ee.FeatureCollection.flatten
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Flattens collections of collections.
Usage | Returns | FeatureCollection.flatten() | FeatureCollection |
Argument | Type | Details | this: collection | FeatureCollection | The input collection of collections. |
Examples
Code Editor (JavaScript)
// Counties in New Mexico, USA.
var counties = ee.FeatureCollection('TIGER/2018/Counties')
.filter('STATEFP == "35"');
// Monthly climate and climatic water balance surfaces for January 2020.
var 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.
var countiesClimate = climate.map(function(image) {
return image.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 using
geemap
for interactive development.
import ee
import geemap.core as geemap
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.
def reduce_mean(image):
return image.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.
print('FeatureCollection of FeatureCollections:', counties_climate.getInfo())
print('Flattened FeatureCollection of FeatureCollections:',
counties_climate.flatten().getInfo())
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Last updated 2023-10-06 UTC.
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