#!pip3 install --upgrade folium
#Use the latest version of folium pulled directly from github
!pip3 install --upgrade  git+git://github.com/python-visualization/folium

import pandas as pd

import folium
from IPython.display import HTML

def inline_map(map):
    """
    Embeds the HTML source of the map directly into the IPython notebook.
    
    This method will not work if the map depends on any files (json data). Also this uses
    the HTML5 srcdoc attribute, which may not be supported in all browsers.
    """
    map._build_map()
    return HTML('<iframe srcdoc="{srcdoc}" style="width: 100%; height: 510px; border: none"></iframe>'.format(srcdoc=map.HTML.replace('"', '&quot;')))

def embed_map(map, path="map.html"):
    """
    Embeds a linked iframe to the map into the IPython notebook.
    
    Note: this method will not capture the source of the map into the notebook.
    This method should work for all maps (as long as they use relative urls).
    """
    map.create_map(path=path)
    return HTML('<iframe src="files/{path}" style="width: 100%; height: 510px; border: none"></iframe>'.format(path=path))


import requests
url='https://github.com/martinjc/UK-GeoJSON/blob/master/json/electoral/gb/wpc.json?raw=true'
r = requests.get(url)
with open("data/wpc.json", "wb") as code:
    code.write(r.content)
r=None

df=pd.read_html('http://www.electionforecast.co.uk/tables/predicted_probability_by_seat.html')

df[0][:10]

#If necessary, grab a local copy... (really should add a timestamp to the filename...)
df[0].to_csv('data/pred.csv',index=False)

tmp=pd.DataFrame({'val':[100],'const':['Aldershot']})
tmp

!head data/pred.csv

map = folium.Map(location=[53, 0],zoom_start=6)
map.geo_json(geo_path='wpc.json', data=df[0],data_out='data.json', columns=['Seat', 'Labour'],
             key_on='feature.properties.PCON13NM',threshold_scale=[0, 20, 40, 60, 80, 100],
             fill_color='OrRd')
embed_map(map)

inline_map(map)

wpc_long=pd.melt(df[0],id_vars=['Seat','Region','2010'])
#http://stackoverflow.com/a/19818942
wpc_favourite=wpc_long.sort('value', ascending=False).groupby('Seat', as_index=False).first()
wpc_favourite[:3]

wpc_favourite[ wpc_favourite['Seat']=='Isle of Wight' ]