Multidimensional numpy.ndarray from multi-indexed pandas.DataFrame

Question

I want to produce a 3-dimensional numpy.ndarray from a multi-indexed pandas.DataFrame. More precisely, say I have:

df = pd.DataFrame([[1, '1' , 1, 10], [1, '2', 2, 20], [2, '1', 5, 30]], columns=['x', 'y', 'z', 't'])
df = df.set_index(['x','y'])
df

which gives me

and I want to write a function which returns, with the above argument, the numpy.ndarray

  [[[1, 10],
    [2, 20]],
   [[5, 30],
    [NaN, NaN]]]

Pandas multi-index looks like a substitute for multidimensional arrays, but it does not provide (or at least does not document) ways to go back and forth...

Thanks.

Thanks, but this does not work: this returns a 2-dimensional array. — user0
– user0, Commented Jan 27, 2020 at 17:58

Tarifazo · Accepted Answer · 2020-01-27 19:22:16Z

1

Use:

df.to_xarray().to_array().values.transpose(1,2,0)

>>[[[ 1. 10.]
  [ 2. 20.]]

 [[ 5. 30.]
  [nan nan]]]

answered Jan 27, 2020 at 19:22

Tarifazo

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3 Comments

user0 Over a year ago

Thanks! So xarray is yet another library on top of numpy and pandas, which has to patch the deficiencies of pandas? PS: I'll upvote your answer when I have enough reputation.

Tarifazo Over a year ago

yes, it's a different data structure. more info here

Tarifazo Over a year ago

note the transpose is just to get the values in the order you specified.

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