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ee.FeatureCollection.flatten

  • FeatureCollection.flatten() is used to flatten collections of collections.

  • The method takes a FeatureCollection as input and returns a flattened FeatureCollection.

 Flattens collections of collections.

UsageReturns
FeatureCollection.flatten()FeatureCollection
ArgumentTypeDetails
this:collectionFeatureCollectionThe input collection of collections.

Examples

Code Editor (JavaScript)

// Counties in New Mexico, USA.varcounties=ee.FeatureCollection('TIGER/2018/Counties').filter('STATEFP == "35"');// Monthly climate and climatic water balance surfaces for January 2020.varclimate=ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE').filterDate('2020-01','2020-02');// Calculate mean climate variables for each county per climate surface// time step. The result is a FeatureCollection of FeatureCollections.varcountiesClimate=climate.map(function(image){returnimage.reduceRegions({collection:counties,reducer:ee.Reducer.mean(),scale:5000,crs:'EPSG:4326'});});// Note that a printed FeatureCollection of FeatureCollections is not// recursively expanded, you cannot view metadata of the features within the// nested collections until you isolate a single collection or flatten the// collections.print('FeatureCollection of FeatureCollections',countiesClimate);print('Flattened FeatureCollection of FeatureCollections',countiesClimate.flatten());

Python setup

See the Python Environment page for information on the Python API and usinggeemap for interactive development.

importeeimportgeemap.coreasgeemap

Colab (Python)

# Counties in New Mexico, USA.counties=ee.FeatureCollection('TIGER/2018/Counties').filter('STATEFP == "35"')# Monthly climate and climatic water balance surfaces for January 2020.climate=ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE').filterDate('2020-01','2020-02')# Calculate mean climate variables for each county per climate surface# time step. The result is a FeatureCollection of FeatureCollections.defreduce_mean(image):returnimage.reduceRegions(**{'collection':counties,'reducer':ee.Reducer.mean(),'scale':5000,'crs':'EPSG:4326'})counties_climate=climate.map(reduce_mean)# Note that a printed FeatureCollection of FeatureCollections is not# recursively expanded, you cannot view metadata of the features within the# nested collections until you isolate a single collection or flatten the# collections.display('FeatureCollection of FeatureCollections:',counties_climate)display('Flattened FeatureCollection of FeatureCollections:',counties_climate.flatten())

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Last updated 2023-10-06 UTC.