shuffling/permutating a DataFrame in pandas

Question

What's a simple and efficient way to shuffle a dataframe in pandas, by rows or by columns? I.e. how to write a function `shuffle(df, n, axis=0)` that takes a dataframe, a number of shuffles `n`, and an axis (`axis=0` is rows, `axis=1` is columns) and returns a copy of the dataframe that has been shuffled `n` times. Edit: key is to do this without destroying the row/column labels of the dataframe. If you just shuffle `df.index` that loses all that information. I want the resulting `df` to be the same as the original except with the order of rows or order of columns different. Edit2: My question was unclear. When I say shuffle the rows, I mean shuffle each row independently. So if you have two columns `a` and `b`, I want each row shuffled on its own, so that you don't have the same associations between `a` and `b` as you do if you just re-order each row as a whole. Something like: ``` for 1...n: for each col in df: shuffle column return new_df ``` But hopefully more efficient than naive…

Accepted Answer

```
In [16]: def shuffle(df, n=1, axis=0):     
    ...:     df = df.copy()
    ...:     for _ in range(n):
    ...:         df.apply(np.random.shuffle, axis=axis)
    ...:     return df
    ...:

In [17]: df = pd.DataFrame({'A':range(10), 'B':range(10)})

In [18]: shuffle(df)

In [19]: df
Out[19]: 
   A  B
0  8  5
1  1  7
2  7  3
3  6  2
4  3  4
5  0  1
6  9  0
7  4  6
8  2  8
9  5  9
```

shuffling/permutating a DataFrame in pandas

Solution

Problem