How to expand array type column in Pandas to individual columns-CodePudding

I have a df, sample data looks like below:

df

user    collaborators
A       {'first_name' : 'jack', 'last_name' : 'abc', 'id' : '[email protected]'}, {'first_name' : 'john', 'last_name' : 'pqr', 'id' : '[email protected]'}
B       {'first_name' : 'tom', 'last_name' : 'pwc', 'id' : '[email protected]'}
C       {'first_name' : 'jill', 'last_name' : 'def', 'id' : '[email protected]'}, {'first_name' : 'jill', 'last_name' : 'def', 'id' : '[email protected]'}

I need to get the first_name, last_name and id in individual columns. Expected Output:

I first tried to remove the square brackets using:

df['collaborators'].str.strip('{}')
df['collaborators'].str.replace('[{}]', '')

But I got only NaN as the result, not sure why. I thought of exploding the column after converting it to a list and extracting first_name, last_name and id. But all this looks like lot of steps. Could someone please let me know if there's a shorter way.

Updated DF:

I used to_list to convert the three columns into list:

df['first_name'] = df['first_name'].to_list()
df['last_name'] = df['last_name'].to_list()
df['id'] = df['id'].to_list()

I then tried below code from SO:

 df.set_index('collaborators').apply(lambda x:
    x.apply(pd.Series).stack()).reset_index().drop('level_1', 1)

But it didn't work for me.

CodePudding user response：

Use ast.literal_eval with add [] for lists of DataFrames, so possible use