In [81]:
import folium
import numpy as np
from IPython.display import IFrame
In [122]:
import json
with open('/Users/ramz.sivagurunathan/hacks/hackathon/code/parramatta_route_path.json') as fp:
route_data = json.load(fp)
with open('/Users/ramz.sivagurunathan/hacks/hackathon/code/passenger_start.json') as fp:
passenger_data = json.load(fp)
In [132]:
import random
import math
map_osm = folium.Map(location=[-33.8176967,151.0032968],
zoom_start=13)
empdata = pd.read_csv('/Users/ramz.sivagurunathan/hacks/hackathon/datasets/curated/empforecast.csv')
map_osm.geo_json(geo_path='test.geojson', line_color='blue', data=empdata, columns=['TZ_CODE11', 'Employment'],
key_on='feature.properties.TZ_CODE11',
fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,
legend_name='Employment')
colors_list = ["red",'blue','black']
for route_id, info in route_data.iteritems():
map_osm.line(locations=info, line_weight=2, line_color=random.choice(colors_list), popup=route_id)
for elem in passenger_data:
if math.isnan(elem['lat']):
continue
map_osm.circle_marker(location=[elem['lat'], elem['long']], radius=10, popup=str(elem['count']))
map_osm.create_map(path='osm.html')
In [ ]:
import random
map_osm = folium.Map(location=[-33.8176967,151.0032968],
zoom_start=13)
empdata = pd.read_csv('/Users/ramz.sivagurunathan/hacks/hackathon/datasets/curated/empforecast.csv')
map_osm.geo_json(geo_path='test.geojson', line_color='blue', data=empdata, columns=['TZ_CODE11', 'Employment'],
key_on='feature.properties.TZ_CODE11',
fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,
legend_name='Employment')
colors_list = ["red",'blue','black']
for route_id, info in route_data.iteritems():
map_osm.line(locations=info, line_weight=2, line_color=random.choice(colors_list), popup=route_id)
map_osm.create_map(path='osm1.html')
In [89]:
%matplotlib inline
import pandas as pd
df = pd.read_csv('/Users/ramz.sivagurunathan/hacks/hackathon/datasets/employment_forecast/bts_employment_forecast.csv')
In [90]:
out = df[df.Year == 2016].groupby('TZ').aggregate(sum).Emp
out.to_csv('/Users/ramz.sivagurunathan/hacks/hackathon/datasets/curated/empforecast.csv')
In [94]:
route_shape = pd.read_csv('/Users/ramz.sivagurunathan/hacks/hackathon/datasets/routes/around_parramatta.csv')
In [100]:
for shape_id,shape_val in route_shape.groupby('shape_id'):
print shape_val.values
break
In [133]:
route_shape
Out[133]:
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