8 Tips for Creating Data Visualizations in Python using Bokeh

Author: Payal Patel

Import Libraries

import numpy as np
import pandas as pd
import bokeh
from bokeh.plotting import figure, output_file, show

# Needed for output_notebook / inline notebook data viz 
from bokeh.io import output_notebook, show
from bokeh.resources import INLINE

# Needed for scatterplot markers
from bokeh.transform import factor_cmap, factor_mark
import warnings
warnings.filterwarnings('ignore')

from bokeh.models import BasicTickFormatter, Label, Title, NumeralTickFormatter

# Needed to add a hover tool 
from bokeh.models.tools import HoverTool

# Needed for bar chart legend example 
from bokeh.models import Legend
from bokeh.models import Range1d

# Needed to create tabs
from bokeh.models.widgets import Tabs, Panel

output_notebook(resources=INLINE)

Loading BokehJS ...

California Wildfires Line Graph

This section shows how to create a line graph displaying the total acres burned by county in California from 2013 to 2019.

Dataset Used: https://www.kaggle.com/datasets/ananthu017/california-wildfire-incidents-20132020

Highlights / Features Implemented:

- Formatted Titles & Axes 
- Interactive Legend  
- Gridline Removal 
- Hover Tool  

caliWildfires = pd.read_csv('California_Fire_Incidents.csv')

caliWildfires.head()

	AcresBurned	Active	AdminUnit	AirTankers	ArchiveYear	CalFireIncident	CanonicalUrl	ConditionStatement	ControlStatement	Counties	...	SearchKeywords	Started	Status	StructuresDamaged	StructuresDestroyed	StructuresEvacuated	StructuresThreatened	UniqueId	Updated	WaterTenders
0	257314.0	False	Stanislaus National Forest/Yosemite National Park	NaN	2013	True	/incidents/2013/8/17/rim-fire/	NaN	NaN	Tuolumne	...	Rim Fire, Stanislaus National Forest, Yosemite...	2013-08-17T15:25:00Z	Finalized	NaN	NaN	NaN	NaN	5fb18d4d-213f-4d83-a179-daaf11939e78	2013-09-06T18:30:00Z	NaN
1	30274.0	False	USFS Angeles National Forest/Los Angeles Count...	NaN	2013	True	/incidents/2013/5/30/powerhouse-fire/	NaN	NaN	Los Angeles	...	Powerhouse Fire, May 2013, June 2013, Angeles ...	2013-05-30T15:28:00Z	Finalized	NaN	NaN	NaN	NaN	bf37805e-1cc2-4208-9972-753e47874c87	2013-06-08T18:30:00Z	NaN
2	27531.0	False	CAL FIRE Riverside Unit / San Bernardino Natio...	NaN	2013	True	/incidents/2013/7/15/mountain-fire/	NaN	NaN	Riverside	...	Mountain Fire, July 2013, Highway 243, Highway...	2013-07-15T13:43:00Z	Finalized	NaN	NaN	NaN	NaN	a3149fec-4d48-427c-8b2c-59e8b79d59db	2013-07-30T18:00:00Z	NaN
3	27440.0	False	Tahoe National Forest	NaN	2013	False	/incidents/2013/8/10/american-fire/	NaN	NaN	Placer	...	American Fire, August 2013, Deadwood Ridge, Fo...	2013-08-10T16:30:00Z	Finalized	NaN	NaN	NaN	NaN	8213f5c7-34fa-403b-a4bc-da2ace6e6625	2013-08-30T08:00:00Z	NaN
4	24251.0	False	Ventura County Fire/CAL FIRE	NaN	2013	True	/incidents/2013/5/2/springs-fire/	Acreage has been reduced based upon more accur...	NaN	Ventura	...	Springs Fire, May 2013, Highway 101, Camarillo...	2013-05-02T07:01:00Z	Finalized	6.0	10.0	NaN	NaN	46731fb8-3350-4920-bdf7-910ac0eb715c	2013-05-11T06:30:00Z	11.0

5 rows × 40 columns

# Subset Dataset
caliWildfires = caliWildfires[['ArchiveYear', 'Counties', 'AcresBurned']]

# Generate Dataframes for Specific Counties
caliWildfiresSanBernardino = caliWildfires[caliWildfires['Counties'] == 'San Bernardino'] 
caliWildfiresSanBernardino_sum = pd.DataFrame(caliWildfiresSanBernardino.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresSanBernardino_sum.rename(columns={'AcresBurned':'San Bernardino'}, inplace=True)

caliWildfiresLA = caliWildfires[caliWildfires['Counties'] == 'Los Angeles'] 
caliWildfiresLA_sum = pd.DataFrame(caliWildfiresLA.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresLA_sum.rename(columns={'AcresBurned':'Los Angeles'}, inplace=True)

caliWildfiresSanDiego = caliWildfires[caliWildfires['Counties'] == 'San Diego'] 
caliWildfiresSanDiego_sum = pd.DataFrame(caliWildfiresSanDiego.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresSanDiego_sum.rename(columns={'AcresBurned':'San Diego'}, inplace=True)

caliWildfiresOrange = caliWildfires[caliWildfires['Counties'] == 'Orange'] 
caliWildfiresOrange_sum = pd.DataFrame(caliWildfiresOrange.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresOrange_sum.rename(columns={'AcresBurned':'Orange'}, inplace=True)

caliWildfiresSB = caliWildfires[caliWildfires['Counties'] == 'Santa Barbara'] 
caliWildfiresSB_sum = pd.DataFrame(caliWildfiresSB.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresSB_sum.rename(columns={'AcresBurned':'Santa Barbara'}, inplace=True)

caliWildfiresSC = caliWildfires[caliWildfires['Counties'] == 'Santa Clara'] 
caliWildfiresSC_sum = pd.DataFrame(caliWildfiresSC.groupby('ArchiveYear')['AcresBurned'].sum())
caliWildfiresSC_sum.rename(columns={'AcresBurned':'Santa Clara'}, inplace=True)

# Combine dataframes
caliWildfires = pd.concat([caliWildfiresLA_sum, caliWildfiresSanDiego_sum, caliWildfiresOrange_sum,
                          caliWildfiresSB_sum, caliWildfiresSC_sum, caliWildfiresSanBernardino_sum], axis=1)

caliWildfires = caliWildfires.reset_index(level=0)
caliWildfires['ArchiveYear'] = caliWildfires['ArchiveYear'].astype(str)

# View combined dataset
caliWildfires

	ArchiveYear	Los Angeles	San Diego	Orange	Santa Barbara	Santa Clara	San Bernardino
0	2013	31503.0	13771.0	40.0	2154.0	90.0	1850.0
1	2014	2416.0	26315.0	968.0	632.0	205.0	2191.0
2	2015	1289.0	2373.0	214.0	284.0	257.0	35883.0
3	2016	11488.0	9115.0	197.0	52813.0	4738.0	44454.0
4	2017	31190.0	10156.0	11879.0	301035.0	639.0	4065.0
5	2018	102232.0	2205.0	23466.0	1804.0	756.0	1820.0
6	2019	14462.0	1459.0	NaN	3846.0	859.0	634.0

# Plot Line Graph

TOOLTIPS = "pan, wheel_zoom, box_zoom, box_select,reset, save" # the tools you want to add to your graph

x = list(caliWildfires['ArchiveYear'])
y = list(caliWildfires['Los Angeles'])
y2 = list(caliWildfires['San Diego'])
y3 = list(caliWildfires['Orange'])
y4 = list(caliWildfires['Santa Barbara'])
y5 = list(caliWildfires['Santa Clara'])
y6 = list(caliWildfires['San Bernardino'])


# Create Figure 
p = figure(x_range = x, title = 'California Wildfires Total Acres Burned by County - 2013 to 2019', 
           x_axis_label='Year', y_axis_label='Total Acres Burned')
l1 = p.line(x,y,legend_label='Los Angeles', color='blue', line_width = 2, muted_color='gray', muted_alpha=0.2)
l2 = p.line(x,y2, legend_label='San Diego', color='green', line_width = 2, muted_color='gray', muted_alpha=0.2)
l3 = p.line(x,y3, legend_label='Orange', color='orange', line_width = 2, muted_color='gray', muted_alpha=0.2)
l4 = p.line(x,y4, legend_label='Santa Barbara', color='midnightblue', line_width = 2, muted_color='gray', muted_alpha=0.2)
l5 = p.line(x,y5, legend_label='Santa Clara', color='gold', line_width = 2, muted_color='gray', muted_alpha=0.2)
l6 = p.line(x,y6, legend_label='San Bernardino', color='sienna', line_width = 2, muted_color='gray', muted_alpha=0.2)



# Adjust Figure Size
p.plot_height=400
p.plot_width=800

# Adjust title and axis fonts
p.title.text_font_size = '18pt'
p.title.align = 'center'


# Modify X & Y Axes 
p.xaxis.axis_label_text_font_size = "12pt"
p.yaxis.axis_label_text_font_size = "12pt"
p.axis.axis_label_text_font_style='bold'
#p.yaxis.formatter = BasicTickFormatter(use_scientific=False)
p.yaxis[0].formatter = NumeralTickFormatter(format="0,0")


# Format Legend 
p.legend.click_policy="mute"
p.legend.title = 'County'
# labelcolor='linecolor'
# p.legend.label_text_color = "blue"

# Hide gridlines
#p.xgrid.grid_line_color = None
#p.ygrid.grid_line_color = None


#p.yaxis.ticker = [1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000]
p.yaxis.major_label_overrides = {50000: '50K', 100000: '100K', 150000: '150K', 
                                200000: '200K', 250000: '250K', 300000: '300K'}

# Add hover text
TOOLTIPS = [('Year', "@x"),
            ('Acres Burned', '@y{0,0}')] 

# p.add_tools(HoverTool(tooltips="Month: @x,  y: @y", renderers=[l1,l2], mode="vline"))
p.add_tools(HoverTool(tooltips=TOOLTIPS, 
                      renderers=[l1,l2,l3,l4,l5, l6]
                      ))

show(p)

Average Chocolate Ratings Bar Graph

This section shows how to create a bar graph displaying the average chocolate rating by country.

Dataset Used: https://www.kaggle.com/datasets/andrewmvd/chocolate-ratings

Highlights / Features Implemented:

- Formatted Titles & Axes 
- Legend outside frame/figure 
- Gridline Removal 
- Hover Tool / Tooltip

# Import Dataset
cacao = pd.read_csv('flavors_of_cacao.csv')

# View dataset
cacao.head()

	Company \n(Maker-if known)	Specific Bean Origin\nor Bar Name	REF	Review\nDate	Cocoa\nPercent	Company\nLocation	Rating	Bean\nType	Broad Bean\nOrigin
0	A. Morin	Agua Grande	1876	2016	63%	France	3.75		Sao Tome
1	A. Morin	Kpime	1676	2015	70%	France	2.75		Togo
2	A. Morin	Atsane	1676	2015	70%	France	3.00		Togo
3	A. Morin	Akata	1680	2015	70%	France	3.50		Togo
4	A. Morin	Quilla	1704	2015	70%	France	3.50		Peru

# Subset dataset where Nationality is England, French, Spanish or German. and include Age column. 
ques1_ds = cacao[['Company\nLocation', 'Rating']]
ques1_ds = ques1_ds.loc[ques1_ds['Company\nLocation'].isin(['Switzerland', 'U.S.A.', 'U.K.', 'Germany', 'Belgium'])]

# groupby nationality 
ques1_ds = pd.DataFrame(ques1_ds.groupby('Company\nLocation')['Rating'].mean().round(decimals=2))

# reset index 
ques1_ds.reset_index(inplace=True)
ques1_ds = ques1_ds.rename(columns = {'index':'Company Location'})

# Plot bar graph
from bokeh.palettes import YlOrBr5

ques1_ds.sort_values('Rating', inplace=True, ascending=False)

types = ques1_ds['Company\nLocation']
values = ques1_ds['Rating']

data=dict(types=types, values=values, color=YlOrBr5)

p = figure(x_range=types,  plot_width = 700, plot_height = 400, 
           title='Average Chocolate Rating by Country', 
           x_axis_label="Country", y_axis_label="Average Rating")

# Modify Legend
p.add_layout(Legend(), 'right')
p.legend.title = "Country"

p.vbar(x='types', top='values', width=0.9, color='color', legend="types", source=data)

# Modify Chart Title 
p.title.align = "center"
p.title.text_color = "black"
p.title.text_font_size = "20px"

# Modify X & Y Axes 
p.axis.axis_label_text_font_style='bold'
p.xaxis.axis_label_text_font_size = '12pt'
p.yaxis.axis_label_text_font_size = '12pt'
p.y_range=Range1d(0, 4)

# Hide gridlines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None


# Add hover text 
p.add_tools(HoverTool(tooltips=[("Country", "@types"), ("Average Rating", "@values")]))


show(p)

Relationship Between Student Math and Reading Scores Scatterplot

This section shows how to create a bar graph displaying student math and reading scores by race.

Dataset Used: https://www.kaggle.com/datasets/whenamancodes/students-performance-in-exams

Highlights / Features Implemented:

- Formatted Titles & Axes 
- Gridline Removal 
- Hover Tool / Tooltip
- Interactive Legend (for scatterplot)

exams = pd.read_csv('exams.csv')

exams = exams.rename(columns={'math score': 'math_score', 'reading score': 'reading_score', 
                              'writing score': 'writing_score','test preparation course':'test_prep_course',
                             'race/ethnicity':'race'})

exams.head()

	gender	race	parental level of education	lunch	test_prep_course	math_score	reading_score	writing_score
0	male	group A	high school	standard	completed	67	67	63
1	female	group D	some high school	free/reduced	none	40	59	55
2	male	group E	some college	free/reduced	none	59	60	50
3	male	group B	high school	standard	none	77	78	68
4	male	group E	associate's degree	standard	completed	78	73	68

exams_GA = exams[exams.race == 'group A']
exams_GB = exams[exams.race == 'group B']
exams_GC = exams[exams.race == 'group C']
exams_GD = exams[exams.race == 'group D']
exams_GE = exams[exams.race == 'group E']

# Create scatterplot 
TOOLS = "pan, wheel_zoom, box_zoom, box_select,reset, save" # the tools you want to add to your graph


# Create a blank figure with necessary arguments
r = figure(plot_width=600, plot_height=500,
           title="Relationship Between Student Math and Reading Scores by Race", 
           tools = TOOLS)



r.circle('math_score','reading_score',source=exams_GA,fill_alpha=0.4, size=7, color = 'royalblue', legend_label='group A',
         muted_color='gray', muted_alpha=0.2)
r.circle('math_score','reading_score',source=exams_GB,fill_alpha=0.4, size=7, color = 'orange', legend_label='group B',
         muted_color='gray', muted_alpha=0.2)
r.circle('math_score','reading_score',source=exams_GC,fill_alpha=0.4, size=7, color = 'hotpink',legend_label='group C',
         muted_color='gray', muted_alpha=0.2)
r.circle('math_score','reading_score',source=exams_GD,fill_alpha=0.4, size=7, color = 'mediumpurple',legend_label='group D',
         muted_color='gray', muted_alpha=0.2)
r.circle('math_score','reading_score',source=exams_GE,fill_alpha=0.4, size=7, color = 'gold',legend_label='group E',
         muted_color='gray', muted_alpha=0.2)

# Examples of other shape alternatives
#r.triangle('math_score','reading_score',source=exams_GE,fill_alpha=0.4, size=7, color = 'gold',legend_label='group E',
#         muted_color='gray', muted_alpha=0.2)
#r.square('math_score','reading_score',source=exams_GE,fill_alpha=0.4, size=7, color = 'gold',legend_label='group E',
#         muted_color='gray', muted_alpha=0.2)

#Adjust title and axis fonts
r.title.text_font_size = '12pt'
r.title.align = 'center'


# Modify X & Y Axes 
r.axis.axis_label_text_font_style='bold'
r.xaxis.axis_label_text_font_size = "12pt"
r.yaxis.axis_label_text_font_size = "12pt"
r.xaxis.axis_label = 'Math Score'
r.yaxis.axis_label = 'Reading Score'

# Add legend 
r.legend.location = "bottom_right"
r.legend.title="Race"
r.legend.label_text_color = "Black"


r.legend.click_policy="mute"

# Hide gridlines
r.xgrid.grid_line_color = None
r.ygrid.grid_line_color = None


# Add Tooltip 
hover = HoverTool()
hover.tooltips=[
    ('Race/Ethnicity', "@race"),
    ('Math Score', "@math_score"),
    ('Reading Score', "@reading_score")
]

r.add_tools(hover)

# Show the figure
show(r) 

# Create scatterplot

x = exams["math_score"]
y = exams["reading_score"]
TOOLS = "pan, wheel_zoom, box_zoom, box_select,reset, save" # the tools you want to add to your graph


# Create a blank figure with necessary arguments
r = figure(plot_width=600, plot_height=500,
           title="Relationship Between Student Math and Reading Scores by Race", 
           tools = TOOLS)


RACE = ['group A', 'group B', 'group C', 'group D', 'group E']
MARKERS = ['triangle', 'circle', 'square', 'plus', 'hex']


# Call a glyph method as needed on the figure
r.scatter("math_score", "reading_score", source=exams, size=5, fill_alpha=0.5, muted_color='gray', muted_alpha=0.2,
         marker=factor_mark('race', MARKERS, RACE),
         color=factor_cmap('race', 'Colorblind5', RACE),
         legend_field="race"
        ) 

#Adjust title and axis fonts
r.title.text_font_size = '12pt'
r.title.align = 'center'


# Modify X & Y Axes 
r.axis.axis_label_text_font_style='bold'
r.xaxis.axis_label_text_font_size = "12pt"
r.yaxis.axis_label_text_font_size = "12pt"
r.xaxis.axis_label = 'Math Score'
r.yaxis.axis_label = 'Reading Score'

# Add legend 
r.legend.title="Race"
r.legend.label_text_color = "Black"
r.legend.location = "bottom_right"

r.legend.click_policy="mute"

# Hide gridlines
r.xgrid.grid_line_color = None
r.ygrid.grid_line_color = None


# Add Tooltip 
hover = HoverTool()
hover.tooltips=[
    ('Race/Ethnicity', "@race"),
    ('Math Score', "@math_score"),
    ('Reading Score', "@reading_score")
]

r.add_tools(hover)


# Show the figure
show(r) 

# Create scatterplot 
TOOLS = "pan, wheel_zoom, box_zoom, box_select,reset, save" # the tools you want to add to your graph

# Create a blank figure with necessary arguments
p = figure(plot_width=600, plot_height=500,
           title="Relationship Between Student Math and Reading Scores by Gender", 
           tools = TOOLS)

GENDER = ['female', 'male']
MARKERS = ['triangle', 'circle']

# Call a glyph method as needed on the figure
p.scatter("math_score", "reading_score", source=exams, size=5, fill_alpha=0.5,
         marker=factor_mark('gender', MARKERS, GENDER),
         color=factor_cmap('gender', 'Colorblind4', GENDER),
         legend_field="gender"
        ) 

#Adjust title and axis fonts
p.title.text_font_size = '12pt'
p.title.align = 'center'


# Modify X & Y Axes 
p.axis.axis_label_text_font_style='bold'
p.xaxis.axis_label_text_font_size = "12pt"
p.yaxis.axis_label_text_font_size = "12pt"
p.xaxis.axis_label = 'Math Score'
p.yaxis.axis_label = 'Reading Score'

# Add legend 
p.legend.title="Gender"
p.legend.label_text_color = "Black"
p.legend.location = "bottom_right"

# Hide gridlines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None


# Add Tooltip 
hover = HoverTool()
hover.tooltips=[
    ('Gender', "@gender"),
    ('Math Score', "@math_score"),
    ('Reading Score', "@reading_score")
]

p.add_tools(hover)

# Show the figure
show(p) 

# Create tab panel for scatterplot visualizations

# Create the two panels 

tab1 = Panel(child = p, title = 'Student Math and Reading Scores by Gender')
tab2 = Panel(child = r, title = 'Student Math and Reading Scores by Race')
#tab3 = Panel(child = (r), title = 'Add Additional Tab') 
#tab4 = Panel(child = (b), title = 'Add Additional Tab')

# Feed the tabs into a Tabs object
tabs_object = Tabs(tabs = [tab1, tab2])

# Output the plot
# output_file('tab_layout.html')

show(tabs_object)

Average Salary by Experience Level Bar Graph

This section shows how to create a bar graph displaying the average salary by experience level for various data science professionals.

Dataset Used: https://www.kaggle.com/datasets/saurabhshahane/data-science-jobs-salaries

Highlights / Features Implemented:

- Formatted Titles & Axes 
- Gridline Removal 
- Hover Tool / Tooltip

salaries = pd.read_csv('Data Science Jobs Salaries.csv')

salaries.head()

	work_year	experience_level	employment_type	job_title	salary	salary_currency	salary_in_usd	employee_residence	remote_ratio	company_location	company_size
0	2021e	EN	FT	Data Science Consultant	54000	EUR	64369	DE	50	DE	L
1	2020	SE	FT	Data Scientist	60000	EUR	68428	GR	100	US	L
2	2021e	EX	FT	Head of Data Science	85000	USD	85000	RU	0	RU	M
3	2021e	EX	FT	Head of Data	230000	USD	230000	RU	50	RU	L
4	2021e	EN	FT	Machine Learning Engineer	125000	USD	125000	US	100	US	S

# Rename values in Experience-Level column
salaries['experience_level'] = salaries['experience_level'].replace('EN','Entry-level')
salaries['experience_level'] = salaries['experience_level'].replace('SE','Senior-level')
salaries['experience_level'] = salaries['experience_level'].replace('EX','Executive-level')
salaries['experience_level'] = salaries['experience_level'].replace('MI','Mid-level')

# Subset dataset where Nationality is England, French, Spanish or German. and include Age column. 
salaries = salaries[['experience_level', 'salary_in_usd']]
#ques1_ds = ques1_ds.loc[ques1_ds['Company\nLocation'].isin(['Switzerland', 'U.S.A.', 'U.K.', 'Germany', 'Belgium'])]

# groupby nationality 
salaries = pd.DataFrame(salaries.groupby('experience_level')['salary_in_usd'].mean().round(decimals=2))

# reset index 
salaries.reset_index(inplace=True)
salaries = salaries.rename(columns = {'index':'Experience Level'})

# View dataset
salaries

	experience_level	salary_in_usd
0	Entry-level	59753.46
1	Executive-level	226288.00
2	Mid-level	85738.14
3	Senior-level	128841.30

# Create bar graph
from bokeh.palettes import Pastel1_5

salaries.sort_values('salary_in_usd', inplace=True, ascending=False)

types = salaries['experience_level']
values = salaries['salary_in_usd']

data=dict(types=types, values=values, color=Pastel1_5)

p = figure(x_range=types,  plot_width = 700, plot_height = 400, 
           title='Average Salary by Experience Level', 
           x_axis_label="Experience Level", y_axis_label="Average Salary (in USD)")

# Format legend
p.add_layout(Legend(), 'right')
p.legend.title = "Experience Level"

p.vbar(x='types', top='values', width=0.9, color='color', legend="types", source=data)

# Modify Chart Title 
p.title.align = "center"
p.title.text_color = "black"
p.title.text_font_size = "20px"

# Modify X & Y Axes 
p.axis.axis_label_text_font_style='bold'
p.xaxis.axis_label_text_font_size = '12pt'
p.yaxis.axis_label_text_font_size = '12pt'
p.y_range=Range1d(0, 250000)

# Hide gridlines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None


# Add hover text 
p.add_tools(HoverTool(tooltips=[("Experience Level", "@types"), 
                                ("Average Salary (USD)", "$@values{0,0}")]))

# Remove scientific notation
p.yaxis[0].formatter = NumeralTickFormatter(format="0,0")

# Format Y Axis Labels 
p.yaxis.ticker = [0, 50000, 100000, 150000, 200000, 250000]
p.yaxis.major_label_overrides = {0: '0K', 50000: '50K', 100000: '100K', 150000: '150K', 
                                200000: '200K', 250000: '250K'}


# Show figure 
show(p)

Color Palettes

Highlights:

- View available color palettes
- Print colors in color palette

# View names of color palettes available  
bokeh.palettes.all_palettes.keys()

dict_keys(['YlGn', 'YlGnBu', 'GnBu', 'BuGn', 'PuBuGn', 'PuBu', 'BuPu', 'RdPu', 'PuRd', 'OrRd', 'YlOrRd', 'YlOrBr', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'Greys', 'PuOr', 'BrBG', 'PRGn', 'PiYG', 'RdBu', 'RdGy', 'RdYlBu', 'Spectral', 'RdYlGn', 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'Category10', 'Category20', 'Category20b', 'Category20c', 'Colorblind', 'Magma', 'Inferno', 'Plasma', 'Viridis', 'Cividis', 'Turbo', 'Bokeh'])

# View all color palettes available
# bokeh.palettes.all_palettes

# View available sizes for a specific color palette 
bokeh.palettes.all_palettes['Set3']

{3: ('#8dd3c7', '#ffffb3', '#bebada'),
 4: ('#8dd3c7', '#ffffb3', '#bebada', '#fb8072'),
 5: ('#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3'),
 6: ('#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3', '#fdb462'),
 7: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69'),
 8: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69',
  '#fccde5'),
 9: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69',
  '#fccde5',
  '#d9d9d9'),
 10: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69',
  '#fccde5',
  '#d9d9d9',
  '#bc80bd'),
 11: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69',
  '#fccde5',
  '#d9d9d9',
  '#bc80bd',
  '#ccebc5'),
 12: ('#8dd3c7',
  '#ffffb3',
  '#bebada',
  '#fb8072',
  '#80b1d3',
  '#fdb462',
  '#b3de69',
  '#fccde5',
  '#d9d9d9',
  '#bc80bd',
  '#ccebc5',
  '#ffed6f')}

# Import Specific Color Palette - Specific Size 
from bokeh.palettes import Set3_3
from bokeh.palettes import Spectral6

# Import Color Palette - All Sizes 
from bokeh.palettes import cividis

# View HEX colors in a color palette - ex for cividis palette, size 6
cividis(6)

# View HEX colors in a color palette - ex for cividis palette, size 3
cividis(3)

('#00204C', '#7B7B78', '#FFE945')

# Import Markdown & display - needed to create function to print color palette 
from IPython.display import Markdown, display

# Create function to print colors in palette 
# Option 1 - Print HEX Color & Color Block 
def printColorPalette(color_palette):
    display(Markdown('<br>'.join(
        f'<span style="color: {color}; font-family: courier;"><span>{color}: </span>&#9608;&#9608;&#9608;&#9608;&#9608;&#9608;&#9608;</span>'
        for color in color_palette
    )))

printColorPalette(cividis(6))

#00204C: ███████</span>
#31446B: ███████</span>
#666870: ███████</span>
#958F78: ███████</span>
#CAB969: ███████</span>
#FFE945: ███████</span>

printColorPalette(Set3_3)

#8dd3c7: ███████</span>
#ffffb3: ███████</span>
#bebada: ███████</span>

printColorPalette(Spectral6)

#3288bd: ███████</span>
#99d594: ███████</span>
#e6f598: ███████</span>
#fee08b: ███████</span>
#fc8d59: ███████</span>
#d53e4f: ███████</span>

# Create function to print colors in palette 

# Option 2 - Print Color Blocks Only 
def printColorPalette2(color_palette):
    display(Markdown(''.join(
        f'<span style="color: {color}">&#9608;&#9608;&#9608;</span>'
        for color in color_palette
    )))

printColorPalette2(cividis(6))

██████████████████