circle ( 'lon', 'lat', radius = 'radius', alpha = 0.6, color = mapper, source = source ) # and we add a color scale to see which values the colors # correspond to color_bar = ColorBar ( color_mapper = mapper, location = ( 0, 0 )) p. ) # we use the mapper for the color of the circles center = p. How to convert your bearings on a survey to get something you can use in Google Earth (heading) when using the ruler tool. Dynamic Google Map with data overlay : we will create a nice interactive plot with bokeh.įrom ansform import linear_cmap from bokeh.palettes import Plasma256 as palette from bokeh.models import ColorBar # we are adding the dataframe as a parameter, # since we are now going to plot # a different dataframe def plot ( df, lat, lng, zoom = 10, map_type = 'roadmap' ): gmap_options = GMapOptions ( lat = lat, lng = lng, map_type = map_type, zoom = zoom ) hover = HoverTool ( tooltips = ) p = gmap ( api_key, gmap_options, title = 'Pays de Gex', width = bokeh_width, height = bokeh_height, tools = ) source = ColumnDataSource ( df ) # defining a color mapper, that will map values of pricem2 # between 20 on the color palette mapper = linear_cmap ( 'pricem2', palette, 2000.How to prepare your data for geographical display : we will use pandas to read the dataset from file, and have a first look at the data before display.Get a Google Map API key : this is necessary to be able to display google maps in your applications.Installation: set up python for this exercise.You'll see and fix bugs in your data processing, and you'll start thinking about ways to extract valuable information from these datasets. As soon as you do that, obvious features will jump at your eyes. To gain insight into such datasets, you need to be able to display or segment them as a function of geographical coordinates. Think about census, real estate, a distributed system of IOT sensors, geological or weather data, etc. In fact, as soon as measurements are done at a given place in the world, the dataset becomes geographical. In real world data science, geographical datasets are everywhere. If you just want to see the prices, you'll find a ready-to-use interactive plot at the end of the post. In this post, you will learn how to use python to overlay your data on top of a dynamic Google map.Īs an example, we will use a dataset containing all the real-estate sells that occurred in 20 in France, near the swiss town of Geneva.
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