def donutchart(*args, **kwargs):
#get info from submitted data
data = kwargs.get('data', 'None')
ids = kwargs.get('ids', 'None')
vals = kwargs.get('vals', 'None')
val_name = kwargs.get('val_name', 'None')
v_name = kwargs.get('v_name', 'None')
out_file = kwargs.get('out_file', 'None')
if vals or data or ids or val_name == 'None':
return "Data must be submitted"
df = df_from_json(data)
df = df.sort("total", ascending=False)
df = pd.melt(df, id_vars=[ids],
value_vars=[vals],
value_name=val_name,
var_name=v_name)
d = Donut(df, label=[ids, v_name],
values=v_name,
text_font_size='8pt',
hover_text='vals')
output_file(out_file)
save(d)
def donutLsi(data):
TOOLS = 'box_zoom,hover,crosshair,resize,reset'
dat = {'object': 'list'} # {'type': 'old', 'contr': -3.5}, {'type': 'cold', 'contr': -4.45}
dat['data'] = data
df = df_from_json(dat)
TITLE = "LSI Topic #"+ str(data[0]['nr'])
d = Donut(df, label='type', text_font_size='10pt', hover_text='Test', values='contr', toolbar_location="right", tools=TOOLS, title=TITLE)
return d
def load_project_funding():
project_funding = df_from_json(json_file_path)
# cleanup column names
cols = project_funding.columns
flat_cols = [col.split('.')[1] if '.' in col else col for col in cols]
project_funding.columns = flat_cols
# convert to dates
project_funding['client_time'] = pd.to_datetime(project_funding['client_time'], unit='s')
return project_funding
def load_project_funding():
project_funding = df_from_json(json_file_path)
# cleanup column names
cols = project_funding.columns
flat_cols = [col.split('.')[1] if '.' in col else col for col in cols]
project_funding.columns = flat_cols
# convert to dates
project_funding['client_time'] = pd.to_datetime(
project_funding['client_time'], unit='s')
return project_funding
from bokeh.charts import Donut, show, output_file
from bokeh.charts.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("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)
<nav class="w3-sidebar w3-bar-block w3-animate-left w3-grey" style="display:none", id="my_sidebar">
<button class="w3-bar-item w3-button w3-xlarge" onclick="w3_close()">Close ×</button>
<a href="#" class="w3-bar-item w3-button w3-padding-large w3-grey">
<i class="fa fa-home w3-xxlarge">
</i>
from bokeh.charts import Donut, show, output_file
from bokeh.charts.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("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")
show(d)
ylabel="Horsepower",
legend='top_right',
tooltips=[('origin', "@origin")])
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')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('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(
scatter2 = Scatter(
df, x='mpg', y='hp', color='cyl', title="x='mpg', y='hp', color='cyl'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
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=[('origin', "@origin")])
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')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('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")
wedge = {
'data': [{
'0': 1,
'1': 1,
'2': 1,
'3': 1,
'4': 1,
'5': 1,
'6': 1,
'7': 1,
'8': 1,
'9': 1
}]
}
df = df_from_json(wedge)
df = pd.melt(df,
value_vars=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'],
value_name='number',
var_name='spectrograph')
wedge_b = Donut(df,
plot_height=220,
plot_width=220,
color=source.data['color'])
wedge_r = Donut(df,
plot_height=220,
plot_width=220,
color=source.data['color'])
wedge_z = Donut(df,
plot_height=220,