ee.FeatureCollection.randomColumn
Restez organisé à l'aide des collections
Enregistrez et classez les contenus selon vos préférences.
Ajoute une colonne de nombres pseudo-aléatoires déterministes à une collection. Les sorties sont des nombres à virgule flottante à double précision. Lorsque vous utilisez la distribution "uniforme" (par défaut), les sorties se situent dans la plage [0, 1). Avec la distribution "normale", les sorties ont μ=0, σ=1, mais n'ont pas de limites explicites.
| Utilisation | Renvoie |
|---|
FeatureCollection.randomColumn(columnName, seed, distribution, rowKeys) | FeatureCollection |
| Argument | Type | Détails |
|---|
ceci: collection | FeatureCollection | Collection d'entrée à laquelle ajouter une colonne aléatoire. |
columnName | Chaîne, valeur par défaut: "random" | Nom de la colonne à ajouter. |
seed | Long, par défaut: 0 | Graine utilisée lors de la génération des nombres aléatoires. |
distribution | Chaîne, valeur par défaut: "uniform" | Type de distribution des nombres aléatoires à générer : "uniforme" ou "normal". |
rowKeys | Liste (facultatif) | Liste de propriétés qui doivent identifier de manière unique et reproductible un élément de la collection, utilisée pour générer le nombre aléatoire. La valeur par défaut est [system:index]. |
Exemples
Éditeur de code (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());
Configuration de Python
Consultez la page
Environnement Python pour en savoir plus sur l'API Python et l'utilisation de geemap pour le développement interactif.
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"')
print('N features in collection:', fc.size().getInfo())
# 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')
print('N features in 30% sample:', random_sample_30.size().getInfo())
random_sample_70 = fc.filter('random >= 0.3')
print('N features in 70% sample:', random_sample_70.size().getInfo())
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2025/07/25 (UTC).
[null,null,["Dernière mise à jour le 2025/07/25 (UTC)."],[[["\u003cp\u003e\u003ccode\u003erandomColumn()\u003c/code\u003e adds a new column of pseudorandom numbers to a FeatureCollection, with the default column name being "random".\u003c/p\u003e\n"],["\u003cp\u003eThe generated random numbers can follow either a uniform distribution ([0, 1)) or a normal distribution (μ=0, σ=1) specified using the \u003ccode\u003edistribution\u003c/code\u003e parameter.\u003c/p\u003e\n"],["\u003cp\u003eUsers can provide a seed value for reproducibility using the \u003ccode\u003eseed\u003c/code\u003e parameter, ensuring the same sequence of random numbers is generated for a given seed.\u003c/p\u003e\n"],["\u003cp\u003eThis function is commonly used for tasks like randomly splitting a FeatureCollection into subsets for training and testing machine learning models, as demonstrated in the examples.\u003c/p\u003e\n"]]],["This tool adds a column of pseudorandom numbers to a FeatureCollection. Users can specify the `columnName`, `seed`, and `distribution`. The default distribution, 'uniform', generates numbers between 0 and 1; 'normal' produces numbers with a mean of 0 and a standard deviation of 1. The `randomColumn` method returns the modified FeatureCollection. This is exemplified by creating random splits into subsets. The outputs are double-precision floating point numbers.\n"],null,["# ee.FeatureCollection.randomColumn\n\nAdds a column of deterministic pseudorandom numbers to a collection. The outputs are double-precision floating point numbers. When using the 'uniform' distribution (default), outputs are in the range of \\[0, 1). Using the 'normal' distribution, outputs have μ=0, σ=1, but have no explicit limits.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|----------------------------------------------------------------------------------------------|-------------------|\n| FeatureCollection.randomColumn`(`*columnName* `, `*seed* `, `*distribution* `, `*rowKeys*`)` | FeatureCollection |\n\n| Argument | Type | Details |\n|--------------------|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `collection` | FeatureCollection | The input collection to which to add a random column. |\n| `columnName` | String, default: \"random\" | The name of the column to add. |\n| `seed` | Long, default: 0 | A seed used when generating the random numbers. |\n| `distribution` | String, default: \"uniform\" | The distribution type of random numbers to produce; one of 'uniform' or 'normal'. |\n| `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\\]. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// FeatureCollection of power plants in Belgium.\nvar fc = ee.FeatureCollection('WRI/GPPD/power_plants')\n .filter('country_lg == \"Belgium\"');\nprint('N features in collection', fc.size());\n\n// Add a uniform distribution random value column to the FeatureCollection.\nfc = fc.randomColumn();\n\n// Randomly split the collection into two sets, 30% and 70% of the total.\nvar randomSample30 = fc.filter('random \u003c 0.3');\nprint('N features in 30% sample', randomSample30.size());\n\nvar randomSample70 = fc.filter('random \u003e= 0.3');\nprint('N features in 70% sample', randomSample70.size());\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# FeatureCollection of power plants in Belgium.\nfc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(\n 'country_lg == \"Belgium\"')\nprint('N features in collection:', fc.size().getInfo())\n\n# Add a uniform distribution random value column to the FeatureCollection.\nfc = fc.randomColumn()\n\n# Randomly split the collection into two sets, 30% and 70% of the total.\nrandom_sample_30 = fc.filter('random \u003c 0.3')\nprint('N features in 30% sample:', random_sample_30.size().getInfo())\n\nrandom_sample_70 = fc.filter('random \u003e= 0.3')\nprint('N features in 70% sample:', random_sample_70.size().getInfo())\n```"]]
