issue_comments

Your both methods worked! Thank you!

@keewis your answer (and a clarification that we can't do real "ragged" arrays) would make a useful cookbook or StackOverflow answer, since I suspect a lot of people have this question.

this ultimately depends on how the last dimension of A and B are related (or rather, how you want to model the relationship). If they are not related at all, simply use different dimension names: python In [2]: da1 = xr.DataArray(np.empty(shape=(2, 5, 100)), dims=("x", "y", "z1")) ...: da2 = xr.DataArray(np.empty(shape=(2, 5, 101)), dims=("x", "y", "z2")) ...: ds = xr.Dataset({"a": da1, "b": da2}) ...: ds Out[2]: <xarray.Dataset> Dimensions: (x: 2, y: 5, z1: 100, z2: 101) Dimensions without coordinates: x, y, z1, z2 Data variables: a (x, y, z1) float64 6.901e-310 6.901e-310 4.67e-310 ... 0.0 0.0 0.0 b (x, y, z2) float64 6.901e-310 6.901e-310 4.67e-310 ... 0.0 0.0 0.0

If they are related, assign coordinates to the dimensions: python In [3]: da1 = xr.DataArray( ...: np.empty(shape=(2, 5, 100)), ...: dims=("x", "y", "z"), ...: coords={"z": np.arange(100)}, ...: ) ...: da2 = xr.DataArray( ...: np.empty(shape=(2, 5, 101)), ...: dims=("x", "y", "z"), ...: coords={"z": np.arange(101)}, ...: ) ...: ds = xr.Dataset({"a": da1, "b": da2}) ...: ds Out[3]: <xarray.Dataset> Dimensions: (x: 2, y: 5, z: 101) Coordinates: * z (z) int64 0 1 2 3 4 5 6 7 8 9 10 ... 91 92 93 94 95 96 97 98 99 100 Dimensions without coordinates: x, y Data variables: a (x, y, z) float64 6.901e-310 6.901e-310 ... 6.917e-323 nan b (x, y, z) float64 6.901e-310 6.901e-310 ... 6.901e-310 -6.35e+53 In this case, A does not have the label z=100, so it is treated as missing (you should be familiar with the concept of "missing values" since you know pandas).

I dont try it, but i know your problem. If you try to create from dataarray df.to_dataset(name='participant_A') df.to_dataset(name='participant_B') and after merge them?

xr.merge([ds1, ds2], compat='no_conflicts')

http://xarray.pydata.org/en/stable/combining.html

In potter case you could create nan values to create the same dimensions.

But i have never tried. I found another solution for my data, but it was my alternative.

El dom., 19 abr. 2020 20:57, (Ray) Jinbiao Yang notifications@github.com escribió:

I always use Pandas to deal with my neuroscience data (multi-dimension). It is annoying to stack and unstack all the time and I heard Xarray is designed for multi-dimension data.

In neuroscience research, we usually have multiple participants and we will test them different times, which means the data may look like this:

• participant A:
  • 25100 matrix
• participant B:
  • 25101 matrix

(100 and 101 are the testing times)

But Dataset doesn't support to have 25100 DataArray and 25101 DataArray together. Is there any solution to deal with that kind of data in Xarray?

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