![]() Import plotly.figure_factory as ff import numpy as np import pandas as pd df_sample = pd. Projection ( type = 'albers usa' ), showlakes = True, # lakes lakecolor = 'rgb(255, 255, 255)' ), ) fig. update_layout ( title_text = '2011 US Agriculture Exports by State(Hover for breakdown)', geo = dict ( scope = 'usa', projection = go. astype ( float ), locationmode = 'USA-states', colorscale = 'Reds', autocolorscale = False, text = df, # hover text marker_line_color = 'white', # line markers between states colorbar_title = "Millions USD" )) fig. ![]() 'Fruits ' + df + ' Veggies ' + df + '' + \ Import aph_objects as go import pandas as pd df = pd. Here we load a GeoJSON file containing the geometry information for US counties, where feature.id is a FIPS code. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. Note the geojson attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases. The GeoJSON data is passed to the geojson argument, and the data is passed into the color argument of px.choropleth ( z if using graph_objects), in the same order as the IDs are passed into the location argument. ![]()
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