如何在 Python 中遍历此列表
[英]How can I iterate through this list in Python
问题描述
I'm trying through iterate through the list below and am having a hard time.
我正在尝试遍历下面的列表，但遇到了困难。 I am trying to access the dictionary element "metrics".我正在尝试访问字典元素“metrics”。 I am trying to print out all the key/values of 'timestamp' and 'values'.我正在尝试打印出“时间戳”和“值”的所有键/值。
I've been able to access the list using the code below and do see how I can individually get within the list but am not sure how to build a loop to get all values I need.我已经能够使用下面的代码访问列表，并查看我如何单独进入列表，但不确定如何构建一个循环来获取我需要的所有值。
flat_file = DATA[0]['environments'][0]['dimensions'][0]
[
{
"environments": [
{
"dimensions": [
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "651.0"
},
{
"timestamp": 1582502400000,
"value": "510.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "TestL"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "477.0"
},
{
"timestamp": 1582502400000,
"value": "338.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "test_y"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "91.0"
},
{
"timestamp": 1582502400000,
"value": "93.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "Testz"
}
],
"name": "prod"
}
],
"metaData": {
"errors": [],
"notices": [
"query served by: something",
"Source:db",
"Table used: something",
"Metric with Avg of request_size was requested. For this a global avg was also computed with name global-avg-request_size"
]
}
}
]
3 个解决方案
解决方案1
0 2020-02-26 04:06:40
This will work:这将起作用：
for i in range(len(l[0]['environments'][0]['dimensions'])):
for j in range(len(l[0]['environments'][0]['dimensions'][i]['metrics'])):
if '{' in str(l[0]['environments'][0]['dimensions'][i]['metrics'][j]['values']):
print(l[0]['environments'][0]['dimensions'][i]['metrics'][j]['values'])
Output:输出：
[{'timestamp': 1582588800000, 'value': '651.0'}, {'timestamp': 1582502400000, 'value': '510.0'}]
[{'timestamp': 1582588800000, 'value': '0.0'}, {'timestamp': 1582502400000, 'value': '0.0'}]
[{'timestamp': 1582588800000, 'value': '477.0'}, {'timestamp': 1582502400000, 'value': '338.0'}]
[{'timestamp': 1582588800000, 'value': '0.0'}, {'timestamp': 1582502400000, 'value': '0.0'}]
[{'timestamp': 1582588800000, 'value': '91.0'}, {'timestamp': 1582502400000, 'value': '93.0'}]
[{'timestamp': 1582588800000, 'value': '0.0'}, {'timestamp': 1582502400000, 'value': '0.0'}]
解决方案2
0 2020-02-26 04:08:34
What you are reading is JSON, to read it in python you need :您正在阅读的是 JSON，要在 Python 中阅读它，您需要：
import json
data = '''{
"environments": [
{
"dimensions": [
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "651.0"
},
{
"timestamp": 1582502400000,
"value": "510.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "TestL"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "477.0"
},
{
"timestamp": 1582502400000,
"value": "338.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "test_y"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "91.0"
},
{
"timestamp": 1582502400000,
"value": "93.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "Testz"
}
],
"name": "prod"
}
],
"metaData": {
"errors": [],
"notices": [
"query served by: something",
"Source:db",
"Table used: something",
"Metric with Avg of request_size was requested. For this a global avg was also computed with name global-avg-request_size"
]
}
}'''
data_dict = json.loads(data)
This will create a python dictionary of your data.这将为您的数据创建一个 python 字典。 So now access the data as you will for a dictionary.所以现在像访问字典一样访问数据。
like: data_dict["environments"]像： data_dict["environments"]
to go deeper try combo of for:更深入地尝试组合 for：
>>> for doc in data_dict['environments']:
... for i in doc['dimensions']:
... print(i['name'])
...
TestL
test_y
Testz
For better data oriented view and usage use Pandas check this link and this为了更好的面向数据的视图和使用使用熊猫检查这个链接和这个
解决方案3
0 已采纳 2020-02-26 04:43:44
lisp = [
{
"environments": [
{
"dimensions": [
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "651.0"
},
{
"timestamp": 1582502400000,
"value": "510.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "TestL"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "477.0"
},
{
"timestamp": 1582502400000,
"value": "338.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "test_y"
},
{
"metrics": [
{
"name": "sum(message_count)",
"values": [
{
"timestamp": 1582588800000,
"value": "91.0"
},
{
"timestamp": 1582502400000,
"value": "93.0"
}
]
},
{
"name": "global-avg-request_size",
"values": [
"0.0"
]
},
{
"name": " avg(request_size)",
"values": [
{
"timestamp": 1582588800000,
"value": "0.0"
},
{
"timestamp": 1582502400000,
"value": "0.0"
}
]
}
],
"name": "Testz"
}
],
"name": "prod"
}
],
"metaData": {
"errors": [],
"notices": [
"query served by: something",
"Source:db",
"Table used: something",
"Metric with Avg of request_size was requested. For this a global avg was also computed with name global-avg-request_size"
]
}
}
]
for element in lisp[0]['environments'][0]['dimensions']:
for inner_element in element['metrics']:
for el in inner_element['values']:
if type(el) == dict:
print(el)
{'timestamp': 1582588800000, 'value': '651.0'}
{'timestamp': 1582502400000, 'value': '510.0'}
{'timestamp': 1582588800000, 'value': '0.0'}
{'timestamp': 1582502400000, 'value': '0.0'}
{'timestamp': 1582588800000, 'value': '477.0'}
{'timestamp': 1582502400000, 'value': '338.0'}
{'timestamp': 1582588800000, 'value': '0.0'}
{'timestamp': 1582502400000, 'value': '0.0'}
{'timestamp': 1582588800000, 'value': '91.0'}
{'timestamp': 1582502400000, 'value': '93.0'}
{'timestamp': 1582588800000, 'value': '0.0'}
{'timestamp': 1582502400000, 'value': '0.0'}
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