98 Choropleth Maps

Choropleth Maps¶

Offline Plotly Usage¶

Get imports and set everything up to be working offline.

In [12]:

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 [5]:

init_notebook_mode(connected=True) 

More info on other options for Offline Plotly usage can be found here.

Choropleth US Maps¶

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 [6]:

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:

• type = 'choropleth',
• locations = list of states
• locationmode = 'USA-states'
• colorscale=

Either a predefined string:

'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'

or create a custom colorscale

• text= list or array of text to display per point
• z= array of values on z axis (color of state)
• colorbar = {'title':'Colorbar Title'})

Here is a simple example:

In [13]:

d = 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':'my titile'})

Then we create the layout nested dictionary:

In [14]:

l = 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 [15]:

choromap = go.Figure(data = [d],layout = l)

In [16]:

iplot(choromap)

Real Data US Map Choropleth¶

Now let's show an example with some real data as well as some other options we can add to the dictionaries in data and layout.

In [258]:

df = pd.read_csv('2011_US_AGRI_Exports')
df.head()

Out[258]:

	code	state	category	total exports	beef	pork	poultry	dairy	fruits fresh	fruits proc	total fruits	veggies fresh	veggies proc	total veggies	corn	wheat	cotton	text
0	AL	Alabama	state	1390.63	34.4	10.6	481.0	4.06	8.0	17.1	25.11	5.5	8.9	14.33	34.9	70.0	317.61	Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...
1	AK	Alaska	state	13.31	0.2	0.1	0.0	0.19	0.0	0.0	0.00	0.6	1.0	1.56	0.0	0.0	0.00	Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...
2	AZ	Arizona	state	1463.17	71.3	17.9	0.0	105.48	19.3	41.0	60.27	147.5	239.4	386.91	7.3	48.7	423.95	Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...
3	AR	Arkansas	state	3586.02	53.2	29.4	562.9	3.53	2.2	4.7	6.88	4.4	7.1	11.45	69.5	114.5	665.44	Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...
4	CA	California	state	16472.88	228.7	11.1	225.4	929.95	2791.8	5944.6	8736.40	803.2	1303.5	2106.79	34.6	249.3	1064.95	California<br>Beef 228.7 Dairy 929.95<br>Frui...

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)

World Choropleth Map¶

Now let's see an example with a World Map:

In [263]:

df = pd.read_csv('2014_World_GDP')
df.head()

Out[263]:

	COUNTRY	GDP (BILLIONS)	CODE
0	Afghanistan	21.71	AFG
1	Albania	13.40	ALB
2	Algeria	227.80	DZA
3	American Samoa	0.75	ASM
4	Andorra	4.80	AND

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)

Great Job!¶