我有一个复杂的 Json 文件,如下所示:
{
"User A" : {
"Obj1" : {
"key1": "val1",
"key2": "val2",
"key3": "val3",
}
"Obj2" : {
"key1": "val1",
"key2": "val2",
"key3": "val3"
}
}
"User B" : {
"Obj1" : {
"key1": "val1",
"key2": "val2",
"key3": "val3",
"key4": "val4"
}
}
}
我想把它变成一个看起来像这样的数据框:
key1 key2 key3 key4
User A Obj1 val1 val2 val3 NaN
Obj2 val1 val2 val3 NaN
User B Obj1 val1 val2 val3 val4
这对 Pandas 来说可能吗?如果是这样,我该如何设法做到这一点?
- 如果更简单,我不介意删除 User 和 Obj 的前两列,只保留键的列。
最佳答案
您可以先将文件读入dict
:
with open('file.json') as data_file:
dd = json.load(data_file)
print(dd)
{'User B': {'Obj1': {'key2': 'val2', 'key4': 'val4', 'key1': 'val1', 'key3': 'val3'}},
'User A': {'Obj1': {'key2': 'val2', 'key1': 'val1', 'key3': 'val3'},
'Obj2': {'key2': 'val2', 'key1': 'val1', 'key3': 'val3'}}}
然后使用 dict comprehension
和 concat
:
df = pd.concat({key:pd.DataFrame(dd[key]).T for key in dd.keys()})
print (df)
key1 key2 key3 key4
User A Obj1 val1 val2 val3 NaN
Obj2 val1 val2 val3 NaN
User B Obj1 val1 val2 val3 val4
另一种解决方案 read_json
, 但首先需要通过 unstack
进行整形并通过 dropna
删除 NaN
行.最后需要DataFrame.from_records
:
df = pd.read_json('file.json').unstack().dropna()
print (df)
User A Obj1 {'key2': 'val2', 'key1': 'val1', 'key3': 'val3'}
Obj2 {'key2': 'val2', 'key1': 'val1', 'key3': 'val3'}
User B Obj1 {'key2': 'val2', 'key4': 'val4', 'key1': 'val1...
dtype: object
df1 = pd.DataFrame.from_records(df.values.tolist())
print (df1)
key1 key2 key3 key4
0 val1 val2 val3 NaN
1 val1 val2 val3 NaN
2 val1 val2 val3 val4
df1 = pd.DataFrame.from_records(df.values.tolist(), index = df.index)
print (df1)
key1 key2 key3 key4
User A Obj1 val1 val2 val3 NaN
Obj2 val1 val2 val3 NaN
User B Obj1 val1 val2 val3 val4
关于Python Pandas - Json 到 DataFrame,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/41135894/