python 2.7 - 3 dimensional numpy array to multiindex pandas dataframe

Question

I have a 3 dimensional numpy array, (z, x, y). z is a time dimension and x and y are coordinates.

I want to convert this to a multiindexed pandas.DataFrame. I want the row index to be the z dimension and each column to have values from a unique x, y coordinate (and so, each column would be multi-indexed).

The simplest case (not multi-indexed):

>>> array.shape
(500L, 120L, 100L)

>>> df = pd.DataFrame(array[:,0,0])

>>> df.shape
(500, 1)

I've been trying to pass the whole array into a multiindex dataframe using pd.MultiIndex.from_arrays but I'm getting an error: NotImplementedError: > 1 ndim Categorical are not supported at this time

Looks like it should be fairly simple but I cant figure it out.

Answer 1

I find that a Series with a Multiindex is the most analagous pandas datatype for a numpy array with arbitrarily many dimensions (presumably 3 or more).

Here is some example code:

import pandas as pd
import numpy as np

time_vals = np.linspace(1, 50, 50)
x_vals = np.linspace(-5, 6, 12)
y_vals = np.linspace(-4, 5, 10)

measurements = np.random.rand(50,12,10)

#setup multiindex
mi = pd.MultiIndex.from_product([time_vals, x_vals, y_vals], names=['time', 'x', 'y'])

#connect multiindex to data and save as multiindexed Series
sr_multi = pd.Series(index=mi, data=measurements.flatten())

#pull out a dataframe of x, y at time=22
sr_multi.xs(22, level='time').unstack(level=0)

#pull out a dataframe of y, time at x=3
sr_multi.xs(3, level='x').unstack(level=1)