99 Real Data US Map Choropleth(1)

In [10]:

import plotly.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

In [11]:

init_notebook_mode(connected=True) 

In [14]:

import pandas as pd

In [15]:

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'})
l = dict(geo = {'scope':'usa'})

In [16]:

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

In [5]:

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

Out[5]:

	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...

In [18]:

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"}
            ) 

In [22]:

layout = dict(title = '2011 US Agriculture Exports by State',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(85,173,240)')
             )

In [23]:

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

In [24]:

iplot(choromap)