Suppose I have a MultiIndex DataFrame:

                                c       o       l       u
major       timestamp                       
ONE         2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008

TWO         2019-01-22 18:12:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:13:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:14:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:15:00 0.00008 0.00008 0.00008 0.00008 
            2019-01-22 18:16:00 0.00008 0.00008 0.00008 0.00008

I want to generate a NumPy array from this DataFrame with a 3-dimensional, given the dataframe has 15 categories in the major column, 4 columns and one time index of length 5. I would like to create a numpy array with a shape of (4,15,5) denoting (columns, categories, time_index) respectively.

should create an array:

array([[[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
        [8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],

       [[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
        [8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],

       [[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
        [8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]],

       [[8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05],
        [8.e-05, 8.e-05, 8.e-05, 8.e-05, 8.e-05]]])

One used to be able to do this with pd.Panel:

panel = pd.Panel(items=[columns], major_axis=[categories], minor_axis=[time_index], dtype=np.float32)
... 

How would I be able to most effectively accomplish this with a multi index dataframe? Thanks

How about using xarray?

res = df.to_xarray().to_array()

Result is an array of shape (4, 15, 5)

In fact the docs now recommend this as an alternative to pandas Panel. Note that you must have the xarray package installed.