I have a pandas DataFrame with one feature, df['Computed Data'].

I want to extract the "membersStatus", "activable" part from every string and to put it in a new column.

I have tried to use ast.literal_eval() and it is working but only when I apply it to one value

x = ast.literal_eval(df["Computed Data"][0])
x["membersStatus"]["activable"]

'10'

It gives me : '10'. Which is what I want but for every dict in "Computed Data" and to put it in a new column.

I tried to do it with a for loop :

for n, i in enumerate(df["Computed Data"]):
    x = ast.literal_eval(df["Computed Data"][n])

ValueError: malformed node or string: <_ast.Name object at 0x13699c610>

I don't know how can I change what I did to make it work.

Can you Help please ?

CodePudding user response：

you can use apply methods to extract them:

df['member Status']=df['Computed Data'].apply(lambda x:eval(x)['membersStatus']['activable'])

CodePudding user response：

you can use:

import json
df['Computed Data']=df['Computed Data'].apply(json.loads)
df['activable']=df['Computed Data'].apply(lambda x: x['membersStatus']['activable'])

if there are nan values:

df['Computed Data']=df['Computed Data'].apply(lambda x: json.loads(x) if pd.notna(x) else np.nan)
df['activable']=df['Computed Data'].apply(lambda x: x['membersStatus']['activable'] if pd.notna(x) else np.nan)