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 looping. This does not work for me: def shuffle(df, n, axis=0): shuffled_df = df.copy() for k in range(n): shuffled_df.apply(np.random.shuffle(shuffled_df.values),axis=axis) return shuffled_df df = pandas.DataFrame({'A':range(10), 'B':range(10)}) shuffle(df, 5) Continue reading...
