id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 937508115,MDU6SXNzdWU5Mzc1MDgxMTU=,5581,Error slicing CFTimeIndex with Pandas 1.3,161133,closed,0,,,4,2021-07-06T04:28:00Z,2021-07-23T21:53:51Z,2021-07-23T21:53:51Z,CONTRIBUTOR,,,," **What happened**: Slicing a DataArray with a CFTime time axis gives an error `TypeError: _maybe_cast_slice_bound() missing 1 required positional argument: 'kind'` **What you expected to happen**: The slice should return elements 31 to 180 **Minimal Complete Verifiable Example**: ```python import xarray as xr import cftime import numpy as np units = 'days since 2000-01-01 00:00' time_365 = cftime.num2date(np.arange(0, 10 * 365), units, '365_day') da = xr.DataArray(np.arange(time_365.size), coords = [time_365], dims = 'time') da.sel(time=slice('2000-02','2000-06')) ``` **Anything else we need to know?**: It appears to be a compatibility issue between Pandas 1.3.0 and Xarray 0.18.2, with Pandas 1.2.5 and Xarray 0.18.2 the slice behaves normally. Possibly there has been an interface change that has broken CFTimeIndex. Using a pure Pandas time axis works fine ```python import xarray as xr import pandas as pd import numpy as np time = pd.date_range('20000101','20100101', freq='D') da = xr.DataArray(np.arange(time.size), coords = [time], dims = 'time') da.sel(time=slice('2000-02','2000-06')) ``` **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:32:32) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 4.18.0-305.7.1.el8.nci.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.18.2 pandas: 1.3.0 numpy: 1.21.0 scipy: 1.7.0 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 3.3.0 Nio: None zarr: 2.8.3 cftime: 1.4.1 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.2.6 cfgrib: 0.9.9.0 iris: 2.4.0 bottleneck: 1.3.2 dask: 2021.06.2 distributed: 2021.06.2 matplotlib: 3.4.2 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: None pint: 0.17 setuptools: 52.0.0.post20210125 pip: 21.1.3 conda: 4.10.3 pytest: 6.2.4 IPython: 7.25.0 sphinx: 4.0.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5581/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 457771111,MDExOlB1bGxSZXF1ZXN0Mjg5NTExOTk2,3033,ENH: keepdims=True for xarray reductions,161133,closed,0,,,3,2019-06-19T02:04:53Z,2019-06-23T09:18:42Z,2019-06-23T09:18:33Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3033,"Add new option `keepdims` to xarray reduce operations, following the behaviour of Numpy. `keepdims` may be passed to reductions on either Datasets or DataArrays, and will result in the reduced dimensions being still present in the output with size 1. Coordinates that depend on the reduced dimensions will be removed from the Dataset/DataArray The name `keepdims` is used here to be consistent with Numpy, `keep_dims` was an alternative name proposed in #2170. The functionality has only been added to `Variable.reduce()`, `Dataarray.reduce()` and `DataSet.reduce()` to start off with, it is not implemented for `GroupBy`, `Rolling` or `Resample`. - [x] Closes #2170 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3033/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 440988633,MDU6SXNzdWU0NDA5ODg2MzM=,2943,Rolling operations loose chunking with dask and bottleneck,161133,closed,0,,,1,2019-05-07T01:52:05Z,2019-05-07T02:01:13Z,2019-05-07T02:01:13Z,CONTRIBUTOR,,,,"#### Code Sample, a copy-pastable example if possible A ""Minimal, Complete and Verifiable Example"" will make it much easier for maintainers to help you: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports ```python import bottleneck import xarray import dask data = dask.array.ones((100,), chunks=(10,)) da = xarray.DataArray(data, dims=['time']) rolled = da.rolling(time=15).mean() # Expect the 'rolled' dataset to be chunked approximately the same as 'data', # however there is only one chunk in 'rolled' instead of 10 assert len(rolled.chunks[0]) > 1 ``` #### Problem description Rolling operations loose chunking over the rolled dimension when using dask datasets with bottleneck installed, which is a problem for large datasets where we don't want to load the entire thing. The issue appears to be caused by `xarray.core.dask_array_ops.dask_rolling_wrapper` calling `dask.array.overlap.overlap` on a DataArray instead of a Dask array. Possibly #2940 is related? #### Expected Output Chunks should be preserved through `.rolling().mean()` #### Output of ``xr.show_versions()``
INSTALLED VERSIONS ------------------ commit: None python: 3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 09:07:38) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-862.14.4.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_AU.utf8 LANG: C LOCALE: en_AU.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.3 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: installed h5netcdf: None h5py: 2.9.0 Nio: None zarr: 2.3.1 cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: None cfgrib: None iris: 2.2.0 bottleneck: 1.2.1 dask: 1.2.0 distributed: 1.27.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.1 pip: 19.1 conda: None pytest: 4.4.1 IPython: 7.5.0 sphinx: None
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