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Just to denote that the issue still happens today with numpy=1.18.1, xarray=0.15.0, pandas=1.0.1 ```

df = nc.to_dataframe() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/xarray/core/dataset.py", line 4465, in to_dataframe return self._to_dataframe(self.dims) File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/xarray/core/dataset.py", line 4451, in _to_dataframe data = [ File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/xarray/core/dataset.py", line 4452, in <listcomp> self._variables[k].set_dims(ordered_dims).values.reshape(-1) File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/xarray/core/variable.py", line 1345, in set_dims expanded_data = duck_array_ops.broadcast_to(self.data, tmp_shape) File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/xarray/core/duck_array_ops.py", line 47, in f return wrapped(args, *kwargs) File "<array_function internals>", line 5, in broadcast_to File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/numpy/lib/stride_tricks.py", line 182, in broadcast_to return _broadcast_to(array, shape, subok=subok, readonly=True) File "/opt/miniconda3/envs/prod/lib/python3.8/site-packages/numpy/lib/stride_tricks.py", line 125, in _broadcast_to it = np.nditer( ValueError: iterator is too large ```

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Something maybe of interest.

I recently converted some tools we have to do the above from Python 2 to 3. When the files were read in the byte chars were not converted to strings. I couldn't actually get this to work on the xarray side and had to loop through the DataFrame columns with apply(.decode("utf-8")) to decode them properly. I'm assuming this might be in the NetCDF4 library, but not 100% sure.

@mogismog Awesome, thanks so much for the workaround :)

Redeeming myself (only a little bit) from my previous message here:

@akrherz Was messing around with this a bit, this seems to work ok. This gets rid of unnecessary dimensions, concatenates string arrays, and turns it into a pandas DataFrame:

``` [In [1]: import xarray as xr

In [2]: ds = xr.open_dataset('20160430_1600.nc', decode_cf=True, mask_and_scale=False, decode_times=False) # xarray has issue decoding the times, so you'll have to do this in pandas.

In [3]: vars_to_drop = [k for k in ds.variables.iterkeys() if ('recNum' not in ds[k].dims)]

In [4]: ds = ds.drop(vars_to_drop)

In [5]: df = ds.to_dataframe()

In [6]: df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 6277 entries, 0 to 6276 Data columns (total 93 columns): invTime 6277 non-null int32 prevRecord 6277 non-null int32 isOverflow 6277 non-null int32 secondsStage1_2 6277 non-null int32 secondsStage3 6277 non-null int32 providerId 6277 non-null object stationId 6277 non-null object handbook5Id 6277 non-null object](url) ~snip~ ```

A bit hacky, but it works.

I thought of something, is the issue here with the unlimited record dimension?

netcdf \20160430_1600 { dimensions: .... recNum = UNLIMITED ; // (2845 currently)

@shoyer: You're right in that MADIS netCDF files are (imo) poorly formatted. There is also the issue of xarray.decode_cf() not concatenating the string arrays even after fixing the _FillValue, missing_value conflict (hence requiring passing decode_cf=False when opening up the MADIS netCDF file). After looking at the decode_cf code, though, I don't think this is a bug per se (some quick debugging revealed that it doesn't seem like any variable in this netCDF file gets past this check), though if you feel this may in fact be a bug, I can look a bit more into it.

Unfortunately, this does mean I have to do a lot of "manual cleaning" of the netCDF file before exporting as a DataFrame, but it is easy to write a set of functions to accomplish this for you. That said, I can't c/p the exact code (for work-related reasons). I'm not sure how helpful this is, but when working with MADIS netCDF data, I more or less do the following as a workaround: 1. Open up the MADIS netCDF file, fix the _FillValue and missing_value conflict in the variables. 2. Drop the variables I don't want (and there is a lot of filler in MADIS netCDF files). 3. Concatenate the string arrays (e.g. stationId, dataProvider). 4. Turn into a pandas DataFrame.

Though reading over it, that is kind of a draw the owl-esque response, though. :/