my_first_timeseriesv4.py

import pandas as pd
from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource
from datetime import datetime
from bokeh.palettes import Spectral3 #@UnresolvedImport
from bokeh.models import BoxAnnotation

output_file('eto_operations.html')

df = pd.read_csv('thor_wwii.csv')

#filter for the European Theater of Operations
filter = df['THEATER']=='PTO'
df = df[filter]

df['MSNDATE'] = pd.to_datetime(df['MSNDATE'], format='%m/%d/%Y')
group = df.groupby(pd.Grouper(key='MSNDATE', freq='M'))['TOTAL_TONS', 'TONS_IC', 'TONS_FRAG'].sum()
group = group / 1000

source = ColumnDataSource(group)

p = figure(x_axis_type="datetime")

p.line(x='MSNDATE', y='TOTAL_TONS', line_width=2, source=source, legend='All Munitions')
p.line(x='MSNDATE', y='TONS_FRAG', line_width=2, source=source, color=Spectral3[1], legend='Fragmentation')
p.line(x='MSNDATE', y='TONS_IC', line_width=2, source=source, color=Spectral3[2], legend='Incendiary')

p.title.text = 'European Theater of Operations'

p.yaxis.axis_label = 'Kilotons of Munitions Dropped'
p.legend.location = 'top_left'


######## , let’s highlight these trends using Bokeh’s annotation features. ###########

box_left = pd.to_datetime('19-2-1945')
box_right = pd.to_datetime('15-8-1944')

box = BoxAnnotation(left=box_left, right=box_right,
                    line_width=1, line_color='black', line_dash='dashed',
                    fill_alpha=0.2, fill_color='orange')

p.add_layout(box)


show(p)