I'm using xarray in ipython to do interactive/exploratory analysis on large multi-file datasets. To avoid having too many files open, I'm wrapping my file-open code in a with block. However, this means that every time I re-run the code the multi-file dataset is re-initialised, causing xarray to re-scan every input datafile to construct the Dataset.

It would be good to have some kind of 'warm start' or caching mechanism to make it easier to re-open multifile datasets without having to re-scan the input files, but equally without having to keep the dataset open which keeps all the file handles open (I've hit the OS max file limit because of this).

Not sure what API would suit this - since it while being a useful usecase it's also a bit wierd. Something like open_cached_mfdataset which closes input files after initialisation but caches the information collected and simply assumes that files don't move or change between accesses.

What happened?

Now that Scipy is moving to support sparse NDarrays, we would expect that Xarray should work with them as any other array like data.

What did you expect to happen?

Doesn't work. It seems that why trying to use a scipy sparse array as the data, Xarray wraps the the sparse array in a 0-D dense array. (there are likely more issues after this but this was the first hurdle)

With sparse array s: print(s) <4x4 sparse array of type '<class 'numpy.float64'>' with 4 stored elements in COOrdinate format> print(xr.DataArray(s).data) array(<4x4 sparse array of type '<class 'numpy.float64'>' with 4 stored elements in COOrdinate format>, dtype=object)

Minimal Complete Verifiable Example

```Python import numpy as np import xarray as xr from scipy.sparse import coo_array

row = np.array([0, 3, 1, 0])

col = np.array([0, 3, 1, 2])

data = np.array([4, 5.4, 7, 9.2])

s= coo_array((data, (row, col)), shape=(4, 4)) da = xr.DataArray(s) print(da.repr_html()) ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python

AttributeError Traceback (most recent call last) Input In [4], in <cell line: 13>() 11 s= coo_array((data, (row, col)), shape=(4, 4)) 12 da = xr.DataArray(s) ---> 13 print(da.repr_html())

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/common.py:167, in AbstractArray.repr_html(self) 165 if OPTIONS["display_style"] == "text": 166 return f"

{escape(repr(self))}

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting_html.py:311, in array_repr(arr) 303 arr_name = f"'{arr.name}'" if getattr(arr, "name", None) else "" 305 header_components = [ 306 f"

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting_html.py:219, in array_section(obj) 213 collapsed = ( 214 "checked" 215 if _get_boolean_with_default("display_expand_data", default=True) 216 else "" 217 ) 218 variable = getattr(obj, "variable", obj) --> 219 preview = escape(inline_variable_array_repr(variable, max_width=70)) 220 data_repr = short_data_repr_html(obj) 221 data_icon = _icon("icon-database")

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting.py:274, in inline_variable_array_repr(var, max_width) 272 return var.data._repr_inline(max_width) 273 if var._in_memory: --> 274 return format_array_flat(var, max_width) 275 dask_array_type = array_type("dask") 276 if isinstance(var._data, dask_array_type):

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting.py:191, in format_array_flat(array, max_width) 188 # every item will take up at least two characters, but we always want to 189 # print at least first and last items 190 max_possibly_relevant = min(max(array.size, 1), max(math.ceil(max_width / 2.0), 2)) --> 191 relevant_front_items = format_items( 192 first_n_items(array, (max_possibly_relevant + 1) // 2) 193 ) 194 relevant_back_items = format_items(last_n_items(array, max_possibly_relevant // 2)) 195 # interleave relevant front and back items: 196 # [a, b, c] and [y, z] -> [a, z, b, y, c]

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting.py:180, in format_items(x) 177 elif np.logical_not(time_needed).all(): 178 timedelta_format = "date" --> 180 formatted = [format_item(xi, timedelta_format) for xi in x] 181 return formatted

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting.py:180, in <listcomp>(.0) 177 elif np.logical_not(time_needed).all(): 178 timedelta_format = "date" --> 180 formatted = [format_item(xi, timedelta_format) for xi in x] 181 return formatted

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/xarray/core/formatting.py:161, in format_item(x, timedelta_format, quote_strings) 159 return repr(x) if quote_strings else x 160 elif hasattr(x, "dtype") and np.issubdtype(x.dtype, np.floating): --> 161 return f"{x.item():.4}" 162 else: 163 return str(x)

File ~/Scratch/.conda/envs/tessa-1/lib/python3.10/site-packages/scipy/sparse/_base.py:771, in spmatrix.getattr(self, attr) 769 return self.getnnz() 770 else: --> 771 raise AttributeError(attr + " not found")

AttributeError: item not found ```

Anything else we need to know?

No response

Environment

The to_dataframe method fails with ValueError if the dataarray has only value

MCVE Code Sample

```python

Your code here

x = np.arange(10) y = np.arange(10)

data = np.zeros((len(x), len(y)))

da = xr.DataArray(data, coords=[x, y], dims=['x', 'y'])

da.sel(x=1,y=1).to_dataframe(name='test') ```

Expected Output

Expect a dataframe with one row

Problem Description

This happened when selecting a single value out of a gridded dataset - in cases where there was only one value output the to_dataframe failed.

Output of xr.show_versions()

When plotting a time series of maps using faceting, my memory use jumps by over 3x, from about 4GB to 14GB.

Using macOS, Python 3.6, xarray 0.9.5, jupyter notebook.

Not really a bug, but I'm finding that the scipy backend is considerably faster than the netCDF backend for netCDF 3 files (using dataset: http://rda.ucar.edu/datasets/ds093.1/). Using Anaconda python with MKL. Not sure if this is always faster, but if it is perhaps xarray should default to scipy backend for netCDF 3 files?

CF conventions code supports: {'microseconds': 'us', 'milliseconds': 'ms', 'seconds': 's', 'minutes': 'm', 'hours': 'h', 'days': 'D'}, but not 'years'. See example file https://www.dropbox.com/s/34dcpliko928yaj/histsoc_population_0.5deg_1861-2005.nc4?dl=0

I'm trying to select data from a collection of weather files. Xarray opens the multifile dataset perfectly, but when I try the following selection:

``` python

cfsr_new = xr.open_mfdataset('*.grb2.nc')

lon_sel = np.array(cfsr_new.lon[np.array([3, 4, 8])]) lat_sel = np.array(cfsr_new.lat[np.array([2, 3, 4])]) time_sel = cfsr_new.time[100:200]

selection = cfsr_new.sel(lon=lon_sel, lat=lat_sel, time=time_sel) selection.to_array()

```

I get:

```

TypeError Traceback (most recent call last) <ipython-input-38-3f04c6458da2> in <module>() ----> 1 selection.to_array()

/Users/<user>/anaconda/lib/python3.5/site-packages/xarray/core/dataset.py in to_array(self, dim, name) 1847 data_vars = [self.variables[k] for k in self.data_vars] 1848 broadcast_vars = broadcast_variables(*data_vars) -> 1849 data = ops.stack([b.data for b in broadcast_vars], axis=0) 1850 1851 coords = dict(self.coords)

/Users/<user>//anaconda/lib/python3.5/site-packages/xarray/core/ops.py in f(args, kwargs) 65 else: 66 module = eager_module ---> 67 return getattr(module, name)(args, kwargs) 68 else: 69 def f(data, *args, kwargs):

/Users/<user>//anaconda/lib/python3.5/site-packages/dask/array/core.py in stack(seq, axis) 1754 1755 if all(a._dtype is not None for a in seq): -> 1756 dt = reduce(np.promote_types, [a._dtype for a in seq]) 1757 else: 1758 dt = None

/Users/<user>//anaconda/lib/python3.5/site-packages/toolz/functoolz.py in call(self, args, kwargs) 217 def call(self, args, kwargs): 218 try: --> 219 return self._partial(*args, kwargs) 220 except TypeError: 221 # If there was a genuine TypeError

TypeError: invalid type promotion

```

pathlib.Path IMHO is one of the best additions to Python. Would be nice if it were possible to open files from Path without having to cast to str

I've been trying to get around some performance issues when subsetting a set of netCDF files opend with open_mfdataset. I managed to print out the generated dask graph for one variable and it doesn't seem right - it's huge, 5000 elements, and seems to have a getitem entry for every requested element for that variable.

The code that generates this select looks roughly like:

```python

paths = WEATHER_MET['latlon'].glob('_resampled.nc') dataset = xr.open_mfdataset([str(p) for p in paths]) selection = dataset.sel(time=time_sel).sel_points(method='nearest', tolerance=0.1, lon=lon, lat=lat) selection = weights ```

and the graph for one variable in the select (the irradiance value) looks like this:

mydask.pdf

WIP to use dask vindex to point based selection