How to fill in rows with repeating data in pandas?

import pandas as pd
import numpy as np

def put(df, column, values):
    df[column] = 0
    np.put(df[column], np.arange(len(df)), values)

df = pd.DataFrame({'first':range(1, 8)})    
put(df, 'second', [1,2,3])

yields

   first  second
0      1       1
1      2       2
2      3       3
3      4       1
4      5       2
5      6       3
6      7       1

Not particularly beautiful, but one "feature" it possesses is that you do not have to worry if the length of the DataFrame is a multiple of the length of the repeated values. np.put repeats the values as necessary.

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My first answer was:

import itertools as IT
df['second'] = list(IT.islice(IT.cycle([1,2,3]), len(df)))

but it turns out this is significantly slower:

In [312]: df = pd.DataFrame({'first':range(10**6)})

In [313]: %timeit df['second'] = list(IT.islice(IT.cycle([1,2,3]), len(df)))
10 loops, best of 3: 143 ms per loop

In [316]: %timeit df['second'] = 0; np.put(df['second'], np.arange(N), [1,2,3])
10 loops, best of 3: 27.9 ms per loop

Seems there is no elegant way. This is the workaround I just figured out. Basically create a repeating list just bigger than original dataframe, and then left join them.

import pandas
df = pandas.DataFrame(range(100), columns=['first'])
repeat_arr = [1, 2, 3]
df = df.join(pandas.DataFrame(repeat_arr * (len(df)/len(repeat_arr)+1),
    columns=['second']))

The cycle method from itertools is good for repeating a common pattern.

from itertools import cycle

seq = cycle([1, 2, 3])
df['Seq'] = [next(seq) for count in range(df.shape[0])]