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ee.FeatureCollection.reduceColumns
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
使用指定選取器判斷輸入內容,並將縮減器套用至集合中的每個元素。
傳回結果字典,並以輸出名稱做為鍵。
| 用量 | 傳回 |
|---|
FeatureCollection.reduceColumns(reducer, selectors, weightSelectors) | 字典 |
| 引數 | 類型 | 詳細資料 |
|---|
這個:collection | FeatureCollection | 要匯總的集合。 |
reducer | 縮減函式 | 要套用的縮減函式。 |
selectors | 清單 | 每個縮減器輸入內容的選取器。 |
weightSelectors | 清單,預設值為空值 | 每個加權縮減輸入內容的選取器。 |
範例
程式碼編輯器 (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 設定
請參閱
Python 環境頁面,瞭解 Python API 和如何使用 geemap 進行互動式開發。
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())
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上次更新時間:2025-07-26 (世界標準時間)。
[null,null,["上次更新時間:2025-07-26 (世界標準時間)。"],[],["The `reduceColumns` function applies a reducer to a FeatureCollection, generating a dictionary of results. It uses `selectors` to specify input properties and can use `weightSelectors` for weighted inputs. The function takes a `reducer`, and a list of `selectors` and `weightSelectors`. This method can calculate means of single or multiple properties and weighted means by using a reducer and specifying properties to calculate on. The results are returned as a dictionary.\n"]]