Increase default `display_max_rows` #5545

st-bender · 2021-06-28T13:46:14Z

This must have been introduced into xr.set_options() somewhere around version 0.17.
First of all this limit breaks backwards compatibility in the output format with print() or something similar on the console. Second, the default of 12 is much too low imo and makes no sense, in particular since terminals usually have a scrollback buffer and notebook cells can be made scrollable.

I use print() frequently to check that all variables made it into the data set correctly, which is meaningless when lines are skipped with this default limit. And it broke my doctests that I wrote to do exactly that (thanks for that btw.). So if not removed, could the default be at least increased to a sensible number like 100 or 1000 or 10000? (I'd personally prefer much higher or even no limit, but I guess that is not an option.)

Cheers.

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kmuehlbauer · 2021-06-28T13:56:32Z

Why not just use:

import xarray as xr
xr.set_options(display_max_rows=N)

where N is your wanted number of lines

The reasons for the restriction are laid out in #4736

Update: Fixed the call to set_options, stupid copy&paste error.

st-bender · 2021-06-28T14:19:01Z

Why not increase that number to a more sensible value (as I suggested), or make it optional if people have problems?
If people are concerned and have problems, then this would be an option to fix that, not the other way around. This enforces such a low limit onto all others.

keewis · 2021-06-28T15:06:23Z

choosing default values is hard and always some kind of a tradeoff. In general we should strive to choose a default that is useful to most users (but how do you measure "useful to most users"?), and if that is not the case for a particular use case it should be configurable. I agree that having to remember to add the set_options call in every script / notebook / interpreter session is annoying, but there is not too much we can do (except adding a configuration file? but that might make the set_options code more complicated...)

For this particular setting I think the idea for the current value was that printing a lot of variables is slow and, most importantly, that the repr should provide an overview of the object, and in my opinion not being able to have all sections visible reduces the usefulness of the repr (which is why I tend to also set display_expand_data=False at the top of my notebooks). In the PR that introduced the setting we somewhat arbitrarily chose the number 12 but I don't think it should be much higher.

Thoughts, @pydata/xarray?

Disabling the restriction should be possible (but again, not the default), maybe xr.set_options(display_max_rows=float("inf")) works?

st-bender · 2021-06-28T19:12:43Z

I switched off html rendering altogether because that really slows down the browser, haven't had any problems with the text output. The text output is (was) also much more concise and does not require additional clicks to open the dataset and see which variables are in there.

The problem with your suggestion is that this approach is not backwards compatible, which is not nice towards long-term users. A larger default would be a bit like meeting half-way. I also respectfully disagree about the purpose of __repr__(), see for example https://docs.python.org/3/reference/datamodel.html#object.__repr__ .
Cutting the output arbitrarily does not allow one to "recreate the object".

max-sixty · 2021-06-28T21:15:39Z

We need to cut some of the output, given a dataset has arbitrary size — same as numpy arrays / pandas dataframes.

If people feel strongly about a default > 12, that seems reasonable. Do people?

shoyer · 2021-06-28T23:18:08Z

I think a bigger number than 12 would be appropriate. Personally I would almost always prefer to see the full output, even (especially?) if it's big. The only exception would be cases where variables are (mis)used instead of a dimension, e.g., variable_1, variable_2, etc, but these cases are relatively rare and are not what Xarray is primarily designed for.

There are plenty of examples of datasets/netCDF files with tens of distinct variables, and I think we should reveal these variables by default. For example, consider this xgcm example of ocean model output with 35 data varaibles:
https://xgcm.readthedocs.io/en/latest/xgcm-examples/01_eccov4.html

I'm thinking that perhaps 50 or 100 would be a better default?

st-bender · 2021-06-29T08:36:04Z

Hi @max-sixty

We need to cut some of the output, given a dataset has arbitrary size — same as numpy arrays / pandas dataframes.

I thought about that too, but I believe these cases are slightly different. In numpy arrays you can almost guess how the full array looks like, you know the shape and get an impression of the magnitude of the entries (of course there can be exceptions which are not shown in the output). Similar for pandas series or dataframes, the skipped index values are quite easy to guess. The names of data variables in a dataset are almost impossible to guess, as are their dimensions and data types. The ellipsis is usually used to indicate some kind of continuation, which is not really the case with the data variables.

If people feel strongly about a default > 12, that seems reasonable. Do people?

I can't speak for other people, but I do, sorry about that. @shoyer 's suggestion sounds good to me, from the top of my head 30-100 variables in a dataset seems to be around what I have come across as a typical case. Which does not mean that it is the typical case.

max-sixty · 2021-06-29T17:41:35Z

Great! I would have vote lower, since 100 cuts off much of a screen, but maybe there's a synthesis of 50 or so?

max-sixty · 2021-06-29T18:50:40Z

If people feel strongly about a default > 12, that seems reasonable. Do people?

I can't speak for other people, but I do, sorry about that.

Also, just to be clear as things can come across in the wrong tone when we're responding quickly on issues — I appreciate you raising the issue and would like to find the right answer — I had meant the original message as a friendly call for opinions!

Illviljan · 2021-06-29T19:24:28Z

If you want to fix your doctests results you can try out: https://github.com/max-sixty/pytest-accept

xarray was the odd one out having an infinite repr when comparing to other data science packages. numpy and pandas (the gold standard?) for example are limiting it. which also probably makes those reprs meaningless for some users that are making sure all the elements/columns/series have been processed correctly.

I like 12 rows because that fills up pretty much 80% of the screen. It was in inline with the guideline suggested by @shoyer of a repr should fit in 1 screen in #4736 and you don't have to waste too much time scrolling up to figure out your calculation error for the 100th time.
I don't mind it being slightly larger but at around 16 rows I feel the scrolling starts to become rather annoying.

Some related ideas:

If only variables are important there could be some prioritization logic so that as many variables are shown within some limited amount of rows. I think I considered this but dropped it because it requires a larger refactor.
I think the __repr__ should be pretty short since it's easy that it gets accidentally triggered. __str__ however could be longer because that requires some intention to display it, for example print(ds).

Lets set up some examples:

import numpy as np
import xarray as xr

a = np.arange(0, 2000)
data_vars = dict()
for i in a:
    data_vars[f"long_variable_name_{i}"] = xr.DataArray(
        name=f"long_variable_name_{i}",
        data=2*np.arange(25),
        dims=[f"long_coord_name"],
        coords={f"long_coord_name": 2*np.arange(25)},
    )
ds0 = xr.Dataset(data_vars)
ds0.attrs = {f"attr_{k}": 2 for k in a}

Now if you convert this to a Pandas dataframe and print it will look like this:

df0 = ds0.to_dataframe()
print(df0)

                 long_variable_name_0  ...  long_variable_name_1999
long_coord_name                        ...                         
0                                   0  ...                        0
2                                   2  ...                        2
4                                   4  ...                        4
6                                   6  ...                        6
8                                   8  ...                        8
                              ...  ...                      ...
190                               190  ...                      190
192                               192  ...                      192
194                               194  ...                      194
196                               196  ...                      196
198                               198  ...                      198

[100 rows x 2000 columns]

The xarray dataset looks like this:

print(ds0)

<xarray.Dataset>
Dimensions:                  (long_coord_name: 100)
Coordinates:
  * long_coord_name          (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
Data variables: (12/2000)
    long_variable_name_0     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_2     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_3     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_4     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_5     (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
                      ...
    long_variable_name_1994  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1995  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1996  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1997  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1998  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
    long_variable_name_1999  (long_coord_name) int32 0 2 4 6 ... 192 194 196 198
Attributes: (12/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
        ...
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

xarray shows a lot more information in fewer rows which is good and I think its size is still manageable.

Lets try some of the suggestions, I apologize for the long post but remember that this is the kind of scrolling you're going to have to do 100 times when figuring out where that invisible typo is:

import numpy as np
import xarray as xr

a = np.arange(0, 2000)
data_vars = dict()
for i in a:
    data_vars[f"long_variable_name_{i}"] = xr.DataArray(
        name=f"long_variable_name_{i}",
        data=2*np.arange(25),
        dims=[f"long_coord_name_{np.mod(i, 25)}"],
        coords={f"long_coord_name_{np.mod(i, 25)}": 2*np.arange(25)},
    )
ds0 = xr.Dataset(data_vars)
ds0.attrs = {f"attr_{k}": 2 for k in a}

with xr.set_options(display_max_rows=12):
    print(ds0)
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates: (12/25)
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (12/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_mame_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (12/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
        ...
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

with xr.set_options(display_max_rows=16):
    print(ds0)
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates: (16/25)
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (16/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 9 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (16/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
        ...
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

with xr.set_options(display_max_rows=20):
    print(ds0)

<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates: (20/25)
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_8        (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_9        (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
  * long_coord_name_15       (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_16       (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (20/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_8     (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_9     (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
                      ...
    long_variable_name_1990  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1991  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (20/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
    attr_8:     2
    attr_9:     2
        ...
    attr_1990:  2
    attr_1991:  2
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1966:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

with xr.set_options(display_max_rows=24):
    print(ds0)
    
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates: (24/25)
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_8        (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_9        (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_10       (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_11       (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
                      ...
  * long_coord_name_13       (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_14       (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_15       (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_16       (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (24/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_varrable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_8     (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_9     (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_10    (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_11    (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
                      ...
    long_variable_name_1988  (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1989  (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1990  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1991  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (24/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
    attr_8:     2
    attr_9:     2
    attr_10:    2
    attr_11:    2
        ...
    attr_1988:  2
    attr_1989:  2
    attr_1990:  2
    attr_1991:  2
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

with xr.set_options(display_max_rows=30):
    print(ds0)
    
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates:
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coood_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_8        (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_9        (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_10       (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_11       (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_12       (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_13       (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_14       (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_15       (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_16       (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (30/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_8     (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_9     (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_10    (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_11    (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_12    (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_13    (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_14    (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
                      ...
    long_variable_name_1985  (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1986  (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1987  (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1988  (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1989  (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1990  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1991  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (30/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
    attr_8:     2
    attr_9:     2
    attr_10:    2
    attr_11:    2
    attr_12:    2
    attr_13:    2
    attr_14:    2
        ...
    attr_1985:  2
    attr_1986:  2
    attr_1987:  2
    attr_1988:  2
    attr_1989:  2
    attr_1990:  2
    attr_1991:  2
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

There's a noticeable slow down around here:

with xr.set_options(display_max_rows=50):
    print(ds0)
    
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates:
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_8        (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_9        (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_10       (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_11       (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_12       (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_13       (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_14       (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_15       (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_16       (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (50/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_8     (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_9     (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_10    (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_11    (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_12    (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_13    (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_14    (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_15    (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_16    (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_17    (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_18    (long_coord_name_18) int16 0 2 4 6 ... 42 44 46 48
    long_variable_name_19    (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_20    (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_21    (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_22    (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_23    (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_24    (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
                      ...
    long_variable_name_1975  (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1976  (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1977  (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1978  (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1979  (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1980  (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1981  (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1982  (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1983  (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1984  (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1985  (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1986  (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1987  (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1988  (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1989  (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1990  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1991  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (50/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
    attr_8:     2
    attr_9:     2
    attr_10:    2
    attr_11:    2
    attr_12:    2
    attr_13:    2
    attr_14:    2
    attr_15:    2
    attr_16:    2
    attr_17:    2
    attr_18:    2
    attr_19:    2
    attr_20:    2
    attr_21:    2
    attr_22:    2
    attr_23:    2
    attr_24:    2
        ...
    attr_1975:  2
    attr_1976:  2
    attr_1977:  2
    attr_1978:  2
    attr_1979:  2
    attr_1980:  2
    attr_1981:  2
    attr_1982:  2
    attr_1983:  2
    attr_1984:  2
    attr_1985:  2
    attr_1986:  2
    attr_1987:  2
    attr_1988:  2
    attr_1989:  2
    attr_1990:  2
    attr_1991:  2
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

with xr.set_options(display_max_rows=100):
    print(ds0)
    
<xarray.Dataset>
Dimensions:                  (long_coord_name_0: 25, long_coord_name_1: 25, long_coord_name_10: 25, long_coord_name_11: 25, long_coord_name_12: 25, long_coord_name_13: 25, long_coord_name_14: 25, long_coord_name_15: 25, long_coord_name_16: 25, long_coord_name_17: 25, long_coord_name_18: 25, long_coord_name_19: 25, long_coord_name_2: 25, long_coord_name_20: 25, long_coord_name_21: 25, long_coord_name_22: 25, long_coord_name_23: 25, long_coord_name_24: 25, long_coord_name_3: 25, long_coord_name_4: 25, long_coord_name_5: 25, long_coord_name_6: 25, long_coord_name_7: 25, long_coord_name_8: 25, long_coord_name_9: 25)
Coordinates:
  * long_coord_name_0        (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_1        (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_2        (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_3        (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_4        (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_5        (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_6        (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_7        (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_8        (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_9        (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
  * long_coord_name_10       (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_11       (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_12       (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_13       (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_14       (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_15       (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_16       (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_17       (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_18       (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_19       (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_20       (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_21       (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_22       (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_23       (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
  * long_coord_name_24       (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Data variables: (100/2000)
    long_variable_name_0     (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1     (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_2     (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_3     (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_4     (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_5     (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_6     (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_7     (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_8     (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_9     (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_10    (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_11    (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_12    (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_13    (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_14    (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_15    (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_16    (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_17    (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_18    (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_19    (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_20    (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_21    (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_22    (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_23    (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_24    (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_25    (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_26    (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_27    (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_28    (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_29    (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_30    (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_31    (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_32    (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_33    (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_34    (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_35    (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_36    (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_37    (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_38    (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_39    (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_40    (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_41    (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_42    (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_43    (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_44    (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_45    (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_46    (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_47    (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_48    (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_49    (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
                      ...
    long_variable_name_1950  (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1951  (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1952  (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1953  (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1954  (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1955  (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1956  (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1957  (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1958  (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1959  (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1960  (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1961  (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1962  (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1963  (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1964  (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1965  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1966  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1967  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1968  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1969  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1970  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1971  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1972  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1973  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1974  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1975  (long_coord_name_0) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1976  (long_coord_name_1) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1977  (long_coord_name_2) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1978  (long_coord_name_3) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1979  (long_coord_name_4) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1980  (long_coord_name_5) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1981  (long_coord_name_6) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1982  (long_coord_name_7) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1983  (long_coord_name_8) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1984  (long_coord_name_9) int32 0 2 4 6 8 ... 42 44 46 48
    long_variable_name_1985  (long_coord_name_10) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1986  (long_coord_name_11) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1987  (long_coord_name_12) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1988  (long_coord_name_13) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1989  (long_coord_name_14) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1990  (long_coord_name_15) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1991  (long_coord_name_16) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1992  (long_coord_name_17) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1993  (long_coord_name_18) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1994  (long_coord_name_19) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1995  (long_coord_name_20) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1996  (long_coord_name_21) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1997  (long_coord_name_22) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1998  (long_coord_name_23) int32 0 2 4 6 ... 42 44 46 48
    long_variable_name_1999  (long_coord_name_24) int32 0 2 4 6 ... 42 44 46 48
Attributes: (100/2000)
    attr_0:     2
    attr_1:     2
    attr_2:     2
    attr_3:     2
    attr_4:     2
    attr_5:     2
    attr_6:     2
    attr_7:     2
    attr_8:     2
    attr_9:     2
    attr_10:    2
    attr_11:    2
    attr_12:    2
    attr_13:    2
    attr_14:    2
    attr_15:    2
    attr_16:    2
    attr_17:    2
    attr_18:    2
    attr_19:    2
    attr_20:    2
    attr_21:    2
    attr_22:    2
    attr_23:    2
    attr_24:    2
    attr_25:    2
    attr_26:    2
    attr_27:    2
    attr_28:    2
    attr_29:    2
    attr_30:    2
    attr_31:    2
    attr_32:    2
    attr_33:    2
    attr_34:    2
    attr_35:    2
    attr_36:    2
    attr_37:    2
    attr_38:    2
    attr_39:    2
    attr_40:    2
    attr_41:    2
    attr_42:    2
    attr_43:    2
    attr_44:    2
    attr_45:    2
    attr_46:    2
    attr_47:    2
    attr_48:    2
    attr_49:    2
        ...
    attr_1950:  2
    attr_1951:  2
    attr_1952:  2
    attr_1953:  2
    attr_1954:  2
    attr_1955:  2
    attr_1956:  2
    attr_1957:  2
    attr_1958:  2
    attr_1959:  2
    attr_1960:  2
    attr_1961:  2
    attr_1962:  2
    attr_1963:  2
    attr_1964:  2
    attr_1965:  2
    attr_1966:  2
    attr_1967:  2
    attr_1968:  2
    attr_1969:  2
    attr_1970:  2
    attr_1971:  2
    att__1972:  2
    attr_1973:  2
    attr_1974:  2
    attr_1975:  2
    attr_1976:  2
    attr_1977:  2
    attr_1978:  2
    attr_1979:  2
    attr_1980:  2
    attr_1981:  2
    attr_1982:  2
    attr_1983:  2
    attr_1984:  2
    attr_1985:  2
    attr_1986:  2
    attr_1987:  2
    attr_1988:  2
    attr_1989:  2
    attr_1990:  2
    attr_1991:  2
    attr_1992:  2
    attr_1993:  2
    attr_1994:  2
    attr_1995:  2
    attr_1996:  2
    attr_1997:  2
    attr_1998:  2
    attr_1999:  2

By the way, I've added some typos in each of these, so you can haves some fun figuring out where they are as you're scrolling up and down the reprs. :)

st-bender · 2021-06-30T19:36:26Z

Hi @Illviljan,
As I mentioned earlier, your "solution" is not backwards compatible, and it would be counterproductive to update the doctest. Which is also not relevant here and a different issue.

I am not sure what you are trying to show, your datasets look very different from what I am working with, and they miss the point. Then again they also prove my point, pandas and numpy shorten in a canonical way (except the finite number of columns, which may make sense, but I don't like that either and would rather have it wrap but show all columns). xarray doesn't because usually the variables are not simply numbered as in your example.

I am talking about medium sized datasets of a few 10 to maybe a few 100 non-canonical data variables. Have a look at http://cfconventions.org/ to get an impression of real-world variable names, or the example linked above in comment #5545 (comment).
There it would be nice to have an overview over all of them.

If too many variables are a problem, imo it would have been better to say:
"We keep it as it is, however, if it is a problem for your large dataset, here is an option to reduce the amount of output: ..." And put that into the docs or the wiki or FAQ or something similar.
Note that the initial point in the linked issue is about the time it takes to print all variables, not the amount that gets shown. And usually the number of coordinates and attributes is smaller than the number of data variables.
It also depends on what you call "screen", my terminal has currently 48 lines (about 56 in fullscreen, depending on fontsize), and a scrollback buffer of 5000 lines, I am also used to scrolling through long jupyter notebooks. Scrolling through your examples might be tedious (not for me actually), but I will never be able to find typos hidden in the three dots.

@max-sixty No worries, I understand that this is a minor cosmetic issue, actually I intended it as a feature request, not a bug. But that must have gone missing along the way.
I guess I could live with 50, any other opinions? I am sure someone else will complain about that too. ;)

max-sixty · 2021-07-01T00:12:14Z

@st-bender I don't imagine you intend it, but the first two paragraphs of your comment read as abrasive to me. Others should weigh in if they disagree with my assessment.

FWIW I found @Illviljan 's examples very helpful to visualize the various options.

Back to the question: for me, 50 is longer than ideal; it spans my 27" portrait monitor. 20 seems good. We could also have fewer attrs (or coords too?). But as before, no strong view, and I'm rarely dealing with datasets like this

benbovy · 2021-07-01T07:47:51Z

My 2 cents (no strong view either, I'm mostly using the HTML repr with a rather small number of variables):

I do agree with both arguments "fit the screen" vs "need to see all the variables", so why not have different display rules for the Dataset, Dataset.data_vars and Dataset.coords reprs?

For the Dataset repr, I think the motivation is mostly to get a quick overview of the whole dataset and basic answers on questions like:

What are the dimensions and their size?
How many coordinates and data variables?
Is the dataset fully loaded in memory or lazily loaded? Are the variables chunked? What's the type of arrays (dask vs. numpy)?
Is there a lot of metadata or no metadata at all (global attributes)?

All those questions can be answered with a short, truncated repr.

For the Dataset.data_vars and Dataset.coords reprs, it's more obvious that we want to see all of them, so I'd suggest not limiting the maximum of rows displayed (or have a much larger limit? but then we duplicate the display options, which is not very nice).

st-bender · 2021-07-01T17:26:23Z

@max-sixty I apologize if I hurt someone, but it is hard to find a solution if we can't agree on the problem. Try the same examples with 50 or 100 instead of 2000 variables to understand what I mean.
And to be honest, I found your comments a bit dismissive and not exactly welcoming too, which is probably also not your intention.

From what I see in the examples by @Illviljan , setting display_max_rows affects everything equally, coords, data_vars, and attrs. So there would be no need to treat them separately. Or I misunderstood your comment.

Anyway, I think I made my point, I leave it up to you to decide what you are comfortable with.

benbovy · 2021-07-01T18:27:04Z

From what I see in the examples by @Illviljan , setting display_max_rows affects everything equally, coords, data_vars, and attrs. So there would be no need to treat them separately. Or I misunderstood your comment.

My suggestion is to ignore display_max_rows for the repr of the Dataset.coords and Dataset.data_vars properties, but not for the "coordinates" and "data variables" sections of the Dataset text-based repr. This way we could do print(ds.data_vars) to check that all variables made it into the data set correctly while keeping concise the output of print(ds).

max-sixty · 2021-07-01T18:51:17Z

For the Dataset.data_vars and Dataset.coords reprs, it's more obvious that we want to see all of them, so I'd suggest not limiting the maximum of rows displayed

IMO this would be an excellent synthesis of the tradeoffs, +1

st-bender · 2021-07-02T18:46:43Z

@benbovy That sounds good to me. If I may add, I would leave __repr__ and __str__ to return the same things, since people seem to use them interchangeably, e.g. in tutorials, and probably in their own code and notebooks.

max-sixty · 2021-07-04T17:19:12Z

Great, I think we're decided!

@st-bender would you be interested in a PR to kick some of this off? It would be fine to do a subset (e.g. ignoring the limit for the .coords and .data_vars methods). I feel like we could parlay any tension above into a nice contribution. Also fine if not!

st-bender · 2021-07-05T15:58:00Z

Hi,
@max-sixty I could give it a try, but my time is quite limited.
Would you be fine with a diff? That would save me a bit from setting up a fork and new repo.

Anyway, here is a quick diff, I tried to keep it small and basically moved the max_rows setting to dataset_repr, only coords_repr takes a new keyword argument, so that should be backwards compatible.
The tests would need to be updated. Maybe it is a good idea to not test _mapping_repr, but instead test coords_repr, data_vars_repr, attrs_repr, and dataset_repr separately, to check that they do what they are supposed to do regardless of their implementation?

Edit: Never mind, I am preparing a PR with updated tests.

diff --git a/xarray/core/formatting.py b/xarray/core/formatting.py
index 07864e81..ab30facf 100644                                    
--- a/xarray/core/formatting.py            
+++ b/xarray/core/formatting.py
@@ -377,14 +377,12 @@ def _mapping_repr(                
 ):                                                              
     if col_width is None:
         col_width = _calculate_col_width(mapping)
-    if max_rows is None:                                           
-        max_rows = OPTIONS["display_max_rows"]                                        
     summary = [f"{title}:"]
     if mapping:                                                 
         len_mapping = len(mapping)
         if not _get_boolean_with_default(expand_option_name, default=True):
             summary = [f"{summary[0]} ({len_mapping})"]
-        elif len_mapping > max_rows:                                 
+        elif max_rows is not None and len_mapping > max_rows:                           
             summary = [f"{summary[0]} ({max_rows}/{len_mapping})"]
             first_rows = max_rows // 2 + max_rows % 2
             items = list(mapping.items())   
@@ -416,7 +414,7 @@ attrs_repr = functools.partial(             
 )
                              

-def coords_repr(coords, col_width=None):                                                                      
+def coords_repr(coords, col_width=None, max_rows=None):                                                       
     if col_width is None:
         col_width = _calculate_col_width(_get_col_items(coords))
     return _mapping_repr(
@@ -425,6 +423,7 @@ def coords_repr(coords, col_width=None):
         summarizer=summarize_coord,
         expand_option_name="display_expand_coords",
         col_width=col_width,
+        max_rows=max_rows,
     )
@@ -542,21 +541,22 @@ def dataset_repr(ds):
     summary = ["<xarray.{}>".format(type(ds).__name__)]

     col_width = _calculate_col_width(_get_col_items(ds.variables))
+    max_rows = OPTIONS["display_max_rows"]

     dims_start = pretty_print("Dimensions:", col_width)
     summary.append("{}({})".format(dims_start, dim_summary(ds)))

     if ds.coords:
-        summary.append(coords_repr(ds.coords, col_width=col_width))
+        summary.append(coords_repr(ds.coords, col_width=col_width, max_rows=max_rows))

     unindexed_dims_str = unindexed_dims_repr(ds.dims, ds.coords)
     if unindexed_dims_str:
         summary.append(unindexed_dims_str)

-    summary.append(data_vars_repr(ds.data_vars, col_width=col_width))
+    summary.append(data_vars_repr(ds.data_vars, col_width=col_width, max_rows=max_rows))

     if ds.attrs:
-        summary.append(attrs_repr(ds.attrs))
+        summary.append(attrs_repr(ds.attrs, max_rows=max_rows))

     return "\n".join(summary)

As discussed in pydata#5545 the default setting of `display_max_rows` is sometimes less useful to inspect datasets for completeness, and changing the option is not backwards compatible. In addition, a concise and short output dataset seems to be preferred by most people. The compromise is to keep the dataset's `__repr__` short and tunable via `xr.set_options(display_max_rows=...)`, and at the same time to enable the output of all items by explicitly requesting `ds.coords`, `ds.data_vars`, and `ds.attrs`. These explicit `__repr__`s also restore backwards compatibility in these cases. Slightly changes the internal implementation of `_mapping_repr()`: Setting (leaving) `max_rows` to `None` means "no limits".

Updates the dataset `__repr__` test to assure that the dataset output honours the `display_max_rows` setting, not the `data_vars` output. Discussed in pydata#5545

Extends the dataset `__repr__` test to ensure that the output of `ds.coords`, `ds.data_vars`, and `ds.attrs` is of full length as desired. Includes more dimensions and coordinates to cover more cases. Discussed in pydata#5545

@keewis

* Delegate `max_rows` from dataset `__repr__` As discussed in #5545 the default setting of `display_max_rows` is sometimes less useful to inspect datasets for completeness, and changing the option is not backwards compatible. In addition, a concise and short output dataset seems to be preferred by most people. The compromise is to keep the dataset's `__repr__` short and tunable via `xr.set_options(display_max_rows=...)`, and at the same time to enable the output of all items by explicitly requesting `ds.coords`, `ds.data_vars`, and `ds.attrs`. These explicit `__repr__`s also restore backwards compatibility in these cases. Slightly changes the internal implementation of `_mapping_repr()`: Setting (leaving) `max_rows` to `None` means "no limits". * tests: Update dataset `__repr__` tests [1/2] Updates the dataset `__repr__` test to assure that the dataset output honours the `display_max_rows` setting, not the `data_vars` output. Discussed in #5545 * tests: Extend dataset `__repr__` tests [2/2] Extends the dataset `__repr__` test to ensure that the output of `ds.coords`, `ds.data_vars`, and `ds.attrs` is of full length as desired. Includes more dimensions and coordinates to cover more cases. Discussed in #5545 * doc: Add what's new entry for `__repr__` changes Sorted as a "breaking change" for 0.18.3 for now. * Revert "doc: Add what's new entry for `__repr__` changes" This reverts commit 3dd645b. * doc: Add what's new entry for `__repr__` changes Sorted as a "breaking change", for 0.19.1 for now. * doc: Remove `attrs` from `__repr__` changes Address comment from @keewis: `.attrs` is a standard python dict, so there's no custom repr. * tests: Remove `ds.attrs` formatting test According to @keewis: I don't think we need to test this because `attrs_repr` will only ever be called by `dataset_repr` / `array_repr`: on its own, the standard python `dict`'s `repr` will be used. * tests: Fix no. of coordinates in formatting_repr The number of coordinates changed to be the same as the number of variables, which only incidentally was fixed to 40. Updates the to-be-tested format string to use the same number of variables instead of the hard-coded one, which might be subject to change.

st-bender mentioned this issue Jul 5, 2021

dataset __repr__ updates #5580

Merged

5 tasks

max-sixty closed this as completed in #5580 Aug 21, 2021

oliverwm1 mentioned this issue Aug 14, 2022

Add --max-rows option with large default oliverwm1/zarrdump#9

Merged

Uh oh!

Increase default display_max_rows #5545

Increase default display_max_rows #5545

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Increase default `display_max_rows` #5545

Increase default `display_max_rows` #5545

st-bender commented Jun 28, 2021 •

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kmuehlbauer commented Jun 28, 2021 •

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max-sixty commented Jul 1, 2021 •

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st-bender commented Jul 5, 2021 •

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