Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
| Usage | Returns | FeatureCollection.aggregate_stats(property) | Dictionary |
| Argument | Type | Details | this: collection | FeatureCollection | The collection to aggregate over. |
property | String | The property to use from each element of the collection. |
Examples
Code Editor (JavaScript)
// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
.filter('country_lg == "Belgium"');
print('Power plant capacities (MW) summary stats',
fc.aggregate_stats('capacitymw'));
/**
* Expected ee.Dictionary output
*
* {
* "max": 2910,
* "mean": 201.34242424242427,
* "min": 1.8,
* "sample_sd": 466.4808892319684,
* "sample_var": 217604.42001864797,
* "sum": 13288.600000000002,
* "sum_sq": 16819846.24,
* "total_count": 66,
* "total_sd": 462.9334545609107,
* "total_var": 214307.38335169878,
* "valid_count": 66,
* "weight_sum": 66,
* "weighted_sum": 13288.600000000002
* }
*/
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)
from pprint import pprint
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
'country_lg == "Belgium"')
print('Power plant capacities (MW) summary stats:')
pprint(fc.aggregate_stats('capacitymw').getInfo())
# Expected ee.Dictionary output
# {
# "max": 2910,
# "mean": 201.34242424242427,
# "min": 1.8,
# "sample_sd": 466.4808892319684,
# "sample_var": 217604.42001864797,
# "sum": 13288.600000000002,
# "sum_sq": 16819846.24,
# "total_count": 66,
# "total_sd": 462.9334545609107,
# "total_var": 214307.38335169878,
# "valid_count": 66,
# "weight_sum": 66,
# "weighted_sum": 13288.600000000002
# }