pandas-from-the-inside.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas from the Inside\n",
    "\n",
    "## PyData Berlin tutorial, 30 June 2017\n",
    "## Stephen Simmons - mail@stevesimmons.com \n",
    "## http://github.com/stevesimmons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports and setting sensible defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy=1.11.3; pandas=0.19.2\n"
     ]
    }
   ],
   "source": [
    "# Sample code from the tutorial 'Pandas from the Inside'\n",
    "# Stephen Simmons - mail@stevesimmons.com\n",
    "# PyData Amsterdam, Fri 7 April 2017\n",
    "#\n",
    "# Requires python3, pandas and numpy. \n",
    "# Jupyter/IPython are also useful. \n",
    "# Best with pandas > 0.18.1. \n",
    "# Pandas 0.18.0 requires a workaround for an indexing bug.\n",
    "\n",
    "import csv\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "print(\"numpy=%s; pandas=%s\" % (np.__version__, pd.__version__))\n",
    "\n",
    "# Don't wrap tables \n",
    "pd.options.display.max_rows = 20\n",
    "pd.options.display.width = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall program flow\n",
    "\n",
    "If run as a standalone script, a `main()` function mirroring the structure of the slides would look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main(name='bg3.txt'):\n",
    "    # Download sample data from www.afltables.com if not present\n",
    "    if not os.path.exists(name):\n",
    "        download_sample_data(names=[name])\n",
    "\n",
    "    # Part 1 - Load sample data as a DataFrame (1 game => 1 row)\n",
    "    raw_df = load_data(name)\n",
    "    # Part 2 - Reshape to give team scores (1 game => 2 rows)\n",
    "    scores_df = prepare_game_scores(raw_df)\n",
    "    # Parts 3 and 4 - GroupBy to get Wins/Draws/Losses/Points\n",
    "    ladder_df = calc_team_ladder(scores_df)\n",
    "    return ladder_df\n",
    "\n",
    "\n",
    "#if __name__ == '__main__':\n",
    "#    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download sample data\n",
    "\n",
    "The sample data here is historical Australian Rules Football (AFL) game results, going back to the start of the first competition in 1897. \n",
    "\n",
    "This data can be downloaded from the web site http://www.afltables.com. The site's focus is week/game-level HTML tables formatted for human footy enthusiasts to read. We want something easier for pandas to consume, so instead grab the large text file ```bg3.txt```.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def download_sample_data(names=('bg3.txt', 'bg7.txt')):\n",
    "    '''\n",
    "    Download results and attendance stats for every AFL match \n",
    "    since 1897 from www.afltables.com into files \n",
    "    'bg3.txt' and 'bg7.txt' in the current directory. \n",
    "    '''\n",
    "    import urllib.request\n",
    "\n",
    "    base_url = 'http://afltables.com/afl/stats/biglists/'\n",
    "    for filename in names:\n",
    "        url = base_url + filename\n",
    "        print(\"Downloading from %s\" % url)\n",
    "        txt = urllib.request.urlopen(url).read()\n",
    "        with open(filename, 'wb') as f:\n",
    "            f.write(txt)\n",
    "            print(\"Wrote %d bytes to %s\" % (len(txt), filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'bg3.txt'\n",
    "if not os.path.exists(name):\n",
    "    download_sample_data([name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note I prepared the slides in today's tutorial last year for PyData London, when the 2016 season was only part-way through.\n",
    "\n",
    "If you download the data now, it includes results for the whole of the 2016 season. The last 2016 match is the Grand Final on Saturday 1 October, where the Western Bulldogs beat the Sydney Swans 13.11.89 to 10.7.67.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load raw data into a pandas DataFrame\n",
    "\n",
    "The pandas function ```pd.read_csv()``` is surprisingly powerful. Here we use its ability to split text into columns using a regular expression. This is a little tricky because most columns are separated by two or more spaces. The exception is the row numbers, where the separator is a fot (from the row number) plus a single space, once the row numbers are above 99,999, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(name='bg3.txt'):\n",
    "    '''\n",
    "    Pandas DataFrames from loading csv files bg3.txt (games) or\n",
    "    bg7.txt (attendance) csvs downloaded from www.afltables.com.\n",
    "    '''\n",
    "    if name == 'bg3.txt':\n",
    "        # Scores with rounds\n",
    "        # - GameNum ends with '.', single space for nums > 100k\n",
    "        # - Rounds are 'R1'-'R22' or 'QF', 'PF', 'GF'.\n",
    "        # - Three grand finals were drawn and replayed the next week\n",
    "        # - Scores are strings '12.5.65' with goals/behinds/points\n",
    "        # - Venue may end with a '.', e.g. 'M.C.G.' though always at EOL\n",
    "        cols = 'GameNum Date Round HomeTeam HomeScore AwayTeam AwayScore Venue'\n",
    "        sep = '[. ] +'\n",
    "        sep = '[. ] +'\n",
    "\n",
    "    elif name == 'bg7.txt':\n",
    "        # Attendance stats\n",
    "        # - RowNum ends with '.', single space for nums > 100k\n",
    "        # - Spectators ends with '*' for finals games\n",
    "        # - Venue may end with a '.', e.g. 'M.C.G.'\n",
    "        # - Dates are 'dd-Mmm-yyyy'.\n",
    "        # - Date/Venue unique, except for two days in 1980s, when\n",
    "        #   M.C.G. hosted games at 2pm and 5pm with same num of spectators.\n",
    "        cols = 'RowNum Spectators HomeTeam HomeScore AwayTeam AwayScore Venue Date'\n",
    "        sep = '(?:(?<=[0-9])[.*] +)|(?:  +)'\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Unexpected data file\")\n",
    "\n",
    "    df = pd.read_csv(name, skiprows=2, sep=sep,\n",
    "                     names=cols.split(), parse_dates=['Date'],\n",
    "                     quoting=csv.QUOTE_NONE, engine='python')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/stephen/projects/pydata-ams2017-pandas/pydata-ams2017-pandas-and-dask-from-the-inside'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14993 entries, 0 to 14992\n",
      "Data columns (total 8 columns):\n",
      "GameNum      14993 non-null int64\n",
      "Date         14993 non-null datetime64[ns]\n",
      "Round        14993 non-null object\n",
      "HomeTeam     14993 non-null object\n",
      "HomeScore    14993 non-null object\n",
      "AwayTeam     14993 non-null object\n",
      "AwayScore    14993 non-null object\n",
      "Venue        14993 non-null object\n",
      "dtypes: datetime64[ns](1), int64(1), object(6)\n",
      "memory usage: 937.1+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_df = load_data('bg3.txt')\n",
    "raw_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of timing an operation with the IPython magic `%timeit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.15 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit raw_df.groupby('HomeTeam').size().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HomeTeam\n",
       "GW Sydney             56\n",
       "University            63\n",
       "Gold Coast            66\n",
       "Kangaroos            104\n",
       "Brisbane Bears       111\n",
       "Western Bulldogs     230\n",
       "Port Adelaide        231\n",
       "Brisbane Lions       235\n",
       "Fremantle            248\n",
       "Adelaide             300\n",
       "                    ... \n",
       "North Melbourne      853\n",
       "Fitzroy              968\n",
       "Hawthorn             974\n",
       "Richmond            1110\n",
       "St Kilda            1160\n",
       "Melbourne           1184\n",
       "Geelong             1187\n",
       "Essendon            1212\n",
       "Carlton             1218\n",
       "Collingwood         1234\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.groupby('HomeTeam').size().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reformat the raw table\n",
    "\n",
    "This function illustrates some powerful ways to process the raw data. In particular, notice how ```Series.str.extract(regex, expand=True)``` can split a Series of strings into a DataFrame with columns given by a regular expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_game_scores(df):\n",
    "    '''\n",
    "    DataFrame with rows giving each team's results in a game\n",
    "    (1 game -> 2 rows for home and away teams)\n",
    "    '''\n",
    "    scores_raw = df.drop('GameNum', axis=1).set_index(['Date', 'Venue', 'Round'])\n",
    "\n",
    "    # Convert into sections for both teams\n",
    "    home_teams = scores_raw['HomeTeam'].rename('Team')\n",
    "    away_teams = scores_raw['AwayTeam'].rename('Team')\n",
    "\n",
    "    # Split the score strings into Goals/Behinds, and points For and Against\n",
    "    regex = '(?P<G>\\d+).(?P<B>\\d+).(?P<F>\\d+)'\n",
    "    home_scores = scores_raw['HomeScore'].str.extract(regex, expand=True).astype(int)\n",
    "    away_scores = scores_raw['AwayScore'].str.extract(regex, expand=True).astype(int)\n",
    "    home_scores['A'] = away_scores['F']\n",
    "    away_scores['A'] = home_scores['F']\n",
    "\n",
    "    home_games = pd.concat([home_teams, home_scores], axis=1)\n",
    "    away_games = pd.concat([away_teams, away_scores], axis=1)\n",
    "\n",
    "    scores = home_games.append(away_games).sort_index().set_index('Team', append=True)\n",
    "    # scores = pd.concat([home_games, away_games], axis=0).sort_index()\n",
    "\n",
    "    # Rather than moving Team to MultiIndex with scores.set_index('Team', append=True),\n",
    "    # keep it as a data column so we can see what an inhomogeneous DataFrame looks like.\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>F</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Round</th>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1897-05-08</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Brunswick St</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Fitzroy</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlton</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Corio Oval</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Geelong</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Essendon</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lake Oval</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>South Melbourne</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Victoria Park</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Collingwood</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St Kilda</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1897-05-15</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">East Melbourne</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R2</th>\n",
       "      <th>Essendon</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collingwood</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-09-16</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">SF</th>\n",
       "      <th>Western Bulldogs</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawthorn</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-09-17</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">S.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">SF</th>\n",
       "      <th>Sydney</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>118</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelaide</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-09-23</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">PF</th>\n",
       "      <th>Sydney</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>97</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geelong</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-09-24</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sydney Showground</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">PF</th>\n",
       "      <th>Western Bulldogs</th>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>89</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GW Sydney</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>83</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-10-01</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">GF</th>\n",
       "      <th>Western Bulldogs</th>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29986 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      G   B    F    A\n",
       "Date       Venue             Round Team                              \n",
       "1897-05-08 Brunswick St      R1    Fitzroy            6  13   49   16\n",
       "                                   Carlton            2   4   16   49\n",
       "           Corio Oval        R1    Geelong            3   6   24   47\n",
       "                                   Essendon           7   5   47   24\n",
       "           Lake Oval         R1    South Melbourne    3   9   27   44\n",
       "                                   Melbourne          6   8   44   27\n",
       "           Victoria Park     R1    Collingwood        5  11   41   16\n",
       "                                   St Kilda           2   4   16   41\n",
       "1897-05-15 East Melbourne    R2    Essendon           4   6   30   50\n",
       "                                   Collingwood        8   2   50   30\n",
       "...                                                  ..  ..  ...  ...\n",
       "2016-09-16 M.C.G.            SF    Western Bulldogs  16  11  107   84\n",
       "                                   Hawthorn          12  12   84  107\n",
       "2016-09-17 S.C.G.            SF    Sydney            18  10  118   82\n",
       "                                   Adelaide          12  10   82  118\n",
       "2016-09-23 M.C.G.            PF    Sydney            15   7   97   60\n",
       "                                   Geelong            8  12   60   97\n",
       "2016-09-24 Sydney Showground PF    Western Bulldogs  13  11   89   83\n",
       "                                   GW Sydney         12  11   83   89\n",
       "2016-10-01 M.C.G.            GF    Western Bulldogs  13  11   89   67\n",
       "                                   Sydney            10   7   67   89\n",
       "\n",
       "[29986 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df = prepare_game_scores(raw_df)\n",
    "scores_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate season ladder\n",
    "\n",
    "To calculate the ladder for a season, we need to pull out all round robin games for that year (i.e. excluding the finals games SF, QF, PF, GF). A quick way of doing this is noting there are never more than 23 rounds in a season (i.e. R1 to R23). So we can select Rounds in the range 'R1':'R9' inclusive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recents versions of pandas can get this directly using multidimensional slicing, so long as the index is sorted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 29986 entries, (1897-05-08 00:00:00, Brunswick St, R1, Fitzroy) to (2016-10-01 00:00:00, M.C.G., GF, Sydney)\n",
      "Data columns (total 4 columns):\n",
      "G    29986 non-null int64\n",
      "B    29986 non-null int64\n",
      "F    29986 non-null int64\n",
      "A    29986 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#scores_df.loc(axis=0)[str(year), :, 'R1':'R9', :]\n",
    "scores_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df.index.is_lexsorted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df.sort_index(inplace=True)\n",
    "scores_df.index.is_lexsorted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the scores DataFrame is sorted, we can easily select subsets of the rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>F</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Round</th>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016-03-24</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Carlton</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond</th>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2016-03-26</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Carrara</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Essendon</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Coast</th>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>121</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Docklands</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Adelaide</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>97</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Melbourne</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>GW Sydney</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">S.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R1</th>\n",
       "      <th>Collingwood</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>53</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>133</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2016-08-27</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R23</th>\n",
       "      <th>Carlton</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Essendon</th>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>103</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">S.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R23</th>\n",
       "      <th>Richmond</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney</th>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>164</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2016-08-28</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Docklands</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R23</th>\n",
       "      <th>Brisbane Lions</th>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>103</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St Kilda</th>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>161</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M.C.G.</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R23</th>\n",
       "      <th>Collingwood</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>111</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawthorn</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>112</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Subiaco</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">R23</th>\n",
       "      <th>Fremantle</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Bulldogs</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              G   B    F    A\n",
       "Date       Venue     Round Team                              \n",
       "2016-03-24 M.C.G.    R1    Carlton           12  11   83   92\n",
       "                           Richmond          14   8   92   83\n",
       "2016-03-26 Carrara   R1    Essendon           9   6   60  121\n",
       "                           Gold Coast        17  19  121   60\n",
       "           Docklands R1    Adelaide          14  13   97  107\n",
       "                           North Melbourne   16  11  107   97\n",
       "           M.C.G.    R1    GW Sydney         10  18   78   80\n",
       "                           Melbourne         12   8   80   78\n",
       "           S.C.G.    R1    Collingwood        7  11   53  133\n",
       "                           Sydney            18  25  133   53\n",
       "...                                          ..  ..  ...  ...\n",
       "2016-08-27 M.C.G.    R23   Carlton           10  19   79  103\n",
       "                           Essendon          15  13  103   79\n",
       "           S.C.G.    R23   Richmond           7   9   51  164\n",
       "                           Sydney            25  14  164   51\n",
       "2016-08-28 Docklands R23   Brisbane Lions    15  13  103  161\n",
       "                           St Kilda          25  11  161  103\n",
       "           M.C.G.    R23   Collingwood       17   9  111  112\n",
       "                           Hawthorn          17  10  112  111\n",
       "           Subiaco   R23   Fremantle         10   9   69   49\n",
       "                           Western Bulldogs   6  13   49   69\n",
       "\n",
       "[396 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df.loc(axis=0)['2016', :, 'R1':'R9', :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the complete code to produce the ladder. \n",
    "\n",
    "Note there is a bug in pandas 0.18.0's multidimensional slicing which we have to work around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_team_ladder(scores_df, year=2016):\n",
    "    '''\n",
    "    DataFrame with championship ladder with round-robin games for the given year.\n",
    "    Wins, draws and losses are worth 4, 2 and 0 points respectively.\n",
    "    '''\n",
    "    # Select a subset of the rows\n",
    "    # df.loc[] matches dates as strings like '20160506' or '2016'.\n",
    "    # Note here rounds are simple strings so sort with R1 < R10 < R2 < .. < R9\n",
    "    #      (we could change this with a CategoricalIndex)\n",
    "    if pd.__version__ > '0.18.0':\n",
    "        # MultiIndex slicing works ok\n",
    "        scores2 = scores_df.sort_index()\n",
    "        x = scores2.loc(axis=0)[str(year), :, 'R1':'R9', :]\n",
    "    else:\n",
    "        # pandas 0.18.0 has a bug with .loc on MultiIndexes\n",
    "        # if dates are the first level. It works as expected if we\n",
    "        # move the dates to the end before slicing\n",
    "        scores2 = scores_df.reorder_levels([1, 2, 3, 0]).sort_index()\n",
    "        x = scores2.loc(axis=0)[:, 'R1':'R9', :, str(year):str(year)]\n",
    "        # Don't need to put levels back in order as we are about to drop 3 of them\n",
    "        # x = x.reorder_levels([3, 0, 1, 2]).sort_index()\n",
    "\n",
    "    # Just keep Team. This does a copy too, avoiding SettingWithCopy warning\n",
    "    y = x.reset_index(['Date', 'Venue', 'Round'], drop=True)\n",
    "\n",
    "    # Add cols with 0/1 for number of games played, won, drawn and lost\n",
    "    y['P'] = 1\n",
    "    y['W'] = (y['F'] > y['A']).astype(int)\n",
    "    y['D'] = 0\n",
    "    y.loc[y['F'] == y['A'], 'D'] = 1\n",
    "    y.eval('L = 1*(A>F)', inplace=True)\n",
    "    #print(y)\n",
    "\n",
    "    # Subtotal by team and then sort by Points/Percentage\n",
    "    t = y.groupby(level='Team').sum()\n",
    "    t['PCT'] = 100.0 * t.F / t.A\n",
    "    t['PTS'] = 4 * t['W'] + 2 * t['D']\n",
    "    ladder = t.sort_values(['PTS', 'PCT'], ascending=False)\n",
    "\n",
    "    # Add ladder position (note: assumes no ties!)\n",
    "    ladder['Pos'] = pd.RangeIndex(1, len(ladder) + 1)\n",
    "    #print(ladder)\n",
    "\n",
    "    return ladder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>F</th>\n",
       "      <th>A</th>\n",
       "      <th>P</th>\n",
       "      <th>W</th>\n",
       "      <th>D</th>\n",
       "      <th>L</th>\n",
       "      <th>PCT</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Pos</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fremantle</th>\n",
       "      <td>273</td>\n",
       "      <td>219</td>\n",
       "      <td>1857</td>\n",
       "      <td>1564</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>118.734015</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Coast</th>\n",
       "      <td>340</td>\n",
       "      <td>290</td>\n",
       "      <td>2330</td>\n",
       "      <td>1572</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>148.218830</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawthorn</th>\n",
       "      <td>365</td>\n",
       "      <td>262</td>\n",
       "      <td>2452</td>\n",
       "      <td>1548</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>158.397933</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney</th>\n",
       "      <td>292</td>\n",
       "      <td>254</td>\n",
       "      <td>2006</td>\n",
       "      <td>1578</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>127.122940</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond</th>\n",
       "      <td>280</td>\n",
       "      <td>250</td>\n",
       "      <td>1930</td>\n",
       "      <td>1568</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>123.086735</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Bulldogs</th>\n",
       "      <td>310</td>\n",
       "      <td>241</td>\n",
       "      <td>2101</td>\n",
       "      <td>1825</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>115.123288</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelaide</th>\n",
       "      <td>308</td>\n",
       "      <td>259</td>\n",
       "      <td>2107</td>\n",
       "      <td>1821</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>115.705656</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Melbourne</th>\n",
       "      <td>303</td>\n",
       "      <td>244</td>\n",
       "      <td>2062</td>\n",
       "      <td>1937</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>106.453278</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Adelaide</th>\n",
       "      <td>296</td>\n",
       "      <td>226</td>\n",
       "      <td>2002</td>\n",
       "      <td>1874</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>106.830309</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geelong</th>\n",
       "      <td>272</td>\n",
       "      <td>221</td>\n",
       "      <td>1853</td>\n",
       "      <td>1833</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>101.091107</td>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GW Sydney</th>\n",
       "      <td>268</td>\n",
       "      <td>264</td>\n",
       "      <td>1872</td>\n",
       "      <td>1891</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>98.995241</td>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collingwood</th>\n",
       "      <td>287</td>\n",
       "      <td>250</td>\n",
       "      <td>1972</td>\n",
       "      <td>1856</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>106.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>225</td>\n",
       "      <td>223</td>\n",
       "      <td>1573</td>\n",
       "      <td>2044</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>76.956947</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St Kilda</th>\n",
       "      <td>241</td>\n",
       "      <td>249</td>\n",
       "      <td>1695</td>\n",
       "      <td>2162</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>78.399630</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Essendon</th>\n",
       "      <td>226</td>\n",
       "      <td>224</td>\n",
       "      <td>1580</td>\n",
       "      <td>2134</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>74.039363</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Coast</th>\n",
       "      <td>235</td>\n",
       "      <td>223</td>\n",
       "      <td>1633</td>\n",
       "      <td>2240</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>72.901786</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brisbane Lions</th>\n",
       "      <td>224</td>\n",
       "      <td>213</td>\n",
       "      <td>1557</td>\n",
       "      <td>2306</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>67.519514</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlton</th>\n",
       "      <td>219</td>\n",
       "      <td>211</td>\n",
       "      <td>1525</td>\n",
       "      <td>2354</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>64.783347</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    G    B     F     A   P   W  D   L         PCT  PTS  Pos\n",
       "Team                                                                       \n",
       "Fremantle         273  219  1857  1564  22  17  0   5  118.734015   68    1\n",
       "West Coast        340  290  2330  1572  22  16  1   5  148.218830   66    2\n",
       "Hawthorn          365  262  2452  1548  22  16  0   6  158.397933   64    3\n",
       "Sydney            292  254  2006  1578  22  16  0   6  127.122940   64    4\n",
       "Richmond          280  250  1930  1568  22  15  0   7  123.086735   60    5\n",
       "Western Bulldogs  310  241  2101  1825  22  14  0   8  115.123288   56    6\n",
       "Adelaide          308  259  2107  1821  21  13  0   8  115.705656   52    7\n",
       "North Melbourne   303  244  2062  1937  22  13  0   9  106.453278   52    8\n",
       "Port Adelaide     296  226  2002  1874  22  12  0  10  106.830309   48    9\n",
       "Geelong           272  221  1853  1833  21  11  1   9  101.091107   46   10\n",
       "GW Sydney         268  264  1872  1891  22  11  0  11   98.995241   44   11\n",
       "Collingwood       287  250  1972  1856  22  10  0  12  106.250000   40   12\n",
       "Melbourne         225  223  1573  2044  22   7  0  15   76.956947   28   13\n",
       "St Kilda          241  249  1695  2162  22   6  1  15   78.399630   26   14\n",
       "Essendon          226  224  1580  2134  22   6  0  16   74.039363   24   15\n",
       "Gold Coast        235  223  1633  2240  22   4  1  17   72.901786   18   16\n",
       "Brisbane Lions    224  213  1557  2306  22   4  0  18   67.519514   16   17\n",
       "Carlton           219  211  1525  2354  22   4  0  18   64.783347   16   18"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ladder_df = calc_team_ladder(scores_df, 2015)\n",
    "ladder_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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