Pandas, Apply Function to Data Frame That Returns One to Many Rows

Question

I have a pandas dataframe that I need to apply a function to. The function however, returns many items for a single row in the dataframe. I would like to create a new dataframe with the values returned from the function. So, far when I applied the function, the new dataframe had the same number of rows as the original dataframe, with the lists/series from the function as rows. What I need in the new dataframe are the values returned from the function as a column (flattned lists). Also, I would like to know whether this is a good way to use Pandas (in terms of harnessing the efficiency of the library) or will I be better off just sticking to pure python?

This is what I used:

def get_children(id):
    children = []
    suff = ['0', '1', '2', '3']
    for s in suff:
        children.append(id+s)

    return pd.Series(children)

df = pd.DataFrame()
df = all_attrbs['id'].apply(get_children)

Input:

all_attrbs:

id

0
1
2
3

Expected Output:

df:

t_id

00
01
02
03
11
12
13
14
20
21
22
23
30
31
32
33

Answer 1

I'm not sure what your data looks like, but here is a way to do it efficiently :

df = pd.DataFrame({'id' : [0,1,2,3,4,5,6]})

flattened_list = ["{}_{}".format(x, i) for i in range(4) for x in df['id']]

df2 = pd.DataFrame(flattened_list)

---

Output flattened_list :

['0_0',
 '1_0',
 '2_0',
 '3_0',
 '4_0',
 '5_0',
 '6_0',
 '0_1',
 '1_1',
 '2_1',
 '3_1',
 '4_1',
 '5_1',
 '6_1',
 '0_2',
 '1_2',
 '2_2',
 '3_2',
 '4_2',
 '5_2',
 '6_2',
 '0_3',
 '1_3',
 '2_3',
 '3_3',
 '4_3',
 '5_3',
 '6_3']