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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
448478648 MDExOlB1bGxSZXF1ZXN0MjgyMjM5MDI0 2991 ENH: str accessor 0x0L 3621629 closed 0     6 2019-05-25T16:10:22Z 2019-06-10T13:11:14Z 2019-06-10T13:11:11Z CONTRIBUTOR   0 pydata/xarray/pulls/2991

Hello,

Some of the pandas str functionalities. Instead of wrapping pandas internal as in #2983 I copy/pasted the code since it's simple and tiny.

Currently it's a bit more restrictive than pandas since it expects all elements to be string like.

  • [x] Closes #2983
  • [x] Tests added
  • [x] Fully documented, including whats-new.rst for all changes and api.rst for new API
 
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     xarray 13221727 pull
447856992 MDU6SXNzdWU0NDc4NTY5OTI= 2983 string accessor 0x0L 3621629 closed 0     1 2019-05-23T20:25:01Z 2019-06-10T13:11:10Z 2019-06-10T13:11:10Z CONTRIBUTOR      

Hello,

I have written a small wrapper around pandas internal methods https://gist.github.com/0x0L/ef78c80a42892c0f832c91357914a5a4

Missing methods are those involving list or list of arrays (join, split, partition, ...)

Let me know if there's enough interest to turn this into a full commit

 
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   completed xarray 13221727 issue
452734140 MDExOlB1bGxSZXF1ZXN0Mjg1NTY0OTEz 3001 BUG: fix safe_cast_to_index 0x0L 3621629 closed 0     3 2019-06-05T21:52:57Z 2019-06-10T04:48:45Z 2019-06-10T04:48:45Z CONTRIBUTOR   0 pydata/xarray/pulls/3001
  • [x] Closes #3000
  • [x] Tests added
  • [x] Fully documented, including whats-new.rst for all changes and api.rst for new API
 
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     xarray 13221727 pull
452729969 MDU6SXNzdWU0NTI3Mjk5Njk= 3000 Slowness when cftime is installed 0x0L 3621629 closed 0     0 2019-06-05T21:40:42Z 2019-06-10T04:48:44Z 2019-06-10T04:48:44Z CONTRIBUTOR      

With cftime installed

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

da = xr.DataArray(np.random.randn(5000, 500)) df = da.to_pandas()

with pandas

%time df_stacked = df.stack()

Wall time: 48.3 ms

%time df_unstacked = df_stacked.unstack()

Wall time: 368 ms

with xarray

%time da_stacked = da.stack(stacked_dim=('dim_0', 'dim_1'))

Wall time: 1.03 s

%time da_unstacked = da_stacked.unstack('stacked_dim')

Wall time: 78.2 ms

```

prun points to CFTimeIndex(index) in _maybe_cast_to_cftimeindex

The behaviour is also incorrect for empty indexes: ```python da[:0].stack(dim=['dim_0', 'dim_1']).dim.to_index()

CFTimeIndex([], dtype='object', name='dim')

```

 
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   completed xarray 13221727 issue
276688437 MDU6SXNzdWUyNzY2ODg0Mzc= 1742 Performance regression when selecting 0x0L 3621629 closed 0     1 2017-11-24T19:34:29Z 2019-06-06T19:08:06Z 2019-06-06T19:08:06Z CONTRIBUTOR      

Hello,

I just noticed a performance drop in 0.10 after a conda update xarray

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

np.random.seed(1234) ds = xr.Dataset({k: pd.DataFrame(np.random.randn(2500, 2000)) for k in range(20)}) mask = (np.random.randn(2000) > -0.2).astype(bool)

%timeit ds.sel(dim_0=slice(50, 1250), dim_1=mask) %timeit ds[0].sel(dim_0=slice(50, 1250), dim_1=mask)

xarray 0.9.6 -> 120 ± 0.4 ms, 4.2 ± 0.02 ms

xarray 0.10 -> 190 ± 0.4 ms, 6.8 ± 0.03 ms

```

This was run in a docker image. Strangely I can't reproduce it natively on macos (performance is the same as in 0.10 in docker for both versions). On a window box, with similar but "real" netcdf dataset performance is halved.

 
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   completed xarray 13221727 issue
274996832 MDU6SXNzdWUyNzQ5OTY4MzI= 1726 Behavior of dataarray with no dimensions 0x0L 3621629 closed 0     3 2017-11-17T21:07:55Z 2018-01-11T21:24:43Z 2018-01-11T21:24:43Z CONTRIBUTOR      

Consider

```python type(np.array([1.0]).mean())

-> numpy.float64

type(pd.Series([1.0]).mean())

-> float

type(xr.DataArray([1.0]).mean())

-> xarray.core.dataarray.DataArray

```

The issue is that this dimensionless data array won't be cast into float by numpy/pandas when constructing a new ndarray/dataframe. You'll have to do it explicitly. Not a big deal but it feels weird.

I'm sure there's a real technical reason (keeping metadata ?) behind this behavior but I couldn't find any discussion about it.

 
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   completed xarray 13221727 issue
275813162 MDExOlB1bGxSZXF1ZXN0MTUzOTY2MTk0 1734 pandas casting issues 0x0L 3621629 closed 0     7 2017-11-21T18:21:56Z 2018-01-11T21:24:43Z 2018-01-11T21:24:43Z CONTRIBUTOR   0 pydata/xarray/pulls/1734
  • [x] Closes #1726

Added a comment about constructing pandas objects from xarray objects

 
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     xarray 13221727 pull
275789502 MDExOlB1bGxSZXF1ZXN0MTUzOTQ4Njk1 1733 Rank Methods 0x0L 3621629 closed 0     9 2017-11-21T17:03:41Z 2017-12-18T16:51:05Z 2017-12-18T16:51:00Z CONTRIBUTOR   0 pydata/xarray/pulls/1733
  • [x] Closes #1731
  • [x] Tests added / passed
  • [x] Passes git diff upstream/master **/*py | flake8 --diff
  • [x] Fully documented, including whats-new.rst for all changes and api.rst for new API
 
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     xarray 13221727 pull
275461273 MDU6SXNzdWUyNzU0NjEyNzM= 1731 Rank function 0x0L 3621629 closed 0     4 2017-11-20T18:55:33Z 2017-12-18T16:51:00Z 2017-12-18T16:51:00Z CONTRIBUTOR      

Hi,

I think xarray is missing a rank function. Is there any reason not to expose a wrapper to bottleneck.nanrankdata ?

See also https://github.com/pydata/xarray/issues/1635

[edit] Although moving rank is mentioned in the whats-new for v0.9.2 I wasn't able to find that functionality nor a trace of it in the code :)

 
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   completed xarray 13221727 issue

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