Linking Axes

import pandas as pd 

# Read the csv files
player_stats = pd.read_csv('data/2017-18_playerBoxScore.csv',
                           parse_dates=['gmDate'])
team_stats = pd.read_csv('data/2017-18_teamBoxScore.csv',
                          parse_dates=['gmDate'])
standings = pd.read_csv('data/2017-18_standings.csv',
                         parse_dates=['stDate'])

# Create west_top_2
west_top_2 = (standings[(standings['teamAbbr'] == 'HOU') | 
              (standings['teamAbbr'] == 'GS')]
              .loc[:, ['stDate', 'teamAbbr', 'gameWon']]
              .sort_values(['teamAbbr', 'stDate']))

# Find players who took at least 1 three-point shot during the season
three_takers = player_stats[player_stats['play3PA'] > 0]

# Clean up the player names, placing them in a single column
three_takers['name'] = [f'{p["playFNm"]} {p["playLNm"]}'
                        for _, p in three_takers.iterrows()]

# Aggregate the total three-point attempts and makes for each player
three_takers = (three_takers.groupby('name')
                            .sum()
                            .loc[:,['play3PA', 'play3PM']]
                            .sort_values('play3PA', ascending=False))

# Filter out anyone who didn't take at least 100 three-point shots
three_takers = three_takers[three_takers['play3PA'] >= 100].reset_index()

# Add a column with a calculated three-point percentage (made/attempted)
three_takers['pct3PM'] = three_takers['play3PM'] / three_takers['play3PA']

# Philadelphia 76ers data isolated
phi_gm_stats = (team_stats[(team_stats['teamAbbr'] == 'PHI') & 
                           (team_stats['seasTyp'] == 'Regular')]
                .loc[:, ['gmDate',
                         'teamPTS',
                         'teamTRB',
                         'teamAST',
                         'teamTO',
                         'opptPTS',]]
                .sort_values('gmDate'))

# Add game number
phi_gm_stats['game_num'] = range(1, len(phi_gm_stats)+1)

# Derive a win_loss column
win_loss = []
for _, row in phi_gm_stats.iterrows():

    # If the 76ers score more poins, its a win
    if row['teamPTS'] > row['opptPTS']:
        win_loss.append('W')
    else:
        win_loss.append('L')

# Add the win_loss data to the DataFrame
phi_gm_stats['winLoss'] = win_loss

# Bokeh Libraries
from bokeh.plotting import figure, show
from bokeh.io import output_file
from bokeh.models import ColumnDataSource, CategoricalColorMapper, Div
from bokeh.layouts import gridplot, column

# Load in Data
from read_nba_data import phi_gm_stats

# Out to file
output_file('phi_gm_linked_stats.html',
            title='76ers Game Log')

# Store the data in a ColumnDataSource
gm_stats_cds = ColumnDataSource(phi_gm_stats)

# Create a CategoricalColorMapper that assigns a color to wins and losses
win_loss_mapper = CategoricalColorMapper(factors = ['W', 'L'],
                                         palette=['green', 'red'])


# Create a dict with the stat name and its corresponding column in the data
stat_names = {'Points': 'teamPTS',
              'Assists': 'teamAST',
              'Rebounds': 'teamTRB',
              'Turnovers': 'teamTO'}

# The figure for each stat will be held in this dict
stat_figs = {}

# For each stat in the dict
for stat_label, stat_col in stat_names.items():

    # Create a figure
    fig = figure(y_axis_label=stat_label,
                 plot_height=200, plot_width=400,
                 x_range=(1, 10), tools=['xpan', 'reset', 'save'])

    # Configure vbar
    fig.vbar(x='game_num', top=stat_col, source=gm_stats_cds, width=0.9,
             color=dict(field='winLoss', transform=win_loss_mapper))

    # Add the figure to stats_figs dict
    stat_figs[stat_label] = fig

# Create layout
grid = gridplot([[stat_figs['Points'], stat_figs['Assists']],
                [stat_figs['Rebounds'], stat_figs['Turnovers']]])

# Link together the x-axes
stat_figs['Points'].x_range = \
    stat_figs['Assists'].x_range = \
    stat_figs['Rebounds'].x_range = \
    stat_figs['Turnovers'].x_range

# Add a title for the entire visualization using Div
html = """<h3>Philadelphia 76ers Game Log</h3>
<b><i>2017-18 Regular Season</i></b>
<br>
<i>Wins in green, losses in red</i>
"""
sup_title = Div(text=html)

# Visualize
show(column(sup_title, grid))