Python / Pandas / Bokeh: plotting multiple lines with legends from dataframe

Question

I have data in a Pandas dataframe that I am trying to plot to a time series line graph.

When plotting one single line, I have been able to do this quite successfully using the p.line function, ensuring I make the x_axis_type 'datetime'.

To plot multiple lines, I have tried using p.multi_line, which worked well but I also need a legend and, according to this post, it's not possible to add a legend to a multiline: Bokeh how to add legend to figure created by multi_line method?

Leo's answer to the question in the link above looks promising, but I can't seem to work out how to apply this when the data is sourced from a dataframe.

Does anyone have any tips?

could you give a bit more information on what you're trying to do exactly? In my experience with using bokeh and pandas, I just create a figure with fig = figure(), then I just loop through whatever data I'm using, creating a line with fig.line(x=x, y=y, legend="Label for this particular line") for each line I need. Then I just break out of the loop and show the figure, which then shows a single figure with multiple lines. — semore_1267
– semore_1267, Commented Nov 26, 2016 at 7:03
Thanks for your answer. I found some code elsewhere that does something very similar. Please see my own answer below. — pottolom
– pottolom, Commented Nov 26, 2016 at 15:30

pottolom · Accepted Answer · 2016-11-26 15:29:57Z

OK, this seems to work:

from bokeh.plotting import figure, output_file, save
from bokeh.models import ColumnDataSource
import pandas as pd
from pandas import HDFStore
from bokeh.palettes import Spectral11

# imports data to dataframe from our storage hdf5 file
# our index column has no name, so this is assigned a name so it can be
# referenced to for plotting
store = pd.HDFStore('<file location>')
df = pd.DataFrame(store['d1'])
df = df.rename_axis('Time')

#the number of columns is the number of lines that we will make
numlines = len(df.columns)

#import color pallet
mypalette = Spectral11[0:numlines]

# remove unwanted columns
col_list = ['Column A', 'Column B']
df = df[col_list]

# make a list of our columns
col = []
[col.append(i) for i in df.columns]

# make the figure, 
p = figure(x_axis_type="datetime", title="<title>", width = 800, height = 450)
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = '<units>'

# loop through our columns and colours
for (columnnames, colore) in zip(col, mypalette):
    p.line(df.index, df[columnnames], legend = columnnames, color = colore )

# creates an output file 
output_file('<output location>')

#save the plot
save(p)

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