How to split a Dataframe column whose data is not unique - python-3.x

Question

I have a column called users in dataframe which doesn't have a unique format. I am doing a data cleanup project as the data looks unreadable.
company Users
A [{"Name":"Martin","Email":"name_1#email.com","EmpType":"Full"},{"Name":"Rick","Email":"name_2#email.com","Dept":"HR"}]
B [{"Name":"John","Email":"name_2#email.com","EmpType":"Full","Dept":"Sales" }]
I used the below query to this has broke down the data frame as below
df2 = df
df2 = df2.join(df['Users_config'].str.split('},{', expand=True).add_prefix('Users'))
company Users0 Users1
A "Name":"Martin","Email":"name_1#email.com","EmpType":"Full" "Name":"Rick","Email":"name_2#email.com","Dept":"HR"
B "Name":"John","Email":"name_2#email.com","EmpType":"Full","Dept":"Sales"
and further breaking the above df with "," using the same query I got the output as
Company Users01 Users02 Users03 Users10 Users11 Users12
1 "Name":"Martin" "Email":"name_1#email.com" "EmpType":"Full" "Name":"Rick" "Email":"name_2#email.com" "Dept":"HR"
2 "Name":"John" "Email":"name_2#email.com" "EmpType":"Full" "Dept":"Sales"
As this dataframe looks messy I want to get the output as below. I feel the best way to name the column is to use the column value "Name" from "Name":"Martin" itself and If we hardcore using df.rename the column name will get mismatch.
Company Name_1 Email_1 EmpType_1 Dept_1 Name_2 Email_2 Dept_2
1 Martin name_1#email.com Full Rick name_2#email.com "HR"
2 John name_2#email.com" Full Sales
Is there any way I can get the above output from the original dataframe.

Answer 1

Use:
df['Users'] = df['Users'].apply(ast.literal_eval)
d = df.explode('Users').reset_index(drop=True)
d = d.join(pd.DataFrame(d.pop('Users').tolist()))
d = d.set_index(['company', d.groupby('company').cumcount().add(1).astype(str)]).unstack()
d.columns = d.columns.map('_'.join)
Details:
First we use ast.literal_eval to evaluate the strings in Users column, then use DataFrame.explode on column Users to create a dataframe d.
print(d)
company Users
0 A {'Name': 'Martin', 'Email': 'name_1#email.com', 'EmpType': 'Full'}
1 A {'Name': 'Rick', 'Email': 'name_2#email.com', 'Dept': 'HR'}
2 B {'Name': 'John', 'Email': 'name_2#email.com', 'EmpType': 'Full', 'Dept': 'Sales'}
Create a new dataframe from the Users column in d and use DataFrame.join to join this new dataframe with d.
print(d)
company Name Email EmpType Dept
0 A Martin name_1#email.com Full NaN
1 A Rick name_2#email.com NaN HR
2 B John name_2#email.com Full Sales
Use DataFrame.groupby on column company then use groupby.cumcount to create a counter for each group, then use DataFrame.set_index to set the index of d as company + counter. Then use DataFrame.unstack to reshape the dataframe creating MultiIndex columns.
print(d)
Name Email EmpType Dept
1 2 1 2 1 2 1 2
company
A Martin Rick name_1#email.com name_2#email.com Full NaN NaN HR
B John NaN name_2#email.com NaN Full NaN Sales NaN
Finally use map along with .join to flatten the MultiIndex columns.
print(d)
Name_1 Name_2 Email_1 Email_2 EmpType_1 EmpType_2 Dept_1 Dept_2
company
A Martin Rick name_1#email.com name_2#email.com Full NaN NaN HR
B John NaN name_2#email.com NaN Full NaN Sales NaN