV', 'Maine': 'ME'}\n",
    "by_state = by_state.reset_index()\n",
    "by_state['state_code'] = by_state['State'].apply(lambda x : state_codes[x])\n",
    "for col in by_state.columns:\n",
    "        by_state[col] = by_state[col].astype(str)\n",
    "by_state['text'] = 'Number of Schools: ' + by_state['Team'] + '<br>' + 'Total Enrollment: ' + by_state['Enrollment']\n",
    "by_state['Total Pay'] = by_state['Total Pay'].astype(float)\n",
    "by_state['Team'] = by_state['Team'].astype(float)\n",
    "by_state['Price Per School'] = by_state['Total Pay']/by_state['Team']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Plot Total Spending By State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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          "Number of Schools: 5<br>Total Enrollment: 124000",
          "Number of Schools: 2<br>Total Enrollment: 97000",
          "Number of Schools: 2<br>Total Enrollment: 49800",
          "Number of Schools: 7<br>Total Enrollment: 242800",
          "Number of Schools: 2<br>Total Enrollment: 67000",
          "Number of Schools: 1<br>Total Enrollment: 32200",
          "Number of Schools: 6<br>Total Enrollment: 304900",
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          "Number of Schools: 2<br>Total Enrollment: 45800",
          "Number of Schools: 2<br>Total Enrollment: 45300",
          "Number of Schools: 5<br>Total Enrollment: 161200",
          "Number of Schools: 1<br>Total Enrollment: 47800",
          "Number of Schools: 3<br>Total Enrollment: 59900",
          "Number of Schools: 1<br>Total Enrollment: 30000",
          "Number of Schools: 1<br>Total Enrollment: 25800",
          "Number of Schools: 2<br>Total Enrollment: 52100",
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          "Number of Schools: 2<br>Total Enrollment: 48200",
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          "Number of Schools: 8<br>Total Enrollment: 277900",
          "Number of Schools: 3<br>Total Enrollment: 58100",
          "Number of Schools: 2<br>Total Enrollment: 53600",
          "Number of Schools: 2<br>Total Enrollment: 75200",
          "Number of Schools: 3<br>Total Enrollment: 85700",
          "Number of Schools: 4<br>Total Enrollment: 85400",
          "Number of Schools: 10<br>Total Enrollment: 363700",
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ce10142a-212f-4ff8-aced-731166d9b5c3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import iplot\n",
    "import plotly.graph_objects as go\n",
    "    \n",
    "data = [dict(\n",
    "    type=\"choropleth\",\n",
    "    autocolorscale= True,\n",
    "    locations = by_state['state_code'],\n",
    "    z = by_state['Total Pay'].astype(float),\n",
    "    locationmode = 'USA-states',\n",
    "    text = by_state['text'],\n",
    "    marker = dict(\n",
    "    line = dict (\n",
    "    color = 'rgb(0,0,0)',\n",
    "    width = 2\n",
    " )),\n",
    "    colorbar = dict(title = \"Total Pay of College Staffs<br>in State\")\n",
    ")]\n",
    "\n",
    "layout = dict(title = '',\n",
    "        geo=dict(scope=\"usa\",showlakes = True,lakecolor = 'rgb(255, 255, 255)'))\n",
    "\n",
    "iplot(go.Figure(data=data,layout=layout),validate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Plot Total Spending Per Student By State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "autocolorscale": true,
         "colorbar": {
          "title": {
           "text": "College Football Staff<br>Price Per Student ($)"
          }
         },
         "locationmode": "USA-states",
         "locations": [
          "AL",
          "AZ",
          "AR",
          "CA",
          "CO",
          "CT",
          "FL",
          "GA",
          "HI",
          "ID",
          "IL",
          "IN",
          "IA",
          "KS",
          "KY",
          "LA",
          "MD",
          "MA",
          "MI",
          "MN",
          "MS",
          "MO",
          "NE",
          "NV",
          "NJ",
          "NM",
          "NY",
          "NC",
          "OH",
          "OK",
          "OR",
          "PA",
          "SC",
          "TN",
          "TX",
          "UT",
          "VA",
          "WA",
          "WV",
          "WI",
          "WY"
         ],
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           "color": "rgb(0,0,0)",
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          "Number of Schools: 5<br>Total Enrollment: 124000",
          "Number of Schools: 2<br>Total Enrollment: 97000",
          "Number of Schools: 2<br>Total Enrollment: 49800",
          "Number of Schools: 7<br>Total Enrollment: 242800",
          "Number of Schools: 2<br>Total Enrollment: 67000",
          "Number of Schools: 1<br>Total Enrollment: 32200",
          "Number of Schools: 6<br>Total Enrollment: 304900",
          "Number of Schools: 4<br>Total Enrollment: 150100",
          "Number of Schools: 1<br>Total Enrollment: 17700",
          "Number of Schools: 1<br>Total Enrollment: 25500",
          "Number of Schools: 3<br>Total Enrollment: 92400",
          "Number of Schools: 4<br>Total Enrollment: 121900",
          "Number of Schools: 2<br>Total Enrollment: 66400",
          "Number of Schools: 2<br>Total Enrollment: 49400",
          "Number of Schools: 3<br>Total Enrollment: 72100",
          "Number of Schools: 5<br>Total Enrollment: 85300",
          "Number of Schools: 2<br>Total Enrollment: 45800",
          "Number of Schools: 2<br>Total Enrollment: 45300",
          "Number of Schools: 5<br>Total Enrollment: 161200",
          "Number of Schools: 1<br>Total Enrollment: 47800",
          "Number of Schools: 3<br>Total Enrollment: 59900",
          "Number of Schools: 1<br>Total Enrollment: 30000",
          "Number of Schools: 1<br>Total Enrollment: 25800",
          "Number of Schools: 2<br>Total Enrollment: 52100",
          "Number of Schools: 1<br>Total Enrollment: 70900",
          "Number of Schools: 2<br>Total Enrollment: 48200",
          "Number of Schools: 2<br>Total Enrollment: 54400",
          "Number of Schools: 7<br>Total Enrollment: 167200",
          "Number of Schools: 8<br>Total Enrollment: 277900",
          "Number of Schools: 3<br>Total Enrollment: 58100",
          "Number of Schools: 2<br>Total Enrollment: 53600",
          "Number of Schools: 2<br>Total Enrollment: 75200",
          "Number of Schools: 3<br>Total Enrollment: 85700",
          "Number of Schools: 4<br>Total Enrollment: 85400",
          "Number of Schools: 10<br>Total Enrollment: 363700",
          "Number of Schools: 2<br>Total Enrollment: 62400",
          "Number of Schools: 3<br>Total Enrollment: 82800",
          "Number of Schools: 2<br>Total Enrollment: 78500",
          "Number of Schools: 2<br>Total Enrollment: 43200",
          "Number of Schools: 1<br>Total Enrollment: 44400",
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    "data = [dict(\n",
    "    type=\"choropleth\",\n",
    "    autocolorscale= True,\n",
    "    locations = by_state['state_code'],\n",
    "    z = by_state['Price Per Student'].astype(float),\n",
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  {
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   "metadata": {},
   "source": [
    "#### Average Price Per Team in State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
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       "                        [{\"autocolorscale\": true, \"colorbar\": {\"title\": {\"text\": \"College Football Staff<br>Average by State ($)\"}}, \"locationmode\": \"USA-states\", \"locations\": [\"AL\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"NE\", \"NV\", \"NJ\", \"NM\", \"NY\", \"NC\", \"OH\", \"OK\", \"OR\", \"PA\", \"SC\", \"TN\", \"TX\", \"UT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"], \"marker\": {\"line\": {\"color\": \"rgb(0,0,0)\", \"width\": 2}}, \"text\": [\"Number of Schools: 5<br>Total Enrollment: 124000\", \"Number of Schools: 2<br>Total Enrollment: 97000\", \"Number of Schools: 2<br>Total Enrollment: 49800\", \"Number of Schools: 7<br>Total Enrollment: 242800\", \"Number of Schools: 2<br>Total Enrollment: 67000\", \"Number of Schools: 1<br>Total Enrollment: 32200\", \"Number of Schools: 6<br>Total Enrollment: 304900\", \"Number of Schools: 4<br>Total Enrollment: 150100\", \"Number of Schools: 1<br>Total Enrollment: 17700\", \"Number of Schools: 1<br>Total Enrollment: 25500\", \"Number of Schools: 3<br>Total Enrollment: 92400\", \"Number of Schools: 4<br>Total Enrollment: 121900\", \"Number of Schools: 2<br>Total Enrollment: 66400\", \"Number of Schools: 2<br>Total Enrollment: 49400\", \"Number of Schools: 3<br>Total Enrollment: 72100\", \"Number of Schools: 5<br>Total Enrollment: 85300\", \"Number of Schools: 2<br>Total Enrollment: 45800\", \"Number of Schools: 2<br>Total Enrollment: 45300\", \"Number of Schools: 5<br>Total Enrollment: 161200\", \"Number of Schools: 1<br>Total Enrollment: 47800\", \"Number of Schools: 3<br>Total Enrollment: 59900\", \"Number of Schools: 1<br>Total Enrollment: 30000\", \"Number of Schools: 1<br>Total Enrollment: 25800\", \"Number of Schools: 2<br>Total Enrollment: 52100\", \"Number of Schools: 1<br>Total Enrollment: 70900\", \"Number of Schools: 2<br>Total Enrollment: 48200\", \"Number of Schools: 2<br>Total Enrollment: 54400\", \"Number of Schools: 7<br>Total Enrollment: 167200\", \"Number of Schools: 8<br>Total Enrollment: 277900\", \"Number of Schools: 3<br>Total Enrollment: 58100\", \"Number of Schools: 2<br>Total Enrollment: 53600\", \"Number of Schools: 2<br>Total Enrollment: 75200\", \"Number of Schools: 3<br>Total Enrollment: 85700\", \"Number of Schools: 4<br>Total Enrollment: 85400\", \"Number of Schools: 10<br>Total Enrollment: 363700\", \"Number of Schools: 2<br>Total Enrollment: 62400\", \"Number of Schools: 3<br>Total Enrollment: 82800\", \"Number of Schools: 2<br>Total Enrollment: 78500\", \"Number of Schools: 2<br>Total Enrollment: 43200\", \"Number of Schools: 1<br>Total Enrollment: 44400\", \"Number of Schools: 1<br>Total Enrollment: 12400\"], \"type\": \"choropleth\", \"z\": [7416063.2, 5902500.0, 5654241.0, 4579155.714285715, 4830937.5, 3636325.0, 6841159.166666667, 6015883.75, 600004.0, 4295153.0, 4869645.666666667, 4866044.25, 9206450.0, 6220350.0, 6487414.666666667, 3817360.0, 4365500.0, 2259656.0, 5797448.8, 6935000.0, 5885000.0, 8169500.0, 9875000.0, 2218087.0, 5811042.0, 1919925.5, 2064743.0, 3944829.8571428573, 3505539.625, 8041285.666666667, 6538534.0, 4778728.0, 9800657.0, 5645649.5, 6348073.5, 5651458.5, 5739437.333333333, 9202669.0, 4370610.0, 8870811.0, 4121794.0]}],\n",
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('75ea2ed9-53d7-43bf-ba59-a651964fc035');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [dict(\n",
    "    type=\"choropleth\",\n",
    "    autocolorscale= True,\n",
    "    locations = by_state['state_code'],\n",
    "    z = by_state['Price Per School'].astype(float),\n",
    "    locationmode = 'USA-states',\n",
    "    text = by_state['text'],\n",
    "    marker = dict(\n",
    "    line = dict (\n",
    "    color = 'rgb(0,0,0)',\n",
    "    width = 2\n",
    " )),\n",
    "    colorbar = dict(title = \"College Football Staff<br>Average by State ($)\")\n",
    ")]\n",
    "\n",
    "layout = dict(title = '',\n",
    "        geo=dict(scope=\"usa\",showlakes = True,lakecolor = 'rgb(255, 255, 255)'))\n",
    "\n",
    "iplot(go.Figure(data=data,layout=layout),validate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Conference"
   ]
  },
  {
   "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>Price Per Student</th>\n",
       "      <th>Total Pay</th>\n",
       "      <th>Enrollment</th>\n",
       "      <th>Team</th>\n",
       "      <th>Average Per Team</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conference</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>253.445845</td>\n",
       "      <td>85031081</td>\n",
       "      <td>335500</td>\n",
       "      <td>13</td>\n",
       "      <td>6540852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American</th>\n",
       "      <td>137.032922</td>\n",
       "      <td>43000931</td>\n",
       "      <td>313800</td>\n",
       "      <td>10</td>\n",
       "      <td>4300093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big 12</th>\n",
       "      <td>282.416893</td>\n",
       "      <td>80262881</td>\n",
       "      <td>284200</td>\n",
       "      <td>10</td>\n",
       "      <td>8026288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big Ten</th>\n",
       "      <td>189.074407</td>\n",
       "      <td>119797544</td>\n",
       "      <td>633600</td>\n",
       "      <td>14</td>\n",
       "      <td>8556967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-USA</th>\n",
       "      <td>91.593778</td>\n",
       "      <td>31297594</td>\n",
       "      <td>341700</td>\n",
       "      <td>13</td>\n",
       "      <td>2407507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independent</th>\n",
       "      <td>76.100556</td>\n",
       "      <td>4931316</td>\n",
       "      <td>64800</td>\n",
       "      <td>3</td>\n",
       "      <td>1643772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAC</th>\n",
       "      <td>68.760912</td>\n",
       "      <td>20813928</td>\n",
       "      <td>302700</td>\n",
       "      <td>12</td>\n",
       "      <td>1734494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mountain West</th>\n",
       "      <td>108.780279</td>\n",
       "      <td>31535403</td>\n",
       "      <td>289900</td>\n",
       "      <td>11</td>\n",
       "      <td>2866854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pac-12</th>\n",
       "      <td>181.126774</td>\n",
       "      <td>80673865</td>\n",
       "      <td>445400</td>\n",
       "      <td>12</td>\n",
       "      <td>6722822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEC</th>\n",
       "      <td>309.310297</td>\n",
       "      <td>150912494</td>\n",
       "      <td>487900</td>\n",
       "      <td>14</td>\n",
       "      <td>10779463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun Belt</th>\n",
       "      <td>88.355427</td>\n",
       "      <td>20401268</td>\n",
       "      <td>230900</td>\n",
       "      <td>10</td>\n",
       "      <td>2040126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price Per Student  Total Pay  Enrollment  Team Average Per Team\n",
       "Conference                                                                    \n",
       "ACC                   253.445845   85031081      335500    13          6540852\n",
       "American              137.032922   43000931      313800    10          4300093\n",
       "Big 12                282.416893   80262881      284200    10          8026288\n",
       "Big Ten               189.074407  119797544      633600    14          8556967\n",
       "C-USA                  91.593778   31297594      341700    13          2407507\n",
       "Independent            76.100556    4931316       64800     3          1643772\n",
       "MAC                    68.760912   20813928      302700    12          1734494\n",
       "Mountain West         108.780279   31535403      289900    11          2866854\n",
       "Pac-12                181.126774   80673865      445400    12          6722822\n",
       "SEC                   309.310297  150912494      487900    14         10779463\n",
       "Sun Belt               88.355427   20401268      230900    10          2040126"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "football_totals['Conference'] = football_totals['Conference'].replace('American[n 6]', 'American')\n",
    "by_conference = football_totals.groupby(\"Conference\").sum()\n",
    "by_conference[\"Price Per Student\"] = by_conference['Total Pay']/by_conference['Enrollment']\n",
    "by_conference_schools = football_totals.groupby(\"Conference\").count()\n",
    "by_conference_schools = by_conference_schools[[\"Team\"]]\n",
    "by_conference = by_conference.merge(by_conference_schools, left_index=True, right_index=True)\n",
    "by_conference[\"Average Per Team\"] = by_conference['Total Pay']/by_conference[\"Team\"]\n",
    "by_conference[\"Average Per Team\"] = by_conference[\"Average Per Team\"].astype(str)\n",
    "by_conference[\"Average Per Team\"] = [x.split(\".\")[0] for x in by_conference[\"Average Per Team\"]]\n",
    "by_conference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End of Script"
   ]
  }
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