Choropleth Maps

In [1]:

    

import plotly.plotly as py
import plotly.graph_objs as go


    

In [2]:

    

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot


    

In [3]:

    

init_notebook_mode(connected=True)


    

In [4]:

    

data = dict(type = 'choropleth',
           locations = ['AZ', 'CA', 'NY'],
           locationmode = 'USA-states',
           colorscale = 'Greens',
           text = ['Arizona', 'Cali', 'New York'],
           z = [1.0, 2.0, 3.0],
           colorbar = {'title': 'Colorbar Title Goes Here'})


    

In [5]:

    

data


    

    
Out[5]:





{'colorbar': {'title': 'Colorbar Title Goes Here'},
 'colorscale': 'Greens',
 'locationmode': 'USA-states',
 'locations': ['AZ', 'CA', 'NY'],
 'text': ['Arizona', 'Cali', 'New York'],
 'type': 'choropleth',
 'z': [1.0, 2.0, 3.0]}

In [6]:

    

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


    

In [7]:

    

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


    

In [8]:

    

iplot(choromap)


    

In [9]:

    

import pandas as pd
df = pd.read_csv('2011_US_AGRI_Exports')


    

In [10]:

    

df.head()


    

    
Out[10]:






  

    

      

      
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 [11]:

    

data = dict(type = 'choropleth', 
           colorscale = 'YIOrRd',
           locations = df['code'],
           locationmode = 'USA-states',
           z = df['total exports'],
           text = df['text'],
           marker = dict(line = dict(color = 'rgb(255, 255, 255)', width = 2)),
           colorbar = {'title': 'Millions USD'})


    

In [12]:

    

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


    

In [13]:

    

layout


    

    
Out[13]:





{'geo': {'lakecolor': 'rgb(85, 173, 240)', 'scope': 'usa', 'showlakes': True},
 'title': '2011 US Agriculture Exports by State'}

In [14]:

    

choromap2 = go.Figure(data = [data], layout = layout)
#iplot(choromap2)


    

In [15]:

    

df = pd.read_csv('2014_World_GDP')


    

In [16]:

    

data = dict(type='choropleth',
           locations= df['CODE'],
           z = df['GDP (BILLIONS)'],
           text = df['COUNTRY'],
           colorbar = {'title': 'GDP in Billions USD'})


    

In [17]:

    

layout = dict(title = '2014 Global GDP', geo = dict(showframe=False, projection = {'type':'Mercator'}))


    

In [18]:

    

choromap3 = go.Figure(data = [data], layout = layout)


    

In [19]:

    

iplot(choromap3)