scrape-nba-statistics-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Web scraping in Python: gathering and analyzing NBA player stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Erick Lu\n",
    "\n",
    "March 31, 2020 - [GitHub Repo](https://github.com/erilu/web-scraping-NBA-statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I use Python to “scrape” ESPN for stats on all the players in the NBA, clean and organize the data into a data science-friendly format, and calculate some interesting statistics. Web scraping is a useful technique for extracting data from websites that don’t offer formatted, raw data for download.\n",
    "\n",
    "As an example, I will be scraping data from the rosters of each team in the NBA for information such as player age, height, weight, and salary. I will also loop through each individual player's stats page and extract career averages such as points per game, free throw percentages, and more (as of currently, March 2020). \n",
    "\n",
    "We can use this data to answer questions such as:\n",
    "* Do factors such as age, height, weight, etc. correlate with player performance? (i.e. does height matter?)\n",
    "* What is the average salary paid by each team in the NBA, and which player earns the most on each team?\n",
    "* How much more do better players cost? Can we model the average price of hiring a player given his performance? If so, what is the cost per increase in points per game?\n",
    "\n",
    "I've exported the data to a nicely organized csv file, accessible in the [GitHub repo](https://github.com/erilu/web-scraping-NBA-statistics) for this project, in case you would like to analyze it yourself. You can also run the python script `scrape_nba_statistics.py` to re-scrape ESPN for up-to-date data.\n",
    "\n",
    "In the following sections, I will describe how to loop through ESPN page sources using `urllib`, extract information using `re` (regular expressions), organize player statistics in `pandas` DataFrames, and perform some simple modeling using `scikit-learn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrape roster information for each NBA team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first take a look at the structure of the website and figure out which web pages we need to scrape information from. The teams page at https://www.espn.com/nba/teams looks like the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ESPN_teams_webpage.png](images/ESPN_teams_webpage.png)<img src=\"attachment:NN.png\" width=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks very promising. All the teams are listed on this page, which means that they can easily be extracted from the page source. Let’s take a look at the page source to see if we can find URLs for each team's roster:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ESPN_teams_source.png](images/ESPN_teams_source.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like URLs for each of the teams rosters are listed in the page source with the following format: https://www.espn.com/nba/team/roster/_/name/team/team-name, as shown in the highlighted portion of the image above. Given that these all follow the same format, we can use regular expressions to pull out a list of all the team names from the page source, and then construct the roster URLs using the format above. Start by importing the `urllib` and `re` packages in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import urllib\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let’s create a function that will extract all the team names from http://www.espn.com/nba/teams and construct roster URLs for each of the teams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This method finds the urls for each of the rosters in the NBA using regexes.\n",
    "def build_team_urls():\n",
    "    # Open the espn teams webpage and extract the names of each roster available.\n",
    "    f = urllib.request.urlopen('https://www.espn.com/nba/teams')\n",
    "    teams_source = f.read().decode('utf-8')\n",
    "    teams = dict(re.findall(\"www\\.espn\\.com/nba/team/_/name/(\\w+)/(.+?)\\\",\", teams_source))\n",
    "    # Using the names of the rosters, create the urls of each roster\n",
    "    roster_urls = []\n",
    "    for key in teams.keys():\n",
    "        # each roster webpage follows this general pattern.\n",
    "        roster_urls.append('https://www.espn.com/nba/team/roster/_/name/' + key + '/' + teams[key])\n",
    "        teams[key] = str(teams[key])\n",
    "    return dict(zip(teams.values(), roster_urls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boston-celtics': 'https://www.espn.com/nba/team/roster/_/name/bos/boston-celtics',\n",
       " 'brooklyn-nets': 'https://www.espn.com/nba/team/roster/_/name/bkn/brooklyn-nets',\n",
       " 'new-york-knicks': 'https://www.espn.com/nba/team/roster/_/name/ny/new-york-knicks',\n",
       " 'philadelphia-76ers': 'https://www.espn.com/nba/team/roster/_/name/phi/philadelphia-76ers',\n",
       " 'toronto-raptors': 'https://www.espn.com/nba/team/roster/_/name/tor/toronto-raptors',\n",
       " 'chicago-bulls': 'https://www.espn.com/nba/team/roster/_/name/chi/chicago-bulls',\n",
       " 'cleveland-cavaliers': 'https://www.espn.com/nba/team/roster/_/name/cle/cleveland-cavaliers',\n",
       " 'detroit-pistons': 'https://www.espn.com/nba/team/roster/_/name/det/detroit-pistons',\n",
       " 'indiana-pacers': 'https://www.espn.com/nba/team/roster/_/name/ind/indiana-pacers',\n",
       " 'milwaukee-bucks': 'https://www.espn.com/nba/team/roster/_/name/mil/milwaukee-bucks',\n",
       " 'atlanta-hawks': 'https://www.espn.com/nba/team/roster/_/name/atl/atlanta-hawks',\n",
       " 'charlotte-hornets': 'https://www.espn.com/nba/team/roster/_/name/cha/charlotte-hornets',\n",
       " 'miami-heat': 'https://www.espn.com/nba/team/roster/_/name/mia/miami-heat',\n",
       " 'orlando-magic': 'https://www.espn.com/nba/team/roster/_/name/orl/orlando-magic',\n",
       " 'washington-wizards': 'https://www.espn.com/nba/team/roster/_/name/wsh/washington-wizards',\n",
       " 'denver-nuggets': 'https://www.espn.com/nba/team/roster/_/name/den/denver-nuggets',\n",
       " 'minnesota-timberwolves': 'https://www.espn.com/nba/team/roster/_/name/min/minnesota-timberwolves',\n",
       " 'oklahoma-city-thunder': 'https://www.espn.com/nba/team/roster/_/name/okc/oklahoma-city-thunder',\n",
       " 'portland-trail-blazers': 'https://www.espn.com/nba/team/roster/_/name/por/portland-trail-blazers',\n",
       " 'utah-jazz': 'https://www.espn.com/nba/team/roster/_/name/utah/utah-jazz',\n",
       " 'golden-state-warriors': 'https://www.espn.com/nba/team/roster/_/name/gs/golden-state-warriors',\n",
       " 'la-clippers': 'https://www.espn.com/nba/team/roster/_/name/lac/la-clippers',\n",
       " 'los-angeles-lakers': 'https://www.espn.com/nba/team/roster/_/name/lal/los-angeles-lakers',\n",
       " 'phoenix-suns': 'https://www.espn.com/nba/team/roster/_/name/phx/phoenix-suns',\n",
       " 'sacramento-kings': 'https://www.espn.com/nba/team/roster/_/name/sac/sacramento-kings',\n",
       " 'dallas-mavericks': 'https://www.espn.com/nba/team/roster/_/name/dal/dallas-mavericks',\n",
       " 'houston-rockets': 'https://www.espn.com/nba/team/roster/_/name/hou/houston-rockets',\n",
       " 'memphis-grizzlies': 'https://www.espn.com/nba/team/roster/_/name/mem/memphis-grizzlies',\n",
       " 'new-orleans-pelicans': 'https://www.espn.com/nba/team/roster/_/name/no/new-orleans-pelicans',\n",
       " 'san-antonio-spurs': 'https://www.espn.com/nba/team/roster/_/name/sa/san-antonio-spurs'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rosters = build_team_urls()\n",
    "rosters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `build_team_urls()` returns a dictionary that matches team names with their corresponding roster URL. Given this information, we can systematically loop through all of the rosters and use regular expressions to extract player information for each team.\n",
    "\n",
    "In order to figure out how to scrape the rosters, let’s take a look at the Golden State Warriors' roster page as an example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![GSW_roster_webpage.png](images/GSW_roster_webpage.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information for each player is nicely laid out in a table, meaning that the data is likely obtainable using regular expressions. Taking a look at the page source reveals that each player’s name and information are all provided in blocks of what apppear to be `json`, highlighted below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![GSW_roster_source.png](images/GSW_roster_source.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the standardized format of the data for each player, this information is indeed extractable using regular expressions. First, we should read in the roster webpage using `urllib.request.urlopen`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.espn.com/nba/team/roster/_/name/gs/golden-state-warriors\"\n",
    "f = urllib.request.urlopen(url)\n",
    "roster_source = f.read().decode('utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we construct the regex that will return information for each of the players on the roster webpage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Ky Bowman',\n",
       "  '\"uid\":\"s:40~l:46~a:4065635\",\"guid\":\"d0ef63e951bb5f842b7357521697dc62\",\"id\":\"4065635\",\"height\":\"6\\' 1\\\\\"\",\"weight\":\"187 lbs\",\"age\":22,\"position\":\"PG\",\"jersey\":\"12\",\"salary\":\"$350,189\",\"birthDate\":\"06/17/97\",\"headshot\":\"https://a.espncdn.com/i/headshots/nba/players/full/4065635.png\",\"lastName\":\"Ky Bowman\",\"experience\":0,\"college\":\"Boston College\"'),\n",
       " ('Marquese Chriss',\n",
       "  '\"uid\":\"s:40~l:46~a:3907487\",\"guid\":\"a320ecf1d6481b7518ddc1dc576c27b4\",\"id\":\"3907487\",\"height\":\"6\\' 9\\\\\"\",\"weight\":\"240 lbs\",\"age\":22,\"position\":\"C\",\"jersey\":\"32\",\"salary\":\"$654,469\",\"birthDate\":\"07/02/97\",\"headshot\":\"https://a.espncdn.com/i/headshots/nba/players/full/3907487.png\",\"lastName\":\"Marquese Chriss\",\"experience\":3,\"college\":\"Washington\",\"birthPlace\":\"Sacramento, CA\"'),\n",
       " ('Stephen Curry',\n",
       "  '\"uid\":\"s:40~l:46~a:3975\",\"guid\":\"5dda51f150c966e12026400b73f34fad\",\"id\":\"3975\",\"height\":\"6\\' 3\\\\\"\",\"weight\":\"185 lbs\",\"age\":32,\"position\":\"PG\",\"jersey\":\"30\",\"salary\":\"$40,231,758\",\"birthDate\":\"03/14/88\",\"headshot\":\"https://a.espncdn.com/i/headshots/nba/players/full/3975.png\",\"lastName\":\"Stephen Curry\",\"experience\":10,\"college\":\"Davidson\",\"birthPlace\":\"Akron, OH\"'),\n",
       " ('Draymond Green',\n",
       "  '\"uid\":\"s:40~l:46~a:6589\",\"guid\":\"de360720e41625f28a6bb5ff82616cb1\",\"id\":\"6589\",\"height\":\"6\\' 6\\\\\"\",\"weight\":\"230 lbs\",\"age\":30,\"position\":\"PF\",\"jersey\":\"23\",\"salary\":\"$18,539,130\",\"birthDate\":\"03/04/90\",\"headshot\":\"https://a.espncdn.com/i/headshots/nba/players/full/6589.png\",\"lastName\":\"Draymond Green\",\"experience\":7,\"college\":\"Michigan State\",\"birthPlace\":\"Saginaw, MI\"')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_regex = ('\\{\\\"name\\\"\\:\\\"(\\w+\\s\\w+)\\\",\\\"href\\\"\\:\\\"https?\\://www\\.espn\\.com/nba/player/.*?\\\",(.*?)\\}')\n",
    "player_regex\n",
    "player_info = re.findall(player_regex, roster_source)\n",
    "player_info[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, `player_info` is a list of tuples, in which each player name is paired with a set of information (height, weight, age, etc.) that is organized in `json` format. We can use the `json` package in Python to convert the information into a Python dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 30,\n",
       " 'birthDate': '03/04/90',\n",
       " 'birthPlace': 'Saginaw, MI',\n",
       " 'college': 'Michigan State',\n",
       " 'experience': 7,\n",
       " 'guid': 'de360720e41625f28a6bb5ff82616cb1',\n",
       " 'headshot': 'https://a.espncdn.com/i/headshots/nba/players/full/6589.png',\n",
       " 'height': '6\\' 6\"',\n",
       " 'id': '6589',\n",
       " 'jersey': '23',\n",
       " 'lastName': 'Draymond Green',\n",
       " 'position': 'PF',\n",
       " 'salary': '$18,539,130',\n",
       " 'uid': 's:40~l:46~a:6589',\n",
       " 'weight': '230 lbs'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "draymond = json.loads(\"{\"+player_info[3][1]+\"}\")\n",
    "draymond"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, all of the pertinent information for Draymond Green is now stored into a Python dictionary named `draymond`. Let's use the snippets of code above to construct a function which loops through each player in a given roster and scrapes their information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_player_info(roster_url):\n",
    "    f = urllib.request.urlopen(roster_url)\n",
    "    roster_source = f.read().decode('utf-8')\n",
    "    sleep(0.5)\n",
    "    player_regex = ('\\{\\\"name\\\"\\:\\\"(\\w+\\s\\w+)\\\",\\\"href\\\"\\:\\\"https?\\://www\\.espn\\.com/nba/player/.*?\\\",(.*?)\\}')\n",
    "    player_info = re.findall(player_regex, roster_source)\n",
    "    player_dict = dict()\n",
    "    for player in player_info:\n",
    "        player_dict[player[0]] = json.loads(\"{\"+player[1]+\"}\")\n",
    "    return(player_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now loop through each team in `rosters` and run `get_player_info()`, storing the output in a dictionary called `all_players`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gathering player info for team: boston-celtics\n",
      "Gathering player info for team: brooklyn-nets\n",
      "Gathering player info for team: new-york-knicks\n",
      "Gathering player info for team: philadelphia-76ers\n",
      "Gathering player info for team: toronto-raptors\n",
      "Gathering player info for team: chicago-bulls\n",
      "Gathering player info for team: cleveland-cavaliers\n",
      "Gathering player info for team: detroit-pistons\n",
      "Gathering player info for team: indiana-pacers\n",
      "Gathering player info for team: milwaukee-bucks\n",
      "Gathering player info for team: atlanta-hawks\n",
      "Gathering player info for team: charlotte-hornets\n",
      "Gathering player info for team: miami-heat\n",
      "Gathering player info for team: orlando-magic\n",
      "Gathering player info for team: washington-wizards\n",
      "Gathering player info for team: denver-nuggets\n",
      "Gathering player info for team: minnesota-timberwolves\n",
      "Gathering player info for team: oklahoma-city-thunder\n",
      "Gathering player info for team: portland-trail-blazers\n",
      "Gathering player info for team: utah-jazz\n",
      "Gathering player info for team: golden-state-warriors\n",
      "Gathering player info for team: la-clippers\n",
      "Gathering player info for team: los-angeles-lakers\n",
      "Gathering player info for team: phoenix-suns\n",
      "Gathering player info for team: sacramento-kings\n",
      "Gathering player info for team: dallas-mavericks\n",
      "Gathering player info for team: houston-rockets\n",
      "Gathering player info for team: memphis-grizzlies\n",
      "Gathering player info for team: new-orleans-pelicans\n",
      "Gathering player info for team: san-antonio-spurs\n"
     ]
    }
   ],
   "source": [
    "all_players = dict()\n",
    "for team in rosters.keys():\n",
    "    print(\"Gathering player info for team: \" + team)\n",
    "    all_players[team] = get_player_info(rosters[team])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running this code, the `all_players` dictionary should be a dictionary of dictionaries of dictionaries. This sounds complicated, but let's walk through what it looks like. The first level of keys should correspond to teams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['boston-celtics', 'brooklyn-nets', 'new-york-knicks', 'philadelphia-76ers', 'toronto-raptors', 'chicago-bulls', 'cleveland-cavaliers', 'detroit-pistons', 'indiana-pacers', 'milwaukee-bucks', 'atlanta-hawks', 'charlotte-hornets', 'miami-heat', 'orlando-magic', 'washington-wizards', 'denver-nuggets', 'minnesota-timberwolves', 'oklahoma-city-thunder', 'portland-trail-blazers', 'utah-jazz', 'golden-state-warriors', 'la-clippers', 'los-angeles-lakers', 'phoenix-suns', 'sacramento-kings', 'dallas-mavericks', 'houston-rockets', 'memphis-grizzlies', 'new-orleans-pelicans', 'san-antonio-spurs'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within a team, the keys should correspond to player names. Let's zoom in on the LA Lakers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Kostas Antetokounmpo', 'Avery Bradley', 'Devontae Cacok', 'Alex Caruso', 'Quinn Cook', 'Anthony Davis', 'Jared Dudley', 'Danny Green', 'Dwight Howard', 'LeBron James', 'Kyle Kuzma', 'JaVale McGee', 'Markieff Morris', 'Rajon Rondo', 'Dion Waiters'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players[\"los-angeles-lakers\"].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can choose which player to look at. Let's choose LeBron James as an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 35,\n",
       " 'birthDate': '12/30/84',\n",
       " 'birthPlace': 'Akron, OH',\n",
       " 'experience': 16,\n",
       " 'guid': '1f6592b3ff53d3218dc56038d48c1786',\n",
       " 'headshot': 'https://a.espncdn.com/i/headshots/nba/players/full/1966.png',\n",
       " 'height': '6\\' 9\"',\n",
       " 'id': '1966',\n",
       " 'jersey': '23',\n",
       " 'lastName': 'LeBron James',\n",
       " 'position': 'SF',\n",
       " 'salary': '$37,436,858',\n",
       " 'uid': 's:40~l:46~a:1966',\n",
       " 'weight': '250 lbs'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players[\"los-angeles-lakers\"][\"LeBron James\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dictionary with information about LeBron James is returned. We can extract information even more precisely by specifying which field we are interested in. Let's get his salary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'$37,436,858'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players[\"los-angeles-lakers\"][\"LeBron James\"][\"salary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make data analysis easier, we can re-format this dictionary into a `pandas` DataFrame. The function `pd.DataFrame.from_dict()` can turn a dictionary of dictionaries into a `pandas` DataFrame, as demonstrated below:"
   ]
  },
  {
   "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>uid</th>\n",
       "      <th>guid</th>\n",
       "      <th>id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>position</th>\n",
       "      <th>jersey</th>\n",
       "      <th>salary</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>headshot</th>\n",
       "      <th>lastName</th>\n",
       "      <th>experience</th>\n",
       "      <th>college</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>hand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alen Smailagic</th>\n",
       "      <td>s:40~l:46~a:4401415</td>\n",
       "      <td>6ed3f8924bfef2e70329ebd6a104ecae</td>\n",
       "      <td>4401415</td>\n",
       "      <td>6' 10\"</td>\n",
       "      <td>215 lbs</td>\n",
       "      <td>19</td>\n",
       "      <td>PF</td>\n",
       "      <td>6</td>\n",
       "      <td>$898,310</td>\n",
       "      <td>08/18/00</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Alen Smailagic</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Wiggins</th>\n",
       "      <td>s:40~l:46~a:3059319</td>\n",
       "      <td>064c19d065276a21ca99fdfb296fe05d</td>\n",
       "      <td>3059319</td>\n",
       "      <td>6' 7\"</td>\n",
       "      <td>197 lbs</td>\n",
       "      <td>25</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>$27,504,630</td>\n",
       "      <td>02/23/95</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Andrew Wiggins</td>\n",
       "      <td>5</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Thornhill, ON</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chasson Randle</th>\n",
       "      <td>s:40~l:46~a:2580898</td>\n",
       "      <td>71b7154a3d81842448b623ee3e65d586</td>\n",
       "      <td>2580898</td>\n",
       "      <td>6' 2\"</td>\n",
       "      <td>185 lbs</td>\n",
       "      <td>27</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02/05/93</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Chasson Randle</td>\n",
       "      <td>2</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>Rock Island, IL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damion Lee</th>\n",
       "      <td>s:40~l:46~a:2595209</td>\n",
       "      <td>41fafb6d47a66d8f79f94161918541a4</td>\n",
       "      <td>2595209</td>\n",
       "      <td>6' 5\"</td>\n",
       "      <td>210 lbs</td>\n",
       "      <td>27</td>\n",
       "      <td>SG</td>\n",
       "      <td>1</td>\n",
       "      <td>$842,327</td>\n",
       "      <td>10/21/92</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Damion Lee</td>\n",
       "      <td>2</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Draymond Green</th>\n",
       "      <td>s:40~l:46~a:6589</td>\n",
       "      <td>de360720e41625f28a6bb5ff82616cb1</td>\n",
       "      <td>6589</td>\n",
       "      <td>6' 6\"</td>\n",
       "      <td>230 lbs</td>\n",
       "      <td>30</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>$18,539,130</td>\n",
       "      <td>03/04/90</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>7</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>Saginaw, MI</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eric Paschall</th>\n",
       "      <td>s:40~l:46~a:3133817</td>\n",
       "      <td>b67e5e0fa5cb209355845d165a49407e</td>\n",
       "      <td>3133817</td>\n",
       "      <td>6' 6\"</td>\n",
       "      <td>255 lbs</td>\n",
       "      <td>23</td>\n",
       "      <td>PF</td>\n",
       "      <td>7</td>\n",
       "      <td>$898,310</td>\n",
       "      <td>11/04/96</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Eric Paschall</td>\n",
       "      <td>0</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>North Tarrytown, NY</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordan Poole</th>\n",
       "      <td>s:40~l:46~a:4277956</td>\n",
       "      <td>4b0492b5a52f267fe84098ef6d2e2bdf</td>\n",
       "      <td>4277956</td>\n",
       "      <td>6' 4\"</td>\n",
       "      <td>194 lbs</td>\n",
       "      <td>20</td>\n",
       "      <td>SG</td>\n",
       "      <td>3</td>\n",
       "      <td>$1,964,760</td>\n",
       "      <td>06/19/99</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Jordan Poole</td>\n",
       "      <td>0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevon Looney</th>\n",
       "      <td>s:40~l:46~a:3155535</td>\n",
       "      <td>10a8e77b877324c69966f0c4618caad6</td>\n",
       "      <td>3155535</td>\n",
       "      <td>6' 9\"</td>\n",
       "      <td>222 lbs</td>\n",
       "      <td>24</td>\n",
       "      <td>PF</td>\n",
       "      <td>5</td>\n",
       "      <td>$4,464,226</td>\n",
       "      <td>02/06/96</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Kevon Looney</td>\n",
       "      <td>4</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Klay Thompson</th>\n",
       "      <td>s:40~l:46~a:6475</td>\n",
       "      <td>3411530a7ab7e8dce4f165d59a559520</td>\n",
       "      <td>6475</td>\n",
       "      <td>6' 6\"</td>\n",
       "      <td>215 lbs</td>\n",
       "      <td>30</td>\n",
       "      <td>SG</td>\n",
       "      <td>11</td>\n",
       "      <td>$32,742,000</td>\n",
       "      <td>02/08/90</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Klay Thompson</td>\n",
       "      <td>8</td>\n",
       "      <td>Washington State</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ky Bowman</th>\n",
       "      <td>s:40~l:46~a:4065635</td>\n",
       "      <td>d0ef63e951bb5f842b7357521697dc62</td>\n",
       "      <td>4065635</td>\n",
       "      <td>6' 1\"</td>\n",
       "      <td>187 lbs</td>\n",
       "      <td>22</td>\n",
       "      <td>PG</td>\n",
       "      <td>12</td>\n",
       "      <td>$350,189</td>\n",
       "      <td>06/17/97</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Ky Bowman</td>\n",
       "      <td>0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marquese Chriss</th>\n",
       "      <td>s:40~l:46~a:3907487</td>\n",
       "      <td>a320ecf1d6481b7518ddc1dc576c27b4</td>\n",
       "      <td>3907487</td>\n",
       "      <td>6' 9\"</td>\n",
       "      <td>240 lbs</td>\n",
       "      <td>22</td>\n",
       "      <td>C</td>\n",
       "      <td>32</td>\n",
       "      <td>$654,469</td>\n",
       "      <td>07/02/97</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Marquese Chriss</td>\n",
       "      <td>3</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Sacramento, CA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mychal Mulder</th>\n",
       "      <td>s:40~l:46~a:3936298</td>\n",
       "      <td>f5a46c489e9aee6a1a74f67f9494132f</td>\n",
       "      <td>3936298</td>\n",
       "      <td>6' 4\"</td>\n",
       "      <td>184 lbs</td>\n",
       "      <td>25</td>\n",
       "      <td>G</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06/12/94</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Mychal Mulder</td>\n",
       "      <td>0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Toronto, ON</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stephen Curry</th>\n",
       "      <td>s:40~l:46~a:3975</td>\n",
       "      <td>5dda51f150c966e12026400b73f34fad</td>\n",
       "      <td>3975</td>\n",
       "      <td>6' 3\"</td>\n",
       "      <td>185 lbs</td>\n",
       "      <td>32</td>\n",
       "      <td>PG</td>\n",
       "      <td>30</td>\n",
       "      <td>$40,231,758</td>\n",
       "      <td>03/14/88</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>10</td>\n",
       "      <td>Davidson</td>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 uid                              guid  \\\n",
       "Alen Smailagic   s:40~l:46~a:4401415  6ed3f8924bfef2e70329ebd6a104ecae   \n",
       "Andrew Wiggins   s:40~l:46~a:3059319  064c19d065276a21ca99fdfb296fe05d   \n",
       "Chasson Randle   s:40~l:46~a:2580898  71b7154a3d81842448b623ee3e65d586   \n",
       "Damion Lee       s:40~l:46~a:2595209  41fafb6d47a66d8f79f94161918541a4   \n",
       "Draymond Green      s:40~l:46~a:6589  de360720e41625f28a6bb5ff82616cb1   \n",
       "Eric Paschall    s:40~l:46~a:3133817  b67e5e0fa5cb209355845d165a49407e   \n",
       "Jordan Poole     s:40~l:46~a:4277956  4b0492b5a52f267fe84098ef6d2e2bdf   \n",
       "Kevon Looney     s:40~l:46~a:3155535  10a8e77b877324c69966f0c4618caad6   \n",
       "Klay Thompson       s:40~l:46~a:6475  3411530a7ab7e8dce4f165d59a559520   \n",
       "Ky Bowman        s:40~l:46~a:4065635  d0ef63e951bb5f842b7357521697dc62   \n",
       "Marquese Chriss  s:40~l:46~a:3907487  a320ecf1d6481b7518ddc1dc576c27b4   \n",
       "Mychal Mulder    s:40~l:46~a:3936298  f5a46c489e9aee6a1a74f67f9494132f   \n",
       "Stephen Curry       s:40~l:46~a:3975  5dda51f150c966e12026400b73f34fad   \n",
       "\n",
       "                      id  height   weight  age position jersey       salary  \\\n",
       "Alen Smailagic   4401415  6' 10\"  215 lbs   19       PF      6     $898,310   \n",
       "Andrew Wiggins   3059319   6' 7\"  197 lbs   25       SF     22  $27,504,630   \n",
       "Chasson Randle   2580898   6' 2\"  185 lbs   27       PG     25          NaN   \n",
       "Damion Lee       2595209   6' 5\"  210 lbs   27       SG      1     $842,327   \n",
       "Draymond Green      6589   6' 6\"  230 lbs   30       PF     23  $18,539,130   \n",
       "Eric Paschall    3133817   6' 6\"  255 lbs   23       PF      7     $898,310   \n",
       "Jordan Poole     4277956   6' 4\"  194 lbs   20       SG      3   $1,964,760   \n",
       "Kevon Looney     3155535   6' 9\"  222 lbs   24       PF      5   $4,464,226   \n",
       "Klay Thompson       6475   6' 6\"  215 lbs   30       SG     11  $32,742,000   \n",
       "Ky Bowman        4065635   6' 1\"  187 lbs   22       PG     12     $350,189   \n",
       "Marquese Chriss  3907487   6' 9\"  240 lbs   22        C     32     $654,469   \n",
       "Mychal Mulder    3936298   6' 4\"  184 lbs   25        G     12          NaN   \n",
       "Stephen Curry       3975   6' 3\"  185 lbs   32       PG     30  $40,231,758   \n",
       "\n",
       "                birthDate                                           headshot  \\\n",
       "Alen Smailagic   08/18/00  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Andrew Wiggins   02/23/95  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Chasson Randle   02/05/93  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Damion Lee       10/21/92  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Draymond Green   03/04/90  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Eric Paschall    11/04/96  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Jordan Poole     06/19/99  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Kevon Looney     02/06/96  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Klay Thompson    02/08/90  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Ky Bowman        06/17/97  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Marquese Chriss  07/02/97  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Mychal Mulder    06/12/94  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "Stephen Curry    03/14/88  https://a.espncdn.com/i/headshots/nba/players/...   \n",
       "\n",
       "                        lastName  experience           college  \\\n",
       "Alen Smailagic    Alen Smailagic           0               NaN   \n",
       "Andrew Wiggins    Andrew Wiggins           5            Kansas   \n",
       "Chasson Randle    Chasson Randle           2          Stanford   \n",
       "Damion Lee            Damion Lee           2        Louisville   \n",
       "Draymond Green    Draymond Green           7    Michigan State   \n",
       "Eric Paschall      Eric Paschall           0         Villanova   \n",
       "Jordan Poole        Jordan Poole           0          Michigan   \n",
       "Kevon Looney        Kevon Looney           4              UCLA   \n",
       "Klay Thompson      Klay Thompson           8  Washington State   \n",
       "Ky Bowman              Ky Bowman           0    Boston College   \n",
       "Marquese Chriss  Marquese Chriss           3        Washington   \n",
       "Mychal Mulder      Mychal Mulder           0          Kentucky   \n",
       "Stephen Curry      Stephen Curry          10          Davidson   \n",
       "\n",
       "                          birthPlace hand  \n",
       "Alen Smailagic                   NaN  NaN  \n",
       "Andrew Wiggins         Thornhill, ON  NaN  \n",
       "Chasson Randle       Rock Island, IL  NaN  \n",
       "Damion Lee                       NaN    L  \n",
       "Draymond Green           Saginaw, MI  NaN  \n",
       "Eric Paschall    North Tarrytown, NY  NaN  \n",
       "Jordan Poole           Milwaukee, WI    B  \n",
       "Kevon Looney           Milwaukee, WI  NaN  \n",
       "Klay Thompson        Los Angeles, CA  NaN  \n",
       "Ky Bowman                        NaN  NaN  \n",
       "Marquese Chriss       Sacramento, CA  NaN  \n",
       "Mychal Mulder            Toronto, ON  NaN  \n",
       "Stephen Curry              Akron, OH  NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "gsw = pd.DataFrame.from_dict(all_players[\"golden-state-warriors\"], orient = \"index\")\n",
    "gsw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the DataFrame above, each of the parameters such as 'age', 'salary', etc. are organized in columns and each player is a row. This makes display of the data much easier to read and understand. Furthermore, it also places null values when pieces of data are missing--for example, Chasson Randle's salary information is missing from the website, so 'NaN' is automatically placed in the DataFrame.\n",
    "\n",
    "DataFrames allow us to quickly make calculations, sort players based on their stats, and compare stats between teams. To make a DataFrame containing data from all the teams, we will loop through each team in `all_players`, construct DataFrames, label them with a `team` column, and aggregate them into a single DataFrame called `all_players_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_players_df = pd.DataFrame()\n",
    "# loop through each team, create a pandas DataFrame, and append\n",
    "for team in all_players.keys():\n",
    "    team_df = pd.DataFrame.from_dict(all_players[team], orient = \"index\")\n",
    "    team_df['team'] = team\n",
    "    all_players_df = all_players_df.append(team_df)"
   ]
  },
  {
   "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>age</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>college</th>\n",
       "      <th>experience</th>\n",
       "      <th>guid</th>\n",
       "      <th>hand</th>\n",
       "      <th>headshot</th>\n",
       "      <th>height</th>\n",
       "      <th>id</th>\n",
       "      <th>jersey</th>\n",
       "      <th>lastName</th>\n",
       "      <th>position</th>\n",
       "      <th>salary</th>\n",
       "      <th>team</th>\n",
       "      <th>uid</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brad Wanamaker</th>\n",
       "      <td>30</td>\n",
       "      <td>07/25/89</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1</td>\n",
       "      <td>5aad35bbbb760e3958107639266768ae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 3\"</td>\n",
       "      <td>6507</td>\n",
       "      <td>9</td>\n",
       "      <td>Brad Wanamaker</td>\n",
       "      <td>PG</td>\n",
       "      <td>$1,445,697</td>\n",
       "      <td>boston-celtics</td>\n",
       "      <td>s:40~l:46~a:6507</td>\n",
       "      <td>210 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>22</td>\n",
       "      <td>03/12/98</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>Purdue</td>\n",
       "      <td>0</td>\n",
       "      <td>4b8ebdfd01221567925035c1e0d0c337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>5' 11\"</td>\n",
       "      <td>4066407</td>\n",
       "      <td>4</td>\n",
       "      <td>Carsen Edwards</td>\n",
       "      <td>PG</td>\n",
       "      <td>$1,228,026</td>\n",
       "      <td>boston-celtics</td>\n",
       "      <td>s:40~l:46~a:4066407</td>\n",
       "      <td>200 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Theis</th>\n",
       "      <td>27</td>\n",
       "      <td>04/04/92</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ce75206c087f83ace6f9a8e3efbd9671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 8\"</td>\n",
       "      <td>2451037</td>\n",
       "      <td>27</td>\n",
       "      <td>Daniel Theis</td>\n",
       "      <td>C</td>\n",
       "      <td>$5,000,000</td>\n",
       "      <td>boston-celtics</td>\n",
       "      <td>s:40~l:46~a:2451037</td>\n",
       "      <td>245 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enes Kanter</th>\n",
       "      <td>27</td>\n",
       "      <td>05/20/92</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>1e039b407b3daa6eeac69432aa6413fd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 10\"</td>\n",
       "      <td>6447</td>\n",
       "      <td>11</td>\n",
       "      <td>Enes Kanter</td>\n",
       "      <td>C</td>\n",
       "      <td>$4,767,000</td>\n",
       "      <td>boston-celtics</td>\n",
       "      <td>s:40~l:46~a:6447</td>\n",
       "      <td>250 lbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon Hayward</th>\n",
       "      <td>30</td>\n",
       "      <td>03/23/90</td>\n",
       "      <td>Indianapolis, IN</td>\n",
       "      <td>Butler</td>\n",
       "      <td>9</td>\n",
       "      <td>56f675cb8f40a5aaee5f5747ec9099c5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 7\"</td>\n",
       "      <td>4249</td>\n",
       "      <td>20</td>\n",
       "      <td>Gordon Hayward</td>\n",
       "      <td>SF</td>\n",
       "      <td>$32,700,690</td>\n",
       "      <td>boston-celtics</td>\n",
       "      <td>s:40~l:46~a:4249</td>\n",
       "      <td>225 lbs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age birthDate        birthPlace     college  experience  \\\n",
       "Brad Wanamaker   30  07/25/89  Philadelphia, PA  Pittsburgh           1   \n",
       "Carsen Edwards   22  03/12/98       Houston, TX      Purdue           0   \n",
       "Daniel Theis     27  04/04/92           Germany         NaN           2   \n",
       "Enes Kanter      27  05/20/92       Switzerland    Kentucky           8   \n",
       "Gordon Hayward   30  03/23/90  Indianapolis, IN      Butler           9   \n",
       "\n",
       "                                            guid hand  \\\n",
       "Brad Wanamaker  5aad35bbbb760e3958107639266768ae  NaN   \n",
       "Carsen Edwards  4b8ebdfd01221567925035c1e0d0c337  NaN   \n",
       "Daniel Theis    ce75206c087f83ace6f9a8e3efbd9671  NaN   \n",
       "Enes Kanter     1e039b407b3daa6eeac69432aa6413fd  NaN   \n",
       "Gordon Hayward  56f675cb8f40a5aaee5f5747ec9099c5  NaN   \n",
       "\n",
       "                                                         headshot  height  \\\n",
       "Brad Wanamaker  https://a.espncdn.com/i/headshots/nba/players/...   6' 3\"   \n",
       "Carsen Edwards  https://a.espncdn.com/i/headshots/nba/players/...  5' 11\"   \n",
       "Daniel Theis    https://a.espncdn.com/i/headshots/nba/players/...   6' 8\"   \n",
       "Enes Kanter     https://a.espncdn.com/i/headshots/nba/players/...  6' 10\"   \n",
       "Gordon Hayward  https://a.espncdn.com/i/headshots/nba/players/...   6' 7\"   \n",
       "\n",
       "                     id jersey        lastName position       salary  \\\n",
       "Brad Wanamaker     6507      9  Brad Wanamaker       PG   $1,445,697   \n",
       "Carsen Edwards  4066407      4  Carsen Edwards       PG   $1,228,026   \n",
       "Daniel Theis    2451037     27    Daniel Theis        C   $5,000,000   \n",
       "Enes Kanter        6447     11     Enes Kanter        C   $4,767,000   \n",
       "Gordon Hayward     4249     20  Gordon Hayward       SF  $32,700,690   \n",
       "\n",
       "                          team                  uid   weight  \n",
       "Brad Wanamaker  boston-celtics     s:40~l:46~a:6507  210 lbs  \n",
       "Carsen Edwards  boston-celtics  s:40~l:46~a:4066407  200 lbs  \n",
       "Daniel Theis    boston-celtics  s:40~l:46~a:2451037  245 lbs  \n",
       "Enes Kanter     boston-celtics     s:40~l:46~a:6447  250 lbs  \n",
       "Gordon Hayward  boston-celtics     s:40~l:46~a:4249  225 lbs  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, `all_players_df` is a DataFrame with all the players in the NBA categorized by team. It contains player information such as age, salary, height, weight, etc. I'll export this data to a csv file, in case you readers out there want to read it in and play around with it yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_players_df.to_csv(\"NBA_roster_info_all_players_mar2020.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrape player stats for career averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to scrape data coresponding to the performance of each player, in terms of points per game, field goal percentage, rebounds per game, etc. Our goal is to append this information to `all_players_df` so that we can compare player performance with traits such as height, salary, etc. We can find performance stats at each player's personal page on ESPN:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![curry_stats_webpage.png](images/curry_stats_webpage.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want to extract the career stats in the bottom row, which can be found in the highlighted section of the source code below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![curry_stats_source.png](images/curry_stats_source.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to extract the information above for each player in our DataFrame, we can construct URLs for player stats pages using the `id` column. Fortunately, the URL is standardized and very easy to construct. For example, using the `id` value of 3975 for Stephen Curry, the URL to open would be: https://www.espn.com/nba/player/stats/_/id/3975. Below is an example of extracting his career stats using regexes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\"699\",\"693\",\"34.3\",\"8.1-17.1\",\"47.6\",\"3.6-8.2\",\"43.5\",\"3.7-4.0\",\"90.6\",\"0.7\",\"3.8\",\"4.5\",\"6.6\",\"0.2\",\"1.7\",\"2.5\",\"3.1\",\"23.5\"']\n"
     ]
    }
   ],
   "source": [
    "url = \"https://www.espn.com/nba/player/stats/_/id/3975\"\n",
    "f = urllib.request.urlopen(url)\n",
    "sleep(0.3)\n",
    "player_source = f.read().decode('utf-8')\n",
    "# extract career stats using this regex\n",
    "stats_regex = ('\\[\\\"Career\\\",\\\"\\\",(.*?)\\]\\},\\{\\\"ttl\\\"\\:\\\"Regular Season Totals\\\"')\n",
    "career_info = re.findall(stats_regex, player_source)\n",
    "print(career_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that some of the stats are complex and contain non-numerical symbols such as \"-\". In the example above, the range \"3.7-4.0\" is for the column \"FT\", which stands for \"Free Throws Made-Attempted Per Game\". We should split this up into two categories, \"Free Throws Made (FTM)\" and \"Free Throws Attempted (FTA)\", and do the same for field goals and 3 pointers. To do so, we can split the string based on \"-\" and then un-nest the list. We also need to convert the strings to floating point values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[699.0, 693.0, 34.3, 8.1, 17.1, 47.6, 3.6, 8.2, 43.5, 3.7, 4.0, 90.6, 0.7, 3.8, 4.5, 6.6, 0.2, 1.7, 2.5, 3.1, 23.5]\n"
     ]
    }
   ],
   "source": [
    "from itertools import chain\n",
    "career_info = career_info[0].replace(\"\\\"\", \"\").split(\",\")\n",
    "career_info = list(chain.from_iterable([i.split(\"-\") for i in career_info]))\n",
    "career_info = list(map(float,career_info))\n",
    "print(career_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can loop through each player in `all_players_df`, open their stats webpage, extract their career stats, and store the stats in a separate data frame called `career_stats_df` using the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "career_stats_df = pd.DataFrame(columns = [\"GP\",\"GS\",\"MIN\",\"FGM\", \"FGA\",\"FG%\",\"3PTM\",\"3PTA\",\"3P%\",\"FTM\",\"FTA\",\"FT%\",\"OR\",\"DR\",\"REB\",\"AST\",\"BLK\",\"STL\",\"PF\",\"TO\",\"PTS\"])\n",
    "for player_index in all_players_df.index:\n",
    "    url = \"https://www.espn.com/nba/player/stats/_/id/\" + str(all_players_df.loc[player_index]['id'])\n",
    "    f = urllib.request.urlopen(url)\n",
    "    sleep(0.3)\n",
    "    player_source = f.read().decode('utf-8')\n",
    "    # extract career stats using this regex\n",
    "    stats_regex = ('\\[\\\"Career\\\",\\\"\\\",(.*?)\\]\\},\\{\\\"ttl\\\"\\:\\\"Regular Season Totals\\\"')\n",
    "    career_info = re.findall(stats_regex, player_source)\n",
    "    try:\n",
    "        # convert the stats to a list of floats, and add the entry to the DataFrame\n",
    "        career_info = career_info[0].replace(\"\\\"\", \"\").split(\",\")\n",
    "        career_info = list(chain.from_iterable([i.split(\"-\") for i in career_info]))\n",
    "        career_info = list(map(float,career_info))\n",
    "        career_stats_df = career_stats_df.append(pd.Series(career_info, index = career_stats_df.columns, name=player_index))\n",
    "    except:\n",
    "        # if no career stats were returned, the player was a rookie with no games played\n",
    "        print(player_index + \" has no info, \", end = \"\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some player webpages did not have career stats, which I found corresponded to rookies which had no games played. This threw an error in the loop, so I used a try/except clause to bypass the error and continue stripping content for the remaining players. Their stats are currently stored in the object `career_stats_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PTM</th>\n",
       "      <th>3PTA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brad Wanamaker</th>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>91.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>84.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Theis</th>\n",
       "      <td>187.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>55.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enes Kanter</th>\n",
       "      <td>634.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>54.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>28.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon Hayward</th>\n",
       "      <td>634.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>45.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>82.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GP     GS   MIN  FGM   FGA   FG%  3PTM  3PTA   3P%  FTM  \\\n",
       "Brad Wanamaker   99.0    1.0  15.7  1.9   4.3  44.0   0.5   1.3  38.0  1.3   \n",
       "Carsen Edwards   35.0    0.0   9.0  1.1   3.2  32.7   0.6   1.9  30.9  0.3   \n",
       "Daniel Theis    187.0   62.0  17.2  2.6   4.7  55.4   0.4   1.1  34.0  1.1   \n",
       "Enes Kanter     634.0  222.0  21.8  4.8   8.8  54.2   0.1   0.2  28.7  2.0   \n",
       "Gordon Hayward  634.0  472.0  30.8  5.2  11.6  45.1   1.3   3.6  36.6  3.5   \n",
       "\n",
       "                ...    FT%   OR   DR  REB  AST  BLK  STL   PF   TO   PTS  \n",
       "Brad Wanamaker  ...   91.7  0.2  1.4  1.7  2.2  0.1  0.6  1.6  0.9   5.6  \n",
       "Carsen Edwards  ...   84.6  0.2  1.1  1.2  0.6  0.1  0.3  0.9  0.4   3.0  \n",
       "Daniel Theis    ...   75.3  1.6  3.1  4.7  1.2  0.9  0.5  2.8  0.7   6.7  \n",
       "Enes Kanter     ...   77.6  2.9  4.7  7.6  0.9  0.5  0.4  2.2  1.5  11.6  \n",
       "Gordon Hayward  ...   82.2  0.7  3.6  4.4  3.5  0.4  1.0  1.7  2.0  15.3  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "career_stats_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stats for each player are now organized in a neat DataFrame. Here is a legend for what each of the abbreviations mean:\n",
    "\n",
    "* GP:Games Played\n",
    "* GS:Games Started\n",
    "* MIN:Minutes Per Game\n",
    "* FGM:Field Goals Made Per Game\n",
    "* FGA:Field Goals Attempted Per Game\n",
    "* FG%:Field Goal Percentage\n",
    "* 3PTM:3-Point Field Goals Made Per Game\n",
    "* 3PTA:3-Point Field Goals Attempted Per Game\n",
    "* 3P%:3-Point Field Goal Percentage\n",
    "* FTM:Free Throws Made Per Game\n",
    "* FTA:Free Throws Attempted\n",
    "* FT%:Free Throw Percentage\n",
    "* OR:Offensive Rebounds Per Game\n",
    "* DR:Defensive Rebounds Per Game\n",
    "* REB:Rebounds Per Game\n",
    "* AST:Assists Per Game\n",
    "* BLK:Blocks Per Game\n",
    "* STL:Steals Per Game\n",
    "* PF:Fouls Per Game\n",
    "* TO:Turnovers Per Game\n",
    "* PTS:Points Per Game\n",
    "\n",
    "I'll also export these stats to a csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "career_stats_df.to_csv(\"NBA_player_stats_all_mar2020.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining and cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now join `career_stats_df` with `all_players_df`, which will merge the content from both data frames based on rows that have the same index (player name). Players in `all_players_df` that are not included in `career_stats_df` will have `NaN` values for the joined columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df = all_players_df.join(career_stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>age</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>college</th>\n",
       "      <th>experience</th>\n",
       "      <th>guid</th>\n",
       "      <th>hand</th>\n",
       "      <th>headshot</th>\n",
       "      <th>height</th>\n",
       "      <th>id</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brad Wanamaker</th>\n",
       "      <td>30</td>\n",
       "      <td>07/25/89</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1</td>\n",
       "      <td>5aad35bbbb760e3958107639266768ae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 3\"</td>\n",
       "      <td>6507</td>\n",
       "      <td>...</td>\n",
       "      <td>91.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carsen Edwards</th>\n",
       "      <td>22</td>\n",
       "      <td>03/12/98</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>Purdue</td>\n",
       "      <td>0</td>\n",
       "      <td>4b8ebdfd01221567925035c1e0d0c337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>5' 11\"</td>\n",
       "      <td>4066407</td>\n",
       "      <td>...</td>\n",
       "      <td>84.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Theis</th>\n",
       "      <td>27</td>\n",
       "      <td>04/04/92</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ce75206c087f83ace6f9a8e3efbd9671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 8\"</td>\n",
       "      <td>2451037</td>\n",
       "      <td>...</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enes Kanter</th>\n",
       "      <td>27</td>\n",
       "      <td>05/20/92</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>1e039b407b3daa6eeac69432aa6413fd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 10\"</td>\n",
       "      <td>6447</td>\n",
       "      <td>...</td>\n",
       "      <td>77.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon Hayward</th>\n",
       "      <td>30</td>\n",
       "      <td>03/23/90</td>\n",
       "      <td>Indianapolis, IN</td>\n",
       "      <td>Butler</td>\n",
       "      <td>9</td>\n",
       "      <td>56f675cb8f40a5aaee5f5747ec9099c5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a.espncdn.com/i/headshots/nba/players/...</td>\n",
       "      <td>6' 7\"</td>\n",
       "      <td>4249</td>\n",
       "      <td>...</td>\n",
       "      <td>82.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                age birthDate        birthPlace     college  experience  \\\n",
       "Brad Wanamaker   30  07/25/89  Philadelphia, PA  Pittsburgh           1   \n",
       "Carsen Edwards   22  03/12/98       Houston, TX      Purdue           0   \n",
       "Daniel Theis     27  04/04/92           Germany         NaN           2   \n",
       "Enes Kanter      27  05/20/92       Switzerland    Kentucky           8   \n",
       "Gordon Hayward   30  03/23/90  Indianapolis, IN      Butler           9   \n",
       "\n",
       "                                            guid hand  \\\n",
       "Brad Wanamaker  5aad35bbbb760e3958107639266768ae  NaN   \n",
       "Carsen Edwards  4b8ebdfd01221567925035c1e0d0c337  NaN   \n",
       "Daniel Theis    ce75206c087f83ace6f9a8e3efbd9671  NaN   \n",
       "Enes Kanter     1e039b407b3daa6eeac69432aa6413fd  NaN   \n",
       "Gordon Hayward  56f675cb8f40a5aaee5f5747ec9099c5  NaN   \n",
       "\n",
       "                                                         headshot  height  \\\n",
       "Brad Wanamaker  https://a.espncdn.com/i/headshots/nba/players/...   6' 3\"   \n",
       "Carsen Edwards  https://a.espncdn.com/i/headshots/nba/players/...  5' 11\"   \n",
       "Daniel Theis    https://a.espncdn.com/i/headshots/nba/players/...   6' 8\"   \n",
       "Enes Kanter     https://a.espncdn.com/i/headshots/nba/players/...  6' 10\"   \n",
       "Gordon Hayward  https://a.espncdn.com/i/headshots/nba/players/...   6' 7\"   \n",
       "\n",
       "                     id  ...    FT%   OR   DR  REB  AST  BLK  STL   PF   TO  \\\n",
       "Brad Wanamaker     6507  ...   91.7  0.2  1.4  1.7  2.2  0.1  0.6  1.6  0.9   \n",
       "Carsen Edwards  4066407  ...   84.6  0.2  1.1  1.2  0.6  0.1  0.3  0.9  0.4   \n",
       "Daniel Theis    2451037  ...   75.3  1.6  3.1  4.7  1.2  0.9  0.5  2.8  0.7   \n",
       "Enes Kanter        6447  ...   77.6  2.9  4.7  7.6  0.9  0.5  0.4  2.2  1.5   \n",
       "Gordon Hayward     4249  ...   82.2  0.7  3.6  4.4  3.5  0.4  1.0  1.7  2.0   \n",
       "\n",
       "                 PTS  \n",
       "Brad Wanamaker   5.6  \n",
       "Carsen Edwards   3.0  \n",
       "Daniel Theis     6.7  \n",
       "Enes Kanter     11.6  \n",
       "Gordon Hayward  15.3  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_stats_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance stats have been added as columns on the right side of the DataFrame.\n",
    "\n",
    "We notice that some of the columns which should contain numerical data such as salary, height, and weight are instead considered strings. This is beacuse they contain non-numerical characters (such as '$' and ',' for salary). To be able to compute statistics on these columns, we need to convert them to numeric values.\n",
    "\n",
    "We can convert salaries to numeric by removing all non-numerical characters and converting to `int` using list comprehension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker    $1,445,697\n",
       "Carsen Edwards    $1,228,026\n",
       "Daniel Theis      $5,000,000\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before converting\n",
    "all_stats_df['salary'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df['salary']=[int(re.sub(r'[^\\d.]+', '', s)) if isinstance(s, str) else s for s in all_stats_df['salary'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker    1445697.0\n",
       "Carsen Edwards    1228026.0\n",
       "Daniel Theis      5000000.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after converting\n",
    "all_stats_df['salary'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Height is also provided in a non-numeric form, in feet plus inches (e.g. 6' 5\"). We should convert this to a numeric form so that statistics can be calculated. To do so, we will write a small function that converts a string of feet plus inches into a numeric value of total inches, `convert_height`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_height(height):\n",
    "    split_height = height.split(\" \")\n",
    "    feet = float(split_height[0].replace(\"\\'\",\"\"))\n",
    "    inches = float(split_height[1].replace(\"\\\"\",\"\"))\n",
    "    return (feet*12 + inches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker     6' 3\"\n",
       "Carsen Edwards    5' 11\"\n",
       "Daniel Theis       6' 8\"\n",
       "Name: height, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before conversion\n",
    "all_stats_df['height'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df['height'] = [convert_height(x) for x in all_stats_df['height']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker    75.0\n",
       "Carsen Edwards    71.0\n",
       "Daniel Theis      80.0\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after conversion\n",
    "all_stats_df['height'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weight is also a non-numerical field, because of the units listed (e.g. weight': '230 lbs'). We will simply strip off the units for each entry by splitting the string in half with `split(\" \")` and taking the left side of the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker    210 lbs\n",
       "Carsen Edwards    200 lbs\n",
       "Daniel Theis      245 lbs\n",
       "Name: weight, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before conversion\n",
    "all_stats_df['weight'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df['weight'] = [float(x.split(\" \")[0]) for x in all_stats_df['weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brad Wanamaker    210.0\n",
       "Carsen Edwards    200.0\n",
       "Daniel Theis      245.0\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after conversion\n",
    "all_stats_df['weight'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should be the last of the values we have to convert to numeric. Now, we have a cleaned-up and joined dataset! I'll save the data to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df.to_csv(\"NBA_player_info_and_stats_joined_mar2020.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to read in the data at a later time, you can use `read_csv()` like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stats_df = pd.read_csv(\"NBA_player_info_and_stats_joined_mar2020.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the data we just compiled to calculate some statistics. Let's start by calculating average stats per team, using `groupby()` with `mean()` in `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>height</th>\n",
       "      <th>salary</th>\n",
       "      <th>weight</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>atlanta-hawks</th>\n",
       "      <td>25.538462</td>\n",
       "      <td>4.307692</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>5.608001e+06</td>\n",
       "      <td>218.615385</td>\n",
       "      <td>314.153846</td>\n",
       "      <td>200.076923</td>\n",
       "      <td>22.215385</td>\n",
       "      <td>3.815385</td>\n",
       "      <td>8.146154</td>\n",
       "      <td>...</td>\n",
       "      <td>74.423077</td>\n",
       "      <td>1.223077</td>\n",
       "      <td>3.184615</td>\n",
       "      <td>4.407692</td>\n",
       "      <td>2.323077</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>0.669231</td>\n",
       "      <td>2.146154</td>\n",
       "      <td>1.430769</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boston-celtics</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>77.687500</td>\n",
       "      <td>7.228917e+06</td>\n",
       "      <td>224.062500</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>121.687500</td>\n",
       "      <td>17.718750</td>\n",
       "      <td>2.787500</td>\n",
       "      <td>6.212500</td>\n",
       "      <td>...</td>\n",
       "      <td>76.050000</td>\n",
       "      <td>0.712500</td>\n",
       "      <td>2.356250</td>\n",
       "      <td>3.081250</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.356250</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>1.631250</td>\n",
       "      <td>1.006250</td>\n",
       "      <td>7.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brooklyn-nets</th>\n",
       "      <td>25.882353</td>\n",
       "      <td>4.529412</td>\n",
       "      <td>78.529412</td>\n",
       "      <td>7.928121e+06</td>\n",
       "      <td>217.352941</td>\n",
       "      <td>310.823529</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>20.905882</td>\n",
       "      <td>3.482353</td>\n",
       "      <td>7.652941</td>\n",
       "      <td>...</td>\n",
       "      <td>71.711765</td>\n",
       "      <td>0.864706</td>\n",
       "      <td>2.970588</td>\n",
       "      <td>3.835294</td>\n",
       "      <td>2.047059</td>\n",
       "      <td>0.476471</td>\n",
       "      <td>0.641176</td>\n",
       "      <td>1.688235</td>\n",
       "      <td>1.288235</td>\n",
       "      <td>9.582353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charlotte-hornets</th>\n",
       "      <td>24.266667</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>78.733333</td>\n",
       "      <td>6.772335e+06</td>\n",
       "      <td>216.066667</td>\n",
       "      <td>203.400000</td>\n",
       "      <td>103.466667</td>\n",
       "      <td>19.566667</td>\n",
       "      <td>2.626667</td>\n",
       "      <td>5.973333</td>\n",
       "      <td>...</td>\n",
       "      <td>75.086667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2.773333</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>1.506667</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>1.746667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chicago-bulls</th>\n",
       "      <td>24.666667</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>5.392607e+06</td>\n",
       "      <td>217.266667</td>\n",
       "      <td>199.400000</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>20.033333</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>6.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>73.613333</td>\n",
       "      <td>0.793333</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>1.826667</td>\n",
       "      <td>0.326667</td>\n",
       "      <td>0.726667</td>\n",
       "      <td>1.773333</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>8.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cleveland-cavaliers</th>\n",
       "      <td>24.866667</td>\n",
       "      <td>2.933333</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>8.744085e+06</td>\n",
       "      <td>224.333333</td>\n",
       "      <td>253.615385</td>\n",
       "      <td>165.538462</td>\n",
       "      <td>20.376923</td>\n",
       "      <td>3.184615</td>\n",
       "      <td>6.953846</td>\n",
       "      <td>...</td>\n",
       "      <td>62.484615</td>\n",
       "      <td>1.246154</td>\n",
       "      <td>3.069231</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.638462</td>\n",
       "      <td>0.353846</td>\n",
       "      <td>0.546154</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>8.407692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dallas-mavericks</th>\n",
       "      <td>26.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>79.250000</td>\n",
       "      <td>7.593353e+06</td>\n",
       "      <td>220.333333</td>\n",
       "      <td>237.750000</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>19.083333</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>7.058333</td>\n",
       "      <td>...</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>2.725000</td>\n",
       "      <td>3.533333</td>\n",
       "      <td>1.808333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>1.033333</td>\n",
       "      <td>8.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>denver-nuggets</th>\n",
       "      <td>25.928571</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>79.285714</td>\n",
       "      <td>8.798127e+06</td>\n",
       "      <td>224.142857</td>\n",
       "      <td>347.153846</td>\n",
       "      <td>185.307692</td>\n",
       "      <td>20.976923</td>\n",
       "      <td>3.423077</td>\n",
       "      <td>7.330769</td>\n",
       "      <td>...</td>\n",
       "      <td>75.776923</td>\n",
       "      <td>1.061538</td>\n",
       "      <td>2.946154</td>\n",
       "      <td>3.984615</td>\n",
       "      <td>2.030769</td>\n",
       "      <td>0.469231</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.846154</td>\n",
       "      <td>1.107692</td>\n",
       "      <td>9.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>detroit-pistons</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.411765</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>6.505785e+06</td>\n",
       "      <td>208.529412</td>\n",
       "      <td>235.058824</td>\n",
       "      <td>132.294118</td>\n",
       "      <td>18.876471</td>\n",
       "      <td>2.911765</td>\n",
       "      <td>6.652941</td>\n",
       "      <td>...</td>\n",
       "      <td>64.917647</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>2.335294</td>\n",
       "      <td>3.029412</td>\n",
       "      <td>1.723529</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>1.635294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golden-state-warriors</th>\n",
       "      <td>25.076923</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>77.538462</td>\n",
       "      <td>1.173546e+07</td>\n",
       "      <td>209.153846</td>\n",
       "      <td>244.692308</td>\n",
       "      <td>187.692308</td>\n",
       "      <td>24.076923</td>\n",
       "      <td>4.023077</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>0.792308</td>\n",
       "      <td>2.853846</td>\n",
       "      <td>3.661538</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>1.430769</td>\n",
       "      <td>11.123077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>houston-rockets</th>\n",
       "      <td>28.800000</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>77.666667</td>\n",
       "      <td>7.617278e+06</td>\n",
       "      <td>212.933333</td>\n",
       "      <td>494.400000</td>\n",
       "      <td>332.266667</td>\n",
       "      <td>21.860000</td>\n",
       "      <td>3.353333</td>\n",
       "      <td>7.773333</td>\n",
       "      <td>...</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>2.766667</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>2.013333</td>\n",
       "      <td>0.406667</td>\n",
       "      <td>0.773333</td>\n",
       "      <td>1.953333</td>\n",
       "      <td>1.406667</td>\n",
       "      <td>9.706667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indiana-pacers</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>7.942772e+06</td>\n",
       "      <td>214.500000</td>\n",
       "      <td>251.166667</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>19.841667</td>\n",
       "      <td>3.241667</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>76.050000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.725000</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>1.716667</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>8.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la-clippers</th>\n",
       "      <td>27.250000</td>\n",
       "      <td>5.312500</td>\n",
       "      <td>78.437500</td>\n",
       "      <td>7.520664e+06</td>\n",
       "      <td>217.500000</td>\n",
       "      <td>362.562500</td>\n",
       "      <td>185.875000</td>\n",
       "      <td>20.318750</td>\n",
       "      <td>3.337500</td>\n",
       "      <td>7.175000</td>\n",
       "      <td>...</td>\n",
       "      <td>74.587500</td>\n",
       "      <td>1.018750</td>\n",
       "      <td>2.862500</td>\n",
       "      <td>3.893750</td>\n",
       "      <td>1.862500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>1.856250</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>9.112500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>los-angeles-lakers</th>\n",
       "      <td>29.133333</td>\n",
       "      <td>7.733333</td>\n",
       "      <td>78.666667</td>\n",
       "      <td>6.905793e+06</td>\n",
       "      <td>222.133333</td>\n",
       "      <td>579.928571</td>\n",
       "      <td>425.071429</td>\n",
       "      <td>25.071429</td>\n",
       "      <td>4.528571</td>\n",
       "      <td>9.514286</td>\n",
       "      <td>...</td>\n",
       "      <td>70.414286</td>\n",
       "      <td>1.121429</td>\n",
       "      <td>3.707143</td>\n",
       "      <td>4.807143</td>\n",
       "      <td>2.507143</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>1.957143</td>\n",
       "      <td>1.614286</td>\n",
       "      <td>11.985714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memphis-grizzlies</th>\n",
       "      <td>24.800000</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>78.866667</td>\n",
       "      <td>6.021349e+06</td>\n",
       "      <td>219.066667</td>\n",
       "      <td>234.857143</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>20.107143</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>73.071429</td>\n",
       "      <td>0.935714</td>\n",
       "      <td>2.892857</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>0.635714</td>\n",
       "      <td>1.692857</td>\n",
       "      <td>1.171429</td>\n",
       "      <td>8.328571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miami-heat</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.439172e+06</td>\n",
       "      <td>222.187500</td>\n",
       "      <td>381.266667</td>\n",
       "      <td>210.200000</td>\n",
       "      <td>22.126667</td>\n",
       "      <td>3.326667</td>\n",
       "      <td>7.326667</td>\n",
       "      <td>...</td>\n",
       "      <td>71.233333</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>2.980000</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>2.013333</td>\n",
       "      <td>0.313333</td>\n",
       "      <td>0.713333</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>9.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milwaukee-bucks</th>\n",
       "      <td>29.466667</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>78.733333</td>\n",
       "      <td>7.836516e+06</td>\n",
       "      <td>227.200000</td>\n",
       "      <td>565.066667</td>\n",
       "      <td>371.266667</td>\n",
       "      <td>23.466667</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>8.233333</td>\n",
       "      <td>...</td>\n",
       "      <td>77.946667</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>3.133333</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>1.966667</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.953333</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>10.413333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minnesota-timberwolves</th>\n",
       "      <td>23.571429</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>77.857143</td>\n",
       "      <td>4.248132e+06</td>\n",
       "      <td>216.214286</td>\n",
       "      <td>172.500000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>16.978571</td>\n",
       "      <td>2.442857</td>\n",
       "      <td>5.721429</td>\n",
       "      <td>...</td>\n",
       "      <td>74.914286</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>2.128571</td>\n",
       "      <td>2.764286</td>\n",
       "      <td>1.457143</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>1.507143</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>6.535714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new-orleans-pelicans</th>\n",
       "      <td>25.200000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>77.933333</td>\n",
       "      <td>7.031152e+06</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>244.200000</td>\n",
       "      <td>150.066667</td>\n",
       "      <td>22.960000</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>8.046667</td>\n",
       "      <td>...</td>\n",
       "      <td>64.346667</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>2.973333</td>\n",
       "      <td>4.020000</td>\n",
       "      <td>2.193333</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>1.440000</td>\n",
       "      <td>9.946667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new-york-knicks</th>\n",
       "      <td>25.066667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>78.266667</td>\n",
       "      <td>6.224969e+06</td>\n",
       "      <td>217.933333</td>\n",
       "      <td>290.357143</td>\n",
       "      <td>137.785714</td>\n",
       "      <td>21.492857</td>\n",
       "      <td>3.342857</td>\n",
       "      <td>7.371429</td>\n",
       "      <td>...</td>\n",
       "      <td>72.535714</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>3.914286</td>\n",
       "      <td>1.914286</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>1.892857</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>8.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oklahoma-city-thunder</th>\n",
       "      <td>25.066667</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>78.533333</td>\n",
       "      <td>9.111964e+06</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>281.533333</td>\n",
       "      <td>187.466667</td>\n",
       "      <td>18.613333</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>5.713333</td>\n",
       "      <td>...</td>\n",
       "      <td>61.446667</td>\n",
       "      <td>0.793333</td>\n",
       "      <td>2.320000</td>\n",
       "      <td>3.106667</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.673333</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>6.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orlando-magic</th>\n",
       "      <td>24.833333</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>79.750000</td>\n",
       "      <td>9.398300e+06</td>\n",
       "      <td>219.083333</td>\n",
       "      <td>234.416667</td>\n",
       "      <td>136.916667</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>3.016667</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>71.383333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>3.841667</td>\n",
       "      <td>1.458333</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>7.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>philadelphia-76ers</th>\n",
       "      <td>25.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>78.642857</td>\n",
       "      <td>8.825191e+06</td>\n",
       "      <td>218.500000</td>\n",
       "      <td>263.214286</td>\n",
       "      <td>156.785714</td>\n",
       "      <td>20.685714</td>\n",
       "      <td>3.492857</td>\n",
       "      <td>7.550000</td>\n",
       "      <td>...</td>\n",
       "      <td>67.207143</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>3.164286</td>\n",
       "      <td>4.021429</td>\n",
       "      <td>2.107143</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.671429</td>\n",
       "      <td>1.835714</td>\n",
       "      <td>1.314286</td>\n",
       "      <td>9.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phoenix-suns</th>\n",
       "      <td>24.142857</td>\n",
       "      <td>2.357143</td>\n",
       "      <td>78.857143</td>\n",
       "      <td>5.896355e+06</td>\n",
       "      <td>213.142857</td>\n",
       "      <td>191.071429</td>\n",
       "      <td>101.357143</td>\n",
       "      <td>19.621429</td>\n",
       "      <td>3.092857</td>\n",
       "      <td>6.892857</td>\n",
       "      <td>...</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>0.907143</td>\n",
       "      <td>2.678571</td>\n",
       "      <td>3.578571</td>\n",
       "      <td>2.007143</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>1.764286</td>\n",
       "      <td>1.157143</td>\n",
       "      <td>8.385714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>portland-trail-blazers</th>\n",
       "      <td>25.400000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.704394e+06</td>\n",
       "      <td>226.533333</td>\n",
       "      <td>336.066667</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>20.186667</td>\n",
       "      <td>3.626667</td>\n",
       "      <td>7.993333</td>\n",
       "      <td>...</td>\n",
       "      <td>70.940000</td>\n",
       "      <td>1.026667</td>\n",
       "      <td>3.040000</td>\n",
       "      <td>4.080000</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>1.913333</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>9.786667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sacramento-kings</th>\n",
       "      <td>26.153846</td>\n",
       "      <td>3.769231</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.316023e+06</td>\n",
       "      <td>214.538462</td>\n",
       "      <td>298.153846</td>\n",
       "      <td>142.615385</td>\n",
       "      <td>19.369231</td>\n",
       "      <td>3.238462</td>\n",
       "      <td>7.061538</td>\n",
       "      <td>...</td>\n",
       "      <td>63.053846</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>2.530769</td>\n",
       "      <td>3.323077</td>\n",
       "      <td>1.584615</td>\n",
       "      <td>0.353846</td>\n",
       "      <td>0.607692</td>\n",
       "      <td>1.653846</td>\n",
       "      <td>0.992308</td>\n",
       "      <td>8.492308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>san-antonio-spurs</th>\n",
       "      <td>26.200000</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>78.600000</td>\n",
       "      <td>7.264785e+06</td>\n",
       "      <td>219.733333</td>\n",
       "      <td>371.866667</td>\n",
       "      <td>218.533333</td>\n",
       "      <td>18.746667</td>\n",
       "      <td>3.120000</td>\n",
       "      <td>7.026667</td>\n",
       "      <td>...</td>\n",
       "      <td>66.606667</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>2.493333</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>1.753333</td>\n",
       "      <td>0.353333</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>1.393333</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>8.213333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toronto-raptors</th>\n",
       "      <td>25.937500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>78.437500</td>\n",
       "      <td>7.590898e+06</td>\n",
       "      <td>214.937500</td>\n",
       "      <td>268.562500</td>\n",
       "      <td>169.625000</td>\n",
       "      <td>17.556250</td>\n",
       "      <td>2.606250</td>\n",
       "      <td>5.893750</td>\n",
       "      <td>...</td>\n",
       "      <td>76.187500</td>\n",
       "      <td>0.712500</td>\n",
       "      <td>2.487500</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.618750</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>1.668750</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>7.143750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utah-jazz</th>\n",
       "      <td>25.857143</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>77.928571</td>\n",
       "      <td>8.142802e+06</td>\n",
       "      <td>219.928571</td>\n",
       "      <td>293.071429</td>\n",
       "      <td>170.214286</td>\n",
       "      <td>18.207143</td>\n",
       "      <td>3.057143</td>\n",
       "      <td>6.535714</td>\n",
       "      <td>...</td>\n",
       "      <td>57.978571</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.514286</td>\n",
       "      <td>3.328571</td>\n",
       "      <td>1.757143</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>1.457143</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>8.185714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>washington-wizards</th>\n",
       "      <td>25.133333</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>78.733333</td>\n",
       "      <td>7.772450e+06</td>\n",
       "      <td>216.733333</td>\n",
       "      <td>231.333333</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>19.233333</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>...</td>\n",
       "      <td>74.406667</td>\n",
       "      <td>0.853333</td>\n",
       "      <td>2.493333</td>\n",
       "      <td>3.346667</td>\n",
       "      <td>1.973333</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.113333</td>\n",
       "      <td>8.360000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age  experience     height        salary  \\\n",
       "team                                                                     \n",
       "atlanta-hawks           25.538462    4.307692  79.000000  5.608001e+06   \n",
       "boston-celtics          25.000000    2.500000  77.687500  7.228917e+06   \n",
       "brooklyn-nets           25.882353    4.529412  78.529412  7.928121e+06   \n",
       "charlotte-hornets       24.266667    2.733333  78.733333  6.772335e+06   \n",
       "chicago-bulls           24.666667    2.533333  79.000000  5.392607e+06   \n",
       "cleveland-cavaliers     24.866667    2.933333  78.333333  8.744085e+06   \n",
       "dallas-mavericks        26.500000    3.250000  79.250000  7.593353e+06   \n",
       "denver-nuggets          25.928571    4.285714  79.285714  8.798127e+06   \n",
       "detroit-pistons         25.000000    3.411765  78.000000  6.505785e+06   \n",
       "golden-state-warriors   25.076923    3.153846  77.538462  1.173546e+07   \n",
       "houston-rockets         28.800000    7.266667  77.666667  7.617278e+06   \n",
       "indiana-pacers          25.250000    3.500000  78.500000  7.942772e+06   \n",
       "la-clippers             27.250000    5.312500  78.437500  7.520664e+06   \n",
       "los-angeles-lakers      29.133333    7.733333  78.666667  6.905793e+06   \n",
       "memphis-grizzlies       24.800000    2.866667  78.866667  6.021349e+06   \n",
       "miami-heat              27.000000    4.937500  79.000000  8.439172e+06   \n",
       "milwaukee-bucks         29.466667    7.666667  78.733333  7.836516e+06   \n",
       "minnesota-timberwolves  23.571429    2.285714  77.857143  4.248132e+06   \n",
       "new-orleans-pelicans    25.200000    3.400000  77.933333  7.031152e+06   \n",
       "new-york-knicks         25.066667    3.666667  78.266667  6.224969e+06   \n",
       "oklahoma-city-thunder   25.066667    3.866667  78.533333  9.111964e+06   \n",
       "orlando-magic           24.833333    3.166667  79.750000  9.398300e+06   \n",
       "philadelphia-76ers      25.714286    3.714286  78.642857  8.825191e+06   \n",
       "phoenix-suns            24.142857    2.357143  78.857143  5.896355e+06   \n",
       "portland-trail-blazers  25.400000    4.666667  80.000000  8.704394e+06   \n",
       "sacramento-kings        26.153846    3.769231  78.000000  7.316023e+06   \n",
       "san-antonio-spurs       26.200000    4.933333  78.600000  7.264785e+06   \n",
       "toronto-raptors         25.937500    3.562500  78.437500  7.590898e+06   \n",
       "utah-jazz               25.857143    3.785714  77.928571  8.142802e+06   \n",
       "washington-wizards      25.133333    3.333333  78.733333  7.772450e+06   \n",
       "\n",
       "                            weight          GP          GS        MIN  \\\n",
       "team                                                                    \n",
       "atlanta-hawks           218.615385  314.153846  200.076923  22.215385   \n",
       "boston-celtics          224.062500  218.000000  121.687500  17.718750   \n",
       "brooklyn-nets           217.352941  310.823529  214.000000  20.905882   \n",
       "charlotte-hornets       216.066667  203.400000  103.466667  19.566667   \n",
       "chicago-bulls           217.266667  199.400000  105.666667  20.033333   \n",
       "cleveland-cavaliers     224.333333  253.615385  165.538462  20.376923   \n",
       "dallas-mavericks        220.333333  237.750000  105.666667  19.083333   \n",
       "denver-nuggets          224.142857  347.153846  185.307692  20.976923   \n",
       "detroit-pistons         208.529412  235.058824  132.294118  18.876471   \n",
       "golden-state-warriors   209.153846  244.692308  187.692308  24.076923   \n",
       "houston-rockets         212.933333  494.400000  332.266667  21.860000   \n",
       "indiana-pacers          214.500000  251.166667  116.250000  19.841667   \n",
       "la-clippers             217.500000  362.562500  185.875000  20.318750   \n",
       "los-angeles-lakers      222.133333  579.928571  425.071429  25.071429   \n",
       "memphis-grizzlies       219.066667  234.857143  100.000000  20.107143   \n",
       "miami-heat              222.187500  381.266667  210.200000  22.126667   \n",
       "milwaukee-bucks         227.200000  565.066667  371.266667  23.466667   \n",
       "minnesota-timberwolves  216.214286  172.500000   56.000000  16.978571   \n",
       "new-orleans-pelicans    221.000000  244.200000  150.066667  22.960000   \n",
       "new-york-knicks         217.933333  290.357143  137.785714  21.492857   \n",
       "oklahoma-city-thunder   217.000000  281.533333  187.466667  18.613333   \n",
       "orlando-magic           219.083333  234.416667  136.916667  19.333333   \n",
       "philadelphia-76ers      218.500000  263.214286  156.785714  20.685714   \n",
       "phoenix-suns            213.142857  191.071429  101.357143  19.621429   \n",
       "portland-trail-blazers  226.533333  336.066667  247.000000  20.186667   \n",
       "sacramento-kings        214.538462  298.153846  142.615385  19.369231   \n",
       "san-antonio-spurs       219.733333  371.866667  218.533333  18.746667   \n",
       "toronto-raptors         214.937500  268.562500  169.625000  17.556250   \n",
       "utah-jazz               219.928571  293.071429  170.214286  18.207143   \n",
       "washington-wizards      216.733333  231.333333  109.000000  19.233333   \n",
       "\n",
       "                             FGM       FGA    ...            FT%        OR  \\\n",
       "team                                          ...                            \n",
       "atlanta-hawks           3.815385  8.146154    ...      74.423077  1.223077   \n",
       "boston-celtics          2.787500  6.212500    ...      76.050000  0.712500   \n",
       "brooklyn-nets           3.482353  7.652941    ...      71.711765  0.864706   \n",
       "charlotte-hornets       2.626667  5.973333    ...      75.086667  0.933333   \n",
       "chicago-bulls           3.100000  6.866667    ...      73.613333  0.793333   \n",
       "cleveland-cavaliers     3.184615  6.953846    ...      62.484615  1.246154   \n",
       "dallas-mavericks        3.233333  7.058333    ...      70.833333  0.791667   \n",
       "denver-nuggets          3.423077  7.330769    ...      75.776923  1.061538   \n",
       "detroit-pistons         2.911765  6.652941    ...      64.917647  0.711765   \n",
       "golden-state-warriors   4.023077  9.100000    ...      77.800000  0.792308   \n",
       "houston-rockets         3.353333  7.773333    ...      71.300000  0.820000   \n",
       "indiana-pacers          3.241667  7.150000    ...      76.050000  0.666667   \n",
       "la-clippers             3.337500  7.175000    ...      74.587500  1.018750   \n",
       "los-angeles-lakers      4.528571  9.514286    ...      70.414286  1.121429   \n",
       "memphis-grizzlies       3.200000  6.800000    ...      73.071429  0.935714   \n",
       "miami-heat              3.326667  7.326667    ...      71.233333  0.840000   \n",
       "milwaukee-bucks         3.820000  8.233333    ...      77.946667  0.973333   \n",
       "minnesota-timberwolves  2.442857  5.721429    ...      74.914286  0.657143   \n",
       "new-orleans-pelicans    3.766667  8.046667    ...      64.346667  1.040000   \n",
       "new-york-knicks         3.342857  7.371429    ...      72.535714  1.035714   \n",
       "oklahoma-city-thunder   2.560000  5.713333    ...      61.446667  0.793333   \n",
       "orlando-magic           3.016667  6.750000    ...      71.383333  0.966667   \n",
       "philadelphia-76ers      3.492857  7.550000    ...      67.207143  0.857143   \n",
       "phoenix-suns            3.092857  6.892857    ...      82.428571  0.907143   \n",
       "portland-trail-blazers  3.626667  7.993333    ...      70.940000  1.026667   \n",
       "sacramento-kings        3.238462  7.061538    ...      63.053846  0.784615   \n",
       "san-antonio-spurs       3.120000  7.026667    ...      66.606667  0.740000   \n",
       "toronto-raptors         2.606250  5.893750    ...      76.187500  0.712500   \n",
       "utah-jazz               3.057143  6.535714    ...      57.978571  0.800000   \n",
       "washington-wizards      3.080000  6.620000    ...      74.406667  0.853333   \n",
       "\n",
       "                              DR       REB       AST       BLK       STL  \\\n",
       "team                                                                       \n",
       "atlanta-hawks           3.184615  4.407692  2.323077  0.569231  0.669231   \n",
       "boston-celtics          2.356250  3.081250  1.562500  0.356250  0.612500   \n",
       "brooklyn-nets           2.970588  3.835294  2.047059  0.476471  0.641176   \n",
       "charlotte-hornets       2.773333  3.680000  1.506667  0.440000  0.626667   \n",
       "chicago-bulls           2.580000  3.360000  1.826667  0.326667  0.726667   \n",
       "cleveland-cavaliers     3.069231  4.300000  1.638462  0.353846  0.546154   \n",
       "dallas-mavericks        2.725000  3.533333  1.808333  0.466667  0.575000   \n",
       "denver-nuggets          2.946154  3.984615  2.030769  0.469231  0.700000   \n",
       "detroit-pistons         2.335294  3.029412  1.723529  0.341176  0.517647   \n",
       "golden-state-warriors   2.853846  3.661538  2.400000  0.384615  0.784615   \n",
       "houston-rockets         2.766667  3.600000  2.013333  0.406667  0.773333   \n",
       "indiana-pacers          2.725000  3.416667  1.716667  0.491667  0.625000   \n",
       "la-clippers             2.862500  3.893750  1.862500  0.406250  0.662500   \n",
       "los-angeles-lakers      3.707143  4.807143  2.507143  0.678571  0.914286   \n",
       "memphis-grizzlies       2.892857  3.821429  1.950000  0.421429  0.635714   \n",
       "miami-heat              2.980000  3.833333  2.013333  0.313333  0.713333   \n",
       "milwaukee-bucks         3.133333  4.120000  1.966667  0.513333  0.760000   \n",
       "minnesota-timberwolves  2.128571  2.764286  1.457143  0.350000  0.585714   \n",
       "new-orleans-pelicans    2.973333  4.020000  2.193333  0.506667  0.720000   \n",
       "new-york-knicks         2.857143  3.914286  1.914286  0.464286  0.657143   \n",
       "oklahoma-city-thunder   2.320000  3.106667  1.566667  0.400000  0.680000   \n",
       "orlando-magic           2.866667  3.841667  1.458333  0.525000  0.625000   \n",
       "philadelphia-76ers      3.164286  4.021429  2.107143  0.571429  0.671429   \n",
       "phoenix-suns            2.678571  3.578571  2.007143  0.314286  0.657143   \n",
       "portland-trail-blazers  3.040000  4.080000  1.640000  0.473333  0.620000   \n",
       "sacramento-kings        2.530769  3.323077  1.584615  0.353846  0.607692   \n",
       "san-antonio-spurs       2.493333  3.240000  1.753333  0.353333  0.553333   \n",
       "toronto-raptors         2.487500  3.200000  1.618750  0.450000  0.593750   \n",
       "utah-jazz               2.514286  3.328571  1.757143  0.371429  0.542857   \n",
       "washington-wizards      2.493333  3.346667  1.973333  0.366667  0.606667   \n",
       "\n",
       "                              PF        TO        PTS  \n",
       "team                                                   \n",
       "atlanta-hawks           2.146154  1.430769  10.300000  \n",
       "boston-celtics          1.631250  1.006250   7.662500  \n",
       "brooklyn-nets           1.688235  1.288235   9.582353  \n",
       "charlotte-hornets       1.746667  1.000000   7.013333  \n",
       "chicago-bulls           1.773333  1.020000   8.180000  \n",
       "cleveland-cavaliers     1.692308  1.200000   8.407692  \n",
       "dallas-mavericks        1.566667  1.033333   8.933333  \n",
       "denver-nuggets          1.846154  1.107692   9.092308  \n",
       "detroit-pistons         1.635294  1.000000   7.900000  \n",
       "golden-state-warriors   2.115385  1.430769  11.123077  \n",
       "houston-rockets         1.953333  1.406667   9.706667  \n",
       "indiana-pacers          1.750000  1.066667   8.633333  \n",
       "la-clippers             1.856250  1.087500   9.112500  \n",
       "los-angeles-lakers      1.957143  1.614286  11.985714  \n",
       "memphis-grizzlies       1.692857  1.171429   8.328571  \n",
       "miami-heat              1.840000  1.140000   9.180000  \n",
       "milwaukee-bucks         1.953333  1.266667  10.413333  \n",
       "minnesota-timberwolves  1.507143  0.850000   6.535714  \n",
       "new-orleans-pelicans    2.020000  1.440000   9.946667  \n",
       "new-york-knicks         1.892857  1.214286   8.757143  \n",
       "oklahoma-city-thunder   1.673333  0.920000   6.973333  \n",
       "orlando-magic           1.575000  0.900000   7.816667  \n",
       "philadelphia-76ers      1.835714  1.314286   9.392857  \n",
       "phoenix-suns            1.764286  1.157143   8.385714  \n",
       "portland-trail-blazers  1.913333  1.240000   9.786667  \n",
       "sacramento-kings        1.653846  0.992308   8.492308  \n",
       "san-antonio-spurs       1.393333  0.946667   8.213333  \n",
       "toronto-raptors         1.668750  0.912500   7.143750  \n",
       "utah-jazz               1.457143  1.142857   8.185714  \n",
       "washington-wizards      1.920000  1.113333   8.360000  \n",
       "\n",
       "[30 rows x 26 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate means and remove irrelevant columns for id and jersey #\n",
    "mean_df = all_stats_df.groupby('team').mean().drop(['id','jersey'],1)\n",
    "mean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the index of the data frame that is returned corresponds to each individual team now, and the mean values are displayed for each of the columns with numerical values. To find the team with the highest averages for a specific stat, we can use the `sort_values()` function. Let's find the top 5 teams with the highest average salary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>height</th>\n",
       "      <th>salary</th>\n",
       "      <th>weight</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OR</th>\n",
       "      <th>DR</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>STL</th>\n",
       "      <th>PF</th>\n",
       "      <th>TO</th>\n",
       "      <th>PTS</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>golden-state-warriors</th>\n",
       "      <td>25.076923</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>77.538462</td>\n",
       "      <td>1.173546e+07</td>\n",
       "      <td>209.153846</td>\n",
       "      <td>244.692308</td>\n",
       "      <td>187.692308</td>\n",
       "      <td>24.076923</td>\n",
       "      <td>4.023077</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>0.792308</td>\n",
       "      <td>2.853846</td>\n",
       "      <td>3.661538</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>1.430769</td>\n",
       "      <td>11.123077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orlando-magic</th>\n",
       "      <td>24.833333</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>79.750000</td>\n",
       "      <td>9.398300e+06</td>\n",
       "      <td>219.083333</td>\n",
       "      <td>234.416667</td>\n",
       "      <td>136.916667</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>3.016667</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>71.383333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>3.841667</td>\n",
       "      <td>1.458333</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>7.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oklahoma-city-thunder</th>\n",
       "      <td>25.066667</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>78.533333</td>\n",
       "      <td>9.111964e+06</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>281.533333</td>\n",
       "      <td>187.466667</td>\n",
       "      <td>18.613333</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>5.713333</td>\n",
       "      <td>...</td>\n",
       "      <td>61.446667</td>\n",
       "      <td>0.793333</td>\n",
       "      <td>2.320000</td>\n",
       "      <td>3.106667</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.673333</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>6.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>philadelphia-76ers</th>\n",
       "      <td>25.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>78.642857</td>\n",
       "      <td>8.825191e+06</td>\n",
       "      <td>218.500000</td>\n",
       "      <td>263.214286</td>\n",
       "      <td>156.785714</td>\n",
       "      <td>20.685714</td>\n",
       "      <td>3.492857</td>\n",
       "      <td>7.550000</td>\n",
       "      <td>...</td>\n",
       "      <td>67.207143</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>3.164286</td>\n",
       "      <td>4.021429</td>\n",
       "      <td>2.107143</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.671429</td>\n",
       "      <td>1.835714</td>\n",
       "      <td>1.314286</td>\n",
       "      <td>9.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>denver-nuggets</th>\n",
       "      <td>25.928571</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>79.285714</td>\n",
       "      <td>8.798127e+06</td>\n",
       "      <td>224.142857</td>\n",
       "      <td>347.153846</td>\n",
       "      <td>185.307692</td>\n",
       "      <td>20.976923</td>\n",
       "      <td>3.423077</td>\n",
       "      <td>7.330769</td>\n",
       "      <td>...</td>\n",
       "      <td>75.776923</td>\n",
       "      <td>1.061538</td>\n",
       "      <td>2.946154</td>\n",
       "      <td>3.984615</td>\n",
       "      <td>2.030769</td>\n",
       "      <td>0.469231</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.846154</td>\n",
       "      <td>1.107692</td>\n",
       "      <td>9.092308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             age  experience     height        salary  \\\n",
       "team                                                                    \n",
       "golden-state-warriors  25.076923    3.153846  77.538462  1.173546e+07   \n",
       "orlando-magic          24.833333    3.166667  79.750000  9.398300e+06   \n",
       "oklahoma-city-thunder  25.066667    3.866667  78.533333  9.111964e+06   \n",
       "philadelphia-76ers     25.714286    3.714286  78.642857  8.825191e+06   \n",
       "denver-nuggets         25.928571    4.285714  79.285714  8.798127e+06   \n",
       "\n",
       "                           weight          GP          GS        MIN  \\\n",
       "team                                                                   \n",
       "golden-state-warriors  209.153846  244.692308  187.692308  24.076923   \n",
       "orlando-magic          219.083333  234.416667  136.916667  19.333333   \n",
       "oklahoma-city-thunder  217.000000  281.533333  187.466667  18.613333   \n",
       "philadelphia-76ers     218.500000  263.214286  156.785714  20.685714   \n",
       "denver-nuggets         224.142857  347.153846  185.307692  20.976923   \n",
       "\n",
       "                            FGM       FGA    ...            FT%        OR  \\\n",
       "team                                         ...                            \n",
       "golden-state-warriors  4.023077  9.100000    ...      77.800000  0.792308   \n",
       "orlando-magic          3.016667  6.750000    ...      71.383333  0.966667   \n",
       "oklahoma-city-thunder  2.560000  5.713333    ...      61.446667  0.793333   \n",
       "philadelphia-76ers     3.492857  7.550000    ...      67.207143  0.857143   \n",
       "denver-nuggets         3.423077  7.330769    ...      75.776923  1.061538   \n",
       "\n",
       "                             DR       REB       AST       BLK       STL  \\\n",
       "team                                                                      \n",
       "golden-state-warriors  2.853846  3.661538  2.400000  0.384615  0.784615   \n",
       "orlando-magic          2.866667  3.841667  1.458333  0.525000  0.625000   \n",
       "oklahoma-city-thunder  2.320000  3.106667  1.566667  0.400000  0.680000   \n",
       "philadelphia-76ers     3.164286  4.021429  2.107143  0.571429  0.671429   \n",
       "denver-nuggets         2.946154  3.984615  2.030769  0.469231  0.700000   \n",
       "\n",
       "                             PF        TO        PTS  \n",
       "team                                                  \n",
       "golden-state-warriors  2.115385  1.430769  11.123077  \n",
       "orlando-magic          1.575000  0.900000   7.816667  \n",
       "oklahoma-city-thunder  1.673333  0.920000   6.973333  \n",
       "philadelphia-76ers     1.835714  1.314286   9.392857  \n",
       "denver-nuggets         1.846154  1.107692   9.092308  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df.sort_values('salary', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the highest average salary is paid by the Golden State Warriors. Similarly, we can find the top 10 highest paid players by sorting `all_stats_df` on salary, then pulling out the top entries for the 'salary' and 'team' columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>salary</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stephen Curry</th>\n",
       "      <td>40231758.0</td>\n",
       "      <td>golden-state-warriors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russell Westbrook</th>\n",
       "      <td>38506482.0</td>\n",
       "      <td>houston-rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Paul</th>\n",
       "      <td>38506482.0</td>\n",
       "      <td>oklahoma-city-thunder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Durant</th>\n",
       "      <td>38199000.0</td>\n",
       "      <td>brooklyn-nets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Harden</th>\n",
       "      <td>38199000.0</td>\n",
       "      <td>houston-rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Wall</th>\n",
       "      <td>38199000.0</td>\n",
       "      <td>washington-wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LeBron James</th>\n",
       "      <td>37436858.0</td>\n",
       "      <td>los-angeles-lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyle Lowry</th>\n",
       "      <td>34996296.0</td>\n",
       "      <td>toronto-raptors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blake Griffin</th>\n",
       "      <td>34449964.0</td>\n",
       "      <td>detroit-pistons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemba Walker</th>\n",
       "      <td>32742000.0</td>\n",
       "      <td>boston-celtics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       salary                   team\n",
       "Stephen Curry      40231758.0  golden-state-warriors\n",
       "Russell Westbrook  38506482.0        houston-rockets\n",
       "Chris Paul         38506482.0  oklahoma-city-thunder\n",
       "Kevin Durant       38199000.0          brooklyn-nets\n",
       "James Harden       38199000.0        houston-rockets\n",
       "John Wall          38199000.0     washington-wizards\n",
       "LeBron James       37436858.0     los-angeles-lakers\n",
       "Kyle Lowry         34996296.0        toronto-raptors\n",
       "Blake Griffin      34449964.0        detroit-pistons\n",
       "Kemba Walker       32742000.0         boston-celtics"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_stats_df.sort_values('salary', ascending=False)[['salary','team']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stephen Curry is the highest paid player in the NBA with a whopping salary of $40,231,758, followed by Russell Westbrook. We can continue to sift through the data this way for whatever piques our interest. Given how many different variables there are, we can write a small function to make things easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_n(df, category, n):\n",
    "    return (df.sort_values(category, ascending=False)[[category,'team']].head(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way, we can quickly identify the top n players for any given category in a DataFrame. Let's cycle through some stats of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>PTS</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LeBron James</th>\n",
       "      <td>27.1</td>\n",
       "      <td>los-angeles-lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Durant</th>\n",
       "      <td>27.0</td>\n",
       "      <td>brooklyn-nets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Harden</th>\n",
       "      <td>25.1</td>\n",
       "      <td>houston-rockets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luka Doncic</th>\n",
       "      <td>24.4</td>\n",
       "      <td>dallas-mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joel Embiid</th>\n",
       "      <td>24.1</td>\n",
       "      <td>philadelphia-76ers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PTS                team\n",
       "LeBron James  27.1  los-angeles-lakers\n",
       "Kevin Durant  27.0       brooklyn-nets\n",
       "James Harden  25.1     houston-rockets\n",
       "Luka Doncic   24.4    dallas-mavericks\n",
       "Joel Embiid   24.1  philadelphia-76ers"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n(all_stats_df, 'PTS', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>REB</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andre Drummond</th>\n",
       "      <td>13.8</td>\n",
       "      <td>cleveland-cavaliers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dwight Howard</th>\n",
       "      <td>12.3</td>\n",
       "      <td>los-angeles-lakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hassan Whiteside</th>\n",
       "      <td>11.8</td>\n",
       "      <td>portland-trail-blazers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joel Embiid</th>\n",
       "      <td>11.5</td>\n",
       "      <td>philadelphia-76ers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Love</th>\n",
       "      <td>11.1</td>\n",
       "      <td>cleveland-cavaliers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   REB                    team\n",
       "Andre Drummond    13.8     cleveland-cavaliers\n",
       "Dwight Howard     12.3      los-angeles-lakers\n",
       "Hassan Whiteside  11.8  portland-trail-blazers\n",
       "Joel Embiid       11.5      philadelphia-76ers\n",
       "Kevin Love        11.1     cleveland-cavaliers"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n(all_stats_df, 'REB', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>height</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tacko Fall</th>\n",
       "      <td>89.0</td>\n",
       "      <td>boston-celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boban Marjanovic</th>\n",
       "      <td>88.0</td>\n",
       "      <td>dallas-mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kristaps Porzingis</th>\n",
       "      <td>87.0</td>\n",
       "      <td>dallas-mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moses Brown</th>\n",
       "      <td>86.0</td>\n",
       "      <td>portland-trail-blazers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bol Bol</th>\n",
       "      <td>86.0</td>\n",
       "      <td>denver-nuggets</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    height                    team\n",
       "Tacko Fall            89.0          boston-celtics\n",
       "Boban Marjanovic      88.0        dallas-mavericks\n",
       "Kristaps Porzingis    87.0        dallas-mavericks\n",
       "Moses Brown           86.0  portland-trail-blazers\n",
       "Bol Bol               86.0          denver-nuggets"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n(all_stats_df, 'height', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>weight</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tacko Fall</th>\n",
       "      <td>311.0</td>\n",
       "      <td>boston-celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jusuf Nurkic</th>\n",
       "      <td>290.0</td>\n",
       "      <td>portland-trail-blazers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boban Marjanovic</th>\n",
       "      <td>290.0</td>\n",
       "      <td>dallas-mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nikola Jokic</th>\n",
       "      <td>284.0</td>\n",
       "      <td>denver-nuggets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zion Williamson</th>\n",
       "      <td>284.0</td>\n",
       "      <td>new-orleans-pelicans</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  weight                    team\n",
       "Tacko Fall         311.0          boston-celtics\n",
       "Jusuf Nurkic       290.0  portland-trail-blazers\n",
       "Boban Marjanovic   290.0        dallas-mavericks\n",
       "Nikola Jokic       284.0          denver-nuggets\n",
       "Zion Williamson    284.0    new-orleans-pelicans"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n(all_stats_df, 'weight', 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, Tacko Fall of the Boston Celtics is both the tallest and the heaviest player in the NBA.\n",
    "\n",
    "To get a high level overview of how each statistic correlates with one another, we can generate a correlation matrix using `corr()` and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = all_stats_df.drop(['id','jersey'],1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11251d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(corr_matrix, fignum=f.number)\n",
    "plt.xticks(range(corr_matrix.shape[1]), corr_matrix.columns, fontsize=14, rotation=45, ha = 'left')\n",
    "plt.yticks(range(corr_matrix.shape[1]), corr_matrix.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can learn a lot about how different statistics are associated with each other from this matrix, and also identify some interesting trends. For example:\n",
    "\n",
    "* As expected, we observe that games played, age, and experience are highly correlated with one another. An older player will have played more games and have more years of experience in the NBA.\n",
    "* As expected, height is most highly correlated with weight (i.e. taller players weigh more). The correlation of either height or weight with other statistics are similar as well.\n",
    "* Aside from weight, height is highly correlated with overal offensive rebounds (OR) and blocks (BLK), which also makes sense. A taller player should be able to get these more easily.\n",
    "* Points per game (PTS) is highly correlated with field goal and free throw attempts, which also makes sense since more shots generally mean more points. Interestingly, the correlation with the percentage made is low.\n",
    "* One of the highest correlates with salary is points per game, which is one of the more important stats when it comes down to performance.\n",
    "\n",
    "We can narrow in on correlations of interest by sorting the correlation matrix. Let's try sorting by salary and identifying the top correlates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary    1.000000\n",
       "PTS       0.712635\n",
       "FTM       0.707054\n",
       "GS        0.703869\n",
       "FGM       0.699154\n",
       "FGA       0.686631\n",
       "FTA       0.681934\n",
       "MIN       0.663697\n",
       "TO        0.648611\n",
       "STL       0.602140\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.sort_values('salary', ascending=False)['salary'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we suspected, points per game (PTS) is most highly correlated with salary, followed by other point-related stats such as free throws made (FTM). Games started (GS) is also highly correlated with salary, which makes sense since highly-paid players are typically better and will be starters.\n",
    "\n",
    "If we want to model how much more a player costs based on increases in points per game, an easy way is to use linear regression (OLS). To do so, we will use `scikit-learn`. The `LinearRegression()` function cannot handle null values, so we will remove players that don't have reported salaries or PTS values first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "# remove rows with null values for regression\n",
    "reg_df = all_stats_df[['salary', 'PTS']].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we will fit the model with the predictor variable (X) being PTS and the dependent variable (Y) being salary. We will set `fit_intercept=False` since players cannot be paid less than $0.00 or score less than 0 PTS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4cd160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = reg_df['PTS'].values.reshape(-1,1)\n",
    "Y = reg_df['salary'].values.reshape(-1,1)\n",
    "reg = LinearRegression(fit_intercept=False).fit(X,Y)\n",
    "y_pred = reg.predict(X)\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, y_pred, color='red')\n",
    "plt.xlabel(\"Points per game (Career)\")\n",
    "plt.ylabel(\"Salary (2020)\")\n",
    "plt.title('Salary vs PTS - simple linear regression', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consistent with the positive correlation we calculated previously, a regression line with a positive slope is fitted to the data. We can extract the slope of the line by getting the coefficient using `.coef_`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[947619.16030932]]\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was only meant to be a demonstration of what could be done with the data that we scraped. Better models can definitely be generated, especially given the nature of the data. Just by looking at the fit above, we can see that the residuals will be heteroskedastic. There are also a small number of players with high career points per game but low salaries in the bottom right corner of the plot which are skewing the regression line.\n",
    "\n",
    "Taking into account these caveats, the value of the slope is ~947619.16. This suggests that for every unit increase in points per game, the predicted salary paid to a player increases by $947,619.16! Looks like making that free throw really does count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I used Python to scrape ESPN for statistics on all the players in the NBA using the `urllib` and `re` packages. Then, I used `pandas` and `scikit-learn` to organize the data and calculate some summary statistics.\n",
    "\n",
    "I hope what you've learned from this project will help you out on your own web scraping quests. The techniques that I've outlined here should be broadly applicable for other websites. In general, webpages that link to subpages within the same site will construct their links in some sort of standardized pattern. If so, you can construct URLs for the subpages and loop through them as we have done here. Next time you find yourself flipping through a website and copy-pasting, consider trying to automate the process using Python!"
   ]
  }
 ],
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