Iterate over list elements in pandas dataframe - each entry has different size and a new column needs to be generated w.r.t each entry in list

Question

I have a dataframe

Here i have a column called "name split" which is a column with lists. Now i want to split the contents of the lists and create separate columns for each.

This is what i have tried so far :

df = pd.read_csv("C:/Users/Transorg-PC/Desktop/Training/py/datase/football.csv")

temp = df.copy()

temp['name'] = temp['name'].apply(lambda x: ' '.join(x.split()))

temp['name split'] = temp['name'].apply(lambda x: x.split())

temp['length'] = temp['name split'].str.len()

for i in range(temp['length'].max()-1):
    temp[i] = temp['name split'].apply(lambda x:x[i])

But i am not able to iterate like this as for some cases the index goes out of bound. So how to split the contents of the list in separate columns.

Answer 1

Something Like Data from jpp

pd.concat([df,pd.DataFrame(df.name.tolist())],1)
Out[1596]: 
   A    name  0  1
0  1  [1, 2]  1  2
1  1  [3, 4]  3  4
2  2  [5, 6]  5  6

Update

df=pd.DataFrame([[1,[1, 2]],
                   [1,[3, 4]],
                   [2,[5, 6,1,1]]],
                  columns=['A','name'])
pd.concat([df,pd.DataFrame(df.name.tolist())],1)
Out[1602]: 
   A          name  0  1    2    3
0  1        [1, 2]  1  2  NaN  NaN
1  1        [3, 4]  3  4  NaN  NaN
2  2  [5, 6, 1, 1]  5  6  1.0  1.0

Answer 2

This is one way:

df = pd.DataFrame([[1,[1, 2, 3]],
                   [1,[3, 4]],
                   [2,[5, 6, 7, 8]]],
                  columns=['A','name'])

df = df.join(pd.DataFrame(df['name'].tolist()))

#    A          name    0    1    2    3
# 0  1     [1, 2, 3]  1.0  2.0  3.0  NaN
# 1  1        [3, 4]  3.0  4.0  NaN  NaN
# 2  2  [5, 6, 7, 8]  5.0  6.0  7.0  8.0