Page Summary
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This function adds a column of deterministic pseudorandom numbers to a FeatureCollection.
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The random numbers can follow either a 'uniform' distribution (range [0, 1)) or a 'normal' distribution (mean 0, standard deviation 1).
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Arguments control the column name, the seed for reproducibility, the distribution type, and the properties used to uniquely identify elements for number generation.
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The function returns the modified FeatureCollection with the added random column.
| Usage | Returns |
|---|---|
FeatureCollection.
randomColumn
( columnName
, seed
, distribution
, rowKeys
)
|
FeatureCollection |
| Argument | Type | Details |
|---|---|---|
|
this:
collection
|
FeatureCollection | The input collection to which to add a random column. |
columnName
|
String, default: "random" | The name of the column to add. |
seed
|
Long, default: 0 | A seed used when generating the random numbers. |
distribution
|
String, default: "uniform" | The distribution type of random numbers to produce; one of 'uniform' or 'normal'. |
rowKeys
|
List, optional | A list of properties that should uniquely and repeatably identify an element of the collection, used to generate the random number. Defaults to [system:index]. |
Examples
Code Editor (JavaScript)
// FeatureCollection of power plants in Belgium. var fc = ee . FeatureCollection ( 'WRI/GPPD/power_plants' ) . filter ( 'country_lg == "Belgium"' ); print ( 'N features in collection' , fc . size ()); // Add a uniform distribution random value column to the FeatureCollection. fc = fc . randomColumn (); // Randomly split the collection into two sets, 30% and 70% of the total. var randomSample30 = fc . filter ( 'random < 0.3' ); print ( 'N features in 30% sample' , randomSample30 . size ()); var randomSample70 = fc . filter ( 'random >= 0.3' ); print ( 'N features in 70% sample' , randomSample70 . size ());
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 ( 'N features in collection:' , fc . size ()) # Add a uniform distribution random value column to the FeatureCollection. fc = fc . randomColumn () # Randomly split the collection into two sets, 30% and 70% of the total. random_sample_30 = fc . filter ( 'random < 0.3' ) display ( 'N features in 30 % s ample:' , random_sample_30 . size ()) random_sample_70 = fc . filter ( 'random >= 0.3' ) display ( 'N features in 70 % s ample:' , random_sample_70 . size ())
