how to spead a list and put into a new column based on number of rows

lets say I have a brand of list:

cat_list = ['a', 'b', 'ab']

and I want this list to repeteadly fill a new column called category as much as my rows have.

I want the first row have 'a', the second row have 'b', and the third row have 'ab', and the cycle repeats until the last rows like the example below:

type     value     category
a         25          a
a         25          b
a         25          ab
b         50          a
b         50          b
b         50          ab

What I have tried so far is:

cat_list = ['a', 'b', 'ab']
df['category'] = cat_list * len(df)

but I got this kind of error

Length of values does not match length of index

how should I fix my script in order to get the desired results?

thanks.

Answer

Use numpy.tile by repeat with integer division for number of repeats:

df = pd.DataFrame({'a':range(8)})

cat_list = ['a', 'b', 'ab']
df['category'] = np.tile(cat_list, (len(df.index) // len(cat_list)) + 1)[:len(df.index)]
print (df)
   a category
0  0        a
1  1        b
2  2       ab
3  3        a
4  4        b
5  5       ab
6  6        a
7  7        b