我有這種格式的json：
{
"fields": {
"tcidte": {
"mode": "required",
"type": "date",
"format": "%Y%m%d"
},
"tcmcid": {
"mode": "required",
"type": "string"
},
"tcacbr": {
"mode": "required",
"type": "string"
}
}
}
我希望它采用資料幀格式，其中三個欄位名稱中的每一個都是單獨的行。如果一行有一個列（例如“格式”），而其他列是空白的，則應假定為 NULL。
我嘗試使用我在此處找到的 flatten_json 函式，但沒有按預期作業，但仍將包括在此處：
def flatten_json(nested_json, exclude=['']):
"""Flatten json object with nested keys into a single level.
Args:
nested_json: A nested json object.
exclude: Keys to exclude from output.
Returns:
The flattened json object if successful, None otherwise.
"""
out = {}
def flatten(x, name='', exclude=exclude):
if type(x) is dict:
for a in x:
if a not in exclude: flatten(x[a], name a '_')
elif type(x) is list:
i = 0
for a in x:
flatten(a, name str(i) '_')
i = 1
else:
out[name[:-1]] = x
flatten(nested_json)
return out
flatten_json_file = pd.DataFrame(flatten_json(nested_json))
pprint.pprint(flatten_json_file)
額外的復雜性 JSON：
{
"fields": {
"action": {
"type": {
"field_type": "string"
},
"mode": "required"
},
"upi": {
"type": {
"field_type": "string"
},
"regex": "^[0-9]{9}$",
"mode": "required"
},
"firstname": {
"type": {
"field_type": "string"
},
"mode": "required"
}
}
}
uj5u.com熱心網友回復：
和
data = {
"fields": {
"tcidte": {
"mode": "required",
"type": "date",
"format": "%Y%m%d"
},
"tcmcid": {
"mode": "required",
"type": "string"
},
"tcacbr": {
"mode": "required",
"type": "string"
}
}
}
這個
df = pd.DataFrame(data["fields"].values())
結果是
mode type format
0 required date %Y%m%d
1 required string NaN
2 required string NaN
那是你的目標嗎？
如果你想要data["fields"]as 索引的鍵：
df = pd.DataFrame(data["fields"]).T
或者
df = pd.DataFrame.from_dict(data["fields"], orient="index")
兩者都導致
mode type format
tcidte required date %Y%m%d
tcmcid required string NaN
tcacbr required string NaN
和
data = {
"fields": {
"action": {
"type": {
"field_type": "string"
},
"mode": "required"
},
"upi": {
"type": {
"field_type": "string"
},
"regex": "^[0-9]{9}$",
"mode": "required"
},
"firstname": {
"type": {
"field_type": "string"
},
"mode": "required"
}
}
}
你可以做
data = {key: {**d, **d["type"]} for key, d in data["fields"].items()}
df = pd.DataFrame.from_dict(data, orient="index").drop(columns="type")
或者
df = pd.DataFrame.from_dict(data["fields"], orient="index")
df = pd.concat(
[df, pd.DataFrame(df.type.to_list(), index=df.index)], axis=1
).drop(columns="type")
結果如（列位置可能不同）
mode field_type regex
action required string NaN
upi required string ^[0-9]{9}$
firstname required string NaN
uj5u.com熱心網友回復：
df= pd.read_json('test.json')
df_fields = pd.DataFrame(df['fields'].values.tolist(), index=df.index)
print(df_fields)
輸出：
mode type format
tcacbr required string NaN
tcidte required date %Y%m%d
tcmcid required string NaN
uj5u.com熱心網友回復：
一種選擇是jmespath庫，它在以下場景中很有用：
# pip install jmespath
import jmespath
import pandas as pd
# think of it like a path
# fields is the first key
# there are sub keys with varying names
# we are only interested in mode, type, format
# hence the * to represent the intermediate key(s)
expression = jmespath.compile('fields.*[mode, type, format]')
pd.DataFrame(expression.search(data), columns = ['mode', 'type', 'format'])
mode type format
0 required date %Y%m%d
1 required string None
2 required string None
jmespath 有很多工具；然而，這應該足夠了，并且涵蓋了子詞典中缺少鍵（模式、型別、格式）的情況。
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