将嵌套列拆分为新列

Question

My dataframe has a nested column (people_info) that contains cells like the sample below.

[{"institution":"some_institution","startMonth":1,"startYear":2563,"course":"any","id":1111,"formation":"any","endMonth":12,"endYear":2556,"status":"complete"}]

As far I know this can be solved using dictionary/json concepts.

I'm trying to split this column in new columns, considering that each key of this nested cell will be a new column with their respective values.

I tried json_normalize, but I'm getting this error: "AttributeError: 'str' object has no attribute 'values'"

I tried to transform those cells in a dict, but I never was able to make python understand that "institution" is a key and "some_institution" is a value in this created dict. It's seems python understand the whole cell as a string.

Can you help me? If I wasn't clear, please tell me. Tks!

Answer 1

IIUC, the following should work:

Input

df = pd.DataFrame({'col1':[1], 'col2':2, 'nested_column':'[{"institution":"some_institution","startMonth":1,"startYear":2563,"course":"any","id":1111,"formation":"any","endMonth":12,"endYear":2556,"status":"complete"}]'})

df

  col1  col2    nested_column
0    1     2    [{"institution":"some_institution","startMonth...

Process

import json
df['nested_column_dict'] = df['nested_column'].transform(lambda x : json.loads(x)[0] if x is not np.nan else {})
df = pd.concat([df, pd.DataFrame.from_records(df['nested_column_dict'])], axis=1)
df.drop('nested_column_dict', axis=1, inplace=True)

Output

 df

 col1   col2    nested_column                                           institution startMonth  startYear   course    id    formation   endMonth    endYear   status
0   1      2    [{"institution":"some_institution","startMonth...   some_institution         1      2563       any  1111          any         12       2556 complete

Answer 2

Maybe this helps.

import pandas as pd

data = [{"institution":"some_institution", "startMonth":1, "startYear":2563, "course":"any", "id":1111, "formation":"any", "endMonth":12, "endYear":2556, "status":"complete"}]

l = next(item for item in data)

df = pd.DataFrame(l, index=[0])

df