

In [1]:

    
from datascience import *

Basic Map



In [2]:

    
Map()









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In [3]:

    
Map(zoom_start=16)









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Markers



In [4]:

    
Marker(37.78, -122.42, 'San Francisco')









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In [5]:

    
m = Marker(37.78, -122.42, 'San Francisco', marker_color='green')
m.show()
Marker(37.78, -122.42, 'San Francisco').format(marker_icon='thumbs-up').show()
m



In [6]:

    
features = [
    Marker(51.5135015, -0.1358392, 'A'), 
    Marker(51.5137, -0.1358392, 'B'), 
    Marker(51.5132, -0.138, 'C'), 
    Marker(51.5143, -0.135, 'D')
]
Map(features)









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In [7]:

    
Map(features, zoom_start=15)









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In [8]:

    
points = [
    (51.5135015, -0.1358392), 
    (51.5137, -0.1358392), 
    (51.5132, -0.138), 
    (51.5143, -0.135),
]
lats, lons = zip(*points)
Marker.map(lats, lons, ['A', 'B', 'C', 'D'], marker_color='black')









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In [9]:

    
colors = ['red', 'blue', 'yellow', 'green']
Circle.map(lats, lons, colors=colors, fill_opacity=0.8, radius=20)









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In [10]:

    
names = colors
t = Table().with_columns(*zip(['lat', 'lon', 'name', 'color'], [lats, lons, names, colors]))
t.show()
Circle.map_table(t, radius=20)









    





    
        
            lat  lon  name  color
        
    
    
        
            51.5135  -0.135839  red    red   
        
    
        
            51.5137  -0.135839  blue   blue  
        
    
        
            51.5132  -0.138    yellow  yellow
        
    
        
            51.5143  -0.135    green  green 
        
    







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Regions



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states = Map.read_geojson('us-states.json')
states









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In [12]:

    
Map([states['CA'], states['NV'].format(fill_color='yellow')])









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Binding Data



In [13]:

    
unemployment = Table.read_table('us-unemployment.csv')
unemployment









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            State  Unemployment
        
    
    
        
            AL    7.1         
        
    
        
            AK    6.8         
        
    
        
            AZ    8.1         
        
    
        
            AR    7.2         
        
    
        
            CA    10.1        
        
    
        
            CO    7.7         
        
    
        
            CT    8.4         
        
    
        
            DE    7.1         
        
    
        
            FL    8.2         
        
    
        
            GA    8.8         
        
    

... (40 rows omitted)



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states.color(unemployment)









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Maps in tables



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Table().with_columns(
    'State', states.keys(),
    'Region', states.values()).show(3)









    





    
        
            State  Region
        
    
    
        
            AL    
        
    
        
            AK    
        
    
        
            AZ    
        
    

... (47 rows omitted)

lat	lon	name	color
51.5135	-0.135839	red	red
51.5137	-0.135839	blue	blue
51.5132	-0.138	yellow	yellow
51.5143	-0.135	green	green