Duyuru:
15 Nisan 2025'ten önce Earth Engine'i kullanmak için kaydedilen tüm ticari olmayan projelerin Earth Engine erişimini sürdürmek için
ticari olmayan uygunluğu doğrulaması gerekir.
ee.ImageCollection.aggregate_count
Koleksiyonlar ile düzeninizi koruyun
İçeriği tercihlerinize göre kaydedin ve kategorilere ayırın.
Bir koleksiyondaki nesnelerin belirli bir özelliği üzerinde toplama işlemi yaparak özelliğin boş olmayan değerlerinin sayısını hesaplar.
Kullanım | İadeler |
---|
ImageCollection.aggregate_count(property) | Sayı |
Bağımsız Değişken | Tür | Ayrıntılar |
---|
bu: collection | FeatureCollection | Üzerinde toplama işlemi yapılacak koleksiyon. |
property | Dize | Koleksiyonun her öğesinden kullanılacak özellik. |
Örnekler
Kod Düzenleyici (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));
Python kurulumu
Python API'si ve etkileşimli geliştirme için geemap
kullanımı hakkında bilgi edinmek üzere
Python Ortamı sayfasına bakın.
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())
Aksi belirtilmediği sürece bu sayfanın içeriği Creative Commons Atıf 4.0 Lisansı altında ve kod örnekleri Apache 2.0 Lisansı altında lisanslanmıştır. Ayrıntılı bilgi için Google Developers Site Politikaları'na göz atın. Java, Oracle ve/veya satış ortaklarının tescilli ticari markasıdır.
Son güncelleme tarihi: 2025-07-26 UTC.
[null,null,["Son güncelleme tarihi: 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```"]]