ee.FeatureCollection.reduceColumns

  • reduceColumns applies a reducer function to properties (columns) of a FeatureCollection, effectively aggregating data across all features.

  • It takes a reducer, a list of selectors specifying the properties to use as input, and optionally, weight selectors for weighted reductions.

  • The output is a dictionary containing the results, keyed by the names defined within the reducer.

  • This function allows for calculating statistics such as mean, sum, or other custom aggregations across the features in a FeatureCollection, using specified properties and optional weights.

  • Examples provided demonstrate calculating mean and weighted mean of properties within a FeatureCollection of power plants.

 Apply a reducer to each element of a collection, using the given selectors to determine the inputs.

Returns a dictionary of results, keyed with the output names.

UsageReturns
FeatureCollection.reduceColumns(reducer, selectors, weightSelectors)Dictionary
ArgumentTypeDetails
this: collectionFeatureCollectionThe collection to aggregate over.
reducerReducerThe reducer to apply.
selectorsListA selector for each input of the reducer.
weightSelectorsList, default: nullA selector for each weighted input of the reducer.

Examples

Code Editor (JavaScript)

// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
            .filter('country_lg == "Belgium"');

// Calculate mean of a single FeatureCollection property.
var propMean = fc.reduceColumns({
  reducer: ee.Reducer.mean(),
  selectors: ['gwh_estimt']
});
print('Mean of a single property', propMean);

// Calculate mean of multiple FeatureCollection properties.
var propsMean = fc.reduceColumns({
  reducer: ee.Reducer.mean().repeat(2),
  selectors: ['gwh_estimt', 'capacitymw']
});
print('Mean of multiple properties', propsMean);

// Calculate weighted mean of a single FeatureCollection property. Add a fuel
// source weight property to the FeatureCollection.
var fuelWeights = ee.Dictionary({
  Wind: 0.9,
  Gas: 0.2,
  Oil: 0.2,
  Coal: 0.1,
  Hydro: 0.7,
  Biomass: 0.5,
  Nuclear: 0.3
});
fc = fc.map(function(feature) {
  return feature.set('weight', fuelWeights.getNumber(feature.get('fuel1')));
});

var weightedMean = fc.reduceColumns({
  reducer: ee.Reducer.mean(),
  selectors: ['gwh_estimt'],
  weightSelectors: ['weight']
});
print('Weighted mean of a single property', weightedMean);

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)

# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
    'country_lg == "Belgium"')

# Calculate mean of a single FeatureCollection property.
prop_mean = fc.reduceColumns(**{
    'reducer': ee.Reducer.mean(),
    'selectors': ['gwh_estimt']
    })
print('Mean of a single property:', prop_mean.getInfo())

# Calculate mean of multiple FeatureCollection properties.
props_mean = fc.reduceColumns(**{
    'reducer': ee.Reducer.mean().repeat(2),
    'selectors': ['gwh_estimt', 'capacitymw']
    })
print('Mean of multiple properties:', props_mean.getInfo())


# Calculate weighted mean of a single FeatureCollection property. Add a fuel
# source weight property to the FeatureCollection.
def get_fuel(feature):
  return feature.set('weight', fuel_weights.getNumber(feature.get('fuel1')))

fuel_weights = ee.Dictionary({
    'Wind': 0.9,
    'Gas': 0.2,
    'Oil': 0.2,
    'Coal': 0.1,
    'Hydro': 0.7,
    'Biomass': 0.5,
    'Nuclear': 0.3
    })

fc = fc.map(get_fuel)

weighted_mean = fc.reduceColumns(**{
    'reducer': ee.Reducer.mean(),
    'selectors': ['gwh_estimt'],
    'weightSelectors': ['weight']
    })
print('Weighted mean of a single property:', weighted_mean.getInfo())