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Merge pull request #17 from leondangio/intro-to-bokeh
Intro to bokeh
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‎intro-to-bokeh/README.md‎

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# Getting Started with Interactive Visualization Using Bokeh
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Data and code snippets from tutorial.
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## Data
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Found in the [`data\`](https://github.com/LeonSD09/materials/tree/intro-to-bokeh/intro-to-bokeh/data) directory.
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The tutorial utilizes the following three files:
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- **2017-18_standings.csv**
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- Contains daily team standing information for the 2017-18 season
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- [Original Kaggle Link](https://www.kaggle.com/pablote/nba-enhanced-stats/downloads/2017-18_standings.csv/26)
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- **2017-18_playerBoxScore.csv**
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- Contains per-game box score player statistics for the 2017-18 season
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- [Original Kaggle Link](https://www.kaggle.com/pablote/nba-enhanced-stats/downloads/2017-18_playerBoxScore.csv/26)
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- **2017-18_teamBoxScore.csv**
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- Contains per-game box score team statistics for the 2017-18 season
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- [Original Kaggle Link](https://www.kaggle.com/pablote/nba-enhanced-stats/downloads/2017-18_teamBoxScore.csv/26)
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The original Kaggle link for this data is: [**NBA Enhanced Box Score and Standings Stats**](https://www.kaggle.com/pablote/nba-enhanced-stats/home)
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This data is licensed under [**CC BY-SA 4.0**](https://creativecommons.org/licenses/by-sa/4.0/).
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## Code Snippets
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Found in the [`code-snippets\`](https://github.com/LeonSD09/materials/tree/intro-to-bokeh/intro-to-bokeh/code-snippets) directory.
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The code snippets found in the tutorial can be found here in individual files.

‎intro-to-bokeh/code-snippets/bokeh-template.py‎

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"""Bokeh Visualization Template
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This template is a general outline for turning your data into a
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visualization using Bokeh.
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"""
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# Data handling
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import pandas as pd
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import numpy as np
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# Bokeh libraries
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from bokeh.io import output_file, output_notebook
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from bokeh.plotting import figure, show
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from bokeh.models import ColumnDataSource
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from bokeh.layouts import row, column, gridplot
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from bokeh.models.widgets import Tabs, Panel
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# Prepare the data
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# Determine where the visualization will be rendered
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output_file('filename.html') # Render to static HTML, or
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output_notebook() # Render inline in a Jupyter Notebook
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# Set up the figure(s)
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fig = figure() # Instantiate a figure() object
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# Connect to and draw the data
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# Organize the layout
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# Preview and save
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show(fig) # See what I made, and save if I like it

‎intro-to-bokeh/code-snippets/east-top-2-race-v0.py‎

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# Bokeh libraries
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from bokeh.plotting import figure, show
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from bokeh.io import output_file
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from bokeh.models import ColumnDataSource, CDSView, GroupFilter
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# Output to file
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output_file('east-top-2-standings-race.html',
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title='Eastern Conference Top 2 Teams Wins Race')
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# Create a ColumnDataSource
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standings_cds = ColumnDataSource(standings)
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# Create views for each team
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celtics_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='BOS')])
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raptors_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='TOR')])
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# Create and configure the figure
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east_fig = figure(x_axis_type='datetime',
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plot_height=300, plot_width=600,
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title='Eastern Conference Top 2 Teams Wins Race, 2017-18',
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x_axis_label='Date', y_axis_label='Wins',
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toolbar_location=None)
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# Render the race as step lines
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east_fig.step('stDate', 'gameWon',
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color='#007A33', legend='Celtics',
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source=standings_cds, view=celtics_view)
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east_fig.step('stDate', 'gameWon',
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color='#CE1141', legend='Raptors',
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source=standings_cds, view=raptors_view)
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# Move the legend to the upper left corner
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east_fig.legend.location = 'top_left'
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# Show the plot
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show(east_fig)

‎intro-to-bokeh/code-snippets/east-west-column-layout.py‎

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# Bokeh library
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from bokeh.plotting import figure, show
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from bokeh.io import output_file
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from bokeh.layouts import column
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# Output to file
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output_file('east-west-top-2-standings-race.html',
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title='Conference Top 2 Teams Wins Race')
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# Plot the two visualizations in a vertical configuration
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show(column(west_fig, east_fig))

‎intro-to-bokeh/code-snippets/east-west-gridplot-layout-with-nones.py‎

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# Bokeh libraries
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from bokeh.io import output_file
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from bokeh.layouts import gridplot
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# Output to file
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output_file('east-west-top-2-gridplot.html',
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title='Conference Top 2 Teams Wins Race')
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# Reduce the width of both figures
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east_fig.plot_width = west_fig.plot_width = 300
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# Edit the titles
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east_fig.title.text = 'Eastern Conference'
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west_fig.title.text = 'Western Conference'
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# Plot the two visualizations with placeholders
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east_west_gridplot = gridplot([[west_fig, None], [None, east_fig]],
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toolbar_location='right')
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# Plot the two visualizations in a horizontal configuration
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show(east_west_gridplot)

‎intro-to-bokeh/code-snippets/east-west-gridplot-layout.py‎

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# Bokeh libraries
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from bokeh.io import output_file
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from bokeh.layouts import gridplot
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# Output to file
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output_file('east-west-top-2-gridplot.html',
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title='Conference Top 2 Teams Wins Race')
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# Reduce the width of both figures
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east_fig.plot_width = west_fig.plot_width = 300
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# Edit the titles
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east_fig.title.text = 'Eastern Conference'
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west_fig.title.text = 'Western Conference'
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# Configure the gridplot
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east_west_gridplot = gridplot([[west_fig, east_fig]],
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toolbar_location='right')
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# Plot the two visualizations in a horizontal configuration
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show(east_west_gridplot)

‎intro-to-bokeh/code-snippets/east-west-layout-prep.py‎

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# Bokeh libraries
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from bokeh.plotting import figure, show
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from bokeh.models import ColumnDataSource, CDSView, GroupFilter
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# Create a ColumnDataSource
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standings_cds = ColumnDataSource(standings)
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# Create the views for each team
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celtics_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='BOS')])
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raptors_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='TOR')])
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rockets_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='HOU')])
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warriors_view = CDSView(source=standings_cds,
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filters=[GroupFilter(column_name='teamAbbr',
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group='GS')])
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# Create and configure the figure
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east_fig = figure(x_axis_type='datetime',
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plot_height=300,
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x_axis_label='Date',
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y_axis_label='Wins',
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toolbar_location=None)
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west_fig = figure(x_axis_type='datetime',
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plot_height=300,
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x_axis_label='Date',
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y_axis_label='Wins',
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toolbar_location=None)
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# Configure the figures for each conference
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east_fig.step('stDate', 'gameWon',
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color='#007A33', legend='Celtics',
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source=standings_cds, view=celtics_view)
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east_fig.step('stDate', 'gameWon',
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color='#CE1141', legend='Raptors',
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source=standings_cds, view=raptors_view)
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west_fig.step('stDate', 'gameWon', color='#CE1141', legend='Rockets',
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source=standings_cds, view=rockets_view)
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west_fig.step('stDate', 'gameWon', color='#006BB6', legend='Warriors',
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source=standings_cds, view=warriors_view)
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# Move the legend to the upper left corner
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east_fig.legend.location = 'top_left'
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west_fig.legend.location = 'top_left'

‎intro-to-bokeh/code-snippets/east-west-tabbed-layout.py‎

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# Bokeh Library
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from bokeh.io import output_file
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from bokeh.models.widgets import Tabs, Panel
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# Output to file
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output_file('east-west-top-2-tabbed_layout.html',
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title='Conference Top 2 Teams Wins Race')
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# Increase the plot widths
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east_fig.plot_width = west_fig.plot_width = 800
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# Create two panels, one for each conference
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east_panel = Panel(child=east_fig, title='Eastern Conference')
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west_panel = Panel(child=west_fig, title='Western Conference')
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# Assign the panels to Tabs
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tabs = Tabs(tabs=[west_panel, east_panel])
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# Show the tabbed layout
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show(tabs)

‎intro-to-bokeh/code-snippets/first-figure-no-gridlines.py‎

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# Bokeh Libraries
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from bokeh.io import output_notebook
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from bokeh.plotting import figure, show
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# The figure will be rendered inline in my Jupyter Notebook
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output_notebook()
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# Example figure
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fig = figure(background_fill_color='gray',
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background_fill_alpha=0.5,
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border_fill_color='blue',
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border_fill_alpha=0.25,
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plot_height=300,
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plot_width=500,
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h_symmetry=True,
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x_axis_label='X Label',
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x_axis_type='datetime',
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x_axis_location='above',
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x_range=('2018-01-01', '2018-06-30'),
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y_axis_label='Y Label',
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y_axis_type='linear',
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y_axis_location='left',
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y_range=(0, 100),
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title='Example Figure',
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title_location='right',
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toolbar_location='below',
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tools='save')
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# Remove the gridlines from the figure() object
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fig.grid.grid_line_color = None
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# See what it looks like
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show(fig)

‎intro-to-bokeh/code-snippets/first-figure.py‎

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# Bokeh Libraries
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from bokeh.io import output_notebook
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from bokeh.plotting import figure, show
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# The figure will be rendered inline in my Jupyter Notebook
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output_notebook()
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# Example figure
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fig = figure(background_fill_color='gray',
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background_fill_alpha=0.5,
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border_fill_color='blue',
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border_fill_alpha=0.25,
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plot_height=300,
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plot_width=500,
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h_symmetry=True,
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x_axis_label='X Label',
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x_axis_type='datetime',
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x_axis_location='above',
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x_range=('2018-01-01', '2018-06-30'),
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y_axis_label='Y Label',
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y_axis_type='linear',
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y_axis_location='left',
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y_range=(0, 100),
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title='Example Figure',
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title_location='right',
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toolbar_location='below',
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tools='save')
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# See what it looks like
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show(fig)

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