Labeled repr

It may be nice to take advantage of labels to show a different, labeled repr - especially for more than 3 dimensions, I personally find the the numpy array one hard to read.

Some sample data and the current repr

In [103]: d = xr.DataArray(np.arange(200).reshape((2,5,2,10)), dims=('a', 'b', 'c', 'd'),
     ...:                  coords={'a': ['A', 'B'], 'b': ['Cat 1', 'Cat 2', 'Cat 3', 'Cat 4', 'Cat 5'],
     ...:                          'c': ['J', 'K']})

In [104]: d
Out[104]: 
<xarray.DataArray (a: 2, b: 5, c: 2, d: 10)>
array([[[[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9],
         [ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19]],

        [[ 20,  21,  22,  23,  24,  25,  26,  27,  28,  29],
         [ 30,  31,  32,  33,  34,  35,  36,  37,  38,  39]],

        [[ 40,  41,  42,  43,  44,  45,  46,  47,  48,  49],
         [ 50,  51,  52,  53,  54,  55,  56,  57,  58,  59]],

        [[ 60,  61,  62,  63,  64,  65,  66,  67,  68,  69],
         [ 70,  71,  72,  73,  74,  75,  76,  77,  78,  79]],

        [[ 80,  81,  82,  83,  84,  85,  86,  87,  88,  89],
         [ 90,  91,  92,  93,  94,  95,  96,  97,  98,  99]]],


       [[[100, 101, 102, 103, 104, 105, 106, 107, 108, 109],
         [110, 111, 112, 113, 114, 115, 116, 117, 118, 119]],

        [[120, 121, 122, 123, 124, 125, 126, 127, 128, 129],
         [130, 131, 132, 133, 134, 135, 136, 137, 138, 139]],

        [[140, 141, 142, 143, 144, 145, 146, 147, 148, 149],
         [150, 151, 152, 153, 154, 155, 156, 157, 158, 159]],

        [[160, 161, 162, 163, 164, 165, 166, 167, 168, 169],
         [170, 171, 172, 173, 174, 175, 176, 177, 178, 179]],

        [[180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
         [190, 191, 192, 193, 194, 195, 196, 197, 198, 199]]]])
Coordinates:
  * a        (a) <U1 'A' 'B'
  * b        (b) <U5 'Cat 1' 'Cat 2' 'Cat 3' 'Cat 4' 'Cat 5'
  * c        (c) <U1 'J' 'K'
  * d        (d) int64 0 1 2 3 4 5 6 7 8 9

The labeled repr could instead look something (not exactly) like this?

<xarray.DataArray (a: 2, b: 5, c: 2, d: 10)>

a: 'A'
b: 'Cat 1'
c x d: 
         0   2   3   4   5   6   7   8   9  10
     J   0   1   2   3   4   5   6   7   8   9
     K  10  11  12  13  14  15  16  17  18  19


a: 'A'
b: 'Cat 2'
c x d
    <repeat>
...

Coordinates:
  * a        (a) <U1 'A' 'B'
  * b        (b) <U5 'Cat 1' 'Cat 2' 'Cat 3' 'Cat 4' 'Cat 5'
  * c        (c) <U1 'J' 'K'
  * d        (d) int64 0 1 2 3 4 5 6 7 8 9

[benbovy]

After seeing the discussion in #680, I’m wondering if showing the firsts values of the flattened array wouldn’t be enough here, e.g., something like this:

>>> d
<xarray.DataArray (a: 2, b: 5, c: 2, d: 10)>
  array          int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ...
Coordinates:
  * a        (a) <U1 'A' 'B'
  * b        (b) <U5 'Cat 1' 'Cat 2' 'Cat 3' 'Cat 4' 'Cat 5'
  * c        (c) <U1 'J' 'K'
  * d        (d) int64 0 1 2 3 4 5 6 7 8 9

This example is more consistent with the repr of Dataset data variables, and similarly we could customize the repr of dask arrays and lazy arrays (loaded from netcdf files) like this:

>>> d.chunk((10, 5, 5, 10))
<xarray.DataArray (a: 2, b: 5, c: 2, d: 10)>
  dask.array     int64 chunksize=(10, 5, 5, 10)
Coordinates:
  * a        (a) <U1 'A' 'B'
  * b        (b) <U5 'Cat 1' 'Cat 2' 'Cat 3' 'Cat 4' 'Cat 5'
  * c        (c) <U1 'J' 'K'
  * d        (d) int64 0 1 2 3 4 5 6 7 8 9

>>> d.name = 'myvar'
>>> d.to_netcdf('data.nc')
>>> xr.open_dataset('data.nc').myvar
<xarray.DataArray 'myvar' (a: 2, b: 5, c: 2, d: 10)>
  lazy-array     int64
Coordinates:
  * a        (a) <U1 'A' 'B'
  * b        (b) <U5 'Cat 1' 'Cat 2' 'Cat 3' 'Cat 4' 'Cat 5'
  * c        (c) <U1 'J' 'K'
  * d        (d) int64 0 1 2 3 4 5 6 7 8 9

[stale[bot]]

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