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amanahuja/womens_stats_2015.ipynb

Created May 26, 2015 22:35
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Women's stats #d1natties (temp)
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womens_stats_2015.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:2e76fe8eb7e724c069e1d7af43889d38e437b869168db3bde772f93899e35144"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle\n",
"import csv\n",
"import os\n",
"import re\n",
"import urllib\n",
"\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd\n",
"import requests\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline\n",
"\n",
"BASE_URL = \"http://play.usaultimate.org\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stats Class and utility functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Stats(object):\n",
" def __init__(self, name, goals, assists, ds, turns, team):\n",
" self.name = name\n",
" self.goals = goals\n",
" self.assists = assists\n",
" self.ds = ds\n",
" self.turns = turns\n",
" self.team = team\n",
"\n",
" def __add__(self, other):\n",
" return Stats(self.name, self.goals+other.goals,\n",
" self.assists+other.assists, self.ds+other.ds,\n",
" self.turns+other.turns, self.team)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def parse_table(div, team):\n",
" \"\"\"\n",
" Parses a stats table on a USA ultimate Match Report webpage\n",
" \"\"\"\n",
" table = {}\n",
" try: \n",
" stats = div.find(\"table\", attrs={\"class\": \"global_table\"})\n",
" for row in stats.findAll(\"tr\")[1:]:\n",
" name, goals, assists, ds, turns = [r.text.strip() for r in row.findAll(\"td\")]\n",
" assert name, \"Couldn't find name in row %s\" % row\n",
" table[name] = Stats(name, goals, assists, ds, turns, team)\n",
" except AttributeError: \n",
" print \"No data found for {}\".format(team)\n",
" return table\n",
"\n",
"def parse_url(match_url):\n",
" \"\"\"\n",
" Parses a USA ultimate Match Report webpage\n",
" e.g. http://play.usaultimate.org/teams/events/match_report/?EventGameId=daI9RYtHla1xWOhvjYOhIMQJV%2f%2fsOsfZ3fhP09IYiWY%3d\n",
" \"\"\"\n",
" gamestats = {}\n",
"\n",
" # load html into Beautiful Soup\n",
" r = requests.get(BASE_URL + match_url)\n",
" soup = BeautifulSoup(r.content)\n",
"\n",
" # Get metadata\n",
" gameid = re.search(\"GameId=(.*)\", match_url).groups()[0]\n",
" \n",
" homediv = soup.find(\"div\", attrs={\"id\": \"home_team\"})\n",
" hometeam = homediv.find('span').text\n",
" homestats = parse_table(homediv, hometeam)\n",
" \n",
" awaydiv = soup.find(\"div\", attrs={\"id\": \"away_team\"})\n",
" awayteam = awaydiv.find('span').text\n",
" awaystats = parse_table(awaydiv, awayteam)\n",
"\n",
" print \"{} vs {}\".format(hometeam, awayteam)\n",
" \n",
" gamestats[gameid] = {\n",
" \"home\": {\n",
" \"name\": hometeam,\n",
" \"stats\": homestats\n",
" },\n",
" \"away\": {\n",
" \"name\": awayteam,\n",
" \"stats\": awaystats\n",
" },\n",
" }\n",
" \n",
" return gamestats"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def getseed(name):\n",
" \"\"\"\n",
" Get seed\n",
" e.g, \"Texas (13)\" --> 13\n",
" \"\"\"\n",
" if name.upper() == 'TBD': \n",
" return 99\n",
" seed = int(name.split('(')[1].split(')')[0])\n",
" return seed"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fetch Data from file or from USA Ultimate"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# http://play.usaultimate.org/events/USA-Ultimate-D-I-College-Championships-2015/schedule/womens/College-Women/\n",
"FORCE_FETCH = False\n",
"url_pklfilename = \"womens_match_urls_2015.pkl\"\n",
"\n",
"if FORCE_FETCH or not os.path.isfile(url_pklfilename):\n",
" # fetch root url\n",
" r = requests.get(BASE_URL + \"/events/USA-Ultimate-D-I-College-Championships-2015/schedule/womens/College-Women/\")\n",
" soup = BeautifulSoup(r.content)\n",
" \n",
" # get all match report urls\n",
" reports = soup.findAll(\"a\", attrs={\"href\": re.compile(\"EventGameId\")})\n",
" match_urls = [report.attrs[\"href\"] for report in reports]\n",
" \n",
" # save urls to file\n",
" cPickle.dump(match_urls, file(url_pklfilename, 'w'))\n",
"\n",
"match_urls = cPickle.load(file(url_pklfilename))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Verify: \", BASE_URL + match_urls[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Verify: http://play.usaultimate.org/teams/events/match_report/?EventGameId=rVVzIL3AsYMErKuPFBAwKWVM9v4Vfg7%2fCltEg1Qn568%3d\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Define filenames\n",
"stats_pklfilename = 'womens_gamestats_2015.pkl'\n",
"stats_csvfilename = 'womens_gamestats_2015.csv'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"FORCE_FETCH = False # if True, will ignore any existing pickled data\n",
"\n",
"if FORCE_FETCH or not os.path.isfile(stats_pklfilename):\n",
" gamestats = {}\n",
"\n",
" print \"Fetching data for: \"\n",
" for match_url in match_urls:\n",
" gamestats.update(parse_url(match_url))\n",
"\n",
" cPickle.dump(gamestats, file(stats_pklfilename, 'w'))\n",
"else: \n",
" print \"Loading stats from file: {}\".format(stats_pklfilename)\n",
" gamestats = cPickle.load(file(stats_pklfilename))\n",
"\n",
"assert len(gamestats) == len(match_urls)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Fetching data for: \n",
"Oregon (1) vs Notre Dame (8)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Florida State (12) vs Central Florida (13)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Notre Dame (8) vs Central Florida (13)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Florida State (12) vs Victoria (17)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Oregon (1) vs Victoria (17)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Oregon (1) vs Florida State (12)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Notre Dame (8) vs Victoria (17)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Oregon (1) vs Central Florida (13)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Notre Dame (8) vs Florida State (12)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Central Florida (13) vs Victoria (17)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanford (2) vs Middlebury (18)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dartmouth (7) vs Ohio State (14)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanford (2) vs Dartmouth (7)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Washington (11) vs Ohio State (14)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Washington (11) vs Middlebury (18)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanford (2) vs Washington (11)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dartmouth (7) vs Middlebury (18)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ohio State (14) vs Middlebury (18)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dartmouth (7) vs Washington (11)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanford (2) vs Ohio State (14)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carleton College (6) vs Pittsburgh (15)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"British Columbia (10) vs Texas (19)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"UCLA (3) vs Carleton College (6)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"British Columbia (10) vs Pittsburgh (15)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"UCLA (3) vs Texas (19)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pittsburgh (15) vs Texas (19)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"UCLA (3) vs British Columbia (10)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carleton College (6) vs Texas (19)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"UCLA (3) vs Pittsburgh (15)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carleton College (6) vs British Columbia (10)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Virginia (5) vs Kansas (16)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Whitman (9) vs Princeton (20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Colorado (4) vs Princeton (20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Whitman (9) vs Kansas (16)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Colorado (4) vs Virginia (5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Colorado (4) vs Whitman (9)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Kansas (16) vs Princeton (20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Virginia (5) vs Princeton (20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Colorado (4) vs Kansas (16)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Virginia (5) vs Whitman (9)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Oregon (1) vs Stanford (2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Oregon (1) vs British Columbia (10)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carleton College (6) vs Stanford (2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dartmouth (7) vs Oregon (1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"British Columbia (10) vs Virginia (5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Whitman (9) vs Carleton College (6)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Colorado (4) vs Stanford (2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dartmouth (7) vs Texas (19)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"British Columbia (10) vs Ohio State (14)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Whitman (9) vs Florida State (12)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Victoria (17) vs Colorado (4)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Notre Dame (8) vs Princeton (20)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Middlebury (18) vs UCLA (3)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Kansas (16) vs Central Florida (13)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Pittsburgh (15) vs Washington (11)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_teams = set()\n",
"\n",
"for k,v in gamestats.iteritems(): \n",
" all_teams.add(v['home']['name'])\n",
" all_teams.add(v['away']['name'])\n",
"\n",
"print \"All Teams\"\n",
"sorted(all_teams, key=getseed)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"All Teams\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[u'Oregon (1)',\n",
" u'Stanford (2)',\n",
" u'UCLA (3)',\n",
" u'Colorado (4)',\n",
" u'Virginia (5)',\n",
" u'Carleton College (6)',\n",
" u'Dartmouth (7)',\n",
" u'Notre Dame (8)',\n",
" u'Whitman (9)',\n",
" u'British Columbia (10)',\n",
" u'Washington (11)',\n",
" u'Florida State (12)',\n",
" u'Central Florida (13)',\n",
" u'Ohio State (14)',\n",
" u'Pittsburgh (15)',\n",
" u'Kansas (16)',\n",
" u'Victoria (17)',\n",
" u'Middlebury (18)',\n",
" u'Texas (19)',\n",
" u'Princeton (20)']"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def team_and_seed(name):\n",
" # Name\n",
" if name.upper() == 'TBD': \n",
" team = 'NOT FOUND'\n",
" else: \n",
" team = name.split('(')[0].strip()\n",
" \n",
" # seed\n",
" seed = getseed(name)\n",
" \n",
" return team, seed\n",
"\n",
"with open(stats_csvfilename, 'wb') as csvfile:\n",
" c = csv.writer(csvfile, quoting=csv.QUOTE_MINIMAL)\n",
" c.writerow([\"game_id\", \"name\", \"team\", \"seed\", \"number\", \"goals\", \"assists\", \"ds\", \"turns\"])\n",
" \n",
" for gameid, game in gamestats.iteritems():\n",
" gameid = urllib.unquote(gameid)\n",
" hometeam, homeseed = team_and_seed(game[\"home\"][\"name\"])\n",
" homestats = game[\"home\"][\"stats\"]\n",
" awayteam, awayseed = team_and_seed(game[\"away\"][\"name\"])\n",
" awaystats = game[\"away\"][\"stats\"]\n",
" \n",
" for _, stat in homestats.iteritems():\n",
" number, name = stat.name.split(' ', 1)\n",
" number = number.strip(\"#\")\n",
" name = name.title()\n",
" c.writerow([gameid, name, hometeam, homeseed, number, stat.goals, stat.assists, stat.ds, stat.turns])\n",
" for _, stat in awaystats.iteritems():\n",
" number, name = stat.name.split(' ', 1)\n",
" number = number.strip(\"#\")\n",
" name = name.title()\n",
" c.writerow([gameid, name, awayteam, awayseed, number, stat.goals, stat.assists, stat.ds, stat.turns])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv(stats_csvfilename)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Visual inspection\n",
"\n",
"df[[\"name\", \"team\", \"number\", \"goals\", \"assists\", \"ds\", \"turns\"]]\\\n",
" .groupby([\"name\", \"team\", \"number\"])\\\n",
" .sum()\\\n",
" .sort_index(by=\"goals\", ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>goals</th>\n",
" <th>assists</th>\n",
" <th>ds</th>\n",
" <th>turns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>name</th>\n",
" <th>team</th>\n",
" <th>number</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Olivia Bartruff</th>\n",
" <th>Oregon</th>\n",
" <th>9</th>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kate Scarth</th>\n",
" <th>Victoria</th>\n",
" <th>8</th>\n",
" <td>19</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mary Andersen</th>\n",
" <th>Notre Dame</th>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Courtney Gegg</th>\n",
" <th>Stanford</th>\n",
" <th>23</th>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Larissa Ferreira</th>\n",
" <th>Florida State</th>\n",
" <th>12</th>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Caitlin Go</th>\n",
" <th>Stanford</th>\n",
" <th>60</th>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Terynn Chan</th>\n",
" <th>British Columbia</th>\n",
" <th>27</th>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kailee Karr</th>\n",
" <th>Kansas</th>\n",
" <th>22</th>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Piper Curtis</th>\n",
" <th>Dartmouth</th>\n",
" <th>6</th>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jane Urheim</th>\n",
" <th>Princeton</th>\n",
" <th>8</th>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Caire Thallon</th>\n",
" <th>Carleton College</th>\n",
" <th>35</th>\n",
" <td>13</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alexa Wood</th>\n",
" <th>Central Florida</th>\n",
" <th>29</th>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tessora Young</th>\n",
" <th>Washington</th>\n",
" <th>91</th>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Camille Wilson</th>\n",
" <th>UCLA</th>\n",
" <th>18</th>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Han Chen</th>\n",
" <th>UCLA</th>\n",
" <th>25</th>\n",
" <td>11</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eva Petzinger</th>\n",
" <th>Dartmouth</th>\n",
" <th>21</th>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shayna Brock</th>\n",
" <th>Central Florida</th>\n",
" <th>8</th>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Claire Revere</th>\n",
" <th>Whitman</th>\n",
" <th>28</th>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Megan Chavez</th>\n",
" <th>Carleton College</th>\n",
" <th>22</th>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Linda Morse</th>\n",
" <th>Pittsburgh</th>\n",
" <th>8</th>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Julia Butterfield</th>\n",
" <th>Notre Dame</th>\n",
" <th>8</th>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Natasha Field-Marsham</th>\n",
" <th>Stanford</th>\n",
" <th>7</th>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sarah Hansen</th>\n",
" <th>Virginia</th>\n",
" <th>31</th>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sarah Lipscomb</th>\n",
" <th>Notre Dame</th>\n",
" <th>84</th>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Keila Strick</th>\n",
" <th>Virginia</th>\n",
" <th>44</th>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Emily Buckner</th>\n",
" <th>Carleton College</th>\n",
" <th>5</th>\n",
" <td>10</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nada Tramonte</th>\n",
" <th>Virginia</th>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bethany Kaylor</th>\n",
" <th>Oregon</th>\n",
" <th>11</th>\n",
" <td>10</td>\n",
" <td>20</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Megan Cousins</th>\n",
" <th>Colorado</th>\n",
" <th>16</th>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Marlena Sloss</th>\n",
" <th>Whitman</th>\n",
" <th>11</th>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Deanna Abrams</th>\n",
" <th>Stanford</th>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pleasant Garner</th>\n",
" <th>Princeton</th>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dora Danko</th>\n",
" <th>Pittsburgh</th>\n",
" <th>32</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Olivia Chow</th>\n",
" <th>Victoria</th>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Elaine Rose</th>\n",
" <th>Notre Dame</th>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nora Bradley</th>\n",
" <th>Princeton</th>\n",
" <th>37</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ellen Jacobus</th>\n",
" <th>Carleton College</th>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nicole Wellman</th>\n",
" <th>Central Florida</th>\n",
" <th>33</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nicole Vujosevich</th>\n",
" <th>Notre Dame</th>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ellen Plane</th>\n",
" <th>Dartmouth</th>\n",
" <th>52</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Emily Hayes</th>\n",
" <th>Central Florida</th>\n",
" <th>88</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Emily Leivy</th>\n",
" <th>Virginia</th>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Emily Steedman</th>\n",
" <th>Ohio State</th>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kelly Ross</th>\n",
" <th>Virginia</th>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nancy Wu</th>\n",
" <th>Princeton</th>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nadine Rowen</th>\n",
" <th>Kansas</th>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evelyn Chan</th>\n",
" <th>British Columbia</th>\n",
" <th>66</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evelyn Ding</th>\n",
" <th>Princeton</th>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Molly Welsh</th>\n",
" <th>Kansas</th>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Miriam Mechache</th>\n",
" <th>Kansas</th>\n",
" <th>25</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Felicia Perez</th>\n",
" <th>Central Florida</th>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Michele Derieux</th>\n",
" <th>Virginia</th>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gabby Sehres</th>\n",
" <th>Florida State</th>\n",
" <th>44</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mia Letterie</th>\n",
" <th>Whitman</th>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gabrielle Doran</th>\n",
" <th>Pittsburgh</th>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gentry Maddox</th>\n",
" <th>Florida State</th>\n",
" <th>50</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heather Fredrickson</th>\n",
" <th>Notre Dame</th>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mary Wheeler</th>\n",
" <th>Central Florida</th>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Isabel Jamerson</th>\n",
" <th>Middlebury</th>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Abigail Leibowitz</th>\n",
" <th>Dartmouth</th>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>392 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" goals assists ds turns\n",
"name team number \n",
"Olivia Bartruff Oregon 9 23 0 2 0\n",
"Kate Scarth Victoria 8 19 14 0 0\n",
"Mary Andersen Notre Dame 15 16 2 0 0\n",
"Courtney Gegg Stanford 23 16 2 1 0\n",
"Larissa Ferreira Florida State 12 14 3 0 0\n",
"Caitlin Go Stanford 60 14 4 1 2\n",
"Terynn Chan British Columbia 27 14 6 0 4\n",
"Kailee Karr Kansas 22 14 10 0 0\n",
"Piper Curtis Dartmouth 6 14 2 0 0\n",
"Jane Urheim Princeton 8 13 9 0 0\n",
"Caire Thallon Carleton College 35 13 6 0 2\n",
"Alexa Wood Central Florida 29 13 4 0 0\n",
"Tessora Young Washington 91 13 2 0 0\n",
"Camille Wilson UCLA 18 12 2 0 0\n",
"Han Chen UCLA 25 11 20 0 0\n",
"Eva Petzinger Dartmouth 21 11 13 0 0\n",
"Shayna Brock Central Florida 8 11 11 0 0\n",
"Claire Revere Whitman 28 11 5 0 0\n",
"Megan Chavez Carleton College 22 11 2 1 0\n",
"Linda Morse Pittsburgh 8 11 5 0 0\n",
"Julia Butterfield Notre Dame 8 11 10 0 0\n",
"Natasha Field-Marsham Stanford 7 11 0 4 1\n",
"Sarah Hansen Virginia 31 10 5 0 0\n",
"Sarah Lipscomb Notre Dame 84 10 4 0 0\n",
"Keila Strick Virginia 44 10 5 0 0\n",
"Emily Buckner Carleton College 5 10 15 1 4\n",
"Nada Tramonte Virginia 10 10 8 0 0\n",
"Bethany Kaylor Oregon 11 10 20 3 10\n",
"Megan Cousins Colorado 16 10 11 0 0\n",
"Marlena Sloss Whitman 11 10 4 0 0\n",
"... ... ... .. ...\n",
"Deanna Abrams Stanford 27 0 0 0 0\n",
"Pleasant Garner Princeton 16 0 0 0 0\n",
"Dora Danko Pittsburgh 32 0 0 0 0\n",
"Olivia Chow Victoria 5 0 1 0 0\n",
"Elaine Rose Notre Dame 22 0 0 0 0\n",
"Nora Bradley Princeton 37 0 0 0 0\n",
"Ellen Jacobus Carleton College 9 0 0 0 0\n",
"Nicole Wellman Central Florida 33 0 0 0 0\n",
"Nicole Vujosevich Notre Dame 19 0 2 0 0\n",
"Ellen Plane Dartmouth 52 0 0 0 0\n",
"Emily Hayes Central Florida 88 0 0 0 0\n",
"Emily Leivy Virginia 12 0 0 0 0\n",
"Emily Steedman Ohio State 14 0 0 0 0\n",
"Kelly Ross Virginia 24 0 0 0 0\n",
"Nancy Wu Princeton 10 0 0 0 0\n",
"Nadine Rowen Kansas 20 0 1 0 0\n",
"Evelyn Chan British Columbia 66 0 0 0 0\n",
"Evelyn Ding Princeton 4 0 0 0 0\n",
"Molly Welsh Kansas 19 0 0 0 0\n",
"Miriam Mechache Kansas 25 0 0 0 0\n",
"Felicia Perez Central Florida 28 0 0 0 0\n",
"Michele Derieux Virginia 23 0 7 0 0\n",
"Gabby Sehres Florida State 44 0 0 0 0\n",
"Mia Letterie Whitman 24 0 0 0 0\n",
"Gabrielle Doran Pittsburgh 18 0 0 0 0\n",
"Gentry Maddox Florida State 50 0 0 0 0\n",
"Heather Fredrickson Notre Dame 24 0 7 0 0\n",
"Mary Wheeler Central Florida 24 0 0 0 0\n",
"Isabel Jamerson Middlebury 14 0 3 0 0\n",
"Abigail Leibowitz Dartmouth 18 0 0 0 0\n",
"\n",
"[392 rows x 4 columns]"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"games_played = df.groupby(\"team\").game_id.nunique()\n",
"games_played.name = 'ngames'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"team_totals = df[[\"team\", \"goals\", \"assists\", \"ds\", \"turns\"]].groupby([\"team\"]).sum()\n",
"team_totals = team_totals.sort_index(by=\"ds\", ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"team_df = team_totals.join(games_played)\n",
"team_df = team_df.sort_index(by='ngames', ascending=False)\n",
"team_df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>goals</th>\n",
" <th>assists</th>\n",
" <th>ds</th>\n",
" <th>turns</th>\n",
" <th>ngames</th>\n",
" </tr>\n",
" <tr>\n",
" <th>team</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>Stanford</th>\n",
" <td>101</td>\n",
" <td>101</td>\n",
" <td>36</td>\n",
" <td>72</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oregon</th>\n",
" <td>102</td>\n",
" <td>102</td>\n",
" <td>32</td>\n",
" <td>67</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>British Columbia</th>\n",
" <td>89</td>\n",
" <td>90</td>\n",
" <td>10</td>\n",
" <td>19</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dartmouth</th>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whitman</th>\n",
" <td>72</td>\n",
" <td>73</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Colorado</th>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Carleton College</th>\n",
" <td>71</td>\n",
" <td>72</td>\n",
" <td>4</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Princeton</th>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Washington</th>\n",
" <td>66</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Virginia</th>\n",
" <td>69</td>\n",
" <td>69</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Victoria</th>\n",
" <td>57</td>\n",
" <td>57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UCLA</th>\n",
" <td>64</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Central Florida</th>\n",
" <td>52</td>\n",
" <td>54</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pittsburgh</th>\n",
" <td>42</td>\n",
" <td>43</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ohio State</th>\n",
" <td>43</td>\n",
" <td>44</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Notre Dame</th>\n",
" <td>48</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Middlebury</th>\n",
" <td>40</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kansas</th>\n",
" <td>62</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Florida State</th>\n",
" <td>53</td>\n",
" <td>53</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Texas</th>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
" goals assists ds turns ngames\n",
"team \n",
"Stanford 101 101 36 72 7\n",
"Oregon 102 102 32 67 7\n",
"British Columbia 89 90 10 19 7\n",
"Dartmouth 71 71 0 0 6\n",
"Whitman 72 73 0 0 6\n",
"Colorado 70 70 0 0 6\n",
"Carleton College 71 72 4 21 6\n",
"Princeton 39 39 0 0 5\n",
"Washington 66 65 0 0 5\n",
"Virginia 69 69 0 0 5\n",
"Victoria 57 57 0 0 5\n",
"UCLA 64 64 0 0 5\n",
"Central Florida 52 54 0 0 5\n",
"Pittsburgh 42 43 0 0 5\n",
"Ohio State 43 44 0 0 5\n",
"Notre Dame 48 51 0 0 5\n",
"Middlebury 40 39 0 0 5\n",
"Kansas 62 63 0 0 5\n",
"Florida State 53 53 0 0 5\n",
"Texas 48 48 0 0 5"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xvar = \"goals\"\n",
"yvar = \"turns\"\n",
"\n",
"g = sns.lmplot(xvar, yvar, team_df, \n",
" scatter_kws={\"marker\":\"\"}, \n",
" size=9\n",
" )\n",
"\n",
"ax = g.axes[0][0]\n",
"\n",
"for name, stats in team_df.iterrows():\n",
" ax.annotate(name, xy=(stats[xvar], stats[yvar]), \n",
" horizontalalignment='center')\n",
"\n",
"ax.set_xlabel(\"Goals\")\n",
"ax.set_ylabel(\"Turnovers\")\n",
"ax.set_title(\"{} vs. {}\\n\".format(xvar, yvar) + \n",
" \"2015 USAU College Championships Men's division\".format())\n",
"\n",
"ax.text(x=50, y=50, s=\"(missing significant data)\",\n",
" fontsize=16, \n",
" color='red')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"<matplotlib.text.Text at 0x7f0f56ded610>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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JxGWXXYbFYiEuLo7Q0FC6du0KQEJCAunp6f7jEhMT/T1CAwYMYOnSpdx8881A7h8VP/74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\nnwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f0f57353ed0>"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Summary stats by player\n",
"player_totals = df[[\"name\", \"team\", \"number\", \"goals\", \"assists\", \"ds\", \"turns\"]]\\\n",
" .groupby([\"name\"])\\\n",
" .sum()\n",
"player_totals.sort_index(by=\"assists\", ascending=False, inplace=True)\n",
"\n",
"# Define \"top players\"\n",
"top_players = player_totals[:20]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"top_players.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>number</th>\n",
" <th>goals</th>\n",
" <th>assists</th>\n",
" <th>ds</th>\n",
" <th>turns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>name</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>Angela Zhu</th>\n",
" <td>48</td>\n",
" <td>6</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lucia Childs-Walker</th>\n",
" <td>60</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Julia Bladin</th>\n",
" <td>60</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Corinne Dunwoody</th>\n",
" <td>65</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stephanie Williams</th>\n",
" <td>30</td>\n",
" <td>5</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
" number goals assists ds turns\n",
"name \n",
"Angela Zhu 48 6 27 0 0\n",
"Lucia Childs-Walker 60 3 24 0 4\n",
"Julia Bladin 60 4 23 0 0\n",
"Corinne Dunwoody 65 4 23 0 0\n",
"Stephanie Williams 30 5 22 0 0"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xvar = 'goals'\n",
"yvar = 'assists'\n",
"g = sns.lmplot(xvar, yvar, top_players, scatter_kws={\"marker\":\"x\"}, size=10)\n",
"ax = g.axes[0][0]\n",
"\n",
"for name, stats in top_players.iterrows():\n",
" ax.annotate(name, xy=(stats[xvar], stats[yvar]), \n",
" horizontalalignment='center')\n",
"\n",
"ax.set_xlabel(xvar)\n",
"ax.set_ylabel(yvar)\n",
"ax.set_title(\"{} vs. {} for the top {} assisters\\n\\\n",
" 2015 USAU College Championships Women's division\".format(\n",
" xvar,\n",
" yvar,\n",
" len(top_players))\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"<matplotlib.text.Text at 0x7f0f567b6b90>"
]
},
{
"metadata": {},
"output_type": "display_data",
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BgwYpMjIy1Xvj4uLUtWtXHThwwL/tyJEjql+/vrp37+7/3++//57uvKNGjfI/yZ9S/fr1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\nG3s+og7sz+La62854joVRaFevQbcPmo8X3z+MV989hG3jbo3dv+/9+4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HqyDAZz/uRtEZ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T9ElS5dhnnz/ovv19+QfMoPm7cTyzJzDrFcuQi++GIRycnJZGVlER//By+++ByhoWF8//1yYmIO\nsGjRfGrWrI3D4QSgV6++9O/fm4SEeOx2b0qUKInd7kVoaDEMw8Df3x+Hw0HXrp0JCSmCw+HMmaoB\n4Ha7eeKJp3j++c4EBARctXnyIiIil8qwCvh3mb/HpZCYeDK/y8i1Y4nxTHxrBAOGjvvHZTynWzSs\nVvofly1oLMsiwOXAz/fC843PvkrC9eLzn/azflccQM50i4Lmyy+XkJSURPv2HS9pvbGfbOS3mOMY\nhsHNpQPo2a7aBZfftm1L9ofMWnWIiTlI794v8fHH86+kdLlC1+Pv3I1A41Y4adwKr9DQ3H9zrjbN\nVbR61fd8Oud9unTtdcHlGlYrTZXy2fNLQwJ8rkVpV8wyLVw+V/9SboVJy7rlqF25GAChQb75XM0/\nu5zh6dmuGrsOHiMwyEXxAOdFly9ZshSDBvXnvfemkZmZSY8eubs8nIiISGGhTrJc0JlwfKkn5elT\nduGkcSu8NHaFk8atcNK4FV7qJMsVM03zz86xI9+vWCEiIiJyrSkkyzks08TH6U2gv6/CsYiIiNyw\nFJIF+OtyboH+LrxsN9Q9ZkRERET+RiH5BmdaFt5eBiEBvjjs+XM5NxEREZGCRiH5BmVZFl6GQZDb\ngY/j2t8+WkRERKQgU0i+AZmmSYDLgb/Lkd+liIiIiBRICsk3ENOycNhthAQVjNtIi4iIiBRUCsk3\nCMuyCPJz4PLR1AoRERGRi1FIvs6Zpomv006Qv/OGvVOeiIiIyKVSSL5OmZaFlwFFAnxxOnTVChER\nEZFLoZB8HTJNE7fLgVsn5omIiIhcFoXk64hlmjgdXgT568Q8ERERkSuhkHwdsCwLmwHBmlohIiIi\nclUoJBdylmXi76upFSIiIiJXk0JyIXXmmsdB/r7YvdQ9FhEREbmaFJILIV3zWERERCRv2fK7AMk9\n07JwetsoHuI6b0COjp7Jyy+/wBNPtKdevZpER88EYO/e3WzevJEnnniMESMG53p/bdu2JiMjg7ff\nHkts7NHzLvPFF4uZOnXSeX+WmJjA2LGjzvuzxx57kGPHjgEQHx/Pv/5Vi/r1axMfHw9Au3ZtiIk5\nQL16NfnyyyX89tuvzJw5HYBmzRqwcuUKunV7lmnT3sn18YiIiIjklkJyIWBZFoYBRQN9CHb7nPem\nIPv27WXVqhWMHz+Fl17qScmSpZg1630Ali9fytq1q0lNTb2k/RqGgWVZvPhiT4oVK/6Py/yTkJAi\n9Oz52nl/VrNmbbZs2QjA6tUrKVcuApfLxbJl33D48O8EB4ewZs1P2GzZb9EKFSryn/90BsDHx4d6\n9epjGAYLF869pGMSERERyQ1NtyjgcnvNY39/f2JjY1myZCF+fv5UrnwbMTEHOHBgP19+uYSTJ0/S\noEEj4uL+4IUXOpOUlMTx40mULRuOYRgUKVKEvXv3kpAQR+nSZUhNTeXUqVOMGTOSFSuWU6ZMOE8/\n3YXp09/hyJEjgEVwcAi33FKZ2NhY7r+/EV5edgIDA3G5/GjX7hH++9+57Nu3hwoVKtG+fUc++iga\nm81GqVKlueuuumzevJEGDRqzevUqqlWrwdKl37B8+Tf4+flx1111WbVqJXa7nQUL5jJnzixOnDjO\nZ58t5tSpUyxYMJejR4+QkpLCqFHDOH48ie3bt5GWlkrJkqXo3r0Hw4YNJDw8goiICLp373FtBkxE\nRESuC+okF1CmaeJtNyge4perK1eEhobxxhtj2bp1M+PGjeKnn34kPDyC7du30qJFa4KDg2nUqAlb\nt26hb98BmGYWrVo9QIsWrfHx8eHQoUN07PgEFSpUIikpieHD3yQlJYXWrdtQoUIlQkPDmD//M+68\nswa9e/ejT59I+vcfzJo1q7Hb7dx8c0X+85+nMQwbN1WqyvzFX9Kv30AiIspTrlwEo0ePYMSIMUya\n9C6hoWEkJCSwdesWsrKyOHz4MEFBwRQrVpysLJNVq1YSEXETYWHFyMzMpFGje3nlld5kZmby22+7\ngOwOdvHiJfD398ftDsDhcNC8eUumTfsQu92bceNGERf3B4MGDVdAFhERkUumTnIBY1kWBgYhAU58\nHLk/Me/33w/h5+dP374D2LBhPbNmzWT16lUkJiZQrFhxSpUqQ0pKMmlpaQwa1I/k5BNs3Pgzdrud\nsmXDSUtLA+DWW28nOTkZp9MJWFSqVBmAoKAgKlSoyJ49uxk3bhROp5M776yBaZpUrHgLO3Zs4847\nq/Pue++Tvi+N5NhkevQbTNrxQ2RlZZGSkkxkZPbUi7S0NGrXroPd7sXq1auoUuVOACIiypOVlcmm\nTRu4+eYKNG16P99++zWBgUEAOJ3O804Z2bdvD7/8soPU1FQWLpxLZmYmxYoVx+0OICAg4EqGQ0RE\nRG5Q6iQXIKZl4eu0UyzE95ICMsDu3b8xbtxoMjMzAXC5XAQFBZGamsqOHdupXPk2XC4/fH19GD58\nNG53II8++jg1a9bmwIH9ZGRkAJCamkpqampOMD0z59iyYOvWLaSmpvLUU8/wf//30J97tnKW2X80\nhYxMEzMrg5Pxewio2IrgIsVwuVy43QGMGjWOiROj6NjxP9SsWZvq1Wvx0UcfUqfO3QBERNzEkSOH\nSU9PZ8uWTVSrVgM4/5xny7Jy6ipbthx16tzNE088RXT0p3Ts+B8aN74PLy+9vUVEROTyqJNcUFhQ\nNMAHh/flXfO4QYNGHDiwj86d/01WVhaJiQn07RtJbOxR5s79lGXLvsHpdHDrrbczZMgADMPizTdH\nULp0GRwOb+x2O7Nnf0By8gkiI4fmnDB3hmFAyZKl+O67pXz//TK8vR3Ur98Qh8OBaZpnL4nNyxtv\n32Bi1n6ALfMEVe9oTokSJejV6yUsy8TPz5/XXx8CwCefzKZ69Zrs2LENt9tNenoaJUuWpGLFWzAM\ng/OfF2jkBPNSpUoTF/cHaWlp/Pe/c/jww/cIDg7h3/9+EsNQSBYREZHLY1hnWnIF1O9xKSQmnszv\nMvKMZVp4e9sICfDBdoErReSlL79cQlJSEu3bd7zibY39ZCO/xRzHMAxuLh1Az3bVrkKFcq2EhrqJ\ni0vO7zLkMmjsCieNW+GkcSu8QkPduV5WneR8ZJoWgX7e+Pnm/y2lr1Y+79muGrsOHiMwyEXxAOfV\n2aiIiIjINaaQnG8sigb54LDn/y2l77+/1VXdXqWywfqULSIiIoWaQvI1ZloWPt5eBLudF7wRh4iI\niIjkH4Xka8i0LAJdBWN6hYiIiIj8M4Xka8SyLIq4fXA68n96hYiIiIhcmEJyHrMsC5vNoGigD3Yv\nBWQRERGRwkAhOQ9ZpoXjz8u7af6xiIiISOGhkJxHTNPE7XLgdmn+sYiIiEhho5CcByzLIiTAecm3\nlhYRERGRgkEh+SqyLAsvm0GRQF+8vHRLZBEREZHCSiH5KjFNE1+nnWC3T36XIiIiIiJXSCH5KrAs\nkyB/Jy4fTa8QERERuR4oJF8hA4sigb54F4DbS4uIiIjI1aGQfJlM08THYdftpUVERESuQwrJl8Gy\nLILdTnydml4hIiIicj1SSL4EpmXh420jyO2LTd1jERERkeuWQnIuWZZFkJ9DJ+eJiIiI3AAUki/C\ntCwcdhshbl9sNnWPRURERG4ECsn/wLIsDAx1j0VERERuQArJ57Ft888s+/Zzhg8biWEYLF/+Le+/\nP40xY94mLKzY35bfsGE9CxfOY/DgEVethi++WMz06VMpVap0znPt2nUgOfkEbncA9erVP+96w4cP\nokmTZtx1V92rVouIiEhBsmHDegYM6EtExE05zwUFBTN06BsXXXfNmp+IjT1K7dp1GDiwH1FR7190\nnYYN63DHHVWxLIvTp0/TufNT1K3b6JJqbtu2NXPmzMPbW423wkIh+SymaeLw9iLQ7cTbbsMwDL75\n5is+/ng2EyZMJTg4+Lzr5cUl4AzDoFmzFnTp0vWS19Ml6URE5HpmGAY1a9Zm0KDhl7zumSbSkSOH\nc71OYGAgEydGAXDyZAodOrRlwYJLC8n621z45HlI3rx5M2PGjCE6OpodO3bw3HPPER4eDkD79u1p\n0aJFXpdwUZZl4WUYBAU48XF4E+OVfWOQr776nLlzP2XChHfw9/cHYM+e3UyYMAbLsggMDKRv3wFY\nlpWzrREjBvP774dIS0vjkUceo1mzFmzc+DPTpr2DzWajVKnS9O7dj+HDB9G0aXPq1q3H/v37mDJl\nAqNHj/9bXZ5mzIiiSJGihIeXY9asD3A4vDl8+Hfuvbcp//73Uznrbd++jQkTxjBs2Kjzdr9FREQK\nK8uyzvs3EqBbt2epUKESe/fuweXypUqVaqxd+xMpKcmMGzeZH374joMHD9CmzcMAxMQcZMiQ15k2\n7UMABgzoS/v2Halc+bbzbj8lJYXAwEDgr7/Jbdo8zIED+xkzZiQTJ0bx448/MHPmNCwLKlasRO/e\n/XLqXrDgM9atW8OgQSPo3LkT1arVYPfu3zAMgzfeGIufnz9Tp05iy5ZNmKZJu3aP06hRE7p1e5aQ\nkCKcOHGcceMmYbPZrvbLKh7yNCRPmzaNRYsW4efnB8D27dt58sknefLJJ/Nyt7l2Zt5xgMuBn6/3\nOc9v2bKJ+Pg4kpOTyczMzPnZqFHD6N9/EOHh5ViyZCGzZ39IrVp3AXDq1Ck2b97Iu+/OBGDt2tV/\nrjOcqVPfIygoiOnTp/Lll0uo36gFCxYtoG7denz++SJatWrzt9q++eYrtm/fCkBwcAhDhow855No\nbOxRPvzwY9LT02nTpnlOSN66dTM//7yO0aPHExQUdPVfOBERkXy2YcN6unfvkvP47rv/Rfv2HTEM\ng1tvvY2XXupJz54v4uvrw1tvTWb48EFs2vTz3zq6ZcqUxen0Yf/+fYSEhHDkyOG/BeQTJ07QvXsX\nLMti9+7feKTd48D5u8NZWVmMH/8m06Z9SFBQEB99FM0ff8QC8Nlnn7B7968MGzYawzA4deoUTZo0\n5+WXezNkSCSrV6/C5fLjyJHDTJkynbS0NJ577klq1aqDYRjcd18z/vWvhlf5lZR/kqchOTw8nEmT\nJvHqq68CsG3bNvbv38/SpUsJDw+nX79+OQH6WrMsC39fb/x9vc/7Ji9SpCjjx09h0aL5DBkSydix\nb2MYBgcPZn9SBMjMzKRMmbI567hcLl58sSejRg3n5MmTNGt2P8eOHSMxMYHIyNcASEtLI7jkLbjK\nlWLztp3899strFu3huee63bO/g3DoGnT+y843aJ8+fLYbDZ8fHxwOp05x7Vu3RpOnz6Fl5dulS0i\nIten6tVr/uO5QJUq3QKAv78/5cplz1t2u92kp6efd/n/+78H+eKLxRQrVpzmzf/+DXdAQAATJ0bx\n+U/7Wb01hv/Of5NMZxnOnl18prN9/HgSbrc7p0n1+OOdcpZZv34tdrvXObmjYsVKAISFFSM9PZ3Y\n2KPs2rUz5wNAVlZWztSQsmXLXeRVkaspT3v1TZs2PSeoVa1alddee41Zs2ZRpkwZJk2alJe7P6/s\n20l7UTzEhdvl+Mc5QqVKlcbb25uHH34Ub287H3wwA4AyZcKJjBzCxIlRdOnSlXvu+esEuoSEeHbt\n+oURI95k9Oi3mDLlbdxuN2FhYYwaNY6JE6P4vwfbk+bMDtYlbr6Ljz+cwh1Va5430P7TV0l/+Xvt\nhmHw9NNdePTR9owde/ETGERERK4/lzb/t2HDe1m7djUrVnxH06bnnwYal3Sa9bvi8PJ24u1wsWN/\nPBmmjYSEeAB+/XUnkP3Nb3JyCidOnADg7bfH8ssv2wEYNWocbrebBQvm/lWpRw4pW7Yc1avXYOLE\nKN56azKNGjXJOYlf85qvrWt64t59992H2+0GoEmTJgwbNixX64WEXHm32TQtfJ12ggN88LrI9Y6D\ng/3w9XUQGppd65gxo3nwwQdp0OAehg8fyhtvDCYrKwubzcbw4cOJjY3F19fBLbdEMGfOCbp3fwYv\nLy+eeaYzJUoEM2BAJH379sg+MdDHRVDFh/C22yh7690sj+7LwyP65+zrDLfbB5hUz5EAAA7KSURB\nVD8/59+e9/NzEhDgS1CQ65wabTYboaFufHy8CQz0pWXLTqxc+R1r166gZcuWV/z6XQ7P2qVw0LgV\nXhq7wknjdumCg/3YtOlnevR4Iec5wzCYNm0a3t5ehIT45fxNDApyERrqxtfXQUCAL2lpNvz8nISE\n+OFw2HNe/7p17+LYsWPcdFPJv+3vxIkTDB7wCjFHk8nKyiAoLIJiZSvT8r5SDB/Ul+3bN3P77bfj\ncNgJCwtgyJBB9O/fE5vNxq233kr9+nWw2QyKFvVn6NDBPPLIIzRt2ggvLxtFi/rjcDhwubLra9Om\nJbt2beXll5/j1KlT3HfffYSHFzvnuOTaMKyLtyuvyKFDh+jZsyeffPIJ7dq1o3///lSpUoXo6Ghi\nY2Pp1avXBdf/PS6FxMSTl71/y7KwGRDk74PTUTCmH3z+037W74oj7dRxDq77iFnvv5ffJV11oaFu\n4uKS87sMuUQat8JLY1c4adwKjrfeGk2DBo2pXr3mPy5z5u+3t91G1fJFaFm33DWrT66OS/mQcU06\nyWe+Hhg8eDCDBw/GbrcTFhbGkCFD8nS/lmni8nUQ6OfI0/1cqpZ1y5F1bCeffPQeffu8nt/liIiI\n3NB69OhGUFDwBQMyZP/9rl25GCEhfniZ5jWqTvJLnneSr9TldJIt08JuNwh2O7Hr5LV8oe5I4aRx\nK7w0doWTxq1w0rgVXgWuk3wtWaZJgL8TP91KWkREREQu0/UVki0oEuSLw67usYiIiIhcvusiJFuW\nhZfNoGiQL7aLXLlCRERERORiCn1ItiwLh91GSICPrh8oIiIiIldFoQ7Jpmni5+Mg0L9gXb1CRERE\nRAq3QhuSTdMiyN+JSyfoiYiIiMhVVihDsmVZFAkoODcHEREREZHrS6EKyX+doOfSCXoiIiIikmcK\nTUi2TAunw4tgt1Mn6ImIiIhInioUIdm0LAJc3vi7dIKeiIiIiOS9Ah+STcsixO3Ex1HgSxURERGR\n64Qtvwu4mBIhLgVkEREREbmmCnxItusW0yIiIiJyjRX4kCwiIiIicq0pJIuIiIiIeFBIFhERERHx\noJAsIiIiIuJBIVlERERExINCsoiIiIiIB4VkEREREREPCskiIiIiIh4UkkVEREREPCgki4iIiIh4\nUEgWEREREfGgkCwiIiIi4kEhWURERETEg0KyiIiIiIgHhWQREREREQ8KySIiIiIiHhSSRUREREQ8\nKCSLiIiIiHhQSBYRERER8aCQLCIiIiLiQSFZRERERMSDQrKIiIiIiAeFZBERERERDwrJIiIiIiIe\nFJJFRERERDwoJIuIiIiIeFBIFhERERHxoJAsIiIiIuJBIVlERERExINCsoiIiIiIB4VkEREREREP\nCskiIiIiIh4UkkVEREREPCgki4iIiIh4UEgWEREREfGgkCwiIiIi4kEhWURERETEg0KyiIiIiIgH\nhWQREREREQ8KySIiIiIiHhSSRUREREQ8KCSLiIiIiHhQSBYRERER8aCQLCIiIiLiQSFZRERERMSD\nQrKIiIiIiAeFZBERERERDwrJIiIiIiIeFJJFRERERDwoJIuIiIiIeFBIFhERERHxoJAsIiIiIuJB\nIVlERERExINCsoiIiIiIB4VkEREREREPCskiIiIiIh4UkkVEREREPCgki4iIiIh4UEgWEREREfGg\nkCwiIiIi4kEhWURERETEg0KyiIiIiIgHhWQREREREQ8KySIiIiIiHhSSRUREREQ8KCSLiIiIiHhQ\nSBYRERER8aCQLCIiIiLiQSFZRERERMSDQrKIiIiIiAeFZBERERERDwrJIiIiIiIeFJJFRERERDwo\nJIuIiIiIeFBIFhERERHxoJAsIiIiIuJBIVlERERExINCsoiIiIiIB4VkEREREREPCskiIiIiIh4U\nkkVEREREPCgki4iIiIh4UEgWEREREfGgkCwiIiIi4kEhWURERETEg0KyiIiIiIgHhWQREREREQ8K\nySIiIiIiHhSSRUREREQ8KCSLiIiIiHhQSBYRERER8aCQLCIiIiLiQSFZRERERMSDQrKIiIiIiAeF\nZBERERERDwrJIiIiIiIeFJJFRERERDwoJIuIiIiIeFBIFhERERHxoJAsIiIiIuJBIVlERERExINC\nsoiIiIiIB4VkEREREREPCskiIiIiIh4UkkVEREREPCgki4iIiIh4UEgWEREREfGgkCwiIiIi4kEh\nWURERETEg0KyiIiIiIgHhWQREREREQ8KySIiIiIiHvI8JG/evJlOnToBcODAAdq3b0+HDh0YNGgQ\nlmXl9e5FRERERC5ZnobkadOm8frrr5ORkQHAyJEj6dGjB7Nnz8ayLJYuXZqXuxcRERERuSx5GpLD\nw8OZNGlSTsd4x44d1KpVC4D69euzatWqvNy9iIiIiMhlydOQ3LRpU7y8vHIenz29wuVykZycnJe7\nFxERERG5LPZruTOb7a9MfvLkSQICAnK1XmioO69KkjykcSucNG6Fl8aucNK4FU4at+vfNb26ReXK\nlVm7di0AK1asoGbNmtdy9yIiIiIiuXJNOsmGYQDQp08fIiMjycjIoHz58jRv3vxa7F5ERERE5JIY\nlq7DJiIiIiJyDt1MRERERETEg0KyiIiIiIgHhWQREREREQ8KySIiIiIiHgpkSDZNkwEDBvDYY4/R\nqVMnDh48mN8lSS5lZGTQu3dvOnTowCOPPMKyZcvyuyS5BAkJCTRo0IB9+/bldymSS1FRUTz22GM8\n/PDDzJ8/P7/LkVwyTZO+ffvSvn17OnTowN69e/O7JLmIzZs306lTJwAOHDiQM3aDBg1C10AouM4e\nt19++YUOHTrQqVMnnn76aRISEi64boEMyd9++y0ZGRl8/PHH9OrVizfeeCO/S5JcWrx4MSEhIcye\nPZvp06czdOjQ/C5JcikjI4MBAwbg6+ub36VILq1Zs4aNGzfy8ccfEx0dTUxMTH6XJLm0cuVKTp8+\nzZw5c+jatSvjx4/P75LkAqZNm8brr79ORkYGACNHjqRHjx7Mnj0by7JYunRpPlco5+M5biNGjCAy\nMpLo6GiaNm3KtGnTLrh+gQzJGzZs4F//+hcAVatWZdu2bflckeRW8+bNefHFF4HsTsnZtyWXgm30\n6NG0b9+e0NDQ/C5FcunHH3+kUqVKvPDCCzz33HM0btw4v0uSXPLx8SE5ORnLskhOTsbb2zu/S5IL\nCA8PZ9KkSTkd4x07dlCrVi0A6tevz6pVq/KzPPkHnuM2btw4brnlFgAyMzNxOp0XXP+a3pY6t1JS\nUvD398957OXlhWma59zWWgoml8sFZI/hSy+9xCuvvJLPFUluzJs3j5CQEOrVq0dUVJS+OiwkEhMT\nOXLkCFFRUcTExPD888/z1Vdf5XdZkgvVq1cnPT2d5s2bk5SUxNSpU/O7JLmApk2bcujQoZzHZ/8b\n6XK5SE5Ozo+y5CI8x+1ME2jDhg3Mnj2b2bNnX3D9Apk6/f39OXnyZM5jBeTC5ciRIzzxxBO0adOG\nli1b5nc5kgvz5s1j1apVdOrUiZ07d9KnTx/i4+Pzuyy5iODgYOrVq4fdbiciIgKn00liYmJ+lyW5\nMH36dKpXr87XX3/NwoUL6dOnD+np6fldluTS2Znk5MmTBAQE5GM1cim++OILBg0axLvvvktwcPAF\nly2QybN69eqsWLECgE2bNlGpUqV8rkhyKz4+nqeeeorevXvz0EMP5Xc5kkuzZs0iOjqa6Ohobrnl\nFkaNGkXRokXzuyy5iBo1avDDDz8AEBsby+nTpy/6j74UDKdPn8bPzw+AgIAAMjIyME0zn6uS3Kpc\nuTJr164FYMWKFdSsWTOfK5LcWLhwIbNnzyY6OprSpUtfdPkCOd3ivvvu48cff+Sxxx4DsifIS+Ew\ndepUkpOTmTx5MpMnTwayOyYXm/cjIpeuYcOGrFu3jrZt22KaJgMHDsQwjPwuS3Lh6aefpm/fvjz+\n+ONkZmbSs2dPfHx88rssuYgzv199+vQhMjKSjIwMypcvT/PmzfO5MrkQwzAwTZMRI0ZQsmRJunXr\nBkDt2rXp3r37P69nafKhiIiIiMg5CuR0CxERERGR/KSQLCIiIiLiQSFZRERERMSDQrKIiIiIiAeF\nZBERERERDwrJIiIiIiIeFJJFRK5TjRs35vDhw/ldhohIoaSQLCIiIiLioUDecU9E5EY3duxY/ve/\n/xEcHExoaCiNGzfGNE1mzpwJwO23305kZCQul4tZs2axaNEiTp8+jWEYvPXWW5QvXz5nWzt37mTg\nwIFkZmbidDoZOXIk4eHh+XRkIiKFgzrJIiIFzLJly9iwYQOff/457777Ljt27ODkyZNERUUxa9Ys\nFi9ejK+vL5MmTSIlJYWlS5fmPN+kSRPmzJmTsy3Lsvjggw948sknmTt3Lh07dmTTpk35eHQiIoWD\nQrKISAGzatUqWrRogd1uJyAggCZNmmAYBo0bNyYwMBCARx99lNWrV+Pv78/YsWNZvHgxY8eOZfny\n5Zw6dSpnW4Zh0LBhQ4YOHUr//v1xOBy0bt06vw5NRKTQUEgWESlgvLy8yMrKOuc50zSxLCvnsWVZ\nZGZmcvToUR599FFSUlJo0KABDz744DnLATRr1ox58+ZRpUoVPvjgAwYOHHhNjkNEpDBTSBYRKWDu\nvvtu/ve//5GRkUFKSgrfffcdx48fZ9myZRw/fhyATz/9lDp16rB161bCw8N54oknqFKlCt9///05\nAduyLHr27MnWrVtp164dL774Itu3b8+vQxMRKTR04p6ISAHToEEDNm7cyIMPPkhgYCBhYWGUL1+e\nZ599lo4dO5KZmcntt9/O4MGDAZgzZw6tWrUiODiYevXqsWLFipxtGYbBM888w+uvv86UKVPw8vKi\nX79++XVoIiKFhmF5fi8nIiL5atOmTezfv582bdqQkZHBY489xsiRI6lYsWJ+lyYicsNQSBYRKWCO\nHz9Oz549iYuLwzRNHnroIZ588sn8LktE5IaikCwiIiIi4kEn7omIiIiIeFBIFhERERHxoJAsIiIi\nIuJBIVlERERExINCsoiIiIiIh/8HbtRjyO6DhsIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f0f57bc8710>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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