ee.ImageCollection.aggregate_count
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Enregistrez et classez les contenus selon vos préférences.
Agrège une propriété donnée des objets d'une collection, en calculant le nombre de valeurs non nulles de la propriété.
Utilisation | Renvoie |
---|
ImageCollection.aggregate_count(property) | Nombre |
Argument | Type | Détails |
---|
ceci : collection | FeatureCollection | Collection à agréger. |
property | Chaîne | Propriété à utiliser pour chaque élément de la collection. |
Exemples
Éditeur de code (JavaScript)
// A Lansat 8 TOA image collection for a specific year and location.
var col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
.filterBounds(ee.Geometry.Point([-122.073, 37.188]))
.filterDate('2018', '2019');
// An image property of interest, percent cloud cover in this case.
var prop = 'CLOUD_COVER';
// Use ee.ImageCollection.aggregate_* functions to fetch information about
// values of a selected property across all images in the collection. For
// example, produce a list of all values, get counts, and calculate statistics.
print('List of property values', col.aggregate_array(prop));
print('Count of property values', col.aggregate_count(prop));
print('Count of distinct property values', col.aggregate_count_distinct(prop));
print('First collection element property value', col.aggregate_first(prop));
print('Histogram of property values', col.aggregate_histogram(prop));
print('Min of property values', col.aggregate_min(prop));
print('Max of property values', col.aggregate_max(prop));
// The following methods are applicable to numerical properties only.
print('Mean of property values', col.aggregate_mean(prop));
print('Sum of property values', col.aggregate_sum(prop));
print('Product of property values', col.aggregate_product(prop));
print('Std dev (sample) of property values', col.aggregate_sample_sd(prop));
print('Variance (sample) of property values', col.aggregate_sample_var(prop));
print('Std dev (total) of property values', col.aggregate_total_sd(prop));
print('Variance (total) of property values', col.aggregate_total_var(prop));
print('Summary stats of property values', col.aggregate_stats(prop));
// Note that if the property is formatted as a string, min and max will
// respectively return the first and last values according to alphanumeric
// order of the property values.
var propString = 'LANDSAT_SCENE_ID';
print('List of property values (string)', col.aggregate_array(propString));
print('Min of property values (string)', col.aggregate_min(propString));
print('Max of property values (string)', col.aggregate_max(propString));
Configuration de Python
Consultez la page
Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap
pour le développement interactif.
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
# A Lansat 8 TOA image collection for a specific year and location.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterBounds(
ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')
# An image property of interest, percent cloud cover in this case.
prop = 'CLOUD_COVER'
# Use ee.ImageCollection.aggregate_* functions to fetch information about
# values of a selected property across all images in the collection. For
# example, produce a list of all values, get counts, and calculate statistics.
print('List of property values:', col.aggregate_array(prop).getInfo())
print('Count of property values:', col.aggregate_count(prop).getInfo())
print('Count of distinct property values:',
col.aggregate_count_distinct(prop).getInfo())
print('First collection element property value:',
col.aggregate_first(prop).getInfo())
print('Histogram of property values:')
pprint(col.aggregate_histogram(prop).getInfo())
print('Min of property values:', col.aggregate_min(prop).getInfo())
print('Max of property values:', col.aggregate_max(prop).getInfo())
# The following methods are applicable to numerical properties only.
print('Mean of property values:', col.aggregate_mean(prop).getInfo())
print('Sum of property values:', col.aggregate_sum(prop).getInfo())
print('Product of property values:', col.aggregate_product(prop).getInfo())
print('Std dev (sample) of property values:',
col.aggregate_sample_sd(prop).getInfo())
print('Variance (sample) of property values:',
col.aggregate_sample_var(prop).getInfo())
print('Std dev (total) of property values:',
col.aggregate_total_sd(prop).getInfo())
print('Variance (total) of property values:',
col.aggregate_total_var(prop).getInfo())
print('Summary stats of property values:')
pprint(col.aggregate_stats(prop).getInfo())
# Note that if the property is formatted as a string, min and max will
# respectively return the first and last values according to alphanumeric
# order of the property values.
prop_string = 'LANDSAT_SCENE_ID'
print('List of property values (string):',
col.aggregate_array(prop_string).getInfo())
print('Min of property values (string):',
col.aggregate_min(prop_string).getInfo())
print('Max of property values (string):',
col.aggregate_max(prop_string).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 2025/07/26 (UTC).
[null,null,["Dernière mise à jour le 2025/07/26 (UTC)."],[[["\u003cp\u003e\u003ccode\u003eaggregate_count\u003c/code\u003e calculates the number of non-null values for a specified property within an \u003ccode\u003eImageCollection\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eIt takes the ImageCollection and the property name as input.\u003c/p\u003e\n"],["\u003cp\u003eThe output is a single numeric value representing the count of non-null property values.\u003c/p\u003e\n"],["\u003cp\u003eUseful for understanding the data availability for a specific property across the collection.\u003c/p\u003e\n"]]],["The `aggregate_count(property)` function calculates the number of non-null values for a specified property within a FeatureCollection. The function takes the collection and the target property as arguments and returns the count as a number. Other aggregation functions are demonstrated, including `aggregate_array`, `aggregate_count_distinct`, `aggregate_first`, and statistical functions. String properties are also demonstrated for `aggregate_array`, `aggregate_min`, and `aggregate_max`. Both Javascript and Python code examples are provided.\n"],null,["# ee.ImageCollection.aggregate_count\n\nAggregates over a given property of the objects in a collection, calculating the number of non-null values of the property.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|---------------------------------------------|---------|\n| ImageCollection.aggregate_count`(property)` | Number |\n\n| Argument | Type | Details |\n|--------------------|-------------------|----------------------------------------------------------|\n| this: `collection` | FeatureCollection | The collection to aggregate over. |\n| `property` | String | The property to use from each element of the collection. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Lansat 8 TOA image collection for a specific year and location.\nvar col = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\")\n .filterBounds(ee.Geometry.Point([-122.073, 37.188]))\n .filterDate('2018', '2019');\n\n// An image property of interest, percent cloud cover in this case.\nvar prop = 'CLOUD_COVER';\n\n// Use ee.ImageCollection.aggregate_* functions to fetch information about\n// values of a selected property across all images in the collection. For\n// example, produce a list of all values, get counts, and calculate statistics.\nprint('List of property values', col.aggregate_array(prop));\nprint('Count of property values', col.aggregate_count(prop));\nprint('Count of distinct property values', col.aggregate_count_distinct(prop));\nprint('First collection element property value', col.aggregate_first(prop));\nprint('Histogram of property values', col.aggregate_histogram(prop));\nprint('Min of property values', col.aggregate_min(prop));\nprint('Max of property values', col.aggregate_max(prop));\n\n// The following methods are applicable to numerical properties only.\nprint('Mean of property values', col.aggregate_mean(prop));\nprint('Sum of property values', col.aggregate_sum(prop));\nprint('Product of property values', col.aggregate_product(prop));\nprint('Std dev (sample) of property values', col.aggregate_sample_sd(prop));\nprint('Variance (sample) of property values', col.aggregate_sample_var(prop));\nprint('Std dev (total) of property values', col.aggregate_total_sd(prop));\nprint('Variance (total) of property values', col.aggregate_total_var(prop));\nprint('Summary stats of property values', col.aggregate_stats(prop));\n\n// Note that if the property is formatted as a string, min and max will\n// respectively return the first and last values according to alphanumeric\n// order of the property values.\nvar propString = 'LANDSAT_SCENE_ID';\nprint('List of property values (string)', col.aggregate_array(propString));\nprint('Min of property values (string)', col.aggregate_min(propString));\nprint('Max of property values (string)', col.aggregate_max(propString));\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\nfrom pprint import pprint\n\n# A Lansat 8 TOA image collection for a specific year and location.\ncol = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\").filterBounds(\n ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')\n\n# An image property of interest, percent cloud cover in this case.\nprop = 'CLOUD_COVER'\n\n# Use ee.ImageCollection.aggregate_* functions to fetch information about\n# values of a selected property across all images in the collection. For\n# example, produce a list of all values, get counts, and calculate statistics.\nprint('List of property values:', col.aggregate_array(prop).getInfo())\nprint('Count of property values:', col.aggregate_count(prop).getInfo())\nprint('Count of distinct property values:',\n col.aggregate_count_distinct(prop).getInfo())\nprint('First collection element property value:',\n col.aggregate_first(prop).getInfo())\nprint('Histogram of property values:')\npprint(col.aggregate_histogram(prop).getInfo())\nprint('Min of property values:', col.aggregate_min(prop).getInfo())\nprint('Max of property values:', col.aggregate_max(prop).getInfo())\n\n# The following methods are applicable to numerical properties only.\nprint('Mean of property values:', col.aggregate_mean(prop).getInfo())\nprint('Sum of property values:', col.aggregate_sum(prop).getInfo())\nprint('Product of property values:', col.aggregate_product(prop).getInfo())\nprint('Std dev (sample) of property values:',\n col.aggregate_sample_sd(prop).getInfo())\nprint('Variance (sample) of property values:',\n col.aggregate_sample_var(prop).getInfo())\nprint('Std dev (total) of property values:',\n col.aggregate_total_sd(prop).getInfo())\nprint('Variance (total) of property values:',\n col.aggregate_total_var(prop).getInfo())\nprint('Summary stats of property values:')\npprint(col.aggregate_stats(prop).getInfo())\n\n# Note that if the property is formatted as a string, min and max will\n# respectively return the first and last values according to alphanumeric\n# order of the property values.\nprop_string = 'LANDSAT_SCENE_ID'\nprint('List of property values (string):',\n col.aggregate_array(prop_string).getInfo())\nprint('Min of property values (string):',\n col.aggregate_min(prop_string).getInfo())\nprint('Max of property values (string):',\n col.aggregate_max(prop_string).getInfo())\n```"]]