Filling all of an empty dataframe's rows or columns with a single series in pandas-CodePudding

Say I have a predefined data frame of size NxM (N rows and M columns), it is initialized with all NaNs.

Now say I have a series of size M, I want to fill all rows with that series. For example, if the series is of ints [3,4,5] where N=2, M=3, my resultant dataframe should be (I have included column and row indices):

	0	1	2
0	3	4	5
1	3	4	5

Alternatively, say I have a series of size N, I want to fill all columns with that series. For example, if the series is of ints [10, 11] where N=2, M=3, my resultant dataframe should be (I have included column and row indices):

	0	1	2
0	10	10	10
1	11	11	11

How do I do both of these operations in pure pandas without resorting to using something like np.repeat or np.tile?

CodePudding user response：

You can use np.broadcast_to, last if necessary transpose ouput for second sample data:

s = pd.Series([3,4,5])
N = 2
M = 3

df = pd.DataFrame(index=range(N), columns=range(M))

df[:] = np.broadcast_to(s.to_numpy(), (N, M))
print (df)
   0  1  2
0  3  4  5
1  3  4  5

s = pd.Series([10, 11])
N = 2
M = 3

df = pd.DataFrame(index=range(N), columns=range(M))
df[:] = np.broadcast_to(s.to_numpy(), (M, N)).T
print (df)
    0   1   2
0  10  10  10
1  11  11  11

s = pd.Series([3,4,5])
N = 2
M = 3

df = pd.DataFrame(np.broadcast_to(s.to_numpy(), (N, M)))
print (df)
   0  1  2
0  3  4  5
1  3  4  5

s = pd.Series([10, 11])
N = 2
M = 3


df = pd.DataFrame(np.broadcast_to(s.to_numpy(), (M, N)).T)
print (df)
    0   1   2
0  10  10  10
1  11  11  11