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Apr 24, 2021
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Skip mean over empty axis #5207

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Skip mean over empty axis
dschwoerer Apr 22, 2021
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dschwoerer Apr 23, 2021
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4 changes: 4 additions & 0 deletions doc/whats-new.rst
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Expand Up @@ -85,6 +85,10 @@ Breaking changes
as positional, all others need to be passed are keyword arguments. This is part of the
refactor to support external backends (:issue:`4309`, :pull:`4989`).
By `Alessandro Amici <https://github.com/alexamici>`_.
- :py:func:`mean` does not change the data if axis is None. This
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:py:func:mean is actually a method (there should be other references in the whats-new which can be copied)

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I haven't found an example, so I have removed the explicit reference

ensures that Datasets where some variables do not have the averaged
dimensions are not accidentially changed (:issue:`4885`).
By `David Schwörer <https://github.com/dschwoerer>`_

Deprecations
~~~~~~~~~~~~
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5 changes: 5 additions & 0 deletions xarray/core/duck_array_ops.py
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Expand Up @@ -537,6 +537,11 @@ def mean(array, axis=None, skipna=None, **kwargs):
dtypes"""
from .common import _contains_cftime_datetimes

# The mean over an empty axis shouldn't change the data
# See https://github.com/pydata/xarray/issues/4885
if axis == tuple():
return array

array = asarray(array)
if array.dtype.kind in "Mm":
offset = _datetime_nanmin(array)
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10 changes: 10 additions & 0 deletions xarray/tests/test_duck_array_ops.py
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Expand Up @@ -373,6 +373,16 @@ def test_cftime_datetime_mean_dask_error():
da.mean()


def test_mean_dtype():
ds = Dataset()
ds["pos"] = [1, 2, 3]
ds["data"] = ("pos", "time"), [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]
ds["var"] = "pos", [2, 3, 4]
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FYI you can construct the dataset in a single expression, but it's also fine as-is (Dataset(dict(pos=..., data=...)))

ds2 = ds.mean(dim="time")
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Convention:

Suggested change
ds2 = ds.mean(dim="time")
result = ds.mean(dim="time")

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I do not store the result but use it directly. Is that ok as well?

assert all(ds2["var"] == ds["var"])
assert ds2["var"].dtype == ds["var"].dtype


@pytest.mark.parametrize("dim_num", [1, 2])
@pytest.mark.parametrize("dtype", [float, int, np.float32, np.bool_])
@pytest.mark.parametrize("dask", [False, True])
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