Get imports and set everything up to be working offline.
In [251]:
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
Now set up everything so that the figures show up in the notebook:
In [252]:
init_notebook_mode(connected=True)
More info on other options for Offline Plotly usage can be found here.
Plotly's mapping can be a bit hard to get used to at first, remember to reference the cheat sheet in the data visualization folder, or find it online here.
In [253]:
import pandas as pd
Now we need to begin to build our data dictionary. Easiest way to do this is to use the dict() function of the general form:
Either a predefined string:
'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'
or create a custom colorscale
Here is a simple example:
In [254]:
data = dict(type = 'choropleth',
locations = ['AZ','CA','NY'],
locationmode = 'USA-states',
colorscale= 'Portland',
text= ['text1','text2','text3'],
z=[1.0,2.0,3.0],
colorbar = {'title':'Colorbar Title'})
Then we create the layout nested dictionary:
In [255]:
layout = dict(geo = {'scope':'usa'})
Then we use:
go.Figure(data = [data],layout = layout)
to set up the object that finally gets passed into iplot()
In [256]:
choromap = go.Figure(data = [data],layout = layout)
In [257]:
iplot(choromap)
In [258]:
df = pd.read_csv('2011_US_AGRI_Exports')
df.head()
Out[258]:
Now out data dictionary with some extra marker and colorbar arguments:
In [259]:
data = dict(type='choropleth',
colorscale = 'YIOrRd',
locations = df['code'],
z = df['total exports'],
locationmode = 'USA-states',
text = df['text'],
marker = dict(line = dict(color = 'rgb(255,255,255)',width = 2)),
colorbar = {'title':"Millions USD"}
)
And our layout dictionary with some more arguments:
In [260]:
layout = dict(title = '2011 US Agriculture Exports by State',
geo = dict(scope='usa',
showlakes = True,
lakecolor = 'rgb(85,173,240)')
)
In [261]:
choromap = go.Figure(data = [data],layout = layout)
In [262]:
iplot(choromap)
In [263]:
df = pd.read_csv('2014_World_GDP')
df.head()
Out[263]:
In [264]:
data = dict(
type = 'choropleth',
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorbar = {'title' : 'GDP Billions US'},
)
In [265]:
layout = dict(
title = '2014 Global GDP',
geo = dict(
showframe = False,
projection = {'type':'Mercator'}
)
)
In [266]:
choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)