Make a vbar with stdev from dataframes for mean and stdeviation - pandas

Question

I have 2 data frames, 1 for mean and standard deviation and I am trying to make them into a bar chart in bokeh with error bars but I am stuck at how to ??groupby?? the 'Design' and Treatment'.
Basically, I am trying to get 3 bars per x-value (T0 to T2). The legend should show something like: 'mouse-yes', 'mouse-no', and 'cat-no'. How do I restructure the dict or dataframe to convert to something for vbar? And then how do I couple that with the stdev dataframe?
Also, is there a way to make the x_range in the figure automatically taking all original values from the 'Time' column? I'd like to be able to interchange the 'Time', 'Design', and 'Treatment' columns for the x-axis. I'm guessing this is where a pivot table comes in handy.
from bokeh.core.properties import value
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from bokeh.transform import dodge
output_file("dodged_bars.html")
import pandas as pd
dat_mean=[['T0','mouse','yes',25],['T0','mouse','no',24],['T0','cat','no',23],['T1','mouse','yes',15],['T1','mouse','no',14],['T1','cat','no',13],['T2','mouse','yes',5],['T2','mouse','no',4],['T2','cat','no',3]]
df_mean= pd.DataFrame(dat_mean,columns = ["Time", "Design", "Treatment", "Mean for Cmpd1"])
dat_std=[['T0','mouse','yes',5],['T0','mouse','no',5],['T0','cat','no',5],['T1','mouse','yes',2.5],['T1','mouse','no',2.5],['T1','cat','no',2.5],['T2','mouse','yes',1],['T2','mouse','no',1],['T2','cat','no',1]]
df_std= pd.DataFrame(dat_std,columns = ["Time", "Design", "Treatment", "Mean for Cmpd1"])
data = df_mean.to_dict(orient='list')*
dates = df_mean['Time'].tolist()
source = ColumnDataSource(data=data)
p = figure(x_range=['T0', 'T1', 'T2'], y_range=(0, 30), plot_height=250, title="Bokeh plot",
toolbar_location=None, tools="")
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
show(p)

Answer 1

from bokeh.core.properties import value
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from bokeh.transform import dodge
output_file("dodged_bars.html")
import pandas as pd
dat_mean=[['T0','mouse','yes',25],['T0','mouse','no',24],['T0','cat','no',23],['T1','mouse','yes',15],['T1','mouse','no',14],['T1','cat','no',13],['T2','mouse','yes',5],['T2','mouse','no',4],['T2','cat','no',3]]
df_mean= pd.DataFrame(dat_mean,columns = ["Time", "Design", "Treatment", "Mean for Cmpd1"])
dat_std=[['T0','mouse','yes',5],['T0','mouse','no',5],['T0','cat','no',5],['T1','mouse','yes',2.5],['T1','mouse','no',2.5],['T1','cat','no',2.5],['T2','mouse','yes',1],['T2','mouse','no',1],['T2','cat','no',1]]
df_std= pd.DataFrame(dat_std,columns = ["Time", "Design", "Treatment", "Mean for Cmpd1"])
time_seq=df_mean['Time'].drop_duplicates()
time_vals=time_seq.tolist()
MEANs=df_mean.groupby(["Design", "Treatment"])["Mean for Cmpd1"].apply(list).to_dict()
keys=[]
for h in range(len(MEANs)):
raw_key=list(MEANs.keys())[h]
keys.append(raw_key[0]+'_'+raw_key[1])
results = {'time_vals' : time_vals,
keys[0] : list(MEANs.values())[0],
keys[1] : list(MEANs.values())[1],
keys[2] : list(MEANs.values())[2]}
source = ColumnDataSource(data=results)
p = figure(x_range=['T0', 'T1', 'T2'], y_range=(0, 30), plot_height=250, title="Bokeh plot",
toolbar_location=None, tools="")
for hh in range(len(MEANs)):
p.vbar(x=dodge('time_vals', -0.25+.2*hh, range=p.x_range), top=keys[hh], width=0.2,
source=source,color=color[hh], legend=value(keys[hh]))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
show(p)
This code works. Errorbars can be done with add_layout and Whisker functions