def create_plot():
(candidatos, porcentajes) = get_plot_data()
# (candidatos, porcentajes) = json_load_unicode("databokeh.json")
primero, segundo = porcentajes[:2]
falta = min(45, max(40 , (segundo + 10))) - primero
if falta <= 0:
sobra = -falta
fserie = [sobra] + [0] * 5
porcentajes[0] -= sobra
resultado = "Gana %s en Primera Vuelta" % candidatos[0]
PALETTE = ["#54BBE2", "#5EDF4D"]
data = OrderedDict([("porcentajes", porcentajes), ("sobra", fserie)])
else:
PALETTE = ["#54BBE2", "#E5162A"]
fserie = [falta] + [0] * 5
resultado = "Hay Ballotage"
data = OrderedDict([("porcentajes", porcentajes), ("falta", fserie)])
# build a dict containing the grouped data
output_file("primervueltometro.html")
# Flyer derecho fijo : 300 x 500
# Zonas de columna auxiliar : 300 x 300 (1er y 2do rolado)
# Puente largo : 975 x 80
# Puente corto : 650 x 80
width, height = 300, 300
z = 2
width *= z
height *= z
params = {
"title": resultado,
"stacked": True,
"width": width,
"height": height,
"tools": "",
"palette": PALETTE,
"legend": 'top_right'
}
bar = Bar(data, candidatos, **params)
show(bar)
def create_plot():
candidatos, (porcentajes, falta) = get_plot_data()
if falta <= 0:
sobra = -falta
fserie = [sobra] + [0] * 5
porcentajes[0] -= sobra
resultado = "Gana %s en Primera Vuelta" % candidatos[0]
data = OrderedDict([("porcentajes", porcentajes), ("sobra", fserie)])
else:
fserie = [falta] + [0] * 5
resultado = "Hay Ballotage"
data = OrderedDict([("porcentajes", porcentajes), ("falta", fserie)])
# build a dict containing the grouped data
output_file("primervueltometro.html")
# Flyer derecho fijo : 300 x 500
# Zonas de columna auxiliar : 300 x 300 (1er y 2do rolado)
# Puente largo : 975 x 80
# Puente corto : 650 x 80
width, height = 300, 300
z = 2
width *= z
height *= z
params = {
"title": "PrimerVueltómetro: %s" % resultado,
"stacked": True,
"width": width,
"height": height,
"tools": "",
"palette": PALETTE,
"legend": 'top_right'
}
bar = Bar(data, candidatos, **params)
show(bar)
def create_plot():
candidatos, (porcentajes, falta) = get_plot_data()
if falta <= 0:
sobra = -falta
fserie = [sobra] + [0] * 5
porcentajes[0] -= sobra
resultado = "Gana %s en Primera Vuelta" % candidatos[0]
data = OrderedDict([("porcentajes", porcentajes), ("sobra", fserie)])
else:
fserie = [falta] + [0] * 5
resultado = "Hay Ballotage"
data = OrderedDict([("porcentajes", porcentajes), ("falta", fserie)])
# build a dict containing the grouped data
output_file("primervueltometro.html")
# Flyer derecho fijo : 300 x 500
# Zonas de columna auxiliar : 300 x 300 (1er y 2do rolado)
# Puente largo : 975 x 80
# Puente corto : 650 x 80
width, height = 300, 300
z = 2
width *= z
height *= z
params = {
"title": "PrimerVueltómetro: %s" % resultado,
"stacked": True,
"width": width,
"height": height,
"tools": "",
"palette": PALETTE,
"legend": 'top_right'
}
bar = Bar(data, candidatos, **params)
show(bar)
from collections import OrderedDict
from bokeh._legacy_charts import Dot, show, output_file
from bokeh.charts import Dot
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26],
pypy=[12, 33, 47, 15, 126],
jython=[22, 43, 10, 25, 26],
)
output_file("dots.html")
from bokeh.charts import Bar, output_file, show, hplot
# 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)
show(dots)
from collections import OrderedDict
from bokeh._legacy_charts import Dot, show, output_file
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26],
pypy=[12, 33, 47, 15, 126],
jython=[22, 43, 10, 25, 26],
)
# any of the following commented are also valid Dot inputs
#xyvalues = pd.DataFrame(xyvalues)
#xyvalues = list(xyvalues.values())
#xyvalues = np.array(list(xyvalues.values()))
output_file("dots.html")
dots = Dot(
xyvalues, cat=['lists','loops','dicts', 'gen exp', 'exceptions'],
title="Dots Example", ylabel='Performance', legend=True
)
show(dots)
from os.path import dirname, join
from blaze import Data
from bokeh.sampledata import iris
from bokeh._legacy_charts import Line, show, output_file
bbvalues = Data(join(dirname(iris.__file__), 'iris.csv'))
columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
result = bbvalues[columns]
output_file("blaze_input.html")
line = Line(
result, title="Line Chart",
ylabel='Petals', notebook=True, legend="top_left"
)
show(line)
import pandas as pd
from bokeh._legacy_charts import Bar, output_file, show
from bokeh.sampledata.olympics2014 import data
df = pd.io.json.json_normalize(data['data'])
# filter by countries with at least one medal and sort
df = df[df['medals.total'] > 0]
df = df.sort("medals.total", ascending=False)
# get the countries and we group the data by medal type
countries = df.abbr.values.tolist()
gold = df['medals.gold'].astype(float).values
silver = df['medals.silver'].astype(float).values
bronze = df['medals.bronze'].astype(float).values
# build a dict containing the grouped data
medals = OrderedDict(bronze=bronze, silver=silver, gold=gold)
# any of the following commented are also alid Bar inputs
#medals = pd.DataFrame(medals)
#medals = list(medals.values())
output_file("stacked_bar.html")
bar = Bar(medals, countries, title="Stacked bars", stacked=True)
show(bar)
from os.path import dirname, join
from blaze import Data
from bokeh.sampledata import iris
from bokeh._legacy_charts import Line, show, output_file
bbvalues = Data(join(dirname(iris.__file__), 'iris.csv'))
columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
result = bbvalues[columns]
output_file("blaze_input.html")
line = Line(result,
title="Line Chart",
ylabel='Petals',
notebook=True,
legend="top_left")
show(line)
from bokeh._legacy_charts import HeatMap, output_file, show
from bokeh.palettes import YlOrRd9 as palette
from bokeh.sampledata.unemployment1948 import data
# pandas magic
df = data[data.columns[:-1]]
df2 = df.set_index(df[df.columns[0]].astype(str))
df2.drop(df.columns[0], axis=1, inplace=True)
df3 = df2.transpose()
output_file("cat_heatmap.html")
palette = palette[::-1] # Reverse the color order so dark red is highest unemployment
hm = HeatMap(df3, title="categorical heatmap", width=800, palette=palette)
show(hm)
xyvalues = OrderedDict(
AAPL=AAPL['Adj Close'],
Date=AAPL['Date'],
MSFT=MSFT['Adj Close'],
IBM=IBM['Adj Close'],
)
# any of the following commented are valid Bar inputs
#xyvalues = pd.DataFrame(xyvalues)
#lindex = xyvalues.pop('Date')
#lxyvalues = list(xyvalues.values())
#lxyvalues = np.array(xyvalues.values())
TOOLS = "resize,pan,wheel_zoom,box_zoom,reset,previewsave"
output_file("stocks_timeseries.html")
ts = TimeSeries(xyvalues,
index='Date',
legend=True,
title="Timeseries",
tools=TOOLS,
ylabel='Stock Prices')
# usage with iterable index
#ts = TimeSeries(
# lxyvalues, index=lindex,
# title="timeseries, pd_input", ylabel='Stock Prices')
show(ts)
from collections import OrderedDict
import numpy as np
from bokeh._legacy_charts 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])
xyvalues = OrderedDict(x=xx, y=yy, y2=yy, y3=yy, y4=yy, y5=yy)
output_file("horizon_folds.html")
hp = Horizon(xyvalues, index='x', title="test horizon", ylabel='Random')
show(hp)
# throw the data into a pandas data frame
df = pd.io.json.json_normalize(data['data'])
# filter by countries with at least one medal and sort
df = df[df['medals.total'] > 8]
df = df.sort("medals.total", ascending=False)
# get the countries and we group the data by medal type
countries = df.abbr.values.tolist()
gold = df['medals.gold'].astype(float).values
silver = df['medals.silver'].astype(float).values
bronze = df['medals.bronze'].astype(float).values
# build a dict containing the grouped data
medals = OrderedDict()
medals['bronze'] = bronze
medals['silver'] = silver
medals['gold'] = gold
# any of the following commented are also valid Donut inputs
#medals = list(medals.values())
#medals = np.array(list(medals.values()))
#medals = pd.DataFrame(medals)
output_file("donut.html")
donut = Donut(medals, countries)
show(donut)
("PuRd9", PuRd9), ("Purples9", Purples9),
("RdBu9", RdBu9), ("RdGy9", RdGy9),
("RdPu9", RdPu9), ("RdYlBu9", RdYlBu9),
("RdYlGn9", RdYlGn9), ("Reds9", Reds9),
("Spectral9", Spectral9), ("YlGn9", YlGn9),
("YlGnBu9", YlGnBu9), ("YlOrBr9", YlOrBr9),
("YlOrRd9", YlOrRd9)])
def create_area_chart(data, palette):
_chart_styling = dict(height=300,
width=300,
xgrid=False,
ygrid=False,
tools=None)
return Area(data,
title=palette,
stacked=True,
palette=standard_palettes.get(palette),
**_chart_styling)
data = np.random.random_integers(low=5, high=13, size=[9,20])
area_charts = [create_area_chart(data, palette)
for palette
in standard_palettes.keys()]
area_charts = np.reshape(area_charts, newshape=[9,3]).tolist()
output_file('palettes.html', title='palettes.py example')
show(gridplot(area_charts))
palette=palette,
title="categorical heatmap, pd_input",
height=400,
width=1000)
hm_data = df3.values.T
hm2 = HeatMap(hm_data,
palette=palette,
title="Unemployment (Array)",
xlabel='Years since 1948',
ylabel='Months',
height=400,
width=1000)
simple_df = pd.DataFrame(
{
'apples': [4, 5, 8, 12, 4],
'pears': [6, 5, 4, 8, 7],
'bananas': [1, 2, 4, 8, 12]
},
index=['2009', '2010', '2011', '2012', '2013'])
hm3 = HeatMap(simple_df,
palette=palette,
title="Fruit comsumption per year",
height=400,
width=1000)
output_file("heatmap.html")
show(vplot(hm1, hm2, hm3))
from collections import OrderedDict
from bokeh._legacy_charts import Area, show, output_file
# create some example data
xyvalues = OrderedDict(
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],
)
output_file(filename="area.html")
area = Area(
xyvalues, title="Responsive Area Chart",
xlabel='time', ylabel='memory',
stacked=True, legend="top_left",
responsive=True
)
show(area)
from collections import OrderedDict
from bokeh._legacy_charts import Area, show, output_file
# create some example data
xyvalues = OrderedDict(
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],
)
output_file(filename="area.html")
area = Area(xyvalues,
title="Responsive Area Chart",
xlabel='time',
ylabel='memory',
stacked=True,
legend="top_left",
responsive=True)
show(area)
from bokeh._legacy_charts 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'])
xyvalues = OrderedDict(
AAPL=AAPL['Adj Close'],
Date=AAPL['Date'],
MSFT=MSFT['Adj Close'],
IBM=IBM['Adj Close'],
)
output_file("horizon.html")
hp = Horizon(
xyvalues, index='Date',
title="horizon plot using stock inputs",
width=800, height=300
)
show(hp)
from collections import OrderedDict
from bokeh._legacy_charts import Step, show, output_file
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26, 81, 44, 93, 94, 105, 66, 67, 90, 83],
pypy=[12, 20, 47, 15, 126, 121, 144, 333, 354, 225, 276, 287, 270, 230],
jython=[22, 43, 70, 75, 76, 101, 114, 123, 194, 215, 201, 227, 139, 160],
)
# any of the following commented are also valid Step inputs
#xyvalues = pd.DataFrame(xyvalues)
#xyvalues = list(xyvalues.values())
#xyvalues = np.array(list(xyvalues.values()))
output_file("steps.html", title="line.py example")
chart = Step(xyvalues, title="Steps", ylabel='measures', legend='top_left')
show(chart)
parse_dates=['Date'])
xyvalues = OrderedDict(
AAPL=AAPL['Adj Close'],
Date=AAPL['Date'],
MSFT=MSFT['Adj Close'],
IBM=IBM['Adj Close'],
)
# any of the following commented are valid Bar inputs
#xyvalues = pd.DataFrame(xyvalues)
#lindex = xyvalues.pop('Date')
#lxyvalues = list(xyvalues.values())
#lxyvalues = np.array(xyvalues.values())
TOOLS="resize,pan,wheel_zoom,box_zoom,reset,previewsave"
output_file("stocks_timeseries.html")
ts = TimeSeries(
xyvalues, index='Date', legend=True,
title="Timeseries", tools=TOOLS, ylabel='Stock Prices')
# usage with iterable index
#ts = TimeSeries(
# lxyvalues, index=lindex,
# title="timeseries, pd_input", ylabel='Stock Prices')
show(ts)
df2 = df.set_index(df[df.columns[0]].astype(str))
df2.drop(df.columns[0], axis=1, inplace=True)
df3 = df2.transpose()
# bokeh magic
hm1 = HeatMap(
df3, palette=palette,
title="categorical heatmap, pd_input",
height=400, width=1000
)
hm_data = df3.values.T
hm2 = HeatMap(
hm_data, palette=palette, title="Unemployment (Array)",
xlabel='Years since 1948', ylabel='Months', height=400, width=1000
)
simple_df = pd.DataFrame(
{'apples':[4,5,8,12,4], 'pears':[6,5,4,8,7], 'bananas':[1,2,4,8,12]},
index=['2009', '2010', '2011', '2012', '2013']
)
hm3 = HeatMap(
simple_df, palette=palette,
title="Fruit comsumption per year",
height=400, width=1000
)
output_file("heatmap.html")
show(vplot(hm1, hm2, hm3))
("RdPu9", RdPu9), ("RdYlBu9", RdYlBu9),
("RdYlGn9", RdYlGn9), ("Reds9", Reds9),
("Spectral9", Spectral9), ("YlGn9", YlGn9),
("YlGnBu9", YlGnBu9), ("YlOrBr9", YlOrBr9),
("YlOrRd9", YlOrRd9)])
def create_area_chart(data, palette):
_chart_styling = dict(height=300,
width=300,
xgrid=False,
ygrid=False,
tools=None)
return Area(data,
title=palette,
stacked=True,
palette=standard_palettes.get(palette),
**_chart_styling)
data = np.random.random_integers(low=5, high=13, size=[9, 20])
area_charts = [
create_area_chart(data, palette) for palette in standard_palettes.keys()
]
area_charts = np.reshape(area_charts, newshape=[9, 3]).tolist()
output_file('palettes.html', title='palettes.py example')
show(gridplot(area_charts))
