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On the one hand, you can use similar working environments such as Google colab notebooks. And, on the other, you could build a backend with something like Flask in combination with Jinja that generates an HTML template containing cartoframes maps.


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You can add a second layer with just the small countries. Using ST_Area(the_geom) could give you an idea about the ones you are interesting to filter. Then you can apply something like ST_PointOnSurface to get the centroid of the polygons. Finally, you should use conditional styling in combination with zoom level values to show and hide the point layer. ...


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You would need to melt your dataset in order to transpose your columns into rows. In doing so, your dates will be stored as a new unique field and CARTO VL will be able to iterate throught them. There are two alternatives to achieve this. a. Pandas' melt function: df_long = pd.melt(df, id_vars=["id"], var_name="date", value_name="...


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CARTO does not have an out of the box search box. On the one hand, you could build one using Airship input component in combination of CARTO's Data Services API (you will need to work a lot if you select this option), or even better, a proper open source geocode. The result should be a geojson or geometry which you can center and zoom in on it. On the other, ...


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You can use Airship checkbox component in combination with CARTO VL hide and show layer methods. You can check how it works in this working example. In this particular case, I have used Vue as the frontend framework but you can use vanilla js or any other framework. The interesting part is this one: this.layers.forEach(layer => { if (layer === '...


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