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"""Mixin classes with reduction operations."""
# This file was generated using xarray.util.generate_aggregations. Do not edit manually.
from __future__ import annotations
from collections.abc import Callable, Mapping, Sequence
from typing import TYPE_CHECKING, Any
from xarray.core import duck_array_ops
from xarray.core.options import OPTIONS
from xarray.core.types import Dims, Self
from xarray.core.utils import contains_only_chunked_or_numpy, module_available
if TYPE_CHECKING:
from xarray.core.coordinates import DatasetCoordinates
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset
flox_available = module_available("flox")
class DataTreeAggregations:
__slots__ = ()
def reduce(
self,
func: Callable[..., Any],
dim: Dims = None,
*,
axis: int | Sequence[int] | None = None,
keep_attrs: bool | None = None,
keepdims: bool = False,
**kwargs: Any,
) -> Self:
raise NotImplementedError()
def count(
self,
dim: Dims = None,
*,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``count`` along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``count``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``count`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``count`` applied to its data and the
indicated dimension(s) removed
See Also
--------
pandas.DataFrame.count
dask.dataframe.DataFrame.count
Dataset.count
DataArray.count
:ref:`agg`
User guide on reduction or aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.count()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo int64 8B 5
"""
out = self.reduce(
duck_array_ops.count,
dim=dim,
numeric_only=False,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def all(
self,
dim: Dims = None,
*,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``all`` along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``all``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``all`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``all`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.all
dask.array.all
Dataset.all
DataArray.all
:ref:`agg`
User guide on reduction or aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(
... foo=(
... "time",
... np.array([True, True, True, True, True, False], dtype=bool),
... )
... ),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) bool 6B True True True True True False
>>> dt.all()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo bool 1B False
"""
out = self.reduce(
duck_array_ops.array_all,
dim=dim,
numeric_only=False,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def any(
self,
dim: Dims = None,
*,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``any`` along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``any``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``any`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``any`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.any
dask.array.any
Dataset.any
DataArray.any
:ref:`agg`
User guide on reduction or aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(
... foo=(
... "time",
... np.array([True, True, True, True, True, False], dtype=bool),
... )
... ),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) bool 6B True True True True True False
>>> dt.any()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo bool 1B True
"""
out = self.reduce(
duck_array_ops.array_any,
dim=dim,
numeric_only=False,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def max(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``max`` (i.e., maximum) along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``max``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``max`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``max`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.max
dask.array.max
Dataset.max
DataArray.max
:ref:`agg`
User guide on reduction or aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.max()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 3.0
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.max(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
"""
out = self.reduce(
duck_array_ops.max,
dim=dim,
skipna=skipna,
numeric_only=False,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def min(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``min`` (i.e., minimum) along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``min``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``min`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``min`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.min
dask.array.min
Dataset.min
DataArray.min
:ref:`agg`
User guide on reduction or aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.min()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 0.0
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.min(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
"""
out = self.reduce(
duck_array_ops.min,
dim=dim,
skipna=skipna,
numeric_only=False,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def mean(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``mean`` along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``mean``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``mean`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``mean`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.mean
dask.array.mean
Dataset.mean
DataArray.mean
:ref:`agg`
User guide on reduction or aggregation operations.
Notes
-----
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.mean()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 1.6
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.mean(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
"""
out = self.reduce(
duck_array_ops.mean,
dim=dim,
skipna=skipna,
numeric_only=True,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def prod(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
min_count: int | None = None,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``prod`` (i.e., product) along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``prod``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
min_count : int or None, optional
The required number of valid values to perform the operation. If
fewer than min_count non-NA values are present the result will be
NA. Only used if skipna is set to True or defaults to True for the
array's dtype. Changed in version 0.17.0: if specified on an integer
array and skipna=True, the result will be a float array.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``prod`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``prod`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.prod
dask.array.prod
Dataset.prod
DataArray.prod
:ref:`agg`
User guide on reduction or aggregation operations.
Notes
-----
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.prod()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 0.0
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.prod(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
Specify ``min_count`` for finer control over when NaNs are ignored.
>>> dt.prod(skipna=True, min_count=2)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 0.0
"""
out = self.reduce(
duck_array_ops.prod,
dim=dim,
skipna=skipna,
min_count=min_count,
numeric_only=True,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def sum(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
min_count: int | None = None,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``sum`` along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``sum``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
min_count : int or None, optional
The required number of valid values to perform the operation. If
fewer than min_count non-NA values are present the result will be
NA. Only used if skipna is set to True or defaults to True for the
array's dtype. Changed in version 0.17.0: if specified on an integer
array and skipna=True, the result will be a float array.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``sum`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``sum`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.sum
dask.array.sum
Dataset.sum
DataArray.sum
:ref:`agg`
User guide on reduction or aggregation operations.
Notes
-----
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.sum()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 8.0
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.sum(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
Specify ``min_count`` for finer control over when NaNs are ignored.
>>> dt.sum(skipna=True, min_count=2)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 8.0
"""
out = self.reduce(
duck_array_ops.sum,
dim=dim,
skipna=skipna,
min_count=min_count,
numeric_only=True,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def std(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
ddof: int = 0,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``std`` (i.e., standard deviation) along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``std``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
ddof : int, default: 0
“Delta Degrees of Freedom”: the divisor used in the calculation is ``N - ddof``,
where ``N`` represents the number of elements.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``std`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``std`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.std
dask.array.std
Dataset.std
DataArray.std
:ref:`agg`
User guide on reduction or aggregation operations.
Notes
-----
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.std()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 1.02
Use ``skipna`` to control whether NaNs are ignored.
>>> dt.std(skipna=False)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
Specify ``ddof=1`` for an unbiased estimate.
>>> dt.std(skipna=True, ddof=1)
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables:
foo float64 8B 1.14
"""
out = self.reduce(
duck_array_ops.std,
dim=dim,
skipna=skipna,
ddof=ddof,
numeric_only=True,
keep_attrs=keep_attrs,
**kwargs,
)
return out
def var(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
ddof: int = 0,
keep_attrs: bool | None = None,
**kwargs: Any,
) -> Self:
"""
Reduce this DataTree's data by applying ``var`` (i.e., variance) along some dimension(s).
Parameters
----------
dim : str, Iterable of Hashable, "..." or None, default: None
Name of dimension[s] along which to apply ``var``. For e.g. ``dim="x"``
or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).
ddof : int, default: 0
“Delta Degrees of Freedom”: the divisor used in the calculation is ``N - ddof``,
where ``N`` represents the number of elements.
keep_attrs : bool or None, optional
If True, ``attrs`` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
**kwargs : Any
Additional keyword arguments passed on to the appropriate array
function for calculating ``var`` on this object's data.
These could include dask-specific kwargs like ``split_every``.
Returns
-------
reduced : DataTree
New DataTree with ``var`` applied to its data and the
indicated dimension(s) removed
See Also
--------
numpy.var
dask.array.var
Dataset.var
DataArray.var
:ref:`agg`
User guide on reduction or aggregation operations.
Notes
-----
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
Examples
--------
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
<xarray.DataTree>
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.var()
<xarray.DataTree>
Group: /
Dimensions: ()
Data variables: