html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/5491#issuecomment-1447776213,https://api.github.com/repos/pydata/xarray/issues/5491,1447776213,IC_kwDOAMm_X85WS0_V,8833517,2023-02-28T08:36:07Z,2023-02-28T08:36:07Z,CONTRIBUTOR,"Both #4697 and https://github.com/corteva/rioxarray/pull/353 have been merged, so this issue can be closed","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,924722006
https://github.com/pydata/xarray/issues/5018#issuecomment-1435670314,https://api.github.com/repos/pydata/xarray/issues/5018,1435670314,IC_kwDOAMm_X85Vkpcq,8833517,2023-02-18T13:31:40Z,2023-02-18T13:31:40Z,CONTRIBUTOR,"The example tests by @yhlam currently pass in `xarray: 2023.2.1.dev7+g21d86450`. Using git bisect, it seems like this issue was fixed as part of the explicit indexes PR #5692. I guess that means this issue can be closed?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,826990294
https://github.com/pydata/xarray/issues/4914#issuecomment-1134360245,https://api.github.com/repos/pydata/xarray/issues/4914,1134360245,IC_kwDOAMm_X85DnPa1,8833517,2022-05-23T08:35:38Z,2022-05-23T08:35:38Z,CONTRIBUTOR,"Now #4896 has been merged, can this issue be closed?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,809332917
https://github.com/pydata/xarray/issues/5386#issuecomment-1010927962,https://api.github.com/repos/pydata/xarray/issues/5386,1010927962,IC_kwDOAMm_X848QYla,8833517,2022-01-12T11:11:16Z,2022-01-12T11:11:16Z,CONTRIBUTOR,"The link to corteva has been added to the rasterio user guide in https://github.com/pydata/xarray/pull/5808, so I think this issue can be closed too.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,903922477
https://github.com/pydata/xarray/issues/4476#issuecomment-1003782259,https://api.github.com/repos/pydata/xarray/issues/4476,1003782259,IC_kwDOAMm_X8471IBz,8833517,2022-01-02T22:03:22Z,2022-01-02T22:03:22Z,CONTRIBUTOR,"Using `git bisect` and @zxdawn's example, I've narrowed it down to commit bdcfab524e.
I'm guessing the exact culprit is the removal of `argmin` and `argmax` from `NAN_REDUCE_METHODS` in [xarray/core/ops.py](https://github.com/pydata/xarray/commit/bdcfab524ef1c852abe6dabcfabc7292f058fddc#diff-53544e03ec0ceff497591f986820b2fb55cbccf5c35fb02c090b291b8628cb44L50) because dedicated implementations were added for Variable/DataSet/DataArray.
","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,712217045
https://github.com/pydata/xarray/pull/3566#issuecomment-1003778566,https://api.github.com/repos/pydata/xarray/issues/3566,1003778566,IC_kwDOAMm_X8471HIG,8833517,2022-01-02T21:31:52Z,2022-01-02T21:31:52Z,CONTRIBUTOR,"Apart from a trivial conflict in `xarray\core\indexing.py`, this PR still looks fine to merge.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527553050
https://github.com/pydata/xarray/issues/2911#issuecomment-753304428,https://api.github.com/repos/pydata/xarray/issues/2911,753304428,MDEyOklzc3VlQ29tbWVudDc1MzMwNDQyOA==,8833517,2021-01-01T11:25:53Z,2021-01-01T11:25:53Z,CONTRIBUTOR,"@tomchor For small snippets including it directly into the docs seems best to me, but an explicit link to the commit/file seems fine too. I've seen links to github issues and blog posts in the docs, so linking to a commit for a larger piece of code doesn't seem out of the ordinary to me.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,435532136
https://github.com/pydata/xarray/issues/2949#issuecomment-728353649,https://api.github.com/repos/pydata/xarray/issues/2949,728353649,MDEyOklzc3VlQ29tbWVudDcyODM1MzY0OQ==,8833517,2020-11-16T21:58:09Z,2020-11-16T21:58:09Z,CONTRIBUTOR,"Since PR #3126 seems to be closed due to performance issues, is this underlying issue still considered an issue, or should it be closed?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,442063346
https://github.com/pydata/xarray/issues/4169#issuecomment-686403682,https://api.github.com/repos/pydata/xarray/issues/4169,686403682,MDEyOklzc3VlQ29tbWVudDY4NjQwMzY4Mg==,8833517,2020-09-03T10:39:09Z,2020-09-03T10:39:09Z,CONTRIBUTOR,"@EliT1626 Can you provide a smaller, faster example including imports etc. that produces the same error?
And what OS are you using? Windows 10?
I've tried to reproduce it on Linux since you mention it as a possible Windows problem, but it took very long to run. It finished without error after changing `end_date` to `dt.date(2010, 1, 31)`, but as I have no idea what your code does, I can't be sure the date range isn't part of the problem.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,643035732
https://github.com/pydata/xarray/issues/1050#issuecomment-670971584,https://api.github.com/repos/pydata/xarray/issues/1050,670971584,MDEyOklzc3VlQ29tbWVudDY3MDk3MTU4NA==,8833517,2020-08-08T20:39:11Z,2020-08-08T20:39:11Z,CONTRIBUTOR,"In [`core/nanops.py`](https://github.com/pydata/xarray/blob/master/xarray/core/nanops.py) there are some explicit defaults of `ddof=0` within xarray, but I'm not sure if those are always used or if there are also cases where `var` (or `std`) are directly passed on to numpy/bottleneck/dask.
I'm considering two different options to clarify this:
1. Add a docstring section on the `ddof` parameter specifying it uses `ddof=0` as default for the reduction methods that use it, i.e. `var` and `std`. Possibly just copied from numpy's [`var`](https://numpy.org/doc/stable/reference/generated/numpy.var.html) page.
2. Refer to numpy's documentation page in the docstring of all reduction methods for further reference.
Both would require some logic in [`core/ops.py`](https://github.com/pydata/xarray/blob/master/xarray/core/ops.py): either to check for which reduce methods need a `ddof` paragraph, or to create the proper url (which has to adjust `min` and `max` to `np.amin` and `np.amax` respectively)
Is there any clear preference from anyone about this?","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,183713222
https://github.com/pydata/xarray/issues/3264#issuecomment-670967112,https://api.github.com/repos/pydata/xarray/issues/3264,670967112,MDEyOklzc3VlQ29tbWVudDY3MDk2NzExMg==,8833517,2020-08-08T19:49:25Z,2020-08-08T19:49:36Z,CONTRIBUTOR,"@rdrussotto Since v0.16.0 (specifically https://github.com/pydata/xarray/commit/bdcfab524ef1c852abe6dabcfabc7292f058fddc), `argmin`/`argmax` have been explicitly defined with dedicated docstrings. Is the issue you raised still a problem?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,485390288
https://github.com/pydata/xarray/issues/4257#issuecomment-664460211,https://api.github.com/repos/pydata/xarray/issues/4257,664460211,MDEyOklzc3VlQ29tbWVudDY2NDQ2MDIxMQ==,8833517,2020-07-27T15:19:07Z,2020-07-27T15:19:07Z,CONTRIBUTOR,@ocefpaf Thanks for trying anyway,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,664458864
https://github.com/pydata/xarray/issues/4257#issuecomment-664456140,https://api.github.com/repos/pydata/xarray/issues/4257,664456140,MDEyOklzc3VlQ29tbWVudDY2NDQ1NjE0MA==,8833517,2020-07-27T15:12:05Z,2020-07-27T15:12:05Z,CONTRIBUTOR,"@ocefpaf :
```
(xarray-docs) rijnsjvan@turbine:~$ echo $0
-bash
(xarray-docs) rijnsjvan@turbine:~$ echo $UDUNITS2_XML_PATH
/home/rijnsjvan/miniconda3/envs/xarray-docs/share/udunits/udunits2.xml
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,664458864
https://github.com/pydata/xarray/issues/4257#issuecomment-664434116,https://api.github.com/repos/pydata/xarray/issues/4257,664434116,MDEyOklzc3VlQ29tbWVudDY2NDQzNDExNg==,8833517,2020-07-27T14:34:53Z,2020-07-27T14:34:53Z,CONTRIBUTOR,"@ocefpaf @dcherian I'm guessing it's some bad package install too, but that surprises me since it's a fresh miniconda install to begin with... Haven't tested miniconda vs anaconda yet.
Just pulled from upstream and recreated the environment, docs make error still persists.
Here are my `.condarc`:
```
(xarray-docs) rijnsjvan@turbine:~$ cat .condarc
channels:
- intel
- defaults
auto_activate_base: false
```
and `conda list`:
```
(xarray-docs) rijnsjvan@turbine:~$ conda list
# packages in environment at /home/rijnsjvan/miniconda3/envs/xarray-docs:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 0_gnu conda-forge
affine 2.3.0 py_0 conda-forge
alabaster 0.7.12 py_0 conda-forge
antlr-python-runtime 4.7.2 py38_1001 conda-forge
asciitree 0.3.3 py_2 conda-forge
attrs 19.3.0 py_0 conda-forge
babel 2.8.0 py_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
bleach 3.1.5 pyh9f0ad1d_0 conda-forge
bokeh 2.1.1 py38h32f6830_0 conda-forge
boost-cpp 1.72.0 h8e57a91_0 conda-forge
bottleneck 1.3.2 py38h8790de6_1 conda-forge
brotlipy 0.7.0 py38h1e0a361_1000 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
ca-certificates 2020.6.20 hecda079_0 conda-forge
cairo 1.16.0 hcf35c78_1003 conda-forge
cartopy 0.18.0 py38h172510d_0 conda-forge
certifi 2020.6.20 py38h32f6830_0 conda-forge
cf-units 2.1.4 py38h8790de6_0 conda-forge
cffi 1.14.1 py38h5bae8af_0 conda-forge
cfgrib 0.9.8.3 py_0 conda-forge
cfitsio 3.470 hce51eda_6 conda-forge
cftime 1.2.1 py38h8790de6_0 conda-forge
chardet 3.0.4 py38h32f6830_1006 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.5.0 py_0 conda-forge
cloudpickle 1.5.0 py_0 conda-forge
cryptography 3.0 py38h766eaa4_0 conda-forge
curl 7.71.1 he644dc0_3 conda-forge
cycler 0.10.0 py_2 conda-forge
cytoolz 0.10.1 py38h516909a_0 conda-forge
dask 2.21.0 py_0 conda-forge
dask-core 2.21.0 py_0 conda-forge
decorator 4.4.2 py_0 conda-forge
defusedxml 0.6.0 py_0 conda-forge
distributed 2.21.0 py38h32f6830_0 conda-forge
docutils 0.16 py38h32f6830_1 conda-forge
eccodes 2.18.0 hf05d9b7_0 conda-forge
entrypoints 0.3 py38h32f6830_1001 conda-forge
expat 2.2.9 he1b5a44_2 conda-forge
fasteners 0.14.1 py_3 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.10.2 he06d7ca_0 conda-forge
freexl 1.0.5 h516909a_1002 conda-forge
fsspec 0.7.4 py_0 conda-forge
gdal 3.0.4 py38h172510d_10 conda-forge
geos 3.8.1 he1b5a44_0 conda-forge
geotiff 1.6.0 h05acad5_0 conda-forge
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
giflib 5.2.1 h516909a_2 conda-forge
glib 2.65.0 h6f030ca_0 conda-forge
h5netcdf 0.8.1 py_0 conda-forge
h5py 2.10.0 nompi_py38hfb01d0b_104 conda-forge
hdf4 4.2.13 hf30be14_1003 conda-forge
hdf5 1.10.6 nompi_h3c11f04_100 conda-forge
heapdict 1.0.1 py_0 conda-forge
icu 64.2 he1b5a44_1 conda-forge
idna 2.10 pyh9f0ad1d_0 conda-forge
imagesize 1.2.0 py_0 conda-forge
importlib-metadata 1.7.0 py38h32f6830_0 conda-forge
importlib_metadata 1.7.0 0 conda-forge
ipykernel 5.3.4 py38h23f93f0_0 conda-forge
ipython 7.16.1 py38h23f93f0_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
iris 2.4.0 py38_0 conda-forge
jasper 1.900.1 h07fcdf6_1006 conda-forge
jedi 0.17.2 py38h32f6830_0 conda-forge
jinja2 2.11.2 pyh9f0ad1d_0 conda-forge
jpeg 9d h516909a_0 conda-forge
json-c 0.13.1 hbfbb72e_1002 conda-forge
jsonschema 3.2.0 py38h32f6830_1 conda-forge
jupyter_client 6.1.6 py_0 conda-forge
jupyter_core 4.6.3 py38h32f6830_1 conda-forge
kealib 1.4.13 h33137a7_1 conda-forge
kiwisolver 1.2.0 py38hbf85e49_0 conda-forge
krb5 1.17.1 hfafb76e_1 conda-forge
lcms2 2.11 hbd6801e_0 conda-forge
ld_impl_linux-64 2.34 hc38a660_9 conda-forge
libaec 1.0.4 he1b5a44_1 conda-forge
libblas 3.8.0 17_openblas conda-forge
libcblas 3.8.0 17_openblas conda-forge
libcurl 7.71.1 hcdd3856_3 conda-forge
libdap4 3.20.6 h1d1bd15_1 conda-forge
libedit 3.1.20191231 h46ee950_1 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 9.2.0 h24d8f2e_2 conda-forge
libgdal 3.0.4 he6a97d6_10 conda-forge
libgfortran-ng 7.5.0 hdf63c60_10 conda-forge
libgomp 9.2.0 h24d8f2e_2 conda-forge
libiconv 1.15 h516909a_1006 conda-forge
libkml 1.3.0 hb574062_1011 conda-forge
liblapack 3.8.0 17_openblas conda-forge
libllvm9 9.0.1 he513fc3_1 conda-forge
libnetcdf 4.7.4 nompi_h84807e1_105 conda-forge
libopenblas 0.3.10 pthreads_hb3c22a3_4 conda-forge
libpng 1.6.37 hed695b0_1 conda-forge
libpq 12.3 h5513abc_0 conda-forge
libsodium 1.0.17 h516909a_0 conda-forge
libspatialite 4.3.0a h2482549_1038 conda-forge
libssh2 1.9.0 hab1572f_4 conda-forge
libstdcxx-ng 9.2.0 hdf63c60_2 conda-forge
libtiff 4.1.0 hc7e4089_6 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libwebp-base 1.1.0 h516909a_3 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxml2 2.9.10 hee79883_0 conda-forge
llvmlite 0.33.0 py38h4f45e52_1 conda-forge
locket 0.2.0 py_2 conda-forge
lz4-c 1.9.2 he1b5a44_1 conda-forge
markupsafe 1.1.1 py38h1e0a361_1 conda-forge
matplotlib-base 3.3.0 py38h91b0d89_1 conda-forge
mistune 0.8.4 py38h1e0a361_1001 conda-forge
monotonic 1.5 py_0 conda-forge
msgpack-python 1.0.0 py38hbf85e49_1 conda-forge
nbconvert 5.6.1 py38h32f6830_1 conda-forge
nbformat 5.0.7 py_0 conda-forge
nbsphinx 0.7.1 pyh9f0ad1d_0 conda-forge
ncurses 6.2 he1b5a44_1 conda-forge
netcdf4 1.5.4 nompi_py38hfd55d45_100 conda-forge
numba 0.50.1 py38hcb8c335_1 conda-forge
numcodecs 0.6.4 py38he1b5a44_0 conda-forge
numpy 1.19.1 py38h8854b6b_0 conda-forge
olefile 0.46 py_0 conda-forge
openjpeg 2.3.1 h981e76c_3 conda-forge
openssl 1.1.1g h516909a_0 conda-forge
owslib 0.20.0 py_0 conda-forge
packaging 20.4 pyh9f0ad1d_0 conda-forge
pandas 1.0.5 py38hcb8c335_0 conda-forge
pandoc 2.10.1 h516909a_0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
parso 0.7.1 pyh9f0ad1d_0 conda-forge
partd 1.1.0 py_0 conda-forge
patsy 0.5.1 py_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
pexpect 4.8.0 py38h32f6830_1 conda-forge
pickleshare 0.7.5 py38h32f6830_1001 conda-forge
pillow 7.2.0 py38h9776b28_1 conda-forge
pip 20.1.1 py_1 conda-forge
pixman 0.38.0 h516909a_1003 conda-forge
poppler 0.87.0 h4190859_1 conda-forge
poppler-data 0.4.9 1 conda-forge
postgresql 12.3 h8573dbc_0 conda-forge
proj 7.0.0 h966b41f_5 conda-forge
prompt-toolkit 3.0.5 py_1 conda-forge
psutil 5.7.2 py38h1e0a361_0 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
ptyprocess 0.6.0 py_1001 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pyepsg 0.4.0 py_0 conda-forge
pygments 2.6.1 py_0 conda-forge
pyke 1.1.1 py38h32f6830_1002 conda-forge
pyopenssl 19.1.0 py_1 conda-forge
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyproj 2.6.1.post1 py38h7521cb9_0 conda-forge
pyrsistent 0.16.0 py38h1e0a361_0 conda-forge
pyshp 2.1.0 py_0 conda-forge
pysocks 1.7.1 py38h32f6830_1 conda-forge
python 3.8.5 h425cb1d_1_cpython conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.8 1_cp38 conda-forge
pytz 2020.1 pyh9f0ad1d_0 conda-forge
pyyaml 5.3.1 py38h1e0a361_0 conda-forge
pyzmq 19.0.1 py38ha71036d_0 conda-forge
rasterio 1.1.5 py38h033e0f6_0 conda-forge
readline 8.0 he28a2e2_2 conda-forge
requests 2.24.0 pyh9f0ad1d_0 conda-forge
scipy 1.5.2 py38h8c5af15_0 conda-forge
seaborn 0.10.1 1 conda-forge
seaborn-base 0.10.1 py_1 conda-forge
setuptools 49.2.0 py38h32f6830_0 conda-forge
shapely 1.7.0 py38hd168ffb_3 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
snowballstemmer 2.0.0 py_0 conda-forge
snuggs 1.4.7 py_0 conda-forge
sortedcontainers 2.2.2 pyh9f0ad1d_0 conda-forge
sphinx 3.1.2 py_0 conda-forge
sphinx_rtd_theme 0.5.0 pyh9f0ad1d_0 conda-forge
sphinxcontrib-applehelp 1.0.2 py_0 conda-forge
sphinxcontrib-devhelp 1.0.2 py_0 conda-forge
sphinxcontrib-htmlhelp 1.0.3 py_0 conda-forge
sphinxcontrib-jsmath 1.0.1 py_0 conda-forge
sphinxcontrib-qthelp 1.0.3 py_0 conda-forge
sphinxcontrib-serializinghtml 1.1.4 py_0 conda-forge
sqlite 3.32.3 hcee41ef_1 conda-forge
statsmodels 0.11.1 py38h1e0a361_2 conda-forge
tbb 2020.1 hc9558a2_0 conda-forge
tblib 1.6.0 py_0 conda-forge
testpath 0.4.4 py_0 conda-forge
tiledb 1.7.7 h8efa9f0_3 conda-forge
tk 8.6.10 hed695b0_0 conda-forge
toolz 0.10.0 py_0 conda-forge
tornado 6.0.4 py38h1e0a361_1 conda-forge
traitlets 4.3.3 py38h32f6830_1 conda-forge
typing_extensions 3.7.4.2 py_0 conda-forge
tzcode 2020a h516909a_0 conda-forge
udunits2 2.2.27.6 h4e0c4b3_1001 conda-forge
urllib3 1.25.10 py_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_0 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.34.2 py_1 conda-forge
xarray 0.16.0 py_0 conda-forge
xerces-c 3.2.2 h8412b87_1004 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.5 h516909a_1 conda-forge
yaml 0.2.5 h516909a_0 conda-forge
zarr 2.4.0 py_0 conda-forge
zeromq 4.3.2 he1b5a44_2 conda-forge
zict 2.0.0 py_0 conda-forge
zipp 3.1.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.5 h6597ccf_2 conda-forge
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,664458864
https://github.com/pydata/xarray/pull/4245#issuecomment-662322092,https://api.github.com/repos/pydata/xarray/issues/4245,662322092,MDEyOklzc3VlQ29tbWVudDY2MjMyMjA5Mg==,8833517,2020-07-22T08:34:09Z,2020-07-24T10:01:14Z,CONTRIBUTOR,"@max-sixty Whoops 😅 Guess I looked over the latest version section as it was still completely empty, thanks for catching that","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,663355647
https://github.com/pydata/xarray/pull/4258#issuecomment-663074248,https://api.github.com/repos/pydata/xarray/issues/4258,663074248,MDEyOklzc3VlQ29tbWVudDY2MzA3NDI0OA==,8833517,2020-07-23T15:31:18Z,2020-07-23T15:31:18Z,CONTRIBUTOR,"@dcherian Docs seem to be correct: `skipna` is removed from [`DataArray.count`](https://xray--4258.org.readthedocs.build/en/4258/generated/xarray.DataArray.count.html), [`DataArray.any`](https://xray--4258.org.readthedocs.build/en/4258/generated/xarray.DataArray.any.html), [`DataArray.all`](https://xray--4258.org.readthedocs.build/en/4258/generated/xarray.DataArray.all.html) while still there in e.g. [`DataArray.sum`](https://xray--4258.org.readthedocs.build/en/4258/generated/xarray.DataArray.sum.html)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,664497733
https://github.com/pydata/xarray/pull/4258#issuecomment-663025279,https://api.github.com/repos/pydata/xarray/issues/4258,663025279,MDEyOklzc3VlQ29tbWVudDY2MzAyNTI3OQ==,8833517,2020-07-23T14:02:36Z,2020-07-23T14:02:36Z,CONTRIBUTOR,"Btw, is there a reason why the functional kwarg `keep_attrs` has an explanation listed, but isn't listed in the signature? I was unable to find how to update that in the docs when working on this PR.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,664497733
https://github.com/pydata/xarray/issues/3007#issuecomment-602508864,https://api.github.com/repos/pydata/xarray/issues/3007,602508864,MDEyOklzc3VlQ29tbWVudDYwMjUwODg2NA==,8833517,2020-03-23T10:27:27Z,2020-03-23T10:27:27Z,CONTRIBUTOR,"I recently had a similar issue and found out the cause: When transforming from a dataframe to an xarray, the xarray allocates memory for all possible combinations of the coordinates. In this particular case, you have 5 unique values for latitude and longitude in your five rows, which means there are 5*5=25 possible combinations of lat/long values. All missing values are then filled in as `NaN`.
Let me illustrate by recreating just your data on latitude, longitude, `wind_surface` and `hurs`:
```python
In [3]: data = [
...: [34.511383, 16.467664, 29.658546, 70.481293],
...: [34.515558, 16.723973, 30.896049, 71.356644],
...: [34.517359, 16.852138, 31.514799, 71.708603],
...: [34.518970, 16.980310, 32.105423, 72.023773],
...: [34.520391, 17.108487, 32.724174, 72.106110],
...: ]
In [4]: df = pd.DataFrame(data=data, columns=['lat', 'long', 'wind_surface', 'hurs']).set_index(['lat', 'long'])
In [5]: df
Out[5]:
wind_surface hurs
lat long
34.511383 16.467664 29.658546 70.481293
34.515558 16.723973 30.896049 71.356644
34.517359 16.852138 31.514799 71.708603
34.518970 16.980310 32.105423 72.023773
34.520391 17.108487 32.724174 72.106110
```
But for the xarray, this means it will end up creating a 5x5 array, of which only 5 values are given along the diagonal. This is very clearly visible when showing just the `DataArray` for a single column:
```python
In [6]: df.to_xarray()['wind_surface']
Out[6]:
array([[29.658546, nan, nan, nan, nan],
[ nan, 30.896049, nan, nan, nan],
[ nan, nan, 31.514799, nan, nan],
[ nan, nan, nan, 32.105423, nan],
[ nan, nan, nan, nan, 32.724174]])
Coordinates:
* lat (lat) float64 34.51 34.52 34.52 34.52 34.52
* long (long) float64 16.47 16.72 16.85 16.98 17.11
```
However, as `to_xarray()` outputs a `DataSet`, each `DataArray`, i.e. column from the dataframe, is summarized as a 1D array, which makes it seem like a lot of data is just 'missing':
```python
In [7]: df.to_xarray()
Out[7]:
Dimensions: (lat: 5, long: 5)
Coordinates:
* lat (lat) float64 34.51 34.52 34.52 34.52 34.52
* long (long) float64 16.47 16.72 16.85 16.98 17.11
Data variables:
wind_surface (lat, long) float64 29.66 nan nan nan ... nan nan nan 32.72
hurs (lat, long) float64 70.48 nan nan nan ... nan nan nan 72.11
```
So it works as intended, but can throw you for a loop if you don't realize it's creating an array the size of all possible index combinations.
@shoyer can you close this issue?","{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,454073421
https://github.com/pydata/xarray/issues/3820#issuecomment-594014351,https://api.github.com/repos/pydata/xarray/issues/3820,594014351,MDEyOklzc3VlQ29tbWVudDU5NDAxNDM1MQ==,8833517,2020-03-03T15:38:05Z,2020-03-03T15:38:05Z,CONTRIBUTOR,Waiting a few more months until it will definitely not be a problem for anyone seems fair to me :+1: ,"{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,574097799
https://github.com/pydata/xarray/issues/3820#issuecomment-593874263,https://api.github.com/repos/pydata/xarray/issues/3820,593874263,MDEyOklzc3VlQ29tbWVudDU5Mzg3NDI2Mw==,8833517,2020-03-03T10:20:08Z,2020-03-03T10:20:08Z,CONTRIBUTOR,"I think that inferring dimension-names from the `coords`-dict is the most intuitive way to define a DataArray.
Passing a dictionary for `coords` is in my opinion the clearest way to indicate which coordinates belong to which dimension, so then why do I have to specify the same dimension names again?
An example of how I create them from my current project:
```
values = xr.DataArray(
values,
coords={'n_high': n_highs,
'n_low': n_lows,
'rep': repetitions,
'model': models,
'idx': range(n_test_samples),},
dims=['n_high', 'n_low', 'rep', 'model', 'idx'], <-- repeated dim names
attrs=attributes,
)
```
If you expect almost everyone to use CPython or 3.7+ anyway, then I don't actually see any drawbacks, while it would regularly make code shorter and less repetitive.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,574097799
https://github.com/pydata/xarray/issues/3820#issuecomment-593673608,https://api.github.com/repos/pydata/xarray/issues/3820,593673608,MDEyOklzc3VlQ29tbWVudDU5MzY3MzYwOA==,8833517,2020-03-02T23:18:47Z,2020-03-02T23:18:47Z,CONTRIBUTOR,"@max-sixty Thanks for the tip. In the end it meant just changing the last line on `dims`. The paragraph on the `coords` argument is still valid after all.
On a related note: according to #727 (PR #993), this was deprecated since key-order in dictionaries was arbitrary at the time of that issue. However, their order is fixed since Python3.7, as noted in [the documentation](https://docs.python.org/3/library/stdtypes.html#dict.values):
> _Changed in version 3.7_: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6.
I guess it's still too soon to 'un-deprecate' this behavior again? 👼 ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,574097799