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ee.FeatureCollection.aggregate_stats
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
对集合中对象的指定属性进行汇总,计算所选属性的总和、最小值、最大值、平均值、样本标准差、样本方差、总体标准差和总体方差。
用法 | 返回 |
---|
FeatureCollection.aggregate_stats(property) | 字典 |
参数 | 类型 | 详细信息 |
---|
此:collection | FeatureCollection | 要汇总的集合。 |
property | 字符串 | 要从集合的每个元素中使用的属性。 |
示例
代码编辑器 (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
* }
*/
Python 设置
如需了解 Python API 和如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
'country_lg == "Belgium"')
print('Power plant capacities (MW) summary stats:')
pprint(fc.aggregate_stats('capacitymw').getInfo())
# 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
# }
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最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003eCalculates descriptive statistics (sum, min, max, mean, standard deviation, and variance) for a specified property within a FeatureCollection.\u003c/p\u003e\n"],["\u003cp\u003eAccepts a FeatureCollection and the property name as input.\u003c/p\u003e\n"],["\u003cp\u003eReturns a dictionary containing the calculated statistics.\u003c/p\u003e\n"],["\u003cp\u003eUseful for understanding the distribution and central tendency of a property across features.\u003c/p\u003e\n"],["\u003cp\u003eExamples demonstrate using the function with power plant data to calculate capacity statistics.\u003c/p\u003e\n"]]],[],null,["# ee.FeatureCollection.aggregate_stats\n\nAggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|-----------------------------------------------|------------|\n| FeatureCollection.aggregate_stats`(property)` | Dictionary |\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// FeatureCollection of power plants in Belgium.\nvar fc = ee.FeatureCollection('WRI/GPPD/power_plants')\n .filter('country_lg == \"Belgium\"');\n\nprint('Power plant capacities (MW) summary stats',\n fc.aggregate_stats('capacitymw'));\n\n/**\n * Expected ee.Dictionary output\n *\n * {\n * \"max\": 2910,\n * \"mean\": 201.34242424242427,\n * \"min\": 1.8,\n * \"sample_sd\": 466.4808892319684,\n * \"sample_var\": 217604.42001864797,\n * \"sum\": 13288.600000000002,\n * \"sum_sq\": 16819846.24,\n * \"total_count\": 66,\n * \"total_sd\": 462.9334545609107,\n * \"total_var\": 214307.38335169878,\n * \"valid_count\": 66,\n * \"weight_sum\": 66,\n * \"weighted_sum\": 13288.600000000002\n * }\n */\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# FeatureCollection of power plants in Belgium.\nfc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(\n 'country_lg == \"Belgium\"')\n\nprint('Power plant capacities (MW) summary stats:')\npprint(fc.aggregate_stats('capacitymw').getInfo())\n\n# Expected ee.Dictionary output\n\n# {\n# \"max\": 2910,\n# \"mean\": 201.34242424242427,\n# \"min\": 1.8,\n# \"sample_sd\": 466.4808892319684,\n# \"sample_var\": 217604.42001864797,\n# \"sum\": 13288.600000000002,\n# \"sum_sq\": 16819846.24,\n# \"total_count\": 66,\n# \"total_sd\": 462.9334545609107,\n# \"total_var\": 214307.38335169878,\n# \"valid_count\": 66,\n# \"weight_sum\": 66,\n# \"weighted_sum\": 13288.600000000002\n# }\n```"]]