ENH: add new method `DataFrame.repeat`

Is your feature request related to a problem?

I sometimes would generate some faker but having rule data. Just like in a (2d) data, repeat some rows or columns serial times.

The steps I do:

1. This is a DataFrame called df.
2. value = df.values # change DataFrame to ndarray
3. use np.repeat or np.tile method to repeat.

Describe the solution you’d like

I thought why not add a method repeat to DataFrame, since Series has already owned the repeat method.

About the DataFrame.repeat parameters

Similar to numpy.repeat(a, repeats, axis=None)

def repeat(
    self,
    repeats: int | list[int],
    axis: int | str = 0,
) -> pd.DataFrame | None:
    ...

DataFrame is a 2d data structure, so it is hard to repeat each element. And the aim of DataFrame.repeat is to repeat one row/column or some rows/columns.

So the axis argument must be specific to 0 or 1.

And if the repeats is a single value int, that means repeat all row/column repeats times.

Examples of this method

>>> df = pd.DataFrame({'a': [1, 2], 'b':[3, 4]})
>>> df
    a  b
0  1  3
1  2  4

Each row repeat two times.

>>> df.repeat(2)
    a  b
0  1  3
0  1  3
1  2  4
1  2  4

Each column repeat two times.

>>> df.repeat(2, 1)
    a  a  b  b
0  1  1  3  3
1  2  2  4  4

``a`` column repeat 1 times, ``b`` column repeat 2 times.

>>> df.repeat([1, 2], 1)
    a  b  b
0  1  3  3
1  2  4  4

[jreback]

-1 on adding even more api to a huge api

happy to take an example though for the doc string

[samukweku]

I’m not sure if it makes sense to have duplicate columns, but this is how you can repeat a dataframe with the index(or columns, which is an index too):

df.reindex(df.index.repeat(2))  # repeat on the index

   a  b
0  1  3
0  1  3
1  2  4
1  2  4


df.reindex(columns=df.columns.repeat(2)) # repeat on the columns

   a  a  b  b
0  1  1  3  3
1  2  2  4  4


 df.reindex(columns=df.columns.repeat([1,2])) # repeat column a once, b twice

   a  b  b
0  1  3  3
1  2  4  4

Reindex does not reset the index, it just conforms it to the new index. It only works if the existing index is unique.