def scatterplot(df):
s1 = Scatter(df,
x='weight',
y='height',
color='species',
marker='species',
title='Fish Data',
legend='top_right')
s2 = Scatter(df,
x='weight',
y='width',
color='species',
marker='species',
title='Fish Data',
legend='top_right')
s3 = Scatter(df,
x='weight',
y='length1',
color='species',
marker='species',
title='Fish Data',
legend='top_right')
s4 = Scatter(df,
x='width',
y='height',
color='species',
marker='species',
title='Fish Data',
legend='top_right')
layout = gridplot([s1, s2], [s3, s4])
output_file('fishdataset.html')
show(layout)
def heatmap3(cs3):
from bkcharts import HeatMap, show, output_file
from bokeh.palettes import RdGy11 as palette # @UnresolvedImport
from bokeh.models import HoverTool
from bokeh.models import CrosshairTool
crosshair = CrosshairTool()
hover = HoverTool(tooltips=[
("index", "$index"),
("(x,y)", "(@x,@y)"),
("score", "@values")
])
print (type(cs3))
score = []
for x in c3.apply(tuple):
score.extend(x)
data = {
'utterance1': list(cs3.index) * len(cs3.columns),
'utterance2': [item for item in list(cs3.columns) for i in range(len(c3.index))],
'score': score,
}
output_file('utterance_heatmap.html')
hm = HeatMap(data, x='utterance1', y='utterance2', values='score', title='Cosine Similarity', tools=[hover,crosshair], stat=None, palette=palette)
#show(hm)
return hm
def get_plot_comp(df, player_name):
values = blend(
'pass', 'shoot', 'turnover',
name='action probability',
labels_name='play_action'
)
labels = cat(columns='region', sort=False)
stacks = cat(columns='play_action', sort=False)
colors = color(
columns='play_action',
palette=['DeepSkyBlue', 'LightGreen', 'LightPink'],
sort=False
)
title = player_name + ' Action Probability per Region'
hover_info = [('action', '@play_action'), ('prob', '@height')]
defaults.width = 450
defaults.height = 450
bar = Bar(
df,
values=values,
label=labels,
stack=stacks,
color=colors,
legend='bottom_left',
title=title,
tooltips=hover_info
)
bar.title.text_font_size = '14pt'
bar.legend.orientation = 'horizontal'
script, div = components(bar)
output_file("test_bar.html")
return script, div, bar
def boxplot(df):
p1 = BoxPlot(df,
values='mpg',
color='origin',
label='origin',
title='AutoInfo by region',
legend='top_right')
p2 = BoxPlot(df,
values='hp',
color='origin',
label='origin',
title='AutoInfo by region',
legend='top_right')
p3 = BoxPlot(df,
values='accel',
color='origin',
label='origin',
title='AutoInfo by region',
legend='top_right')
p4 = BoxPlot(df,
values='weight',
color='origin',
label='origin',
title='AutoInfo by region',
legend='top_right')
layout = gridplot([p1, p2], [p3, p4])
output_file('autodataset.html')
show(layout)
def histograms(df):
p = Histogram(df,
'sepal length',
color='class',
label='class',
legend='top_right')
output_file('irisdataset.html')
show(p)
iphover.mode = 'mouse'
iphover.line_policy = 'interp'
tphover = point_line.select(HoverTool)
tphover.mode = 'mouse'
shover = scatter.select(HoverTool)
shover.mode = 'vline'
shoverp = scatter_point.select(HoverTool)
shoverp.mode = 'mouse'
# set up tooltips
int_vhover.tooltips = int_hhover.tooltips = TOOLTIPS
tphover.tooltips = iphover.tooltips = TOOLTIPS
shover.tooltips = shoverp.tooltips = TOOLTIPS
vhover.tooltips = hhover.tooltips = TOOLTIPS
output_file("hover_span.html", title="hover_span.py example")
show(
gridplot(hline,
vline,
int_hline,
int_vline,
int_point_line,
point_line,
scatter_point,
scatter,
ncols=2))
from bkcharts import Area, show, output_file, defaults
from bokeh.layouts import row
defaults.width = 400
defaults.height = 400
# create some example data
data = dict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
area1 = Area(data,
title="Area Chart",
legend="top_left",
xlabel='time',
ylabel='memory')
area2 = Area(data,
title="Stacked Area Chart",
legend="top_left",
stack=True,
xlabel='time',
ylabel='memory')
output_file("area.html", title="area.py example")
show(row(area1, area2))
import pandas as pd
from bkcharts import Horizon, output_file, show
# read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
MSFT = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
IBM = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
data = pd.DataFrame(data=dict(AAPL=AAPL['Adj Close'],
MSFT=MSFT['Adj Close'],
IBM=IBM['Adj Close'],
Date=AAPL['Date'])).set_index('Date')
hp = Horizon(data, plot_width=800, plot_height=300,
title="horizon plot using stock inputs", xlabel='Date')
output_file("horizon.html", title="horizon.py example")
show(hp)
from bkcharts import BoxPlot, output_file, show
from bokeh.sampledata.autompg import autompg as df
# origin = the source of the data that makes up the autompg dataset
title = "MPG by Cylinders and Data Source, Colored by Cylinders"
# color by one dimension and label by two dimensions
# coloring by one of the columns visually groups them together
box_plot = BoxPlot(df,
label=['cyl', 'origin'],
values='mpg',
color='cyl',
title=title)
output_file("boxplot_single.html", title="boxplot_single.py example")
show(box_plot)
dot_plot8 = Dot(df,
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
color='origin',
legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
# infer labels from index
df = df.set_index('cyl')
dot_plot9 = Dot(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
output_file("dots_multi.html", title="dots_multi.py example")
show(
gridplot(dot_plot,
dot_plot2,
dot_plot3,
dot_plot4,
dot_plot5,
dot_plot6,
dot_plot7,
dot_plot8,
dot_plot9,
ncols=2))
df['date'] = pd.date_range('1/1/2015', periods=len(df.index), freq='D')
# default behavior for dataframe input is to plot each numerical column as a line
line = Line(df)
# build the line plots
line0 = Line(df, y=['python', 'pypy', 'jython'],
title="Interpreters (y=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line1 = Line(df, x='date', y=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line2 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, dash=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line3 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
color=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, dash, color=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line4 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash='test',
color=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, color=['python', 'pypy', 'jython'], dash='test') with tooltips", ylabel='Duration',
legend=True, tooltips=[('series', '@series'), ('test', '@test')])
output_file("line_multi.html", title="line examples")
show(gridplot(line, line0, line1, line2, line3, line4, ncols=2))
from bkcharts import Histogram, output_file, show
from bokeh.sampledata.autompg import autompg as df
df.sort_values(by='cyl', inplace=True)
hist = Histogram(df, values='hp', color='cyl',
title="HP Distribution by Cylinder Count", legend='top_right')
output_file("histogram_single.html", title="histogram_single.py example")
show(hist)
#pip install bokeh=0.12.6
#pip intsall bkcharts
#Bokeh User Guide https://bokeh.pydata.org/en/latest/docs/user_guide.html
from bkcharts import Scatter, output_file, show
import pandas
df = pandas.DataFrame(columns=["X","Y"])
df["X"]=[1,3,5,7,9]
df["Y"]=[2,4,6,8,0]
p = Scatter(df, x="X",y="Y", title="Scatter Test", xlabel="Odd Numbers", ylabel="Even Numbers")
# tools="" this will remove tool bar from the graph
# tools = "pan, resize" this will show only the listed tools
# logo = None this will remove the bokeh logo as well
output_file("Scatter_Example.html", mode="relative")
# default mode is cdn, we can pass mode ="relative"/"absolute"/"inline"
#cdn is content delivery network, the js and css file are donwloaded from intreenet, we can store them locally with relative or absolute.
#inline: javascript file and stylesheet will load in line in the html file
show(p)
import pandas as pd
from bkcharts import output_file, Chord
from bokeh.io import show
from bokeh.sampledata.les_mis import data
nodes = data['nodes']
links = data['links']
nodes_df = pd.DataFrame(nodes)
links_df = pd.DataFrame(links)
source_data = links_df.merge(nodes_df,
how='left',
left_on='source',
right_index=True)
source_data = source_data.merge(nodes_df,
how='left',
left_on='target',
right_index=True)
source_data = source_data[source_data["value"] > 5]
chord_from_df = Chord(source_data,
source="name_x",
target="name_y",
value="value")
output_file('chord_from_df.html', mode="inline")
show(chord_from_df)
from bkcharts import Bar, output_file, show
from bkcharts.attributes import cat, color
from bkcharts.operations import blend
from bkcharts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 0]
df = df.sort_values(by="total", ascending=False)
bar = Bar(df,
values=blend('bronze', 'silver', 'gold', name='medals', labels_name='medal'),
label=cat(columns='abbr', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal', palette=['SaddleBrown', 'Silver', 'Goldenrod'],
sort=False),
legend='top_right',
title="Medals per Country, Sorted by Total Medals",
tooltips=[('medal', '@medal'), ('country', '@abbr')])
output_file("stacked_bar.html", title="stacked_bar.py example")
show(bar)
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'",
legend='top_right')
bar_plot8.title.text_font_size = '9pt'
# infer labels from index
df = df.set_index('cyl')
bar_plot9 = Bar(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
bar_plot9.title.text_font_size = '10pt'
output_file("bar_multi.html", title="bar_multi.py example")
show(
gridplot(bar_plot,
bar_plot2,
bar_plot3,
bar_plot4,
bar_plot5,
bar_plot6,
bar_plot7,
bar_plot8,
bar_plot9,
ncols=2))
from bkcharts import Donut, show, output_file
from bkcharts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
import pandas as pd
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 8]
df = df.sort_values(by="total", ascending=False)
df = pd.melt(df,
id_vars=['abbr'],
value_vars=['bronze', 'silver', 'gold'],
value_name='medal_count',
var_name='medal')
# original example
d = Donut(df,
label=['abbr', 'medal'],
values='medal_count',
text_font_size='8pt',
hover_text='medal_count')
output_file("donut.html", title="donut.py example")
show(d)
from bkcharts import Histogram, defaults, show, output_file
from bokeh.layouts import gridplot
from bokeh.sampledata.autompg import autompg as df
defaults.plot_width = 400
defaults.plot_height = 350
# input options
hist = Histogram(df['mpg'], title="df['mpg']")
hist2 = Histogram(df, 'displ', title="df, 'displ'")
hist3 = Histogram(df, values='hp', title="df, values='hp'", density=True)
hist4 = Histogram(df, values='hp', color='cyl',
title="df, values='hp', color='cyl'", legend='top_right')
hist5 = Histogram(df, values='mpg', bins=50,
title="df, values='mpg', bins=50")
hist6 = Histogram(df, values='mpg', bins=[10, 15, 25, 100], tooltips=[('Bin', "@label")],
title="df, values='mpg', bins=[10, 15, 25, 100]")
output_file("histogram_multi.html", title="histogram_multi.py example")
show(gridplot(hist, hist2, hist3, hist4,
hist5, hist6, ncols=2))
from bkcharts import Dot, show, output_file
# best support is with data in a format that is table-like
data = {
'sample': ['1st', '2nd', '1st', '2nd', '1st', '2nd'],
'interpreter': ['python', 'python', 'pypy', 'pypy', 'jython', 'jython'],
'timing': [-2, 5, 12, 40, 22, 30],
}
# x-axis labels pulled from the interpreter column, stacking labels from sample column
dots = Dot(data,
values='timing',
label='interpreter',
group='sample',
agg='mean',
title="Python Interpreter Sampling",
legend='top_right',
width=600)
output_file("dots.html", title="dots.py example")
show(dots)
values='cyl',
stat='mean',
legend='top_right')
hm9 = HeatMap(fruits, y='year', x='fruit', values='fruit_count', stat=None)
hm10 = HeatMap(unempl,
x='Year',
y='Month',
values='Unemployment',
stat=None,
sort_dim={'x': False},
width=900,
plot_height=500)
output_file("heatmap.html", title="heatmap.py example")
show(
column(
gridplot(hm1,
hm2,
hm3,
hm4,
hm5,
hm6,
hm7,
hm8,
hm9,
ncols=2,
plot_width=400,
plot_height=400), hm10))
Source: https://en.wikipedia.org/wiki/History_of_United_States_postage_rates
"""
from bkcharts import Step, show, output_file
# build a dataset where multiple columns measure the same thing
data = dict(
stamp=[
.33, .33, .34, .37, .37, .37, .37, .39, .41, .42, .44, .44, .44, .45,
.46, .49, .49
],
postcard=[
.20, .20, .21, .23, .23, .23, .23, .24, .26, .27, .28, .28, .29, .32,
.33, .34, .35
],
)
# create a line chart where each column of measures receives a unique color and dash style
line = Step(data,
y=['stamp', 'postcard'],
dash=['stamp', 'postcard'],
color=['stamp', 'postcard'],
title="U.S. Postage Rates (1999-2015)",
ylabel='Rate per ounce',
legend=True)
output_file("steps.html", title="steps.py example")
show(line)
""" This example uses the Iris data to demonstrate the specification of
combined variables using chart operations.
This specific instance uses a blend, which stacks columns, and renames
the combined column. This can be used where the column itself is a type
of categorical variable. Here, length and width are derived from the
petal and sepal measurements.
"""
from bkcharts import Scatter, output_file, show
from bkcharts.operations import blend
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(data,
x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'),
color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("iris_blend.html", title="iris_blend.py example")
show(scatter)
y='Adj Close',
x='Date',
title="Timeseries (Tooltips)",
tooltips=tooltips)
# step
tsstep = TimeSeries(data,
y=['IBM', 'MSFT', 'AAPL'],
legend=True,
builder_type='step',
title="Timeseries (Step)",
tools=TOOLS,
ylabel='Stock Prices',
xlabel='Date')
# point
tspoint = TimeSeries(data,
y=['IBM', 'MSFT', 'AAPL'],
legend=True,
builder_type='point',
marker=['IBM', 'MSFT', 'AAPL'],
color=['IBM', 'MSFT', 'AAPL'],
title="Timeseries (Point)",
tools=TOOLS,
ylabel='Stock Prices',
xlabel='Date')
output_file("timeseries.html", title="timeseries.py example")
show(column(tsline, tsline2, tsline3, tsstep, tspoint))
from bkcharts import Line, show, output_file
# build a dataset where multiple columns measure the same thing
data = dict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
test=[
'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'bar'
])
# create a line chart where each column of measures receives a unique color and dash style
line = Line(data,
y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
color=['python', 'pypy', 'jython'],
legend_sort_field='color',
legend_sort_direction='ascending',
title="Interpreter Sample Data",
ylabel='Duration',
legend=True)
output_file("line_single.html", title="line_single.py example")
show(line)
from bkcharts import Scatter, output_file, show
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(data,
x='petal_length',
y='petal_width',
color='species',
marker='species',
title='Iris Dataset Color and Marker by Species',
legend=True)
output_file("iris_simple.html", title="iris_simple.py example")
show(scatter)
scatter3 = Scatter(
df, x='mpg', y='hp', color='origin', title="x='mpg', y='hp', color='origin', with tooltips",
xlabel="Miles Per Gallon", ylabel="Horsepower",
legend='top_right', tooltips=tooltips)
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right',
tooltips=tooltips)
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort_values(by='total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("scatter_multi.html", title="scatter_multi.py example")
show(gridplot(scatter0, scatter2, scatter3, scatter4,
scatter5, scatter6, ncols=2))
import numpy as np
from bkcharts import Horizon, output_file, show
x = np.linspace(0, np.pi * 4, 137)
y = (2 * np.random.normal(size=137) + x**2)
xx = np.hstack([-1 * x[::-1], x])
yy = np.hstack([-1 * y[::-1], y])
data = dict([('x', xx), ('y', yy), ('y2', yy), ('y3', yy), ('y4', yy),
('y5', yy)])
hp = Horizon(data, x='x', title="test horizon", ylabel='Random')
output_file("horizon_folds.html", title="horizon_folds.py example")
show(hp)
title="label='cyl', values='mpg', marker='cross'")
# color whisker by cylinder
box_plot7 = BoxPlot(df,
label='cyl',
values='mpg',
whisker_color='cyl',
title="label='cyl', values='mpg', whisker_color='cyl'")
# remove outliers
box_plot8 = BoxPlot(
df,
label='cyl',
values='mpg',
outliers=False,
title="label='cyl', values='mpg', outliers=False, tooltips=True",
tooltips=True)
output_file("boxplot_multi.html", title="boxplot_multi.py example")
show(
gridplot(box_plot,
box_plot2,
box_plot3,
box_plot4,
box_plot5,
box_plot6,
box_plot7,
box_plot8,
ncols=2))
