如何使用flatten_json递归地扁平化嵌套的JSON? [英] How to flatten nested JSON recursively, with flatten_json?
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问题描述
- 该软件包位于pypi flatten-json 0.1.7 上，可以与
pip install flatten-json - 此问题特定于软件包的以下组件:
- The package is on pypi flatten-json 0.1.7 and can be installed with
pip install flatten-json - This question is specific to the following component of the package:
def flatten_json(nested_json: dict, exclude: list=[''], sep: str='_') -> dict:
"""
Flatten a list of nested dicts.
"""
out = dict()
def flatten(x: (list, dict, str), name: str='', exclude=exclude):
if type(x) is dict:
for a in x:
if a not in exclude:
flatten(x[a], f'{name}{a}{sep}')
elif type(x) is list:
i = 0
for a in x:
flatten(a, f'{name}{i}{sep}')
i += 1
else:
out[name[:-1]] = x
flatten(nested_json)
return out
使用递归展平嵌套的dicts
- 在Python中进行递归思考
- 在Python中平整JSON对象
- Thinking Recursively in Python
- Flattening JSON objects in Python
-
flatten_json已用于解压缩最终超过100000列的文件 flatten_jsonhas been used to unpack a file that ended up being over 100000 columns- 是的，这个问题不能解决这个问题.但是，如果安装
flatten软件包，则有一个unflatten方法，但我尚未对其进行测试. - Yes, this question doesn't cover that. However, if you install the
flattenpackage, there is anunflattenmethod, but I haven't tested it. - 此答案的重点是使用
flatten_json递归展平嵌套的dict或JSON - This answer focuses on using
flatten_jsonto recursively flatten a nesteddictorJSON - 此答案假设您已经将
JSON或dict加载到某些变量(例如文件，api等)中- 在这种情况下，我们将使用
data
- This answer assumes you already have the
JSONordictloaded into some variable (e.g. file, api, etc.)- In this case we will use
data
- 它接受
dict，如功能类型提示所示.
- It accepts a
dict, as shown by the function type hint.
- 仅是字典:
{}-
flatten_json(data)
- Just a dict:
{}flatten_json(data)
-
[flatten_json(x) for x in data]
-
[flatten_json(data[key]) for key in data.keys()]
-
{'key': [{}, {}, {}]}:[flatten_json(x) for x in data['key']]
- 我通常将
data展平为pandas.DataFrame- 用
import pandas as pd加载
pandas- I typically flatten
datainto apandas.DataFrame- Load
pandaswithimport pandas as pd
{ "id": 1, "class": "c1", "owner": "myself", "metadata": { "m1": { "value": "m1_1", "timestamp": "d1" }, "m2": { "value": "m1_2", "timestamp": "d2" }, "m3": { "value": "m1_3", "timestamp": "d3" }, "m4": { "value": "m1_4", "timestamp": "d4" } }, "a1": { "a11": [ ] }, "m1": {}, "comm1": "COMM1", "comm2": "COMM21529089656387", "share": "xxx", "share1": "yyy", "hub1": "h1", "hub2": "h2", "context": [ ] }Flatten 1:
df = pd.DataFrame([flatten_json(data)]) id class owner metadata_m1_value metadata_m1_timestamp metadata_m2_value metadata_m2_timestamp metadata_m3_value metadata_m3_timestamp metadata_m4_value metadata_m4_timestamp comm1 comm2 share share1 hub1 hub2 1 c1 myself m1_1 d1 m1_2 d2 m1_3 d3 m1_4 d4 COMM1 COMM21529089656387 xxx yyy h1 h2数据2:
[{ 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 }, { 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 }, { 'accuracy': 17, 'activity': [{ 'activity': [{ 'confidence': 100, 'type': 'STILL' } ], 'timestampMs': '1542652' } ], 'altitude': -10, 'latitudeE7': 3777321, 'longitudeE7': -122423125, 'timestampMs': '1542654', 'verticalAccuracy': 2 } ]Flatten 2:
df = pd.DataFrame([flatten_json(x) for x in data]) accuracy activity_0_activity_0_confidence activity_0_activity_0_type activity_0_timestampMs altitude latitudeE7 longitudeE7 timestampMs verticalAccuracy 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2 17 100 STILL 1542652 -10 3777321 -122423125 1542654 2数据3:
{ "1": { "VENUE": "JOEBURG", "COUNTRY": "HAE", "ITW": "XAD", "RACES": { "1": { "NO": 1, "TIME": "12:35" }, "2": { "NO": 2, "TIME": "13:10" }, "3": { "NO": 3, "TIME": "13:40" }, "4": { "NO": 4, "TIME": "14:10" }, "5": { "NO": 5, "TIME": "14:55" }, "6": { "NO": 6, "TIME": "15:30" }, "7": { "NO": 7, "TIME": "16:05" }, "8": { "NO": 8, "TIME": "16:40" } } }, "2": { "VENUE": "FOOBURG", "COUNTRY": "ABA", "ITW": "XAD", "RACES": { "1": { "NO": 1, "TIME": "12:35" }, "2": { "NO": 2, "TIME": "13:10" }, "3": { "NO": 3, "TIME": "13:40" }, "4": { "NO": 4, "TIME": "14:10" }, "5": { "NO": 5, "TIME": "14:55" }, "6": { "NO": 6, "TIME": "15:30" }, "7": { "NO": 7, "TIME": "16:05" }, "8": { "NO": 8, "TIME": "16:40" } } } }Flatten 3:
df = pd.DataFrame([flatten_json(data[key]) for key in data.keys()]) VENUE COUNTRY ITW RACES_1_NO RACES_1_TIME RACES_2_NO RACES_2_TIME RACES_3_NO RACES_3_TIME RACES_4_NO RACES_4_TIME RACES_5_NO RACES_5_TIME RACES_6_NO RACES_6_TIME RACES_7_NO RACES_7_TIME RACES_8_NO RACES_8_TIME JOEBURG HAE XAD 1 12:35 2 13:10 3 13:40 4 14:10 5 14:55 6 15:30 7 16:05 8 16:40 FOOBURG ABA XAD 1 12:35 2 13:10 3 13:40 4 14:10 5 14:55 6 15:30 7 16:05 8 16:40其他示例:
- Python Pandas - Flatten Nested JSON
- handling nested json in pandas
- How to flatten a nested JSON from the NASA Weather Insight API in Python
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- Load
- 用
-
- In this case we will use
- 在这种情况下，我们将使用
Use recursion to flatten nested dicts
推荐答案
如何展平JSON或dict是常见问题，对此有很多答案.
How to flatten a JSON or dict is a common question, to which, there are many answers.
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