import folium import pandas as pd folium.initialize_notebook() #Standard OSM map_osm = folium.Map(location=[45.5236, -122.6750]) map_osm stamen = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner', zoom_start=13) stamen mt_hood = folium.Map(location=[45.372, -121.6972], zoom_start=12, tiles='Stamen Terrain') mt_hood.simple_marker([45.3288, -121.6625], popup='Mt. Hood Meadows') mt_hood.simple_marker([45.3311, -121.7113], popup='Timberline Lodge') mt_hood portland = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner', zoom_start=13) portland.simple_marker(location=[45.5244, -122.6699], popup='The Waterfront') portland.circle_marker(location=[45.5215, -122.6261], radius=500, popup='Laurelhurst Park', line_color='#3186cc', fill_color='#3186cc') portland latlng = folium.Map(location=[46.1991, -122.1889], tiles='Stamen Terrain', zoom_start=13) latlng.lat_lng_popover() latlng waypoints = folium.Map(location=[46.8527, -121.7649], tiles='Stamen Terrain', zoom_start=13) waypoints.simple_marker(location=[46.8354, -121.7325], popup='Camp Muir') waypoints.click_for_marker(popup='Waypoint') waypoints polygons = folium.Map(location=[45.5236, -122.6750], zoom_start=13) polygons.polygon_marker(location=[45.5012, -122.6655], popup='Ross Island Bridge', fill_color='#132b5e', num_sides=3, radius=10) polygons.polygon_marker(location=[45.5132, -122.6708], popup='Hawthorne Bridge', fill_color='#45647d', num_sides=4, radius=10) polygons.polygon_marker(location=[45.5275, -122.6692], popup='Steel Bridge', fill_color='#769d96', num_sides=6, radius=10) polygons.polygon_marker(location=[45.5318, -122.6745], popup='Broadway Bridge', fill_color='#769d96', num_sides=8, radius=10) polygons import vincent NOAA_46041 = pd.read_csv(r'NOAA_46041.csv', index_col=3, parse_dates=True) NOAA_46050 = pd.read_csv(r'NOAA_46050_WS.csv', index_col=3, parse_dates=True) NOAA_46243 = pd.read_csv(r'NOAA_46243.csv', index_col=3, parse_dates=True) NOAA_46041 = NOAA_46041.dropna() #Binned wind speeds for NOAA 46050 bins = range(0, 13, 1) cuts = pd.cut(NOAA_46050['wind_speed_cwind (m/s)'], bins) ws_binned = pd.value_counts(cuts).reindex(cuts.levels) #NOAA 46401 Wave Period vis1 = vincent.Line(NOAA_46041['dominant_wave_period (s)'], width=400, height=200) vis1.axis_titles(x='Time', y='Dominant Wave Period (s)') vis1.to_json('vis1.json') #NOAA 46050 Binned Wind Speed vis2 = vincent.Bar(ws_binned, width=400, height=200) vis2.axis_titles(x='Wind Speed (m/s)', y='# of Obs') vis2.to_json('vis2.json') #NOAA 46243 Wave Height vis3 = vincent.Area(NOAA_46243['significant_wave_height (m)'], width=400, height=200) vis3.axis_titles(x='Time', y='Significant Wave Height (m)') vis3.to_json('vis3.json') #Map all buoys buoy_map = folium.Map(location=[46.3014, -123.7390], zoom_start=7, tiles='Stamen Terrain') buoy_map.polygon_marker(location=[47.3489, -124.708], fill_color='#43d9de', radius=12, popup=(vis1, 'vis1.json')) buoy_map.polygon_marker(location=[44.639, -124.5339], fill_color='#43d9de', radius=12, popup=(vis2, 'vis2.json')) buoy_map.polygon_marker(location=[46.216, -124.1280], fill_color='#43d9de', radius=12, popup=(vis3, 'vis3.json')) buoy_map.create_map(path='NOAA_buoys.html') buoy_map.render_iframe = True buoy_map state_geo = r'us-states.json' state_unemployment = r'US_Unemployment_Oct2012.csv' state_data = pd.read_csv(state_unemployment) #Let Folium determine the scale states = folium.Map(location=[48, -102], zoom_start=3) states.geo_json(geo_path=state_geo, data=state_data, columns=['State', 'Unemployment'], key_on='feature.id', fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2, legend_name='Unemployment Rate (%)') states.create_map(path='us_state_map.html') states states2 = folium.Map(location=[48, -102], zoom_start=3) states2.geo_json(geo_path=state_geo, data=state_data, columns=['State', 'Unemployment'], threshold_scale=[5, 6, 7, 8, 9, 10], key_on='feature.id', fill_color='BuPu', fill_opacity=0.7, line_opacity=0.5, legend_name='Unemployment Rate (%)', reset=True) states2.create_map(path='us_state_map_2.html') states2 county_data = r'us_county_data.csv' county_geo = r'us_counties_20m_topo.json' #Read into Dataframe, cast to string for consistency df = pd.read_csv(county_data, na_values=[' ']) df['FIPS_Code'] = df['FIPS_Code'].astype(str) def set_id(fips): '''Modify FIPS code to match GeoJSON property''' if fips == '0': return None elif len(fips) <= 4: return ''.join(['0500000US0', fips]) else: return ''.join(['0500000US', fips]) #Apply set_id, drop NaN df['GEO_ID'] = df['FIPS_Code'].apply(set_id) df = df.dropna() #Number of employed with auto scale map_1 = folium.Map(location=[48, -102], zoom_start=3) map_1.geo_json(geo_path=county_geo, data_out='data1.json', data=df, columns=['GEO_ID', 'Employed_2011'], key_on='feature.id', fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.3, topojson='objects.us_counties_20m') map_1.create_map(path='map_1.html') map_1 map_2 = folium.Map(location=[40, -99], zoom_start=4) map_2.geo_json(geo_path=county_geo, data_out='data2.json', data=df, columns=['GEO_ID', 'Unemployment_rate_2011'], key_on='feature.id', threshold_scale=[0, 5, 7, 9, 11, 13], fill_color='YlGnBu', line_opacity=0.3, legend_name='Unemployment Rate 2011 (%)', topojson='objects.us_counties_20m') map_2.create_map(path='map_2.html') map_2 map_3 = folium.Map(location=[40, -99], zoom_start=4) map_3.geo_json(geo_path=county_geo, data_out='data3.json', data=df, columns=['GEO_ID', 'Median_Household_Income_2011'], key_on='feature.id', fill_color='PuRd', line_opacity=0.3, legend_name='Median Household Income 2011 ($)', topojson='objects.us_counties_20m') map_3.create_map(path='map_3.html') map_3