This started as a question and I add it as reference. Maybe you have a comment.

There are several ways to calculate time series of seasonal data (starting from monthly or daily data):

```

load libraries

import pandas as pd import matplotlib.pyplot import numpy as np import xarray as xr

Create Example Dataset

time = pd.date_range('2000.01.01', '2010.12.31', freq='M') data = np.random.rand(*time.shape) ds = xr.DataArray(data, coords=dict(time=time))

(1) using resample

ds_res = ds.resample('Q-FEB', 'time') ds_res = ds_res.sel(time=ds_res['time.month'] == 2) ds_res = ds_res.groupby('time.year').mean('time')

(2) this is wrong

ds_season = ds.where(ds['time.season'] == 'DJF').groupby('time.year').mean('time')

(3) using where and rolling

mask other months with nan

ds_DJF = ds.where(ds['time.season'] == 'DJF')

rolling mean -> only Jan is not nan

however, we loose Jan/ Feb in the first year and Dec in the last

ds_DJF = ds_DJF.rolling(min_periods=3, center=True, time=3).mean()

make annual mean

ds_DJF = ds_DJF.groupby('time.year').mean('time')

ds_res.plot(marker='*') ds_season.plot() ds_DJF.plot()

plt.show() ```

(1) The first is to use resample with 'Q-FEB' as argument. This works fine. It does include Jan/ Feb in the first year, and Dec in the last year + 1. If this makes sense can be debated. One case where this does not work is when you have, say, two regions in your data set, for one you want to calculate DJF and for the other you want NovDecJan.

(2) Using 'time.season' is wrong as it combines Jan, Feb and Dec from the same year.

(3) The third uses where and rolling and you lose 'incomplete' seasons. If you replace ds.where(ds['time.season'] == 'DJF') with ds.groupby('time.month').where(summer_months), where summer_months is a boolean array it works also for non-standard 'summers' (or seasons) across the globe.

What happened:

Our doctests fail since numpy 1.20 came out:

https://github.com/pydata/xarray/pull/4760/checks?check_run_id=1818512841#step:8:69

What you expected to happen:

They don't ;-)

Minimal Complete Verifiable Example:

The following fails with numpy 1.20 while it converted np.NaN to an integer before (xarray.DataArray.pad at the bottom)

```python import numpy as np

x = np.arange(10) x = np.pad(x, 1, "constant", constant_values=np.nan) ``` requires numpy 1.20

Anything else we need to know?:

• that's probably related to https://numpy.org/doc/stable/release/1.20.0-notes.html#numpy-scalars-are-cast-when-assigned-to-arrays
• I asked if this behavior will stay: https://github.com/numpy/numpy/issues/16499#issuecomment-772342087
• One possibility is to add a check np.can_cast(constant_values.dtype, array.dtype) (or similar) for a better error message.

Another follow up from #4060: quantile now emits a FutureWarning:

Minimal Complete Verifiable Example:

python xr.DataArray([1, 2, 3]).quantile(q=0.5)

~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/core/variable.py:1866: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version. kwargs={"q": q, "axis": axis, "interpolation": interpolation},

We should probably check the warnings in the test suite - there may be others.

MCVE Code Sample

Add arbitrary change to the file doc/pandas.rst

bash git add doc/pandas.rst git commit -m "test"

The pre-commit hook will fail.

Expected Output

the pre-commit hook to pass

Problem Description

running isort -rc doc/* will change the following files: bash modified: contributing.rst modified: howdoi.rst modified: internals.rst modified: io.rst modified: pandas.rst modified: quick-overview.rst unfortunately it does not behave properly and deletes/ changes arbitrary lines. Can the pre-commit hook be told to only run on *.py files? On the command line this would be isort -rc *.py

MCVE Code Sample

``` python import numpy as np import xarray as xr time = xr.cftime_range("2006-01-01", periods=2, calendar="360_day")

da = xr.DataArray(time, dims=["time"]) da.encoding["dtype"] = np.float da.to_netcdf("tst.nc", format="NETCDF4_CLASSIC")

ds = xr.open_dataset("tst.nc") ds.to_netcdf("tst2.nc", format="NETCDF4_CLASSIC") ```

yields: python ValueError: could not safely cast array from dtype int64 to int32

Or an example without to_netcdf:

```python import numpy as np import xarray as xr

time = xr.cftime_range("2006-01-01", periods=2, calendar="360_day")

da = xr.DataArray(time, dims=["time"]) da.encoding["_FillValue"] = np.array([np.nan])

xr.backends.netcdf3.encode_nc3_variable(xr.conventions.encode_cf_variable(da)) ```

Expected Output

Xarray can save the dataset/ an xr.Variable.

Problem Description

If there is a time variable that can be encoded using integers only, but that has a _FillValue set to NaN, saving to_netcdf(name, format="NETCDF4_CLASSIC") fails. The problem is that xarray adds a (unnecessary) _FillValue when saving a file.

Note: if the time cannot be encoded using integers only, it works: ``` python da = xr.DataArray(time, dims=["time"]) da.encoding["_FillValue"] = np.array([np.nan]) da.encoding["units"] = "days since 2006-01-01T12:00:00"

xr.backends.netcdf3.encode_nc3_variable(xr.conventions.encode_cf_variable(da))

```

Another note: when saving with NETCDF4 ``` python da = xr.DataArray(time, dims=["time"]) da.encoding["_FillValue"] = np.array([np.nan])

xr.backends.netCDF4_._encode_nc4_variable(xr.conventions.encode_cf_variable(da)) The following is returned: <xarray.Variable (time: 2)> array([0, 1]) Attributes: units: days since 2006-01-01 00:00:00.000000 calendar: proleptic_gregorian _FillValue: [-9223372036854775808] ```

Output of xr.show_versions()

Assume I have two datasets with the same lat/ lon grid. However, one has lon = 0...359and the other lon = -180...179. How can I wrap around one of them?

Code Sample, a copy-pastable example if possible

```python

Your code here

import numpy as np import xarray as xr x = np.arange(10) da = xr.Dataset(data_vars=dict(data=('dim1', x)), coords=dict(dim1=('dim1', x), dim2=('dim2', x))) da.to_netcdf('tst.nc', format='NETCDF4_CLASSIC', unlimited_dims='dim1')

```

Problem description

This creates the error RuntimeError: NetCDF: NC_UNLIMITED size already in use. With format='NETCDF4' silently creates the dimensions d, i, m, and \1.

The correct syntax is unlimited_dims=['dim1'].

With format='NETCDF4_CLASSIC' and unlimited_dims=['dim1', 'dim2'], still raises the not-so-helpful NC_UNLIMITED error.

I only tested with netCDF4 as backend.

Expected Output

• better error message
• work with unlimited_dims='dim1'

Output of xr.show_versions()

It would be nice if where accepts an other argument:

def where(self, cond, other=np.NaN): pass

Once I take a mean over virtual variables, they are not available any more.

import pandas as pd import numpy as np import xray t = pd.date_range('2000-01-01', '2000-12-31', freq='6H') x = np.random.rand(*t.shape) time = xray.DataArray(t, name='t', dims='time') ts = xray.Dataset({'x' : ('time', x), 'time' : time}) ts_mean = ts.groupby('time.date').mean() ts_mean.virtual_variables

Is this intended behaviour? And could I get them back somehow?