Map Subplots in Python
How to make map subplots and map small multiples in Python.
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US map small multiples¶
In [1]:
import plotly.graph_objects as go import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv') df.head() data = [] layout = dict( title = 'New Walmart Stores per year 1962-2006<br>\ Source: <a href="http://www.econ.umn.edu/~holmes/data/WalMart/index.html">\ University of Minnesota</a>', # showlegend = False, autosize = False, width = 1000, height = 900, hovermode = False, legend = dict( x=0.7, y=-0.1, bgcolor="rgba(255, 255, 255, 0)", font = dict( size=11 ), ) ) years = df['YEAR'].unique() for i in range(len(years)): geo_key = 'geo'+str(i+1) if i != 0 else 'geo' lons = list(df[ df['YEAR'] == years[i] ]['LON']) lats = list(df[ df['YEAR'] == years[i] ]['LAT']) # Walmart store data data.append( dict( type = 'scattergeo', showlegend=False, lon = lons, lat = lats, geo = geo_key, name = int(years[i]), marker = dict( color = "rgb(0, 0, 255)", opacity = 0.5 ) ) ) # Year markers data.append( dict( type = 'scattergeo', showlegend = False, lon = [-78], lat = [47], geo = geo_key, text = [years[i]], mode = 'text', ) ) layout[geo_key] = dict( scope = 'usa', showland = True, landcolor = 'rgb(229, 229, 229)', showcountries = False, domain = dict( x = [], y = [] ), subunitcolor = "rgb(255, 255, 255)", ) def draw_sparkline( domain, lataxis, lonaxis ): ''' Returns a sparkline layout object for geo coordinates ''' return dict( showland = False, showframe = False, showcountries = False, showcoastlines = False, domain = domain, lataxis = lataxis, lonaxis = lonaxis, bgcolor = 'rgba(255,200,200,0.0)' ) # Stores per year sparkline layout['geo44'] = draw_sparkline({'x':[0.6,0.8], 'y':[0,0.15]}, \ {'range':[-5.0, 30.0]}, {'range':[0.0, 40.0]} ) data.append( dict( type = 'scattergeo', mode = 'lines', lat = list(df.groupby(by=['YEAR']).count()['storenum']/1e1), lon = list(range(len(df.groupby(by=['YEAR']).count()['storenum']/1e1))), line = dict( color = "rgb(0, 0, 255)" ), name = "New stores per year<br>Peak of 178 stores per year in 1990", geo = 'geo44', ) ) # Cumulative sum sparkline layout['geo45'] = draw_sparkline({'x':[0.8,1], 'y':[0,0.15]}, \ {'range':[-5.0, 50.0]}, {'range':[0.0, 50.0]} ) data.append( dict( type = 'scattergeo', mode = 'lines', lat = list(df.groupby(by=['YEAR']).count().cumsum()['storenum']/1e2), lon = list(range(len(df.groupby(by=['YEAR']).count()['storenum']/1e1))), line = dict( color = "rgb(214, 39, 40)" ), name ="Cumulative sum<br>3176 stores total in 2006", geo = 'geo45', ) ) z = 0 COLS = 5 ROWS = 9 for y in reversed(range(ROWS)): for x in range(COLS): geo_key = 'geo'+str(z+1) if z != 0 else 'geo' layout[geo_key]['domain']['x'] = [float(x)/float(COLS), float(x+1)/float(COLS)] layout[geo_key]['domain']['y'] = [float(y)/float(ROWS), float(y+1)/float(ROWS)] z=z+1 if z > 42: break fig = go.Figure(data=data, layout=layout) fig.update_layout(width=800) fig.show() Reference¶
See https://plotly.com/python/reference/scattergeo/ for more information and chart attribute options!
What About Dash?¶
Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash at https://dash.plot.ly/installation.
Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:
import plotly.graph_objects as go # or plotly.express as px fig = go.Figure() # or any Plotly Express function e.g. px.bar(...) # fig.add_trace( ... ) # fig.update_layout( ... ) from dash import Dash, dcc, html app = Dash() app.layout = html.Div([ dcc.Graph(figure=fig) ]) app.run(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter 