AI-generated Key Takeaways
-
The
aggregate_statsmethod calculates various statistical properties of a specified property across all objects in a FeatureCollection. -
It returns a dictionary containing the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation, and total variance of the selected property.
-
The method requires the FeatureCollection to aggregate over and the name of the property to use from each element.
| 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 * } */
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"' ) display ( '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 # }
