ee.FeatureCollection.reduceColumns

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.

Usage	Returns
`FeatureCollection.reduceColumns(reducer, selectors, weightSelectors)`	Dictionary

Argument	Type	Details
this: `collection`	FeatureCollection	The collection to aggregate over.
`reducer`	Reducer	The reducer to apply.
`selectors`	List	A selector for each input of the reducer.
`weightSelectors`	List, default: null	A 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())