Below is what I came up with.
Its not pretty but it works.
Im still new to Bokeh (& Python for that matter) so if anyone wants to suggest a better way to do this, please feel free.
import pandas as pd
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start="01-01-2015",periods=5, freq='d'))
_tools_to_show = 'box_zoom,pan,save,hover,resize,reset,tap,wheel_zoom'
p = figure(width=1200, height=900, x_axis_type="datetime", tools=_tools_to_show)
# FIRST plot ALL lines (This is a hack to get it working, why can't i pass in a dataframe to multi_line?)
# It's not pretty but it works.
# what I want to do!: p.multi_line(df)
ts_list_of_list = []
for i in range(0,len(toy_df.columns)):
ts_list_of_list.append(toy_df.index.T)
vals_list_of_list = toy_df.values.T.tolist()
# Define colors because otherwise multi_line will use blue for all lines...
cols_to_use = ['Black', 'Red', 'Lime']
p.multi_line(ts_list_of_list, vals_list_of_list, line_color=cols_to_use)
# THEN put scatter one at a time on top of each one to get tool tips (HACK! lines with tooltips not yet supported by Bokeh?)
for (name, series) in toy_df.iteritems():
# need to repmat the name to be same dimension as index
name_for_display = np.tile(name, [len(toy_df.index),1])
source = ColumnDataSource({'x': toy_df.index, 'y': series.values, 'series_name': name_for_display, 'Date': toy_df.index.format()})
# trouble formating x as datestring, so pre-formating and using an extra column. It's not pretty but it works.
p.scatter('x', 'y', source = source, fill_alpha=0, line_alpha=0.3, line_color="grey")
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Series", "@series_name"), ("Date", "@Date"), ("Value", "@y{0.00%}"),]
hover.mode="mouse"
show(p)