I want to create a combined bar and line chart with a hover tool. As I wanted to add a hover tool I initially created a figure and then tried to add the bars with vbar and the line with line_glyph. This doesnt work, as it only creates a blank white canvas.
from bokeh.charts import Bar, output_file, show
from bokeh.plotting import figure
from bokeh.models.ranges import Range1d
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.models.glyphs import Line as Line_glyph
import pandas as pd
import numpy as np
data_2015_2016=pd.DataFrame({
'year':[2015,2015,2015,2015,2015],
'volume_neutral':[420,430,440,400,np.nan],
'volume_promo':[np.nan,np.nan,np.nan,np.nan,2000],
'volume_neutral_promo': [420,430,440,400,2000],
'Promo':['No','No','No','No','Yes'],
'Product':['Lemonade','Lemonade','Lemonade','Lemonade','Lemonade'],
'yw':['2015-W01','2015-W02','2015-W03','2015-W04','2015-W05']
})
hover=HoverTool(
tooltips=[
( 'Date', '@yw' ),
( 'Volume (in kg)', '@volume_neutral_promo' ), # use @{ } for field names with spaces
( 'Promo', '@Promo' ),
( 'Product', '@Product' )
])
p = figure(plot_width=1000, plot_height=800, tools=[hover],
title="Weekly Sales 2015-2016",toolbar_location="below")
source = ColumnDataSource(data=data_2015_2016)
#Bar Chart
#This worked however I donno how to combine it with a hoover
#p.Bar = Bar(data_2015_2016, label='yw', values='volume_promo', title="Sales",legend=False,plot_width=1000, plot_height=800)
p.vbar(x='yw', width=0.5, bottom=0,top='volume_promo', color="firebrick",source=source)
# create a line glyph object which references columns from source data
glyph = Line_glyph(x='yw', y='volume_neutral', line_color='green', line_width=2)
# add the glyph to the chart
p.add_glyph(source, glyph)
p.xaxis.axis_label = "Week and Year"
# change just some things about the y-axes
p.yaxis.axis_label = "Volume"
p.yaxis.major_label_orientation = "vertical"
p.y_range = Range1d(0, max(data_2015_2016.volume_neutral_promo))
output_file("bar_line.html")
show(p)
There are a few things wrong with your code:
You've mixed up some of your column names, e.g. volume_neutral_promo
is what is actually in your data source, but instead you have the glyphs mistakenly reference volume_promo_neutral
If you want to use a categorical range with bokeh.plotting
plots, you have to explicitly tell that:
p = figure(plot_width=1000, plot_height=800, tools=[hover],
title="Weekly Sales 2015-2016",toolbar_location="below",
# this part is new
x_range=['2015-W01','2015-W02','2015-W03','2015-W04','2015-W05'])
Here is your complete code updated. I have removed the parts about bokeh.charts
as I would not recommend using that API anymore (it's basically unmaintained at this point). Also I used the simpler p.line
instead of the low level Line
glyph:
from numpy import nan
import pandas as pd
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
data_2015_2016 = pd.DataFrame({
'year': [2015, 2015, 2015, 2015, 2015],
'volume_neutral': [420, 430, 440, 400, nan],
'volume_promo': [nan, nan, nan, nan, 2000],
'volume_neutral_promo': [420, 430, 440, 400, 2000],
'Promo': ['No', 'No', 'No', 'No', 'Yes'],
'Product': ['Lemonade', 'Lemonade', 'Lemonade', 'Lemonade', 'Lemonade'],
'yw': ['2015-W01', '2015-W02', '2015-W03', '2015-W04', '2015-W05']
})
source = ColumnDataSource(data=data_2015_2016)
hover=HoverTool(tooltips=[
( 'Date', '@yw' ),
( 'Volume (in kg)', '@volume_neutral_promo' ),
( 'Promo', '@Promo' ),
( 'Product', '@Product' ),
])
p = figure(plot_width=1000, plot_height=800, tools=[hover],
title="Weekly Sales 2015-2016",toolbar_location="below",
x_range=['2015-W01', '2015-W02', '2015-W03', '2015-W04', '2015-W05'])
p.vbar(x='yw', width=0.5, bottom=0, top='volume_promo', color="firebrick", source=source)
p.line(x='yw', y='volume_neutral', line_color='green', line_width=2, source=source)
p.xaxis.axis_label = "Week and Year"
p.yaxis.axis_label = "Volume"
p.yaxis.major_label_orientation = "vertical"
p.y_range.start = 0
p.y_range.range_padding = 0
output_file("bar_line.html")
show(p)
This results in the following plot with hover tool: