How to convert a pandas MultiIndex DataFrame into a 3D array

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.

Since df.values is a (15*100, 4)-shaped array, you can call reshape to make it a (15, 100, 4)-shaped array:

arr = df.values.reshape(15, 100, 4)

Then call transpose to rearrange the order of the axes:

arr = arr.transpose(2, 0, 1)

Now arr has shape (4, 15, 100).

---

Using reshape/transpose is ~960x faster than to_xarray().to_array():

In [21]: df = pd.DataFrame(np.random.randint(10, size=(15*100, 4)), index=pd.MultiIndex.from_product([range(15), range(100)], names=['A','B']), columns=list('colu'))

In [22]: %timeit arr = df.values.reshape(15, 100, 4).transpose(2, 0, 1)
3.31 µs ± 23.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [24]: %timeit df.to_xarray().to_array()
3.18 ms ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [25]: 3180/3.31
Out[25]: 960.7250755287009