"""Mixin classes with reduction operations.""" # This file was generated using xarray.util.generate_aggregations. Do not edit manually. from __future__ import annotations from typing import TYPE_CHECKING, Any, Callable, Sequence from xarray.core import duck_array_ops from xarray.core.options import OPTIONS from xarray.core.types import Dims from xarray.core.utils import contains_only_dask_or_numpy, module_available if TYPE_CHECKING: from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset flox_available = module_available("flox") class DatasetAggregations: __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, ) -> Dataset: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 : Dataset New Dataset with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count DataArray.count :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.count() Dimensions: () Data variables: da int64 5 """ return self.reduce( duck_array_ops.count, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 : Dataset New Dataset with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all DataArray.all :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.all() Dimensions: () Data variables: da bool False """ return self.reduce( duck_array_ops.array_all, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 : Dataset New Dataset with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any DataArray.any :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.any() Dimensions: () Data variables: da bool True """ return self.reduce( duck_array_ops.array_any, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``max`` 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 : Dataset New Dataset with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max DataArray.max :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.max() Dimensions: () Data variables: da float64 3.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.max(skipna=False) Dimensions: () Data variables: da float64 nan """ return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``min`` 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 : Dataset New Dataset with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min DataArray.min :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.min() Dimensions: () Data variables: da float64 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.min(skipna=False) Dimensions: () Data variables: da float64 nan """ return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 : Dataset New Dataset with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean DataArray.mean :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.mean() Dimensions: () Data variables: da float64 1.8 Use ``skipna`` to control whether NaNs are ignored. >>> ds.mean(skipna=False) Dimensions: () Data variables: da float64 nan """ return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``prod`` 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 : Dataset New Dataset with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod DataArray.prod :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.prod() Dimensions: () Data variables: da float64 12.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.prod(skipna=False) Dimensions: () Data variables: da float64 nan Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.prod(skipna=True, min_count=2) Dimensions: () Data variables: da float64 12.0 """ return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 : Dataset New Dataset with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum DataArray.sum :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.sum() Dimensions: () Data variables: da float64 9.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.sum(skipna=False) Dimensions: () Data variables: da float64 nan Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.sum(skipna=True, min_count=2) Dimensions: () Data variables: da float64 9.0 """ return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``std`` 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 : Dataset New Dataset with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std DataArray.std :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.std() Dimensions: () Data variables: da float64 0.7483 Use ``skipna`` to control whether NaNs are ignored. >>> ds.std(skipna=False) Dimensions: () Data variables: da float64 nan Specify ``ddof=1`` for an unbiased estimate. >>> ds.std(skipna=True, ddof=1) Dimensions: () Data variables: da float64 0.8367 """ return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``var`` 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 : Dataset New Dataset with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var DataArray.var :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.var() Dimensions: () Data variables: da float64 0.56 Use ``skipna`` to control whether NaNs are ignored. >>> ds.var(skipna=False) Dimensions: () Data variables: da float64 nan Specify ``ddof=1`` for an unbiased estimate. >>> ds.var(skipna=True, ddof=1) Dimensions: () Data variables: da float64 0.7 """ return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median DataArray.median :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.median() Dimensions: () Data variables: da float64 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.median(skipna=False) Dimensions: () Data variables: da float64 nan """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum DataArray.cumsum :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.cumsum() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 3.0 6.0 7.0 9.0 9.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.cumsum(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 3.0 6.0 7.0 9.0 nan """ return self.reduce( duck_array_ops.cumsum, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumprod( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod DataArray.cumprod :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.cumprod() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 6.0 6.0 12.0 12.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.cumprod(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 6.0 6.0 12.0 nan """ return self.reduce( duck_array_ops.cumprod, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) class DataArrayAggregations: __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, ) -> DataArray: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 : DataArray New DataArray with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count Dataset.count :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.count() array(5) """ return self.reduce( duck_array_ops.count, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 : DataArray New DataArray with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all Dataset.all :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.all() array(False) """ return self.reduce( duck_array_ops.array_all, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 : DataArray New DataArray with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any Dataset.any :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.any() array(True) """ return self.reduce( duck_array_ops.array_any, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``max`` 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 : DataArray New DataArray with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max Dataset.max :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.max() array(3.) Use ``skipna`` to control whether NaNs are ignored. >>> da.max(skipna=False) array(nan) """ return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``min`` 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 : DataArray New DataArray with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min Dataset.min :ref:`agg` User guide on reduction or aggregation operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.min() array(1.) Use ``skipna`` to control whether NaNs are ignored. >>> da.min(skipna=False) array(nan) """ return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 : DataArray New DataArray with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean Dataset.mean :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.mean() array(1.8) Use ``skipna`` to control whether NaNs are ignored. >>> da.mean(skipna=False) array(nan) """ return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``prod`` 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 : DataArray New DataArray with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod Dataset.prod :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.prod() array(12.) Use ``skipna`` to control whether NaNs are ignored. >>> da.prod(skipna=False) array(nan) Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.prod(skipna=True, min_count=2) array(12.) """ return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 : DataArray New DataArray with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum Dataset.sum :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.sum() array(9.) Use ``skipna`` to control whether NaNs are ignored. >>> da.sum(skipna=False) array(nan) Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.sum(skipna=True, min_count=2) array(9.) """ return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``std`` 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 : DataArray New DataArray with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std Dataset.std :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.std() array(0.74833148) Use ``skipna`` to control whether NaNs are ignored. >>> da.std(skipna=False) array(nan) Specify ``ddof=1`` for an unbiased estimate. >>> da.std(skipna=True, ddof=1) array(0.83666003) """ return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``var`` 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 : DataArray New DataArray with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var Dataset.var :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.var() array(0.56) Use ``skipna`` to control whether NaNs are ignored. >>> da.var(skipna=False) array(nan) Specify ``ddof=1`` for an unbiased estimate. >>> da.var(skipna=True, ddof=1) array(0.7) """ return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median Dataset.median :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.median() array(2.) Use ``skipna`` to control whether NaNs are ignored. >>> da.median(skipna=False) array(nan) """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum Dataset.cumsum :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.cumsum() array([1., 3., 6., 7., 9., 9.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.cumsum(skipna=False) array([ 1., 3., 6., 7., 9., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) DataArray: """ Reduce this DataArray's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod Dataset.cumprod :ref:`agg` User guide on reduction or aggregation operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.cumprod() array([ 1., 2., 6., 6., 12., 12.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.cumprod(skipna=False) array([ 1., 2., 6., 6., 12., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) Dataset: raise NotImplementedError() def _flox_reduce( self, dim: Dims, **kwargs: Any, ) -> Dataset: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count Dataset.count :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").count() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) int64 1 2 2 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="count", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.count, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all Dataset.all :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").all() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) bool False True True """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="all", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_all, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any Dataset.any :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").any() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) bool True True True """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="any", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_any, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``max`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max Dataset.max :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").max() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 2.0 3.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").max(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 2.0 3.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="max", dim=dim, skipna=skipna, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``min`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min Dataset.min :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").min() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 2.0 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").min(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 2.0 1.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="min", dim=dim, skipna=skipna, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean Dataset.mean :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").mean() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").mean(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 2.0 2.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="mean", dim=dim, skipna=skipna, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``prod`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod Dataset.prod :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").prod() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 4.0 3.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").prod(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 4.0 3.0 Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.groupby("labels").prod(skipna=True, min_count=2) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 4.0 3.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="prod", dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum Dataset.sum :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").sum() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 4.0 4.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").sum(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 4.0 4.0 Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.groupby("labels").sum(skipna=True, min_count=2) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 4.0 4.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="sum", dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``std`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std Dataset.std :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").std() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 0.0 0.0 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").std(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 0.0 1.0 Specify ``ddof=1`` for an unbiased estimate. >>> ds.groupby("labels").std(skipna=True, ddof=1) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 0.0 1.414 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="std", dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``var`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : Dataset New Dataset with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var Dataset.var :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").var() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 0.0 0.0 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").var(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 0.0 1.0 Specify ``ddof=1`` for an unbiased estimate. >>> ds.groupby("labels").var(skipna=True, ddof=1) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 0.0 2.0 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="var", dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median Dataset.median :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").median() Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 1.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").median(skipna=False) Dimensions: (labels: 3) Coordinates: * labels (labels) object 'a' 'b' 'c' Data variables: da (labels) float64 nan 2.0 2.0 """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum Dataset.cumsum :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").cumsum() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 3.0 4.0 4.0 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").cumsum(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 3.0 4.0 4.0 nan """ return self.reduce( duck_array_ops.cumsum, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumprod( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod Dataset.cumprod :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.groupby("labels").cumprod() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 3.0 3.0 4.0 1.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.groupby("labels").cumprod(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 3.0 3.0 4.0 nan """ return self.reduce( duck_array_ops.cumprod, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) class DatasetResampleAggregations: _obj: Dataset 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, ) -> Dataset: raise NotImplementedError() def _flox_reduce( self, dim: Dims, **kwargs: Any, ) -> Dataset: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count Dataset.count :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").count() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) int64 1 3 1 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="count", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.count, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all Dataset.all :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").all() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) bool True True False """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="all", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_all, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any Dataset.any :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").any() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) bool True True True """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="any", dim=dim, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_any, dim=dim, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``max`` 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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max Dataset.max :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").max() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 3.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").max(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 3.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="max", dim=dim, skipna=skipna, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``min`` 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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min Dataset.min :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").min() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 1.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").min(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 1.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="min", dim=dim, skipna=skipna, numeric_only=False, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, numeric_only=False, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean Dataset.mean :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").mean() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").mean(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 2.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="mean", dim=dim, skipna=skipna, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``prod`` 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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod Dataset.prod :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").prod() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 6.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").prod(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 6.0 nan Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.resample(time="3M").prod(skipna=True, min_count=2) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 nan 6.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="prod", dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset'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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum Dataset.sum :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").sum() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 6.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").sum(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 6.0 nan Specify ``min_count`` for finer control over when NaNs are ignored. >>> ds.resample(time="3M").sum(skipna=True, min_count=2) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 nan 6.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="sum", dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``std`` 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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std Dataset.std :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").std() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 0.0 0.8165 0.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").std(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 0.0 0.8165 nan Specify ``ddof=1`` for an unbiased estimate. >>> ds.resample(time="3M").std(skipna=True, ddof=1) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 nan 1.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="std", dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``var`` 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 None, will reduce over the Resample dimensions. If "...", 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 : Dataset New Dataset with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var Dataset.var :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").var() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 0.0 0.6667 0.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").var(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 0.0 0.6667 nan Specify ``ddof=1`` for an unbiased estimate. >>> ds.resample(time="3M").var(skipna=True, ddof=1) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 nan 1.0 nan """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="var", dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median Dataset.median :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").median() Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").median(skipna=False) Dimensions: (time: 3) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Data variables: da (time) float64 1.0 2.0 nan """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum Dataset.cumsum :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").cumsum() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 5.0 6.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").cumsum(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 5.0 6.0 2.0 nan """ return self.reduce( duck_array_ops.cumsum, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) def cumprod( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> Dataset: """ Reduce this Dataset's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : Dataset New Dataset with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod Dataset.cumprod :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> ds = xr.Dataset(dict(da=da)) >>> ds Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> ds.resample(time="3M").cumprod() Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 6.0 6.0 2.0 2.0 Use ``skipna`` to control whether NaNs are ignored. >>> ds.resample(time="3M").cumprod(skipna=False) Dimensions: (time: 6) Dimensions without coordinates: time Data variables: da (time) float64 1.0 2.0 6.0 6.0 2.0 nan """ return self.reduce( duck_array_ops.cumprod, dim=dim, skipna=skipna, numeric_only=True, keep_attrs=keep_attrs, **kwargs, ) class DataArrayGroupByAggregations: _obj: DataArray 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, ) -> DataArray: raise NotImplementedError() def _flox_reduce( self, dim: Dims, **kwargs: Any, ) -> DataArray: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count DataArray.count :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").count() array([1, 2, 2]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="count", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.count, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all DataArray.all :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").all() array([False, True, True]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="all", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_all, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any DataArray.any :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").any() array([ True, True, True]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="any", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_any, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``max`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max DataArray.max :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").max() array([1., 2., 3.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").max(skipna=False) array([nan, 2., 3.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="max", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``min`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min DataArray.min :ref:`groupby` User guide on groupby operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").min() array([1., 2., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").min(skipna=False) array([nan, 2., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="min", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean DataArray.mean :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").mean() array([1., 2., 2.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").mean(skipna=False) array([nan, 2., 2.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="mean", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``prod`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod DataArray.prod :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").prod() array([1., 4., 3.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").prod(skipna=False) array([nan, 4., 3.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.groupby("labels").prod(skipna=True, min_count=2) array([nan, 4., 3.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="prod", dim=dim, skipna=skipna, min_count=min_count, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum DataArray.sum :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").sum() array([1., 4., 4.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").sum(skipna=False) array([nan, 4., 4.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.groupby("labels").sum(skipna=True, min_count=2) array([nan, 4., 4.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="sum", dim=dim, skipna=skipna, min_count=min_count, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``std`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std DataArray.std :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").std() array([0., 0., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").std(skipna=False) array([nan, 0., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Specify ``ddof=1`` for an unbiased estimate. >>> da.groupby("labels").std(skipna=True, ddof=1) array([ nan, 0. , 1.41421356]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="std", dim=dim, skipna=skipna, ddof=ddof, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``var`` 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 None, will reduce over the GroupBy dimensions. If "...", 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 : DataArray New DataArray with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var DataArray.var :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").var() array([0., 0., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").var(skipna=False) array([nan, 0., 1.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Specify ``ddof=1`` for an unbiased estimate. >>> da.groupby("labels").var(skipna=True, ddof=1) array([nan, 0., 2.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="var", dim=dim, skipna=skipna, ddof=ddof, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median DataArray.median :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").median() array([1., 2., 2.]) Coordinates: * labels (labels) object 'a' 'b' 'c' Use ``skipna`` to control whether NaNs are ignored. >>> da.groupby("labels").median(skipna=False) array([nan, 2., 2.]) Coordinates: * labels (labels) object 'a' 'b' 'c' """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum DataArray.cumsum :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").cumsum() array([1., 2., 3., 4., 4., 1.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").cumsum(skipna=False) array([ 1., 2., 3., 4., 4., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) DataArray: """ Reduce this DataArray's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions. If "...", 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod DataArray.cumprod :ref:`groupby` User guide on groupby operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").cumprod() array([1., 2., 3., 3., 4., 1.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.groupby("labels").cumprod(skipna=False) array([ 1., 2., 3., 3., 4., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) DataArray: raise NotImplementedError() def _flox_reduce( self, dim: Dims, **kwargs: Any, ) -> DataArray: raise NotImplementedError() def count( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``count`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.count dask.array.count DataArray.count :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").count() array([1, 3, 1]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="count", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.count, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def all( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``all`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.all dask.array.all DataArray.all :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").all() array([ True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="all", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_all, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def any( self, dim: Dims = None, *, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``any`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.any dask.array.any DataArray.any :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([True, True, True, True, True, False], dtype=bool), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ True, True, True, True, True, False]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").any() array([ True, True, True]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="any", dim=dim, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.array_any, dim=dim, keep_attrs=keep_attrs, **kwargs, ) def max( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``max`` 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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``max`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.max dask.array.max DataArray.max :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").max() array([1., 3., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").max(skipna=False) array([ 1., 3., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="max", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.max, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def min( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``min`` 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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``min`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.min dask.array.min DataArray.min :ref:`resampling` User guide on resampling operations. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").min() array([1., 1., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").min(skipna=False) array([ 1., 1., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="min", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.min, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def mean( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``mean`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.mean dask.array.mean DataArray.mean :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").mean() array([1., 2., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").mean(skipna=False) array([ 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="mean", dim=dim, skipna=skipna, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.mean, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def prod( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``prod`` 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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``prod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.prod dask.array.prod DataArray.prod :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").prod() array([1., 6., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").prod(skipna=False) array([ 1., 6., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.resample(time="3M").prod(skipna=True, min_count=2) array([nan, 6., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="prod", dim=dim, skipna=skipna, min_count=min_count, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.prod, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def sum( self, dim: Dims = None, *, skipna: bool | None = None, min_count: int | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray'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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``sum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.sum dask.array.sum DataArray.sum :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").sum() array([1., 6., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").sum(skipna=False) array([ 1., 6., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Specify ``min_count`` for finer control over when NaNs are ignored. >>> da.resample(time="3M").sum(skipna=True, min_count=2) array([nan, 6., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="sum", dim=dim, skipna=skipna, min_count=min_count, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.sum, dim=dim, skipna=skipna, min_count=min_count, keep_attrs=keep_attrs, **kwargs, ) def std( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``std`` 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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``std`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.std dask.array.std DataArray.std :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").std() array([0. , 0.81649658, 0. ]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").std(skipna=False) array([0. , 0.81649658, nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Specify ``ddof=1`` for an unbiased estimate. >>> da.resample(time="3M").std(skipna=True, ddof=1) array([nan, 1., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="std", dim=dim, skipna=skipna, ddof=ddof, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.std, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def var( self, dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``var`` 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 None, will reduce over the Resample dimensions. If "...", 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 : DataArray New DataArray with ``var`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.var dask.array.var DataArray.var :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").var() array([0. , 0.66666667, 0. ]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").var(skipna=False) array([0. , 0.66666667, nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Specify ``ddof=1`` for an unbiased estimate. >>> da.resample(time="3M").var(skipna=True, ddof=1) array([nan, 1., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ if ( flox_available and OPTIONS["use_flox"] and contains_only_dask_or_numpy(self._obj) ): return self._flox_reduce( func="var", dim=dim, skipna=skipna, ddof=ddof, # fill_value=fill_value, keep_attrs=keep_attrs, **kwargs, ) else: return self.reduce( duck_array_ops.var, dim=dim, skipna=skipna, ddof=ddof, keep_attrs=keep_attrs, **kwargs, ) def median( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``median`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``median``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``median`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``median`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.median dask.array.median DataArray.median :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").median() array([1., 2., 2.]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 Use ``skipna`` to control whether NaNs are ignored. >>> da.resample(time="3M").median(skipna=False) array([ 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31 """ return self.reduce( duck_array_ops.median, dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs, ) def cumsum( self, dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any, ) -> DataArray: """ Reduce this DataArray's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``cumsum`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumsum`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumsum dask.array.cumsum DataArray.cumsum :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").cumsum() array([1., 2., 5., 6., 2., 2.]) Coordinates: labels (time) >> da.resample(time="3M").cumsum(skipna=False) array([ 1., 2., 5., 6., 2., nan]) Coordinates: labels (time) DataArray: """ Reduce this DataArray's data by applying ``cumprod`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumprod``. For e.g. ``dim="x"`` or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions. If "...", 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 ``cumprod`` on this object's data. These could include dask-specific kwargs like ``split_every``. Returns ------- reduced : DataArray New DataArray with ``cumprod`` applied to its data and the indicated dimension(s) removed See Also -------- numpy.cumprod dask.array.cumprod DataArray.cumprod :ref:`resampling` User guide on resampling operations. Notes ----- Non-numeric variables will be removed prior to reducing. Examples -------- >>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) >> da.resample(time="3M").cumprod() array([1., 2., 6., 6., 2., 2.]) Coordinates: labels (time) >> da.resample(time="3M").cumprod(skipna=False) array([ 1., 2., 6., 6., 2., nan]) Coordinates: labels (time)