ee.FeatureCollection.reduceColumns

Aplica un reductor a cada elemento de una colección, usando los selectores proporcionados para determinar las entradas.

Devuelve un diccionario de resultados, con los nombres de salida como claves.

Uso	Muestra
FeatureCollection.reduceColumns(reducer, selectors, weightSelectors)	Diccionario

Argumento	Tipo	Detalles
esta: collection	FeatureCollection	Es la colección para agregar.
reducer	Reductor	Es el reductor que se aplicará.
selectors	Lista	Es un selector para cada entrada del reductor.
weightSelectors	Lista, valor predeterminado: null	Es un selector para cada entrada ponderada del reductor.

Ejemplos

Editor de código (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);

Configuración de Python

Consulta la página Entorno de Python para obtener información sobre la API de Python y el uso de geemap para el desarrollo interactivo.

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())