In [1]:

    

import numpy as np


    

In [2]:

    

import pandas as pd
pd.__version__


    

    
Out[2]:





u'0.17.1'

In [3]:

    

url = "https://en.wikipedia.org/wiki/List_of_national_parks_of_the_United_States"
df = pd.read_html(url, header=0)[0]
df.head()


    

    
Out[3]:






  

    

      

      
Name
      
Photo
      
Location
      
Date established[2][4]
      
Area[2]
      
Recreation Visitors (2014)[5]
      
Description
    
  
  

    

      
0
      
Acadia
      
NaN
      
Maine 44°21′N 68°13′W / 44.35°N 68.21°W / 4...
      
000000001919-02-26-0000February 26, 1919
      
7008191779190394916♠47,389.67 acres (191.8 km2)
      
2563129
      
Covering most of Mount Desert Island and other...
    
    

      
1
      
American Samoa
      
NaN
      
American Samoa 14°15′S 170°41′W / 14.25°S 17...
      
000000001988-10-31-0000October 31, 1988
      
7007364217078016000♠9,000.00 acres (36.4 km2)
      
13953
      
The southernmost national park is on three Sam...
    
    

      
2
      
Arches
      
NaN
      
Utah 38°41′N 109°34′W / 38.68°N 109.57°W / ...
      
000000001929-04-12-0000April 12, 1929
      
7008309661325648497♠76,518.98 acres (309.7 km2)
      
1284767
      
This site features more than 2,000 natural san...
    
    

      
3
      
Badlands
      
NaN
      
South Dakota 43°45′N 102°30′W / 43.75°N 102....
      
000000001978-11-10-0000November 10, 1978
      
7008982398434864749♠242,755.94 acres (982.4 km2)
      
868094
      
The Badlands are a collection of buttes, pinna...
    
    

      
4
      
Big Bend
      
NaN
      
Texas 29°15′N 103°15′W / 29.25°N 103.25°W /...
      
000000001944-06-12-0000June 12, 1944
      
7009324219248177910♠801,163.21 acres (3,242.2 ...
      
314102
      
Named for the prominent bend in the Rio Grande...
    
  

In [4]:

    

locations = df['Location'].str.extract(u'(\D+) (\d+°\d+′[NS]) (\d+°\d+′[WE]).*')
locations.columns = ['State', 'lat', 'lon']

locations['lat'] = locations['lat'].str.replace(u'°', '.')
locations['lon'] = locations['lon'].str.replace(u'°', '.')

locations.loc[locations['lat'].str.endswith('S'), 'lat'] = '-' + locations['lat']
locations.loc[locations['lon'].str.endswith('W'), 'lon'] = '-' + locations['lon']

locations['lat'] = locations['lat'].str.slice_replace(start=-2)
locations['lon'] = locations['lon'].str.slice_replace(start=-2)
locations[['lat', 'lon']] = locations[['lat', 'lon']].astype(float)

locations.head()


    

    
Out[4]:






  

    

      

      
State
      
lat
      
lon
    
  
  

    

      
0
      
Maine
      
44.21
      
-68.13
    
    

      
1
      
American Samoa
      
-14.15
      
-170.41
    
    

      
2
      
Utah
      
38.41
      
-109.34
    
    

      
3
      
South Dakota
      
43.45
      
-102.30
    
    

      
4
      
Texas
      
29.15
      
-103.15
    
  

In [5]:

    

df = pd.concat([df, locations], axis=1)


    

In [6]:

    

import cesiumpy


    

In [7]:

    

v = cesiumpy.Viewer()
for i, row in df.iterrows():
    l = row['Recreation Visitors (2014)[5]']
    cyl = cesiumpy.Cylinder(position=[row['lon'], row['lat'], l / 2.], length=l,
                            topRadius=10e4, bottomRadius=10e4, material='aqua')
    v.entities.add(cyl)
v


    

    
Out[7]:

In [8]:

    

v = cesiumpy.Viewer()
for i, row in df.iterrows():
    l = row['Recreation Visitors (2014)[5]']
    p= cesiumpy.Point(position=[row['lon'], row['lat'], 0],
                      pixelSize=np.sqrt(l / 10000), color='blue')
    v.entities.add(p)
v


    

    
Out[8]:

In [9]:

    

v = cesiumpy.Viewer()
pin = cesiumpy.Pin()
for i, row in df.iterrows():
    l = row['Recreation Visitors (2014)[5]']
    b = cesiumpy.Billboard(position=[row['lon'], row['lat'], 0], image = pin, scale=0.4)
    v.entities.add(b)
v


    

    
Out[9]:

In [ ]: