Linking Axes

Interactive Data Visualization in Python With Bokeh Christopher Bailey 12:56

In this lesson you will practice linking the axes of multiple plots. Linking is the process of syncing elements of different visualizations within a layout.

For this example, the visualization will be able to pan to different segments of a team’s schedule and examine various game stats. Each stat will be represented by its own plot in a two-by-two gridplot().

You will start by collecting the data from the team_stats DataFrame within the read_nba_data.py file, selecting the Philadelphia 76ers as the team of interest.

For more on the CategoricalColorMapper, see the Colors section of Handling Categorical Data on Bokeh’s User Guide.

For additional details on linking plots can be found at Linking Plots in the Bokeh User Guide.

File: read_nba_data.py

Python
      
    
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

File: LinkAxes01.py

Python
      
    
# 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))