def plot_cancer_map(cancer_type):
trc = []
# create layout
layout = dict(
title = ‘U.S {} Cancer’.format(cancer_type),
autosize = False,
width = 1000,
height = 900,
hovermode = ‘closest’,
legend=dict(orientation=“v”),
geo = dict(x=0.7, y=-0.1,bgcolor=“rgba(255, 255, 255, 0)”,font = dict( size=11 ))
)
# select relevant data
years = cancer_group_data.Year.unique()
for i in range(len(years)):
geo_key = 'geo'+str(i+1) if i != 0 else 'geo'
temp_df = cancer_group_data[(cancer_group_data['Year'] == 1999+i) &
(cancer_group_data['Leading Cancer Sites'] == cancer_type)]
# create data
trc.append(
dict(type = 'choropleth',
z= temp_df.Count,
locations = temp_df.StatesAbb,
locationmode = 'USA-states',
colorscale = 'Jet',
geo = geo_key,
#name = years[i],
hoverinfo = 'text+z',
text = temp_df['States'],
colorbar=dict(title='Rate Per 100,000')
)
)
layout[geo_key] = dict(
scope = 'usa',
showland = True,
landcolor = 'rgb(229, 229, 229)',
showcountries = False,
domain = dict( x = [], y = [] ),
subunitcolor = "rgb(255, 255, 255)",
)
z = 0
COLS = 4
ROWS = 4
for y in reversed(range(ROWS)):
for x in range(COLS):
geo_key = 'geo'+str(z+1) if z != 0 else 'geo'
layout[geo_key]['domain']['x'] = [float(x)/float(COLS), float(x+1)/float(COLS)]
layout[geo_key]['domain']['y'] = [float(y)/float(ROWS), float(y+1)/float(ROWS)]
z = z + 1
# create figure
fig = { 'data':trc, 'layout':layout }
# plot in jupyter notebook
iplot(fig)
