tidy3d.ModeIndexDataArray#

class tidy3d.ModeIndexDataArray(data: typing.Any = <NA>, coords: typing.Optional[typing.Union[collections.abc.Sequence[collections.abc.Sequence[Any] | pandas.core.indexes.base.Index | xarray.core.dataarray.DataArray], collections.abc.Mapping[typing.Any, typing.Any]]] = None, dims: typing.Optional[typing.Union[collections.abc.Hashable, collections.abc.Sequence[collections.abc.Hashable]]] = None, name: typing.Optional[collections.abc.Hashable] = None, attrs: typing.Optional[collections.abc.Mapping] = None, indexes: typing.Optional[dict[collections.abc.Hashable, xarray.core.indexes.Index]] = None, fastpath: bool = False)#

Bases: tidy3d.components.data.data_array.DataArray

Complex-valued effective propagation index of a mode.

Example

>>> f = [2e14, 3e14]
>>> mode_index = np.arange(4)
>>> coords = dict(f=f, mode_index=mode_index)
>>> data = ModeIndexDataArray((1+1j) * np.random.random((2,4)), coords=coords)
__init__(data: typing.Any = <NA>, coords: typing.Optional[typing.Union[collections.abc.Sequence[collections.abc.Sequence[Any] | pandas.core.indexes.base.Index | xarray.core.dataarray.DataArray], collections.abc.Mapping[typing.Any, typing.Any]]] = None, dims: typing.Optional[typing.Union[collections.abc.Hashable, collections.abc.Sequence[collections.abc.Hashable]]] = None, name: typing.Optional[collections.abc.Hashable] = None, attrs: typing.Optional[collections.abc.Mapping] = None, indexes: typing.Optional[dict[collections.abc.Hashable, xarray.core.indexes.Index]] = None, fastpath: bool = False) None#

Methods

__init__([data, coords, dims, name, attrs, ...])

all([dim, keep_attrs])

Reduce this DataArray's data by applying all along some dimension(s).

any([dim, keep_attrs])

Reduce this DataArray's data by applying any along some dimension(s).

argmax([dim, axis, keep_attrs, skipna])

Index or indices of the maximum of the DataArray over one or more dimensions.

argmin([dim, axis, keep_attrs, skipna])

Index or indices of the minimum of the DataArray over one or more dimensions.

argsort([axis, kind, order])

Returns the indices that would sort this array.

as_numpy()

Coerces wrapped data and coordinates into numpy arrays, returning a DataArray.

assign_attrs(*args, **kwargs)

Assign new attrs to this object.

assign_coord_attrs(val)

Assign the correct coordinate attributes to the DataArray.

assign_coords([coords])

Assign new coordinates to this object.

assign_data_attrs(val)

Assign the correct data attributes to the DataArray.

astype(dtype, *[, order, casting, subok, ...])

Copy of the xarray object, with data cast to a specified type.

bfill(dim[, limit])

Fill NaN values by propagating values backward

broadcast_equals(other)

Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions.

broadcast_like(other[, exclude])

Broadcast this DataArray against another Dataset or DataArray.

check_unloaded_data(val)

If the data comes in as the raw data array string, raise a custom warning.

chunk([chunks, name_prefix, token, lock, ...])

Coerce this array's data into a dask arrays with the given chunks.

clip([min, max, keep_attrs])

Return an array whose values are limited to [min, max].

close()

Release any resources linked to this object.

coarsen([dim, boundary, side, coord_func])

Coarsen object for DataArrays.

combine_first(other)

Combine two DataArray objects, with union of coordinates.

compute(**kwargs)

Manually trigger loading of this array's data from disk or a remote source into memory and return a new array.

conj()

Complex-conjugate all elements.

conjugate()

Return the complex conjugate, element-wise.

convert_calendar(calendar[, dim, align_on, ...])

Convert the DataArray to another calendar.

copy([deep, data])

Returns a copy of this array.

count([dim, keep_attrs])

Reduce this DataArray's data by applying count along some dimension(s).

cumprod([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying cumprod along some dimension(s).

cumsum([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying cumsum along some dimension(s).

cumulative_integrate([coord, datetime_unit])

Integrate cumulatively along the given coordinate using the trapezoidal rule.

curvefit(coords, func[, reduce_dims, ...])

Curve fitting optimization for arbitrary functions.

diff(dim[, n, label])

Calculate the n-th order discrete difference along given axis.

differentiate(coord[, edge_order, datetime_unit])

Differentiate the array with the second order accurate central differences.

dot(other[, dims])

Perform dot product of two DataArrays along their shared dims.

drop([labels, dim, errors])

Backward compatible method based on drop_vars and drop_sel

drop_duplicates(dim[, keep])

Returns a new DataArray with duplicate dimension values removed.

drop_indexes(coord_names, *[, errors])

Drop the indexes assigned to the given coordinates.

drop_isel([indexers])

Drop index positions from this DataArray.

drop_sel([labels, errors])

Drop index labels from this DataArray.

drop_vars(names, *[, errors])

Returns an array with dropped variables.

dropna(dim[, how, thresh])

Returns a new array with dropped labels for missing values along the provided dimension.

equals(other)

True if two DataArrays have the same dimensions, coordinates and values; otherwise False.

expand_dims([dim, axis])

Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape.

ffill(dim[, limit])

Fill NaN values by propagating values forward

fillna(value)

Fill missing values in this object.

from_cdms2(variable)

Convert a cdms2.Variable into an xarray.DataArray

from_dict(d)

Convert a dictionary into an xarray.DataArray

from_file(fname, group_path)

Load an DataArray from an hdf5 file with a given path to the group.

from_hdf5(fname, group_path)

Load an DataArray from an hdf5 file with a given path to the group.

from_iris(cube)

Convert a iris.cube.Cube into an xarray.DataArray

from_series(series[, sparse])

Convert a pandas.Series into an xarray.DataArray.

get_axis_num(dim)

Return axis number(s) corresponding to dimension(s) in this array.

get_index(key)

Get an index for a dimension, with fall-back to a default RangeIndex

groupby(group[, squeeze, restore_coord_dims])

Returns a DataArrayGroupBy object for performing grouped operations.

groupby_bins(group, bins[, right, labels, ...])

Returns a DataArrayGroupBy object for performing grouped operations.

head([indexers])

Return a new DataArray whose data is given by the the first n values along the specified dimension(s).

identical(other)

Like equals, but also checks the array name and attributes, and attributes on all coordinates.

idxmax([dim, skipna, fill_value, keep_attrs])

Return the coordinate label of the maximum value along a dimension.

idxmin([dim, skipna, fill_value, keep_attrs])

Return the coordinate label of the minimum value along a dimension.

integrate([coord, datetime_unit])

Integrate along the given coordinate using the trapezoidal rule.

interp([coords, method, assume_sorted, kwargs])

Interpolate a DataArray onto new coordinates

interp_calendar(target[, dim])

Interpolates the DataArray to another calendar based on decimal year measure.

interp_like(other[, method, assume_sorted, ...])

Interpolate this object onto the coordinates of another object, filling out of range values with NaN.

interpolate_na([dim, method, limit, ...])

Fill in NaNs by interpolating according to different methods.

isel([indexers, drop, missing_dims])

Return a new DataArray whose data is given by selecting indexes along the specified dimension(s).

isin(test_elements)

Tests each value in the array for whether it is in test elements.

isnull([keep_attrs])

Test each value in the array for whether it is a missing value.

item(*args)

Copy an element of an array to a standard Python scalar and return it.

load(**kwargs)

Manually trigger loading of this array's data from disk or a remote source into memory and return this array.

map_blocks(func[, args, kwargs, template])

Apply a function to each block of this DataArray.

max([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying max along some dimension(s).

mean([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying mean along some dimension(s).

median([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying median along some dimension(s).

min([dim, skipna, keep_attrs])

Reduce this DataArray's data by applying min along some dimension(s).

notnull([keep_attrs])

Test each value in the array for whether it is not a missing value.

pad([pad_width, mode, stat_length, ...])

Pad this array along one or more dimensions.

persist(**kwargs)

Trigger computation in constituent dask arrays

pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs)

polyfit(dim, deg[, skipna, rcond, w, full, cov])

Least squares polynomial fit.

prod([dim, skipna, min_count, keep_attrs])

Reduce this DataArray's data by applying prod along some dimension(s).

quantile(q[, dim, method, keep_attrs, ...])

Compute the qth quantile of the data along the specified dimension.

query([queries, parser, engine, missing_dims])

Return a new data array indexed along the specified dimension(s), where the indexers are given as strings containing Python expressions to be evaluated against the values in the array.

rank(dim[, pct, keep_attrs])

Ranks the data.

reduce(func[, dim, axis, keep_attrs, keepdims])

Reduce this array by applying func along some dimension(s).

reindex([indexers, method, tolerance, copy, ...])

Conform this object onto the indexes of another object, filling in missing values with fill_value.

reindex_like(other[, method, tolerance, ...])

Conform this object onto the indexes of another object, filling in missing values with fill_value.

rename([new_name_or_name_dict])

Returns a new DataArray with renamed coordinates, dimensions or a new name.

reorder_levels([dim_order])

Rearrange index levels using input order.

resample([indexer, skipna, closed, label, ...])

Returns a Resample object for performing resampling operations.

reset_coords([names, drop])

Given names of coordinates, reset them to become variables.

reset_encoding()

Return a new DataArray without encoding on the array or any attached coords.

reset_index(dims_or_levels[, drop])

Reset the specified index(es) or multi-index level(s).

roll([shifts, roll_coords])

Roll this array by an offset along one or more dimensions.

rolling([dim, min_periods, center])

Rolling window object for DataArrays.

rolling_exp([window, window_type])

Exponentially-weighted moving window.

round(*args, **kwargs)

Evenly round to the given number of decimals.

searchsorted(v[, side, sorter])

Find indices where elements of v should be inserted in a to maintain order.

sel([indexers, method, tolerance, drop])

Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).

set_close(close)

Register the function that releases any resources linked to this object.

set_index([indexes, append])

Set DataArray (multi-)indexes using one or more existing coordinates.

set_xindex(coord_names[, index_cls])

Set a new, Xarray-compatible index from one or more existing coordinate(s).

shift([shifts, fill_value])

Shift this DataArray by an offset along one or more dimensions.

sortby(variables[, ascending])

Sort object by labels or values (along an axis).

squeeze([dim, drop, axis])

Return a new object with squeezed data.

stack([dimensions, create_index, index_cls])

Stack any number of existing dimensions into a single new dimension.

std([dim, skipna, ddof, keep_attrs])

Reduce this DataArray's data by applying std along some dimension(s).

sum([dim, skipna, min_count, keep_attrs])

Reduce this DataArray's data by applying sum along some dimension(s).

swap_dims([dims_dict])

Returns a new DataArray with swapped dimensions.

tail([indexers])

Return a new DataArray whose data is given by the the last n values along the specified dimension(s).

thin([indexers])

Return a new DataArray whose data is given by each n value along the specified dimension(s).

to_cdms2()

Convert this array into a cdms2.Variable

to_dask_dataframe([dim_order, set_index])

Convert this array into a dask.dataframe.DataFrame.

to_dataframe([name, dim_order])

Convert this array and its coordinates into a tidy pandas.DataFrame.

to_dataset([dim, name, promote_attrs])

Convert a DataArray to a Dataset.

to_dict([data, encoding])

Convert this xarray.DataArray into a dictionary following xarray naming conventions.

to_hdf5(fname, group_path)

Save an xr.DataArray to the hdf5 file with a given path to the group.

to_index()

Convert this variable to a pandas.Index.

to_iris()

Convert this array into a iris.cube.Cube

to_masked_array([copy])

Convert this array into a numpy.ma.MaskedArray

to_netcdf([path, mode, format, group, ...])

Write DataArray contents to a netCDF file.

to_numpy()

Coerces wrapped data to numpy and returns a numpy.ndarray.

to_pandas()

Convert this array into a pandas object with the same shape.

to_series()

Convert this array into a pandas.Series.

to_unstacked_dataset(dim[, level])

Unstack DataArray expanding to Dataset along a given level of a stacked coordinate.

to_zarr([store, chunk_store, mode, ...])

Write DataArray contents to a Zarr store

transpose(*dims[, transpose_coords, ...])

Return a new DataArray object with transposed dimensions.

unify_chunks()

Unify chunk size along all chunked dimensions of this DataArray.

unstack([dim, fill_value, sparse])

Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.

validate_dims(val)

Make sure the dims are the same as _dims, then put them in the correct order.

var([dim, skipna, ddof, keep_attrs])

Reduce this DataArray's data by applying var along some dimension(s).

weighted(weights)

Weighted DataArray operations.

where(cond[, other, drop])

Filter elements from this object according to a condition.

Attributes

T

abs

Absolute value of data array.

attrs

Dictionary storing arbitrary metadata with this array.

chunks

Tuple of block lengths for this dataarray's data, in order of dimensions, or None if the underlying data is not a dask array.

chunksizes

Mapping from dimension names to block lengths for this dataarray's data, or None if the underlying data is not a dask array.

coords

Dictionary-like container of coordinate arrays.

data

The DataArray's data as an array.

dims

Tuple of dimension names associated with this array.

dt

alias of xarray.core.accessor_dt.CombinedDatetimelikeAccessor[DataArray]

dtype

Data-type of the array’s elements.

encoding

Dictionary of format-specific settings for how this array should be serialized.

imag

The imaginary part of the array.

indexes

Mapping of pandas.Index objects used for label based indexing.

loc

Attribute for location based indexing like pandas.

name

The name of this array.

nbytes

Total bytes consumed by the elements of this DataArray's data.

ndim

Number of array dimensions.

real

The real part of the array.

shape

Tuple of array dimensions.

size

Number of elements in the array.

sizes

Ordered mapping from dimension names to lengths.

str

alias of xarray.core.accessor_str.StringAccessor[DataArray]

values

The array's data as a numpy.ndarray.

variable

Low level interface to the Variable object for this DataArray.

xindexes

Mapping of xarray Index objects used for label based indexing.

__abs__()#

Same as abs(a).

__add__(other)#

Same as a + b.

__and__(other)#

Same as a & b.

__dir__() list[str]#

Provide method name lookup and completion. Only provide ‘public’ methods.

__eq__(other) bool#

Whether two data array objects are equal.

__floordiv__(other)#

Same as a // b.

__ge__(other)#

Same as a >= b.

classmethod __get_validators__()#

Validators that get run when DataArray objects are added to pydantic models.

__gt__(other)#

Same as a > b.

__hash__() int#

Generate hash value for a :class:.`DataArray` instance, needed for custom components.

__iadd__(other)#

Same as a += b.

__iand__(other)#

Same as a &= b.

__ifloordiv__(other)#

Same as a //= b.

__ilshift__(other)#

Same as a <<= b.

__imod__(other)#

Same as a %= b.

__imul__(other)#

Same as a *= b.

classmethod __init_subclass__(**kwargs)#

Verify that all subclasses explicitly define __slots__. If they don’t, raise error in the core xarray module and a FutureWarning in third-party extensions.

__invert__()#

Same as ~a.

__ior__(other)#

Same as a |= b.

__ipow__(other)#

Same as a **= b.

__irshift__(other)#

Same as a >>= b.

__isub__(other)#

Same as a -= b.

__itruediv__(other)#

Same as a /= b.

__ixor__(other)#

Same as a ^= b.

__le__(other)#

Same as a <= b.

__lshift__(other)#

Same as a << b.

__lt__(other)#

Same as a < b.

__mod__(other)#

Same as a % b.

classmethod __modify_schema__(field_schema)#

Sets the schema of DataArray object.

__mul__(other)#

Same as a * b.

__neg__()#

Same as -a.

__or__(other)#

Same as a | b.

__pos__()#

Same as +a.

__pow__(other)#

Same as a ** b.

__radd__(other)#

Same as a + b.

__rand__(other)#

Same as a & b.

__rfloordiv__(other)#

Same as a // b.

__rmod__(other)#

Same as a % b.

__rmul__(other)#

Same as a * b.

__ror__(other)#

Same as a | b.

__rpow__(other)#

Same as a ** b.

__rshift__(other)#

Same as a >> b.

__rsub__(other)#

Same as a - b.

__rtruediv__(other)#

Same as a / b.

__rxor__(other)#

Same as a ^ b.

__setattr__(name: str, value: Any) None#

Objects with __slots__ raise AttributeError if you try setting an undeclared attribute. This is desirable, but the error message could use some improvement.

__sub__(other)#

Same as a - b.

__truediv__(other)#

Same as a / b.

__xor__(other)#

Same as a ^ b.

property abs#

Absolute value of data array.

all(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 – New DataArray with all applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.all, dask.array.all, Dataset.all

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ True,  True,  True,  True,  True, False])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.all()
<xarray.DataArray ()>
array(False)
any(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 – New DataArray with any applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.any, dask.array.any, Dataset.any

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ True,  True,  True,  True,  True, False])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.any()
<xarray.DataArray ()>
array(True)
argmax(dim: Dims = None, axis: int | None = None, keep_attrs: bool | None = None, skipna: bool | None = None) DataArray | dict[Hashable, DataArray]#

Index or indices of the maximum of the DataArray over one or more dimensions.

If a sequence is passed to ‘dim’, then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to ‘dim’ then returns a DataArray with dtype int.

If there are multiple maxima, the indices of the first one found will be returned.

Parameters
  • dim ("...", str, Iterable of Hashable or None, optional) – The dimensions over which to find the maximum. By default, finds maximum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass ‘…’.

  • axis (int or None, optional) – Axis over which to apply argmax. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

  • 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).

Returns

result

Return type

DataArray or dict of DataArray

See also

Variable.argmax, DataArray.idxmax

Examples

>>> array = xr.DataArray([0, 2, -1, 3], dims="x")
>>> array.max()
<xarray.DataArray ()>
array(3)
>>> array.argmax(...)
{'x': <xarray.DataArray ()>
array(3)}
>>> array.isel(array.argmax(...))
<xarray.DataArray ()>
array(3)
>>> array = xr.DataArray(
...     [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, 5, 1], [2, 3, 1]]],
...     dims=("x", "y", "z"),
... )
>>> array.max(dim="x")
<xarray.DataArray (y: 3, z: 3)>
array([[3, 3, 2],
       [3, 5, 2],
       [2, 3, 3]])
Dimensions without coordinates: y, z
>>> array.argmax(dim="x")
<xarray.DataArray (y: 3, z: 3)>
array([[0, 1, 1],
       [0, 1, 0],
       [0, 1, 0]])
Dimensions without coordinates: y, z
>>> array.argmax(dim=["x"])
{'x': <xarray.DataArray (y: 3, z: 3)>
array([[0, 1, 1],
       [0, 1, 0],
       [0, 1, 0]])
Dimensions without coordinates: y, z}
>>> array.max(dim=("x", "z"))
<xarray.DataArray (y: 3)>
array([3, 5, 3])
Dimensions without coordinates: y
>>> array.argmax(dim=["x", "z"])
{'x': <xarray.DataArray (y: 3)>
array([0, 1, 0])
Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
array([0, 1, 2])
Dimensions without coordinates: y}
>>> array.isel(array.argmax(dim=["x", "z"]))
<xarray.DataArray (y: 3)>
array([3, 5, 3])
Dimensions without coordinates: y
argmin(dim: Dims = None, axis: int | None = None, keep_attrs: bool | None = None, skipna: bool | None = None) DataArray | dict[Hashable, DataArray]#

Index or indices of the minimum of the DataArray over one or more dimensions.

If a sequence is passed to ‘dim’, then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to ‘dim’ then returns a DataArray with dtype int.

If there are multiple minima, the indices of the first one found will be returned.

Parameters
  • dim ("...", str, Iterable of Hashable or None, optional) – The dimensions over which to find the minimum. By default, finds minimum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass ‘…’.

  • axis (int or None, optional) – Axis over which to apply argmin. Only one of the ‘dim’ and ‘axis’ arguments can be supplied.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

  • 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).

Returns

result

Return type

DataArray or dict of DataArray

See also

Variable.argmin, DataArray.idxmin

Examples

>>> array = xr.DataArray([0, 2, -1, 3], dims="x")
>>> array.min()
<xarray.DataArray ()>
array(-1)
>>> array.argmin(...)
{'x': <xarray.DataArray ()>
array(2)}
>>> array.isel(array.argmin(...))
<xarray.DataArray ()>
array(-1)
>>> array = xr.DataArray(
...     [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, -5, 1], [2, 3, 1]]],
...     dims=("x", "y", "z"),
... )
>>> array.min(dim="x")
<xarray.DataArray (y: 3, z: 3)>
array([[ 1,  2,  1],
       [ 2, -5,  1],
       [ 2,  1,  1]])
Dimensions without coordinates: y, z
>>> array.argmin(dim="x")
<xarray.DataArray (y: 3, z: 3)>
array([[1, 0, 0],
       [1, 1, 1],
       [0, 0, 1]])
Dimensions without coordinates: y, z
>>> array.argmin(dim=["x"])
{'x': <xarray.DataArray (y: 3, z: 3)>
array([[1, 0, 0],
       [1, 1, 1],
       [0, 0, 1]])
Dimensions without coordinates: y, z}
>>> array.min(dim=("x", "z"))
<xarray.DataArray (y: 3)>
array([ 1, -5,  1])
Dimensions without coordinates: y
>>> array.argmin(dim=["x", "z"])
{'x': <xarray.DataArray (y: 3)>
array([0, 1, 0])
Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
array([2, 1, 1])
Dimensions without coordinates: y}
>>> array.isel(array.argmin(dim=["x", "z"]))
<xarray.DataArray (y: 3)>
array([ 1, -5,  1])
Dimensions without coordinates: y
argsort(axis=- 1, kind=None, order=None)#

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

See also

numpy.argsort

equivalent function

as_numpy() T_DataArray#

Coerces wrapped data and coordinates into numpy arrays, returning a DataArray.

See also

DataArray.to_numpy

Same but returns only the data as a numpy.ndarray object.

Dataset.as_numpy

Converts all variables in a Dataset.

DataArray.values, DataArray.data

assign_attrs(*args: Any, **kwargs: Any) T_DataWithCoords#

Assign new attrs to this object.

Returns a new object equivalent to self.attrs.update(*args, **kwargs).

Parameters
  • *args – positional arguments passed into attrs.update.

  • **kwargs – keyword arguments passed into attrs.update.

Returns

assigned – A new object with the new attrs in addition to the existing data.

Return type

same type as caller

See also

Dataset.assign

classmethod assign_coord_attrs(val)#

Assign the correct coordinate attributes to the DataArray.

assign_coords(coords: Mapping[Any, Any] | None = None, **coords_kwargs: Any) T_DataWithCoords#

Assign new coordinates to this object.

Returns a new object with all the original data in addition to the new coordinates.

Parameters
  • coords (dict-like or None, optional) – A dict where the keys are the names of the coordinates with the new values to assign. If the values are callable, they are computed on this object and assigned to new coordinate variables. If the values are not callable, (e.g. a DataArray, scalar, or array), they are simply assigned. A new coordinate can also be defined and attached to an existing dimension using a tuple with the first element the dimension name and the second element the values for this new coordinate.

  • **coords_kwargs (optional) – The keyword arguments form of coords. One of coords or coords_kwargs must be provided.

Returns

assigned – A new object with the new coordinates in addition to the existing data.

Return type

same type as caller

Examples

Convert DataArray longitude coordinates from 0-359 to -180-179:

>>> da = xr.DataArray(
...     np.random.rand(4),
...     coords=[np.array([358, 359, 0, 1])],
...     dims="lon",
... )
>>> da
<xarray.DataArray (lon: 4)>
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 358 359 0 1
>>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))
<xarray.DataArray (lon: 4)>
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 -2 -1 0 1

The function also accepts dictionary arguments:

>>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)})
<xarray.DataArray (lon: 4)>
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 -2 -1 0 1

New coordinate can also be attached to an existing dimension:

>>> lon_2 = np.array([300, 289, 0, 1])
>>> da.assign_coords(lon_2=("lon", lon_2))
<xarray.DataArray (lon: 4)>
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 358 359 0 1
    lon_2    (lon) int64 300 289 0 1

Note that the same result can also be obtained with a dict e.g.

>>> _ = da.assign_coords({"lon_2": ("lon", lon_2)})

Note the same method applies to Dataset objects.

Convert Dataset longitude coordinates from 0-359 to -180-179:

>>> temperature = np.linspace(20, 32, num=16).reshape(2, 2, 4)
>>> precipitation = 2 * np.identity(4).reshape(2, 2, 4)
>>> ds = xr.Dataset(
...     data_vars=dict(
...         temperature=(["x", "y", "time"], temperature),
...         precipitation=(["x", "y", "time"], precipitation),
...     ),
...     coords=dict(
...         lon=(["x", "y"], [[260.17, 260.68], [260.21, 260.77]]),
...         lat=(["x", "y"], [[42.25, 42.21], [42.63, 42.59]]),
...         time=pd.date_range("2014-09-06", periods=4),
...         reference_time=pd.Timestamp("2014-09-05"),
...     ),
...     attrs=dict(description="Weather-related data"),
... )
>>> ds
<xarray.Dataset>
Dimensions:         (x: 2, y: 2, time: 4)
Coordinates:
    lon             (x, y) float64 260.2 260.7 260.2 260.8
    lat             (x, y) float64 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 ... 2014-09-09
    reference_time  datetime64[ns] 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 20.0 20.8 21.6 22.4 ... 30.4 31.2 32.0
    precipitation   (x, y, time) float64 2.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 2.0
Attributes:
    description:  Weather-related data
>>> ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180))
<xarray.Dataset>
Dimensions:         (x: 2, y: 2, time: 4)
Coordinates:
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    lat             (x, y) float64 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 ... 2014-09-09
    reference_time  datetime64[ns] 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 20.0 20.8 21.6 22.4 ... 30.4 31.2 32.0
    precipitation   (x, y, time) float64 2.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 2.0
Attributes:
    description:  Weather-related data

Notes

Since coords_kwargs is a dictionary, the order of your arguments may not be preserved, and so the order of the new variables is not well defined. Assigning multiple variables within the same assign_coords is possible, but you cannot reference other variables created within the same assign_coords call.

See also

Dataset.assign, Dataset.swap_dims, Dataset.set_coords

classmethod assign_data_attrs(val)#

Assign the correct data attributes to the DataArray.

astype(dtype, *, order=None, casting=None, subok=None, copy=None, keep_attrs=True) T_DataWithCoords#

Copy of the xarray object, with data cast to a specified type. Leaves coordinate dtype unchanged.

Parameters
  • dtype (str or dtype) – Typecode or data-type to which the array is cast.

  • order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible.

  • casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –

    Controls what kind of data casting may occur.

    • ’no’ means the data types should not be cast at all.

    • ’equiv’ means only byte-order changes are allowed.

    • ’safe’ means only casts which can preserve values are allowed.

    • ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

    • ’unsafe’ means any data conversions may be done.

  • subok (bool, optional) – If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array.

  • copy (bool, optional) – By default, astype always returns a newly allocated array. If this is set to False and the dtype requirement is satisfied, the input array is returned instead of a copy.

  • keep_attrs (bool, optional) – By default, astype keeps attributes. Set to False to remove attributes in the returned object.

Returns

out – New object with data cast to the specified type.

Return type

same as object

Notes

The order, casting, subok and copy arguments are only passed through to the astype method of the underlying array when a value different than None is supplied. Make sure to only supply these arguments if the underlying array class supports them.

See also

numpy.ndarray.astype, dask.array.Array.astype, sparse.COO.astype

property attrs: dict[Any, Any]#

Dictionary storing arbitrary metadata with this array.

bfill(dim: Hashable, limit: int | None = None) T_DataArray#

Fill NaN values by propagating values backward

Requires bottleneck.

Parameters
  • dim (str) – Specifies the dimension along which to propagate values when filling.

  • limit (int or None, default: None) – The maximum number of consecutive NaN values to backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Must be None or greater than or equal to axis length if filling along chunked axes (dimensions).

Returns

filled

Return type

DataArray

Examples

>>> temperature = np.array(
...     [
...         [0, 1, 3],
...         [0, np.nan, 5],
...         [5, np.nan, np.nan],
...         [3, np.nan, np.nan],
...         [np.nan, 2, 0],
...     ]
... )
>>> da = xr.DataArray(
...     data=temperature,
...     dims=["Y", "X"],
...     coords=dict(
...         lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75, -21.0])),
...         lon=("X", np.array([10.0, 10.25, 10.5])),
...     ),
... )
>>> da
<xarray.DataArray (Y: 5, X: 3)>
array([[ 0.,  1.,  3.],
       [ 0., nan,  5.],
       [ 5., nan, nan],
       [ 3., nan, nan],
       [nan,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X

Fill all NaN values:

>>> da.bfill(dim="Y", limit=None)
<xarray.DataArray (Y: 5, X: 3)>
array([[ 0.,  1.,  3.],
       [ 0.,  2.,  5.],
       [ 5.,  2.,  0.],
       [ 3.,  2.,  0.],
       [nan,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X

Fill only the first of consecutive NaN values:

>>> da.bfill(dim="Y", limit=1)
<xarray.DataArray (Y: 5, X: 3)>
array([[ 0.,  1.,  3.],
       [ 0., nan,  5.],
       [ 5., nan, nan],
       [ 3.,  2.,  0.],
       [nan,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X
broadcast_equals(other: T_DataArray) bool#

Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions.

Parameters

other (DataArray) – DataArray to compare to.

Returns

equal – True if the two DataArrays are broadcast equal.

Return type

bool

See also

DataArray.equals, DataArray.identical

Examples

>>> a = xr.DataArray([1, 2], dims="X")
>>> b = xr.DataArray([[1, 1], [2, 2]], dims=["X", "Y"])
>>> a
<xarray.DataArray (X: 2)>
array([1, 2])
Dimensions without coordinates: X
>>> b
<xarray.DataArray (X: 2, Y: 2)>
array([[1, 1],
       [2, 2]])
Dimensions without coordinates: X, Y

.equals returns True if two DataArrays have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two DataArrays against eachother have the same values, dimensions, and coordinates.

>>> a.equals(b)
False
>>> a2, b2 = xr.broadcast(a, b)
>>> a2.equals(b2)
True
>>> a.broadcast_equals(b)
True
broadcast_like(other: DataArray | Dataset, exclude: Iterable[Hashable] | None = None) T_DataArray#

Broadcast this DataArray against another Dataset or DataArray.

This is equivalent to xr.broadcast(other, self)[1]

xarray objects are broadcast against each other in arithmetic operations, so this method is not be necessary for most uses.

If no change is needed, the input data is returned to the output without being copied.

If new coords are added by the broadcast, their values are NaN filled.

Parameters
  • other (Dataset or DataArray) – Object against which to broadcast this array.

  • exclude (iterable of Hashable, optional) – Dimensions that must not be broadcasted

Returns

new_da – The caller broadcasted against other.

Return type

DataArray

Examples

>>> arr1 = xr.DataArray(
...     np.random.randn(2, 3),
...     dims=("x", "y"),
...     coords={"x": ["a", "b"], "y": ["a", "b", "c"]},
... )
>>> arr2 = xr.DataArray(
...     np.random.randn(3, 2),
...     dims=("x", "y"),
...     coords={"x": ["a", "b", "c"], "y": ["a", "b"]},
... )
>>> arr1
<xarray.DataArray (x: 2, y: 3)>
array([[ 1.76405235,  0.40015721,  0.97873798],
       [ 2.2408932 ,  1.86755799, -0.97727788]])
Coordinates:
  * x        (x) <U1 'a' 'b'
  * y        (y) <U1 'a' 'b' 'c'
>>> arr2
<xarray.DataArray (x: 3, y: 2)>
array([[ 0.95008842, -0.15135721],
       [-0.10321885,  0.4105985 ],
       [ 0.14404357,  1.45427351]])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'
  * y        (y) <U1 'a' 'b'
>>> arr1.broadcast_like(arr2)
<xarray.DataArray (x: 3, y: 3)>
array([[ 1.76405235,  0.40015721,  0.97873798],
       [ 2.2408932 ,  1.86755799, -0.97727788],
       [        nan,         nan,         nan]])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'
  * y        (y) <U1 'a' 'b' 'c'
classmethod check_unloaded_data(val)#

If the data comes in as the raw data array string, raise a custom warning.

chunk(chunks: int | Literal['auto'] | tuple[int, ...] | tuple[tuple[int, ...], ...] | Mapping[Any, None | int | tuple[int, ...]] = {}, name_prefix: str = 'xarray-', token: str | None = None, lock: bool = False, inline_array: bool = False, chunked_array_type: str | ChunkManagerEntrypoint | None = None, from_array_kwargs=None, **chunks_kwargs: Any) T_DataArray#

Coerce this array’s data into a dask arrays with the given chunks.

If this variable is a non-dask array, it will be converted to dask array. If it’s a dask array, it will be rechunked to the given chunk sizes.

If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block.

Parameters
  • chunks (int, "auto", tuple of int or mapping of Hashable to int, optional) – Chunk sizes along each dimension, e.g., 5, "auto", (5, 5) or {"x": 5, "y": 5}.

  • name_prefix (str, optional) – Prefix for the name of the new dask array.

  • token (str, optional) – Token uniquely identifying this array.

  • lock (bool, default: False) – Passed on to dask.array.from_array(), if the array is not already as dask array.

  • inline_array (bool, default: False) – Passed on to dask.array.from_array(), if the array is not already as dask array.

  • chunked_array_type (str, optional) – Which chunked array type to coerce the underlying data array to. Defaults to ‘dask’ if installed, else whatever is registered via the ChunkManagerEntryPoint system. Experimental API that should not be relied upon.

  • from_array_kwargs (dict, optional) – Additional keyword arguments passed on to the ChunkManagerEntrypoint.from_array method used to create chunked arrays, via whichever chunk manager is specified through the chunked_array_type kwarg. For example, with dask as the default chunked array type, this method would pass additional kwargs to dask.array.from_array(). Experimental API that should not be relied upon.

  • **chunks_kwargs ({dim: chunks, ...}, optional) – The keyword arguments form of chunks. One of chunks or chunks_kwargs must be provided.

Returns

chunked

Return type

xarray.DataArray

See also

DataArray.chunks, DataArray.chunksizes, xarray.unify_chunks, dask.array.from_array

property chunks: tuple[tuple[int, ...], ...] | None#

Tuple of block lengths for this dataarray’s data, in order of dimensions, or None if the underlying data is not a dask array.

See also

DataArray.chunk, DataArray.chunksizes, xarray.unify_chunks

property chunksizes: collections.abc.Mapping[Any, tuple[int, ...]]#

Mapping from dimension names to block lengths for this dataarray’s data, or None if the underlying data is not a dask array. Cannot be modified directly, but can be modified by calling .chunk().

Differs from DataArray.chunks because it returns a mapping of dimensions to chunk shapes instead of a tuple of chunk shapes.

See also

DataArray.chunk, DataArray.chunks, xarray.unify_chunks

clip(min: ScalarOrArray | None = None, max: ScalarOrArray | None = None, *, keep_attrs: bool | None = None) T_DataWithCoords#

Return an array whose values are limited to [min, max]. At least one of max or min must be given.

Parameters
  • min (None or Hashable, optional) – Minimum value. If None, no lower clipping is performed.

  • max (None or Hashable, optional) – Maximum value. If None, no upper clipping is performed.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

Returns

clipped – This object, but with with values < min are replaced with min, and those > max with max.

Return type

same type as caller

See also

numpy.clip

equivalent function

close() None#

Release any resources linked to this object.

coarsen(dim: Mapping[Any, int] | None = None, boundary: CoarsenBoundaryOptions = 'exact', side: SideOptions | Mapping[Any, SideOptions] = 'left', coord_func: str | Callable | Mapping[Any, str | Callable] = 'mean', **window_kwargs: int) DataArrayCoarsen#

Coarsen object for DataArrays.

Parameters
  • dim (mapping of hashable to int, optional) – Mapping from the dimension name to the window size.

  • boundary ({"exact", "trim", "pad"}, default: "exact") – If ‘exact’, a ValueError will be raised if dimension size is not a multiple of the window size. If ‘trim’, the excess entries are dropped. If ‘pad’, NA will be padded.

  • side ({"left", "right"} or mapping of str to {"left", "right"}, default: "left") –

  • coord_func (str or mapping of hashable to str, default: "mean") – function (name) that is applied to the coordinates, or a mapping from coordinate name to function (name).

Return type

core.rolling.DataArrayCoarsen

Examples

Coarsen the long time series by averaging over every three days.

>>> da = xr.DataArray(
...     np.linspace(0, 364, num=364),
...     dims="time",
...     coords={"time": pd.date_range("1999-12-15", periods=364)},
... )
>>> da  # +doctest: ELLIPSIS
<xarray.DataArray (time: 364)>
array([  0.        ,   1.00275482,   2.00550964,   3.00826446,
         4.01101928,   5.0137741 ,   6.01652893,   7.01928375,
         8.02203857,   9.02479339,  10.02754821,  11.03030303,
...
       356.98071625, 357.98347107, 358.9862259 , 359.98898072,
       360.99173554, 361.99449036, 362.99724518, 364.        ])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
>>> da.coarsen(time=3, boundary="trim").mean()  # +doctest: ELLIPSIS
<xarray.DataArray (time: 121)>
array([  1.00275482,   4.01101928,   7.01928375,  10.02754821,
        13.03581267,  16.04407713,  19.0523416 ,  22.06060606,
        25.06887052,  28.07713499,  31.08539945,  34.09366391,
...
       349.96143251, 352.96969697, 355.97796143, 358.9862259 ,
       361.99449036])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10
>>>

See also

core.rolling.DataArrayCoarsen, Dataset.coarsen

combine_first(other: T_DataArray) T_DataArray#

Combine two DataArray objects, with union of coordinates.

This operation follows the normal broadcasting and alignment rules of join='outer'. Default to non-null values of array calling the method. Use np.nan to fill in vacant cells after alignment.

Parameters

other (DataArray) – Used to fill all matching missing values in this array.

Return type

DataArray

compute(**kwargs) T_DataArray#

Manually trigger loading of this array’s data from disk or a remote source into memory and return a new array. The original is left unaltered.

Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk.

Parameters

**kwargs (dict) – Additional keyword arguments passed on to dask.compute.

See also

dask.compute

conj()#

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

conjugate()#

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

convert_calendar(calendar: str, dim: str = 'time', align_on: Optional[str] = None, missing: Optional[Any] = None, use_cftime: Optional[bool] = None) xarray.core.dataarray.DataArray#

Convert the DataArray to another calendar.

Only converts the individual timestamps, does not modify any data except in dropping invalid/surplus dates or inserting missing dates.

If the source and target calendars are either no_leap, all_leap or a standard type, only the type of the time array is modified. When converting to a leap year from a non-leap year, the 29th of February is removed from the array. In the other direction the 29th of February will be missing in the output, unless missing is specified, in which case that value is inserted.

For conversions involving 360_day calendars, see Notes.

This method is safe to use with sub-daily data as it doesn’t touch the time part of the timestamps.

Parameters
  • calendar (str) – The target calendar name.

  • dim (str) – Name of the time coordinate.

  • align_on ({None, 'date', 'year'}) – Must be specified when either source or target is a 360_day calendar, ignored otherwise. See Notes.

  • missing (Optional[any]) – By default, i.e. if the value is None, this method will simply attempt to convert the dates in the source calendar to the same dates in the target calendar, and drop any of those that are not possible to represent. If a value is provided, a new time coordinate will be created in the target calendar with the same frequency as the original time coordinate; for any dates that are not present in the source, the data will be filled with this value. Note that using this mode requires that the source data have an inferable frequency; for more information see xarray.infer_freq(). For certain frequency, source, and target calendar combinations, this could result in many missing values, see notes.

  • use_cftime (boolean, optional) – Whether to use cftime objects in the output, only used if calendar is one of {“proleptic_gregorian”, “gregorian” or “standard”}. If True, the new time axis uses cftime objects. If None (default), it uses numpy.datetime64 values if the date range permits it, and cftime.datetime objects if not. If False, it uses numpy.datetime64 or fails.

Returns

Copy of the dataarray with the time coordinate converted to the target calendar. If ‘missing’ was None (default), invalid dates in the new calendar are dropped, but missing dates are not inserted. If missing was given, the new data is reindexed to have a time axis with the same frequency as the source, but in the new calendar; any missing datapoints are filled with missing.

Return type

DataArray

Notes

Passing a value to missing is only usable if the source’s time coordinate as an inferable frequencies (see infer_freq()) and is only appropriate if the target coordinate, generated from this frequency, has dates equivalent to the source. It is usually not appropriate to use this mode with:

  • Period-end frequencies : ‘A’, ‘Y’, ‘Q’ or ‘M’, in opposition to ‘AS’ ‘YS’, ‘QS’ and ‘MS’

  • Sub-monthly frequencies that do not divide a day evenly‘W’, ‘nD’ where N != 1

    or ‘mH’ where 24 % m != 0).

If one of the source or target calendars is “360_day”, align_on must be specified and two options are offered.

  • “year”

    The dates are translated according to their relative position in the year, ignoring their original month and day information, meaning that the missing/surplus days are added/removed at regular intervals.

    From a 360_day to a standard calendar, the output will be missing the following dates (day of year in parentheses):

    To a leap year:

    January 31st (31), March 31st (91), June 1st (153), July 31st (213), September 31st (275) and November 30th (335).

    To a non-leap year:

    February 6th (36), April 19th (109), July 2nd (183), September 12th (255), November 25th (329).

    From a standard calendar to a “360_day”, the following dates in the source array will be dropped:

    From a leap year:

    January 31st (31), April 1st (92), June 1st (153), August 1st (214), September 31st (275), December 1st (336)

    From a non-leap year:

    February 6th (37), April 20th (110), July 2nd (183), September 13th (256), November 25th (329)

    This option is best used on daily and subdaily data.

  • “date”

    The month/day information is conserved and invalid dates are dropped from the output. This means that when converting from a “360_day” to a standard calendar, all 31st (Jan, March, May, July, August, October and December) will be missing as there is no equivalent dates in the “360_day” calendar and the 29th (on non-leap years) and 30th of February will be dropped as there are no equivalent dates in a standard calendar.

    This option is best used with data on a frequency coarser than daily.

property coords: xarray.core.coordinates.DataArrayCoordinates#

Dictionary-like container of coordinate arrays.

copy(deep: bool = True, data: Any = None) T_DataArray#

Returns a copy of this array.

If deep=True, a deep copy is made of the data array. Otherwise, a shallow copy is made, and the returned data array’s values are a new view of this data array’s values.

Use data to create a new object with the same structure as original but entirely new data.

Parameters
  • deep (bool, optional) – Whether the data array and its coordinates are loaded into memory and copied onto the new object. Default is True.

  • data (array_like, optional) – Data to use in the new object. Must have same shape as original. When data is used, deep is ignored for all data variables, and only used for coords.

Returns

copy – New object with dimensions, attributes, coordinates, name, encoding, and optionally data copied from original.

Return type

DataArray

Examples

Shallow versus deep copy

>>> array = xr.DataArray([1, 2, 3], dims="x", coords={"x": ["a", "b", "c"]})
>>> array.copy()
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'
>>> array_0 = array.copy(deep=False)
>>> array_0[0] = 7
>>> array_0
<xarray.DataArray (x: 3)>
array([7, 2, 3])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'
>>> array
<xarray.DataArray (x: 3)>
array([7, 2, 3])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'

Changing the data using the data argument maintains the structure of the original object, but with the new data. Original object is unaffected.

>>> array.copy(data=[0.1, 0.2, 0.3])
<xarray.DataArray (x: 3)>
array([0.1, 0.2, 0.3])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'
>>> array
<xarray.DataArray (x: 3)>
array([7, 2, 3])
Coordinates:
  * x        (x) <U1 'a' 'b' 'c'

See also

pandas.DataFrame.copy

count(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 – New DataArray with count applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.count, dask.array.count, Dataset.count

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.count()
<xarray.DataArray ()>
array(5)
cumprod(dim: Dims = None, *, skipna: bool | None = None, keep_attrs: bool | None = None, **kwargs: Any) 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 – New DataArray with cumprod applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.cumprod, dask.array.cumprod, Dataset.cumprod

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.cumprod()
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  6.,  6., 12., 12.])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'

Use skipna to control whether NaNs are ignored.

>>> da.cumprod(skipna=False)
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  6.,  6., 12., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
cumsum(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 – New DataArray with cumsum applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.cumsum, dask.array.cumsum, Dataset.cumsum

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.cumsum()
<xarray.DataArray (time: 6)>
array([1., 3., 6., 7., 9., 9.])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'

Use skipna to control whether NaNs are ignored.

>>> da.cumsum(skipna=False)
<xarray.DataArray (time: 6)>
array([ 1.,  3.,  6.,  7.,  9., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
cumulative_integrate(coord: Hashable | Sequence[Hashable] = None, datetime_unit: DatetimeUnitOptions = None) DataArray#

Integrate cumulatively along the given coordinate using the trapezoidal rule.

Note

This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional.

The first entry of the cumulative integral is always 0, in order to keep the length of the dimension unchanged between input and output.

Parameters
  • coord (Hashable, or sequence of Hashable) – Coordinate(s) used for the integration.

  • datetime_unit ({'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as', None}, optional) – Specify the unit if a datetime coordinate is used.

Returns

integrated

Return type

DataArray

See also

Dataset.cumulative_integrate

scipy.integrate.cumulative_trapezoid

corresponding scipy function

Examples

>>> da = xr.DataArray(
...     np.arange(12).reshape(4, 3),
...     dims=["x", "y"],
...     coords={"x": [0, 0.1, 1.1, 1.2]},
... )
>>> da
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
>>>
>>> da.cumulative_integrate("x")
<xarray.DataArray (x: 4, y: 3)>
array([[0.  , 0.  , 0.  ],
       [0.15, 0.25, 0.35],
       [4.65, 5.75, 6.85],
       [5.4 , 6.6 , 7.8 ]])
Coordinates:
  * x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
curvefit(coords: str | DataArray | Iterable[str | DataArray], func: Callable[..., Any], reduce_dims: Dims = None, skipna: bool = True, p0: dict[str, Any] | None = None, bounds: dict[str, Any] | None = None, param_names: Sequence[str] | None = None, kwargs: dict[str, Any] | None = None) Dataset#

Curve fitting optimization for arbitrary functions.

Wraps scipy.optimize.curve_fit with apply_ufunc.

Parameters
  • coords (Hashable, DataArray, or sequence of DataArray or Hashable) – Independent coordinate(s) over which to perform the curve fitting. Must share at least one dimension with the calling object. When fitting multi-dimensional functions, supply coords as a sequence in the same order as arguments in func. To fit along existing dimensions of the calling object, coords can also be specified as a str or sequence of strs.

  • func (callable) – User specified function in the form f(x, *params) which returns a numpy array of length len(x). params are the fittable parameters which are optimized by scipy curve_fit. x can also be specified as a sequence containing multiple coordinates, e.g. f((x0, x1), *params).

  • reduce_dims (str, Iterable of Hashable or None, optional) – Additional dimension(s) over which to aggregate while fitting. For example, calling ds.curvefit(coords=’time’, reduce_dims=[‘lat’, ‘lon’], …) will aggregate all lat and lon points and fit the specified function along the time dimension.

  • skipna (bool, default: True) – Whether to skip missing values when fitting. Default is True.

  • p0 (dict-like or None, optional) – Optional dictionary of parameter names to initial guesses passed to the curve_fit p0 arg. If none or only some parameters are passed, the rest will be assigned initial values following the default scipy behavior.

  • bounds (dict-like or None, optional) – Optional dictionary of parameter names to bounding values passed to the curve_fit bounds arg. If none or only some parameters are passed, the rest will be unbounded following the default scipy behavior.

  • param_names (sequence of Hashable or None, optional) – Sequence of names for the fittable parameters of func. If not supplied, this will be automatically determined by arguments of func. param_names should be manually supplied when fitting a function that takes a variable number of parameters.

  • **kwargs (optional) – Additional keyword arguments to passed to scipy curve_fit.

Returns

curvefit_results – A single dataset which contains:

[var]_curvefit_coefficients

The coefficients of the best fit.

[var]_curvefit_covariance

The covariance matrix of the coefficient estimates.

Return type

Dataset

See also

DataArray.polyfit, scipy.optimize.curve_fit

property data: Any#

The DataArray’s data as an array. The underlying array type (e.g. dask, sparse, pint) is preserved.

See also

DataArray.to_numpy, DataArray.as_numpy, DataArray.values

diff(dim: Hashable, n: int = 1, label: Literal['upper', 'lower'] = 'upper') T_DataArray#

Calculate the n-th order discrete difference along given axis.

Parameters
  • dim (Hashable) – Dimension over which to calculate the finite difference.

  • n (int, default: 1) – The number of times values are differenced.

  • label ({"upper", "lower"}, default: "upper") – The new coordinate in dimension dim will have the values of either the minuend’s or subtrahend’s coordinate for values ‘upper’ and ‘lower’, respectively.

Returns

difference – The n-th order finite difference of this object.

Return type

DataArray

Notes

n matches numpy’s behavior and is different from pandas’ first argument named periods.

Examples

>>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ["x"])
>>> arr.diff("x")
<xarray.DataArray (x: 3)>
array([0, 1, 0])
Coordinates:
  * x        (x) int64 2 3 4
>>> arr.diff("x", 2)
<xarray.DataArray (x: 2)>
array([ 1, -1])
Coordinates:
  * x        (x) int64 3 4

See also

DataArray.differentiate

differentiate(coord: Hashable, edge_order: Literal[1, 2] = 1, datetime_unit: DatetimeUnitOptions = None) T_DataArray#

Differentiate the array with the second order accurate central differences.

Note

This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional.

Parameters
  • coord (Hashable) – The coordinate to be used to compute the gradient.

  • edge_order ({1, 2}, default: 1) – N-th order accurate differences at the boundaries.

  • datetime_unit ({"Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as", None}, optional) – Unit to compute gradient. Only valid for datetime coordinate.

Returns

differentiated

Return type

DataArray

See also

numpy.gradient

corresponding numpy function

Examples

>>> da = xr.DataArray(
...     np.arange(12).reshape(4, 3),
...     dims=["x", "y"],
...     coords={"x": [0, 0.1, 1.1, 1.2]},
... )
>>> da
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
>>>
>>> da.differentiate("x")
<xarray.DataArray (x: 4, y: 3)>
array([[30.        , 30.        , 30.        ],
       [27.54545455, 27.54545455, 27.54545455],
       [27.54545455, 27.54545455, 27.54545455],
       [30.        , 30.        , 30.        ]])
Coordinates:
  * x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
property dims: tuple[collections.abc.Hashable, ...]#

Tuple of dimension names associated with this array.

Note that the type of this property is inconsistent with Dataset.dims. See Dataset.sizes and DataArray.sizes for consistently named properties.

See also

DataArray.sizes, Dataset.dims

dot(other: T_DataArray, dims: Dims = None) T_DataArray#

Perform dot product of two DataArrays along their shared dims.

Equivalent to taking taking tensordot over all shared dims.

Parameters
  • other (DataArray) – The other array with which the dot product is performed.

  • dims (..., str, Iterable of Hashable or None, optional) – Which dimensions to sum over. Ellipsis () sums over all dimensions. If not specified, then all the common dimensions are summed over.

Returns

result – Array resulting from the dot product over all shared dimensions.

Return type

DataArray

See also

dot, numpy.tensordot

Examples

>>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))
>>> da = xr.DataArray(da_vals, dims=["x", "y", "z"])
>>> dm_vals = np.arange(4)
>>> dm = xr.DataArray(dm_vals, dims=["z"])
>>> dm.dims
('z',)
>>> da.dims
('x', 'y', 'z')
>>> dot_result = da.dot(dm)
>>> dot_result.dims
('x', 'y')
drop(labels: Mapping[Any, Any] | None = None, dim: Hashable | None = None, *, errors: ErrorOptions = 'raise', **labels_kwargs) T_DataArray#

Backward compatible method based on drop_vars and drop_sel

Using either drop_vars or drop_sel is encouraged

See also

DataArray.drop_vars, DataArray.drop_sel

drop_duplicates(dim: Hashable | Iterable[Hashable], keep: Literal['first', 'last', False] = 'first') T_DataArray#

Returns a new DataArray with duplicate dimension values removed.

Parameters
  • dim (dimension label or labels) – Pass to drop duplicates along all dimensions.

  • keep ({"first", "last", False}, default: "first") –

    Determines which duplicates (if any) to keep.

    • "first" : Drop duplicates except for the first occurrence.

    • "last" : Drop duplicates except for the last occurrence.

    • False : Drop all duplicates.

Return type

DataArray

See also

Dataset.drop_duplicates

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     dims=("x", "y"),
...     coords={"x": np.array([0, 0, 1, 2, 3]), "y": np.array([0, 1, 2, 3, 3])},
... )
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 0 0 1 2 3
  * y        (y) int64 0 1 2 3 3
>>> da.drop_duplicates(dim="x")
<xarray.DataArray (x: 4, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 0 1 2 3
  * y        (y) int64 0 1 2 3 3
>>> da.drop_duplicates(dim="x", keep="last")
<xarray.DataArray (x: 4, y: 5)>
array([[ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 0 1 2 3
  * y        (y) int64 0 1 2 3 3

Drop all duplicate dimension values:

>>> da.drop_duplicates(dim=...)
<xarray.DataArray (x: 4, y: 4)>
array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [15, 16, 17, 18],
       [20, 21, 22, 23]])
Coordinates:
  * x        (x) int64 0 1 2 3
  * y        (y) int64 0 1 2 3
drop_indexes(coord_names: Hashable | Iterable[Hashable], *, errors: ErrorOptions = 'raise') T_DataArray#

Drop the indexes assigned to the given coordinates.

Parameters
  • coord_names (hashable or iterable of hashable) – Name(s) of the coordinate(s) for which to drop the index.

  • errors ({"raise", "ignore"}, default: "raise") – If ‘raise’, raises a ValueError error if any of the coordinates passed have no index or are not in the dataset. If ‘ignore’, no error is raised.

Returns

dropped – A new dataarray with dropped indexes.

Return type

DataArray

drop_isel(indexers: Mapping[Any, Any] | None = None, **indexers_kwargs) T_DataArray#

Drop index positions from this DataArray.

Parameters
  • indexers (mapping of Hashable to Any or None, default: None) – Index locations to drop

  • **indexers_kwargs ({dim: position, ...}, optional) – The keyword arguments form of dim and positions

Returns

dropped

Return type

DataArray

Raises

IndexError

Examples

>>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("X", "Y"))
>>> da
<xarray.DataArray (X: 5, Y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: X, Y
>>> da.drop_isel(X=[0, 4], Y=2)
<xarray.DataArray (X: 3, Y: 4)>
array([[ 5,  6,  8,  9],
       [10, 11, 13, 14],
       [15, 16, 18, 19]])
Dimensions without coordinates: X, Y
>>> da.drop_isel({"X": 3, "Y": 3})
<xarray.DataArray (X: 4, Y: 4)>
array([[ 0,  1,  2,  4],
       [ 5,  6,  7,  9],
       [10, 11, 12, 14],
       [20, 21, 22, 24]])
Dimensions without coordinates: X, Y
drop_sel(labels: Mapping[Any, Any] | None = None, *, errors: ErrorOptions = 'raise', **labels_kwargs) T_DataArray#

Drop index labels from this DataArray.

Parameters
  • labels (mapping of Hashable to Any) – Index labels to drop

  • errors ({"raise", "ignore"}, default: "raise") – If ‘raise’, raises a ValueError error if any of the index labels passed are not in the dataset. If ‘ignore’, any given labels that are in the dataset are dropped and no error is raised.

  • **labels_kwargs ({dim: label, ...}, optional) – The keyword arguments form of dim and labels

Returns

dropped

Return type

DataArray

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     coords={"x": np.arange(0, 9, 2), "y": np.arange(0, 13, 3)},
...     dims=("x", "y"),
... )
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 0 2 4 6 8
  * y        (y) int64 0 3 6 9 12
>>> da.drop_sel(x=[0, 2], y=9)
<xarray.DataArray (x: 3, y: 4)>
array([[10, 11, 12, 14],
       [15, 16, 17, 19],
       [20, 21, 22, 24]])
Coordinates:
  * x        (x) int64 4 6 8
  * y        (y) int64 0 3 6 12
>>> da.drop_sel({"x": 6, "y": [0, 3]})
<xarray.DataArray (x: 4, y: 3)>
array([[ 2,  3,  4],
       [ 7,  8,  9],
       [12, 13, 14],
       [22, 23, 24]])
Coordinates:
  * x        (x) int64 0 2 4 8
  * y        (y) int64 6 9 12
drop_vars(names: Hashable | Iterable[Hashable], *, errors: ErrorOptions = 'raise') DataArray#

Returns an array with dropped variables.

Parameters
  • names (Hashable or iterable of Hashable) – Name(s) of variables to drop.

  • errors ({"raise", "ignore"}, default: "raise") – If ‘raise’, raises a ValueError error if any of the variable passed are not in the dataset. If ‘ignore’, any given names that are in the DataArray are dropped and no error is raised.

Returns

dropped – New Dataset copied from self with variables removed.

Return type

Dataset

Examples

>>> data = np.arange(12).reshape(4, 3)
>>> da = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
... )
>>> da
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) int64 10 20 30 40
  * y        (y) int64 70 80 90

Removing a single variable:

>>> da.drop_vars("x")
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * y        (y) int64 70 80 90
Dimensions without coordinates: x

Removing a list of variables:

>>> da.drop_vars(["x", "y"])
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Dimensions without coordinates: x, y
dropna(dim: Hashable, how: Literal['any', 'all'] = 'any', thresh: int | None = None) T_DataArray#

Returns a new array with dropped labels for missing values along the provided dimension.

Parameters
  • dim (Hashable) – Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported.

  • how ({"any", "all"}, default: "any") –

    • any : if any NA values are present, drop that label

    • all : if all values are NA, drop that label

  • thresh (int or None, default: None) – If supplied, require this many non-NA values.

Returns

dropped

Return type

DataArray

Examples

>>> temperature = [
...     [0, 4, 2, 9],
...     [np.nan, np.nan, np.nan, np.nan],
...     [np.nan, 4, 2, 0],
...     [3, 1, 0, 0],
... ]
>>> da = xr.DataArray(
...     data=temperature,
...     dims=["Y", "X"],
...     coords=dict(
...         lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75])),
...         lon=("X", np.array([10.0, 10.25, 10.5, 10.75])),
...     ),
... )
>>> da
<xarray.DataArray (Y: 4, X: 4)>
array([[ 0.,  4.,  2.,  9.],
       [nan, nan, nan, nan],
       [nan,  4.,  2.,  0.],
       [ 3.,  1.,  0.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75
    lon      (X) float64 10.0 10.25 10.5 10.75
Dimensions without coordinates: Y, X
>>> da.dropna(dim="Y", how="any")
<xarray.DataArray (Y: 2, X: 4)>
array([[0., 4., 2., 9.],
       [3., 1., 0., 0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.75
    lon      (X) float64 10.0 10.25 10.5 10.75
Dimensions without coordinates: Y, X

Drop values only if all values along the dimension are NaN:

>>> da.dropna(dim="Y", how="all")
<xarray.DataArray (Y: 3, X: 4)>
array([[ 0.,  4.,  2.,  9.],
       [nan,  4.,  2.,  0.],
       [ 3.,  1.,  0.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.5 -20.75
    lon      (X) float64 10.0 10.25 10.5 10.75
Dimensions without coordinates: Y, X
property dtype: numpy.dtype#

Data-type of the array’s elements.

See also

ndarray.dtype, numpy.dtype

property encoding: dict[Any, Any]#

Dictionary of format-specific settings for how this array should be serialized.

equals(other: T_DataArray) bool#

True if two DataArrays have the same dimensions, coordinates and values; otherwise False.

DataArrays can still be equal (like pandas objects) if they have NaN values in the same locations.

This method is necessary because v1 == v2 for DataArray does element-wise comparisons (like numpy.ndarrays).

Parameters

other (DataArray) – DataArray to compare to.

Returns

equal – True if the two DataArrays are equal.

Return type

bool

See also

DataArray.broadcast_equals, DataArray.identical

Examples

>>> a = xr.DataArray([1, 2, 3], dims="X")
>>> b = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m"))
>>> c = xr.DataArray([1, 2, 3], dims="Y")
>>> d = xr.DataArray([3, 2, 1], dims="X")
>>> a
<xarray.DataArray (X: 3)>
array([1, 2, 3])
Dimensions without coordinates: X
>>> b
<xarray.DataArray (X: 3)>
array([1, 2, 3])
Dimensions without coordinates: X
Attributes:
    units:    m
>>> c
<xarray.DataArray (Y: 3)>
array([1, 2, 3])
Dimensions without coordinates: Y
>>> d
<xarray.DataArray (X: 3)>
array([3, 2, 1])
Dimensions without coordinates: X
>>> a.equals(b)
True
>>> a.equals(c)
False
>>> a.equals(d)
False
expand_dims(dim: Union[None, collections.abc.Hashable, collections.abc.Sequence[collections.abc.Hashable], collections.abc.Mapping[Any, Any]] = None, axis: Union[None, int, collections.abc.Sequence[int]] = None, **dim_kwargs: Any) xarray.core.dataarray.DataArray#

Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape. The new object is a view into the underlying array, not a copy.

If dim is already a scalar coordinate, it will be promoted to a 1D coordinate consisting of a single value.

Parameters
  • dim (Hashable, sequence of Hashable, dict, or None, optional) – Dimensions to include on the new variable. If provided as str or sequence of str, then dimensions are inserted with length 1. If provided as a dict, then the keys are the new dimensions and the values are either integers (giving the length of the new dimensions) or sequence/ndarray (giving the coordinates of the new dimensions).

  • axis (int, sequence of int, or None, default: None) – Axis position(s) where new axis is to be inserted (position(s) on the result array). If a sequence of integers is passed, multiple axes are inserted. In this case, dim arguments should be same length list. If axis=None is passed, all the axes will be inserted to the start of the result array.

  • **dim_kwargs (int or sequence or ndarray) – The keywords are arbitrary dimensions being inserted and the values are either the lengths of the new dims (if int is given), or their coordinates. Note, this is an alternative to passing a dict to the dim kwarg and will only be used if dim is None.

Returns

expanded – This object, but with additional dimension(s).

Return type

DataArray

See also

Dataset.expand_dims

Examples

>>> da = xr.DataArray(np.arange(5), dims=("x"))
>>> da
<xarray.DataArray (x: 5)>
array([0, 1, 2, 3, 4])
Dimensions without coordinates: x

Add new dimension of length 2:

>>> da.expand_dims(dim={"y": 2})
<xarray.DataArray (y: 2, x: 5)>
array([[0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4]])
Dimensions without coordinates: y, x
>>> da.expand_dims(dim={"y": 2}, axis=1)
<xarray.DataArray (x: 5, y: 2)>
array([[0, 0],
       [1, 1],
       [2, 2],
       [3, 3],
       [4, 4]])
Dimensions without coordinates: x, y

Add a new dimension with coordinates from array:

>>> da.expand_dims(dim={"y": np.arange(5)}, axis=0)
<xarray.DataArray (y: 5, x: 5)>
array([[0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4]])
Coordinates:
  * y        (y) int64 0 1 2 3 4
Dimensions without coordinates: x
ffill(dim: Hashable, limit: int | None = None) T_DataArray#

Fill NaN values by propagating values forward

Requires bottleneck.

Parameters
  • dim (Hashable) – Specifies the dimension along which to propagate values when filling.

  • limit (int or None, default: None) – The maximum number of consecutive NaN values to forward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Must be None or greater than or equal to axis length if filling along chunked axes (dimensions).

Returns

filled

Return type

DataArray

Examples

>>> temperature = np.array(
...     [
...         [np.nan, 1, 3],
...         [0, np.nan, 5],
...         [5, np.nan, np.nan],
...         [3, np.nan, np.nan],
...         [0, 2, 0],
...     ]
... )
>>> da = xr.DataArray(
...     data=temperature,
...     dims=["Y", "X"],
...     coords=dict(
...         lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75, -21.0])),
...         lon=("X", np.array([10.0, 10.25, 10.5])),
...     ),
... )
>>> da
<xarray.DataArray (Y: 5, X: 3)>
array([[nan,  1.,  3.],
       [ 0., nan,  5.],
       [ 5., nan, nan],
       [ 3., nan, nan],
       [ 0.,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X

Fill all NaN values:

>>> da.ffill(dim="Y", limit=None)
<xarray.DataArray (Y: 5, X: 3)>
array([[nan,  1.,  3.],
       [ 0.,  1.,  5.],
       [ 5.,  1.,  5.],
       [ 3.,  1.,  5.],
       [ 0.,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X

Fill only the first of consecutive NaN values:

>>> da.ffill(dim="Y", limit=1)
<xarray.DataArray (Y: 5, X: 3)>
array([[nan,  1.,  3.],
       [ 0.,  1.,  5.],
       [ 5., nan,  5.],
       [ 3., nan, nan],
       [ 0.,  2.,  0.]])
Coordinates:
    lat      (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0
    lon      (X) float64 10.0 10.25 10.5
Dimensions without coordinates: Y, X
fillna(value: Any) T_DataArray#

Fill missing values in this object.

This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (join='left') instead of aligned to the intersection of index coordinates (join='inner').

Parameters

value (scalar, ndarray or DataArray) – Used to fill all matching missing values in this array. If the argument is a DataArray, it is first aligned with (reindexed to) this array.

Returns

filled

Return type

DataArray

Examples

>>> da = xr.DataArray(
...     np.array([1, 4, np.nan, 0, 3, np.nan]),
...     dims="Z",
...     coords=dict(
...         Z=("Z", np.arange(6)),
...         height=("Z", np.array([0, 10, 20, 30, 40, 50])),
...     ),
... )
>>> da
<xarray.DataArray (Z: 6)>
array([ 1.,  4., nan,  0.,  3., nan])
Coordinates:
  * Z        (Z) int64 0 1 2 3 4 5
    height   (Z) int64 0 10 20 30 40 50

Fill all NaN values with 0:

>>> da.fillna(0)
<xarray.DataArray (Z: 6)>
array([1., 4., 0., 0., 3., 0.])
Coordinates:
  * Z        (Z) int64 0 1 2 3 4 5
    height   (Z) int64 0 10 20 30 40 50

Fill NaN values with corresponding values in array:

>>> da.fillna(np.array([2, 9, 4, 2, 8, 9]))
<xarray.DataArray (Z: 6)>
array([1., 4., 4., 0., 3., 9.])
Coordinates:
  * Z        (Z) int64 0 1 2 3 4 5
    height   (Z) int64 0 10 20 30 40 50
classmethod from_cdms2(variable: cdms2_Variable) DataArray#

Convert a cdms2.Variable into an xarray.DataArray

classmethod from_dict(d: Mapping[str, Any]) T_DataArray#

Convert a dictionary into an xarray.DataArray

Parameters

d (dict) – Mapping with a minimum structure of {“dims”: […], “data”: […]}

Returns

obj

Return type

xarray.DataArray

See also

DataArray.to_dict, Dataset.from_dict

Examples

>>> d = {"dims": "t", "data": [1, 2, 3]}
>>> da = xr.DataArray.from_dict(d)
>>> da
<xarray.DataArray (t: 3)>
array([1, 2, 3])
Dimensions without coordinates: t
>>> d = {
...     "coords": {
...         "t": {"dims": "t", "data": [0, 1, 2], "attrs": {"units": "s"}}
...     },
...     "attrs": {"title": "air temperature"},
...     "dims": "t",
...     "data": [10, 20, 30],
...     "name": "a",
... }
>>> da = xr.DataArray.from_dict(d)
>>> da
<xarray.DataArray 'a' (t: 3)>
array([10, 20, 30])
Coordinates:
  * t        (t) int64 0 1 2
Attributes:
    title:    air temperature
classmethod from_file(fname: str, group_path: str) tidy3d.components.data.data_array.DataArray#

Load an DataArray from an hdf5 file with a given path to the group.

classmethod from_hdf5(fname: str, group_path: str) tidy3d.components.data.data_array.DataArray#

Load an DataArray from an hdf5 file with a given path to the group.

classmethod from_iris(cube: iris_Cube) DataArray#

Convert a iris.cube.Cube into an xarray.DataArray

classmethod from_series(series: pandas.core.series.Series, sparse: bool = False) xarray.core.dataarray.DataArray#

Convert a pandas.Series into an xarray.DataArray.

If the series’s index is a MultiIndex, it will be expanded into a tensor product of one-dimensional coordinates (filling in missing values with NaN). Thus this operation should be the inverse of the to_series method.

Parameters
  • series (Series) – Pandas Series object to convert.

  • sparse (bool, default: False) – If sparse=True, creates a sparse array instead of a dense NumPy array. Requires the pydata/sparse package.

See also

DataArray.to_series, Dataset.from_dataframe

get_axis_num(dim: collections.abc.Hashable | collections.abc.Iterable[collections.abc.Hashable]) int | tuple[int, ...]#

Return axis number(s) corresponding to dimension(s) in this array.

Parameters

dim (str or iterable of str) – Dimension name(s) for which to lookup axes.

Returns

Axis number or numbers corresponding to the given dimensions.

Return type

int or tuple of int

get_index(key: collections.abc.Hashable) pandas.core.indexes.base.Index#

Get an index for a dimension, with fall-back to a default RangeIndex

groupby(group: Hashable | DataArray | IndexVariable, squeeze: bool = True, restore_coord_dims: bool = False) DataArrayGroupBy#

Returns a DataArrayGroupBy object for performing grouped operations.

Parameters
  • group (Hashable, DataArray or IndexVariable) – Array whose unique values should be used to group this array. If a Hashable, must be the name of a coordinate contained in this dataarray.

  • squeeze (bool, default: True) – If “group” is a dimension of any arrays in this dataset, squeeze controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out.

  • restore_coord_dims (bool, default: False) – If True, also restore the dimension order of multi-dimensional coordinates.

Returns

grouped – A DataArrayGroupBy object patterned after pandas.GroupBy that can be iterated over in the form of (unique_value, grouped_array) pairs.

Return type

DataArrayGroupBy

Examples

Calculate daily anomalies for daily data:

>>> da = xr.DataArray(
...     np.linspace(0, 1826, num=1827),
...     coords=[pd.date_range("2000-01-01", "2004-12-31", freq="D")],
...     dims="time",
... )
>>> da
<xarray.DataArray (time: 1827)>
array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,
       1.826e+03])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
>>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
<xarray.DataArray (time: 1827)>
array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])
Coordinates:
  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
    dayofyear  (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366

See also

groupby

Users guide explanation of how to group and bin data.

DataArray.groupby_bins, Dataset.groupby, core.groupby.DataArrayGroupBy, pandas.DataFrame.groupby

groupby_bins(group: Hashable | DataArray | IndexVariable, bins: ArrayLike, right: bool = True, labels: ArrayLike | Literal[False] | None = None, precision: int = 3, include_lowest: bool = False, squeeze: bool = True, restore_coord_dims: bool = False) DataArrayGroupBy#

Returns a DataArrayGroupBy object for performing grouped operations.

Rather than using all unique values of group, the values are discretized first by applying pandas.cut [1]_ to group.

Parameters
  • group (Hashable, DataArray or IndexVariable) – Array whose binned values should be used to group this array. If a Hashable, must be the name of a coordinate contained in this dataarray.

  • bins (int or array-like) – If bins is an int, it defines the number of equal-width bins in the range of x. However, in this case, the range of x is extended by .1% on each side to include the min or max values of x. If bins is a sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case.

  • right (bool, default: True) – Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4].

  • labels (array-like, False or None, default: None) – Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, string bin labels are assigned by pandas.cut.

  • precision (int, default: 3) – The precision at which to store and display the bins labels.

  • include_lowest (bool, default: False) – Whether the first interval should be left-inclusive or not.

  • squeeze (bool, default: True) – If “group” is a dimension of any arrays in this dataset, squeeze controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out.

  • restore_coord_dims (bool, default: False) – If True, also restore the dimension order of multi-dimensional coordinates.

Returns

grouped – A DataArrayGroupBy object patterned after pandas.GroupBy that can be iterated over in the form of (unique_value, grouped_array) pairs. The name of the group has the added suffix _bins in order to distinguish it from the original variable.

Return type

DataArrayGroupBy

See also

groupby

Users guide explanation of how to group and bin data.

DataArray.groupby, Dataset.groupby_bins, core.groupby.DataArrayGroupBy, pandas.DataFrame.groupby

References

1

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html

head(indexers: Mapping[Any, int] | int | None = None, **indexers_kwargs: Any) T_DataArray#

Return a new DataArray whose data is given by the the first n values along the specified dimension(s). Default n = 5

See also

Dataset.head, DataArray.tail, DataArray.thin

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     dims=("x", "y"),
... )
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y
>>> da.head(x=1)
<xarray.DataArray (x: 1, y: 5)>
array([[0, 1, 2, 3, 4]])
Dimensions without coordinates: x, y
>>> da.head({"x": 2, "y": 2})
<xarray.DataArray (x: 2, y: 2)>
array([[0, 1],
       [5, 6]])
Dimensions without coordinates: x, y
identical(other: T_DataArray) bool#

Like equals, but also checks the array name and attributes, and attributes on all coordinates.

Parameters

other (DataArray) – DataArray to compare to.

Returns

equal – True if the two DataArrays are identical.

Return type

bool

See also

DataArray.broadcast_equals, DataArray.equals

Examples

>>> a = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m"), name="Width")
>>> b = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m"), name="Width")
>>> c = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="ft"), name="Width")
>>> a
<xarray.DataArray 'Width' (X: 3)>
array([1, 2, 3])
Dimensions without coordinates: X
Attributes:
    units:    m
>>> b
<xarray.DataArray 'Width' (X: 3)>
array([1, 2, 3])
Dimensions without coordinates: X
Attributes:
    units:    m
>>> c
<xarray.DataArray 'Width' (X: 3)>
array([1, 2, 3])
Dimensions without coordinates: X
Attributes:
    units:    ft
>>> a.equals(b)
True
>>> a.identical(b)
True
>>> a.equals(c)
True
>>> a.identical(c)
False
idxmax(dim: typing.Optional[collections.abc.Hashable] = None, skipna: typing.Optional[bool] = None, fill_value: typing.Any = <NA>, keep_attrs: typing.Optional[bool] = None) xarray.core.dataarray.DataArray#

Return the coordinate label of the maximum value along a dimension.

Returns a new DataArray named after the dimension with the values of the coordinate labels along that dimension corresponding to maximum values along that dimension.

In comparison to argmax(), this returns the coordinate label while argmax() returns the index.

Parameters
  • dim (Hashable, optional) – Dimension over which to apply idxmax. This is optional for 1D arrays, but required for arrays with 2 or more dimensions.

  • skipna (bool or None, default: None) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float, complex, and object dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (datetime64 or timedelta64).

  • fill_value (Any, default: NaN) – Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if skipna is False.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

Returns

reduced – New DataArray object with idxmax applied to its data and the indicated dimension removed.

Return type

DataArray

See also

Dataset.idxmax, DataArray.idxmin, DataArray.max, DataArray.argmax

Examples

>>> array = xr.DataArray(
...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
... )
>>> array.max()
<xarray.DataArray ()>
array(2)
>>> array.argmax(...)
{'x': <xarray.DataArray ()>
array(1)}
>>> array.idxmax()
<xarray.DataArray 'x' ()>
array('b', dtype='<U1')
>>> array = xr.DataArray(
...     [
...         [2.0, 1.0, 2.0, 0.0, -2.0],
...         [-4.0, np.NaN, 2.0, np.NaN, -2.0],
...         [np.NaN, np.NaN, 1.0, np.NaN, np.NaN],
...     ],
...     dims=["y", "x"],
...     coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2},
... )
>>> array.max(dim="x")
<xarray.DataArray (y: 3)>
array([2., 2., 1.])
Coordinates:
  * y        (y) int64 -1 0 1
>>> array.argmax(dim="x")
<xarray.DataArray (y: 3)>
array([0, 2, 2])
Coordinates:
  * y        (y) int64 -1 0 1
>>> array.idxmax(dim="x")
<xarray.DataArray 'x' (y: 3)>
array([0., 4., 4.])
Coordinates:
  * y        (y) int64 -1 0 1
idxmin(dim: typing.Optional[collections.abc.Hashable] = None, skipna: typing.Optional[bool] = None, fill_value: typing.Any = <NA>, keep_attrs: typing.Optional[bool] = None) xarray.core.dataarray.DataArray#

Return the coordinate label of the minimum value along a dimension.

Returns a new DataArray named after the dimension with the values of the coordinate labels along that dimension corresponding to minimum values along that dimension.

In comparison to argmin(), this returns the coordinate label while argmin() returns the index.

Parameters
  • dim (str, optional) – Dimension over which to apply idxmin. This is optional for 1D arrays, but required for arrays with 2 or more dimensions.

  • skipna (bool or None, default: None) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float, complex, and object dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (datetime64 or timedelta64).

  • fill_value (Any, default: NaN) – Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if skipna is False.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

Returns

reduced – New DataArray object with idxmin applied to its data and the indicated dimension removed.

Return type

DataArray

See also

Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin

Examples

>>> array = xr.DataArray(
...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
... )
>>> array.min()
<xarray.DataArray ()>
array(-2)
>>> array.argmin(...)
{'x': <xarray.DataArray ()>
array(4)}
>>> array.idxmin()
<xarray.DataArray 'x' ()>
array('e', dtype='<U1')
>>> array = xr.DataArray(
...     [
...         [2.0, 1.0, 2.0, 0.0, -2.0],
...         [-4.0, np.NaN, 2.0, np.NaN, -2.0],
...         [np.NaN, np.NaN, 1.0, np.NaN, np.NaN],
...     ],
...     dims=["y", "x"],
...     coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2},
... )
>>> array.min(dim="x")
<xarray.DataArray (y: 3)>
array([-2., -4.,  1.])
Coordinates:
  * y        (y) int64 -1 0 1
>>> array.argmin(dim="x")
<xarray.DataArray (y: 3)>
array([4, 0, 2])
Coordinates:
  * y        (y) int64 -1 0 1
>>> array.idxmin(dim="x")
<xarray.DataArray 'x' (y: 3)>
array([16.,  0.,  4.])
Coordinates:
  * y        (y) int64 -1 0 1
property imag: T_DataArray#

The imaginary part of the array.

See also

numpy.ndarray.imag

property indexes: xarray.core.indexes.Indexes#

Mapping of pandas.Index objects used for label based indexing.

Raises an error if this Dataset has indexes that cannot be coerced to pandas.Index objects.

See also

DataArray.xindexes

integrate(coord: Hashable | Sequence[Hashable] = None, datetime_unit: DatetimeUnitOptions = None) DataArray#

Integrate along the given coordinate using the trapezoidal rule.

Note

This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional.

Parameters
  • coord (Hashable, or sequence of Hashable) – Coordinate(s) used for the integration.

  • datetime_unit ({'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as', None}, optional) – Specify the unit if a datetime coordinate is used.

Returns

integrated

Return type

DataArray

See also

Dataset.integrate

numpy.trapz

corresponding numpy function

Examples

>>> da = xr.DataArray(
...     np.arange(12).reshape(4, 3),
...     dims=["x", "y"],
...     coords={"x": [0, 0.1, 1.1, 1.2]},
... )
>>> da
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
>>>
>>> da.integrate("x")
<xarray.DataArray (y: 3)>
array([5.4, 6.6, 7.8])
Dimensions without coordinates: y
interp(coords: Mapping[Any, Any] | None = None, method: InterpOptions = 'linear', assume_sorted: bool = False, kwargs: Mapping[str, Any] | None = None, **coords_kwargs: Any) T_DataArray#

Interpolate a DataArray onto new coordinates

Performs univariate or multivariate interpolation of a DataArray onto new coordinates using scipy’s interpolation routines. If interpolating along an existing dimension, scipy.interpolate.interp1d is called. When interpolating along multiple existing dimensions, an attempt is made to decompose the interpolation into multiple 1-dimensional interpolations. If this is possible, scipy.interpolate.interp1d is called. Otherwise, scipy.interpolate.interpn() is called.

Parameters
  • coords (dict, optional) – Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. If DataArrays are passed as new coordinates, their dimensions are used for the broadcasting. Missing values are skipped.

  • method ({"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear") –

    The method used to interpolate. The method should be supported by the scipy interpolator:

    • interp1d: {“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”, “polynomial”}

    • interpn: {“linear”, “nearest”}

    If "polynomial" is passed, the order keyword argument must also be provided.

  • assume_sorted (bool, default: False) – If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values.

  • kwargs (dict-like or None, default: None) – Additional keyword arguments passed to scipy’s interpolator. Valid options and their behavior depend whether interp1d or interpn is used.

  • **coords_kwargs ({dim: coordinate, ...}, optional) – The keyword arguments form of coords. One of coords or coords_kwargs must be provided.

Returns

interpolated – New dataarray on the new coordinates.

Return type

DataArray

Notes

scipy is required.

See also

scipy.interpolate.interp1d, scipy.interpolate.interpn

Examples

>>> da = xr.DataArray(
...     data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
...     dims=("x", "y"),
...     coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
... )
>>> da
<xarray.DataArray (x: 3, y: 4)>
array([[ 1.,  4.,  2.,  9.],
       [ 2.,  7.,  6., nan],
       [ 6., nan,  5.,  8.]])
Coordinates:
  * x        (x) int64 0 1 2
  * y        (y) int64 10 12 14 16

1D linear interpolation (the default):

>>> da.interp(x=[0, 0.75, 1.25, 1.75])
<xarray.DataArray (x: 4, y: 4)>
array([[1.  , 4.  , 2.  ,  nan],
       [1.75, 6.25, 5.  ,  nan],
       [3.  ,  nan, 5.75,  nan],
       [5.  ,  nan, 5.25,  nan]])
Coordinates:
  * y        (y) int64 10 12 14 16
  * x        (x) float64 0.0 0.75 1.25 1.75

1D nearest interpolation:

>>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
<xarray.DataArray (x: 4, y: 4)>
array([[ 1.,  4.,  2.,  9.],
       [ 2.,  7.,  6., nan],
       [ 2.,  7.,  6., nan],
       [ 6., nan,  5.,  8.]])
Coordinates:
  * y        (y) int64 10 12 14 16
  * x        (x) float64 0.0 0.75 1.25 1.75

1D linear extrapolation:

>>> da.interp(
...     x=[1, 1.5, 2.5, 3.5],
...     method="linear",
...     kwargs={"fill_value": "extrapolate"},
... )
<xarray.DataArray (x: 4, y: 4)>
array([[ 2. ,  7. ,  6. ,  nan],
       [ 4. ,  nan,  5.5,  nan],
       [ 8. ,  nan,  4.5,  nan],
       [12. ,  nan,  3.5,  nan]])
Coordinates:
  * y        (y) int64 10 12 14 16
  * x        (x) float64 1.0 1.5 2.5 3.5

2D linear interpolation:

>>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
<xarray.DataArray (x: 4, y: 3)>
array([[2.5  , 3.   ,   nan],
       [4.   , 5.625,   nan],
       [  nan,   nan,   nan],
       [  nan,   nan,   nan]])
Coordinates:
  * x        (x) float64 0.0 0.75 1.25 1.75
  * y        (y) int64 11 13 15
interp_calendar(target: pandas.core.indexes.datetimes.DatetimeIndex | xarray.coding.cftimeindex.CFTimeIndex | xarray.core.dataarray.DataArray, dim: str = 'time') xarray.core.dataarray.DataArray#

Interpolates the DataArray to another calendar based on decimal year measure.

Each timestamp in source and target are first converted to their decimal year equivalent then source is interpolated on the target coordinate. The decimal year of a timestamp is its year plus its sub-year component converted to the fraction of its year. For example “2000-03-01 12:00” is 2000.1653 in a standard calendar or 2000.16301 in a “noleap” calendar.

This method should only be used when the time (HH:MM:SS) information of time coordinate is not important.

Parameters
  • target (DataArray or DatetimeIndex or CFTimeIndex) – The target time coordinate of a valid dtype (np.datetime64 or cftime objects)

  • dim (str) – The time coordinate name.

Returns

The source interpolated on the decimal years of target,

Return type

DataArray

interp_like(other: DataArray | Dataset, method: InterpOptions = 'linear', assume_sorted: bool = False, kwargs: Mapping[str, Any] | None = None) T_DataArray#

Interpolate this object onto the coordinates of another object, filling out of range values with NaN.

If interpolating along a single existing dimension, scipy.interpolate.interp1d is called. When interpolating along multiple existing dimensions, an attempt is made to decompose the interpolation into multiple 1-dimensional interpolations. If this is possible, scipy.interpolate.interp1d is called. Otherwise, scipy.interpolate.interpn() is called.

Parameters
  • other (Dataset or DataArray) – Object with an ‘indexes’ attribute giving a mapping from dimension names to an 1d array-like, which provides coordinates upon which to index the variables in this dataset. Missing values are skipped.

  • method ({"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear") –

    The method used to interpolate. The method should be supported by the scipy interpolator:

    • {“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”, “polynomial”} when interp1d is called.

    • {“linear”, “nearest”} when interpn is called.

    If "polynomial" is passed, the order keyword argument must also be provided.

  • assume_sorted (bool, default: False) – If False, values of coordinates that are interpolated over can be in any order and they are sorted first. If True, interpolated coordinates are assumed to be an array of monotonically increasing values.

  • kwargs (dict, optional) – Additional keyword passed to scipy’s interpolator.

Returns

interpolated – Another dataarray by interpolating this dataarray’s data along the coordinates of the other object.

Return type

DataArray

Examples

>>> data = np.arange(12).reshape(4, 3)
>>> da1 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
... )
>>> da1
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) int64 10 20 30 40
  * y        (y) int64 70 80 90
>>> da2 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [10, 20, 29, 39], "y": [70, 80, 90]},
... )
>>> da2
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) int64 10 20 29 39
  * y        (y) int64 70 80 90

Interpolate the values in the coordinates of the other DataArray with respect to the source’s values:

>>> da2.interp_like(da1)
<xarray.DataArray (x: 4, y: 3)>
array([[0. , 1. , 2. ],
       [3. , 4. , 5. ],
       [6.3, 7.3, 8.3],
       [nan, nan, nan]])
Coordinates:
  * x        (x) int64 10 20 30 40
  * y        (y) int64 70 80 90

Could also extrapolate missing values:

>>> da2.interp_like(da1, kwargs={"fill_value": "extrapolate"})
<xarray.DataArray (x: 4, y: 3)>
array([[ 0. ,  1. ,  2. ],
       [ 3. ,  4. ,  5. ],
       [ 6.3,  7.3,  8.3],
       [ 9.3, 10.3, 11.3]])
Coordinates:
  * x        (x) int64 10 20 30 40
  * y        (y) int64 70 80 90

Notes

scipy is required. If the dataarray has object-type coordinates, reindex is used for these coordinates instead of the interpolation.

See also

DataArray.interp, DataArray.reindex_like

interpolate_na(dim: Hashable | None = None, method: InterpOptions = 'linear', limit: int | None = None, use_coordinate: bool | str = True, max_gap: None | int | float | str | pd.Timedelta | np.timedelta64 | datetime.timedelta = None, keep_attrs: bool | None = None, **kwargs: Any) T_DataArray#

Fill in NaNs by interpolating according to different methods.

Parameters
  • dim (Hashable or None, optional) – Specifies the dimension along which to interpolate.

  • method ({"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear") –

    String indicating which method to use for interpolation:

    • ’linear’: linear interpolation. Additional keyword arguments are passed to numpy.interp()

    • ’nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘polynomial’: are passed to scipy.interpolate.interp1d(). If method='polynomial', the order keyword argument must also be provided.

    • ’barycentric’, ‘krog’, ‘pchip’, ‘spline’, ‘akima’: use their respective scipy.interpolate classes.

  • use_coordinate (bool or str, default: True) – Specifies which index to use as the x values in the interpolation formulated as y = f(x). If False, values are treated as if equally-spaced along dim. If True, the IndexVariable dim is used. If use_coordinate is a string, it specifies the name of a coordinate variable to use as the index.

  • limit (int or None, default: None) – Maximum number of consecutive NaNs to fill. Must be greater than 0 or None for no limit. This filling is done regardless of the size of the gap in the data. To only interpolate over gaps less than a given length, see max_gap.

  • max_gap (int, float, str, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, default: None) –

    Maximum size of gap, a continuous sequence of NaNs, that will be filled. Use None for no limit. When interpolating along a datetime64 dimension and use_coordinate=True, max_gap can be one of the following:

    • a string that is valid input for pandas.to_timedelta

    • a numpy.timedelta64 object

    • a pandas.Timedelta object

    • a datetime.timedelta object

    Otherwise, max_gap must be an int or a float. Use of max_gap with unlabeled dimensions has not been implemented yet. Gap length is defined as the difference between coordinate values at the first data point after a gap and the last value before a gap. For gaps at the beginning (end), gap length is defined as the difference between coordinate values at the first (last) valid data point and the first (last) NaN. For example, consider:

    <xarray.DataArray (x: 9)>
    array([nan, nan, nan,  1., nan, nan,  4., nan, nan])
    Coordinates:
      * x        (x) int64 0 1 2 3 4 5 6 7 8
    

    The gap lengths are 3-0 = 3; 6-3 = 3; and 8-6 = 2 respectively

  • keep_attrs (bool or None, default: None) – If True, the dataarray’s attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

  • **kwargs (dict, optional) – parameters passed verbatim to the underlying interpolation function

Returns

interpolated – Filled in DataArray.

Return type

DataArray

See also

numpy.interp, scipy.interpolate

Examples

>>> da = xr.DataArray(
...     [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]}
... )
>>> da
<xarray.DataArray (x: 5)>
array([nan,  2.,  3., nan,  0.])
Coordinates:
  * x        (x) int64 0 1 2 3 4
>>> da.interpolate_na(dim="x", method="linear")
<xarray.DataArray (x: 5)>
array([nan, 2. , 3. , 1.5, 0. ])
Coordinates:
  * x        (x) int64 0 1 2 3 4
>>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
<xarray.DataArray (x: 5)>
array([1. , 2. , 3. , 1.5, 0. ])
Coordinates:
  * x        (x) int64 0 1 2 3 4
isel(indexers: Mapping[Any, Any] | None = None, drop: bool = False, missing_dims: ErrorOptionsWithWarn = 'raise', **indexers_kwargs: Any) T_DataArray#

Return a new DataArray whose data is given by selecting indexes along the specified dimension(s).

Parameters
  • indexers (dict, optional) – A dict with keys matching dimensions and values given by integers, slice objects or arrays. indexer can be a integer, slice, array-like or DataArray. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See indexing for the details. One of indexers or indexers_kwargs must be provided.

  • drop (bool, default: False) – If drop=True, drop coordinates variables indexed by integers instead of making them scalar.

  • missing_dims ({"raise", "warn", "ignore"}, default: "raise") – What to do if dimensions that should be selected from are not present in the DataArray: - “raise”: raise an exception - “warn”: raise a warning, and ignore the missing dimensions - “ignore”: ignore the missing dimensions

  • **indexers_kwargs ({dim: indexer, ...}, optional) – The keyword arguments form of indexers.

Returns

indexed

Return type

xarray.DataArray

See also

Dataset.isel, DataArray.sel

Examples

>>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y
>>> tgt_x = xr.DataArray(np.arange(0, 5), dims="points")
>>> tgt_y = xr.DataArray(np.arange(0, 5), dims="points")
>>> da = da.isel(x=tgt_x, y=tgt_y)
>>> da
<xarray.DataArray (points: 5)>
array([ 0,  6, 12, 18, 24])
Dimensions without coordinates: points
isin(test_elements: Any) T_DataWithCoords#

Tests each value in the array for whether it is in test elements.

Parameters

test_elements (array_like) – The values against which to test each value of element. This argument is flattened if an array or array_like. See numpy notes for behavior with non-array-like parameters.

Returns

isin – Has the same type and shape as this object, but with a bool dtype.

Return type

DataArray or Dataset

Examples

>>> array = xr.DataArray([1, 2, 3], dims="x")
>>> array.isin([1, 3])
<xarray.DataArray (x: 3)>
array([ True, False,  True])
Dimensions without coordinates: x

See also

numpy.isin

isnull(keep_attrs: bool | None = None) T_DataWithCoords#

Test each value in the array for whether it is a missing value.

Parameters

keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

Returns

isnull – Same type and shape as object, but the dtype of the data is bool.

Return type

DataArray or Dataset

See also

pandas.isnull

Examples

>>> array = xr.DataArray([1, np.nan, 3], dims="x")
>>> array
<xarray.DataArray (x: 3)>
array([ 1., nan,  3.])
Dimensions without coordinates: x
>>> array.isnull()
<xarray.DataArray (x: 3)>
array([False,  True, False])
Dimensions without coordinates: x
item(*args)#

Copy an element of an array to a standard Python scalar and return it.

Parameters

*args (Arguments (variable number and type)) –

  • none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.

  • int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.

  • tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.

Returns

z – A copy of the specified element of the array as a suitable Python scalar

Return type

Standard Python scalar object

Notes

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.

Examples

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1
load(**kwargs) T_DataArray#

Manually trigger loading of this array’s data from disk or a remote source into memory and return this array.

Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk.

Parameters

**kwargs (dict) – Additional keyword arguments passed on to dask.compute.

See also

dask.compute

property loc: xarray.core.dataarray._LocIndexer#

Attribute for location based indexing like pandas.

map_blocks(func: Callable[..., T_Xarray], args: Sequence[Any] = (), kwargs: Mapping[str, Any] | None = None, template: DataArray | Dataset | None = None) T_Xarray#

Apply a function to each block of this DataArray.

Warning

This method is experimental and its signature may change.

Parameters
  • func (callable) –

    User-provided function that accepts a DataArray as its first parameter. The function will receive a subset or ‘block’ of this DataArray (see below), corresponding to one chunk along each chunked dimension. func will be executed as func(subset_dataarray, *subset_args, **kwargs).

    This function must return either a single DataArray or a single Dataset.

    This function cannot add a new chunked dimension.

  • args (sequence) – Passed to func after unpacking and subsetting any xarray objects by blocks. xarray objects in args must be aligned with this object, otherwise an error is raised.

  • kwargs (mapping) – Passed verbatim to func after unpacking. xarray objects, if any, will not be subset to blocks. Passing dask collections in kwargs is not allowed.

  • template (DataArray or Dataset, optional) – xarray object representing the final result after compute is called. If not provided, the function will be first run on mocked-up data, that looks like this object but has sizes 0, to determine properties of the returned object such as dtype, variable names, attributes, new dimensions and new indexes (if any). template must be provided if the function changes the size of existing dimensions. When provided, attrs on variables in template are copied over to the result. Any attrs set by func will be ignored.

Returns

  • A single DataArray or Dataset with dask backend, reassembled from the outputs of the

  • function.

Notes

This function is designed for when func needs to manipulate a whole xarray object subset to each block. Each block is loaded into memory. In the more common case where func can work on numpy arrays, it is recommended to use apply_ufunc.

If none of the variables in this object is backed by dask arrays, calling this function is equivalent to calling func(obj, *args, **kwargs).

See also

dask.array.map_blocks, xarray.apply_ufunc, xarray.Dataset.map_blocks, xarray.DataArray.map_blocks

Examples

Calculate an anomaly from climatology using .groupby(). Using xr.map_blocks() allows for parallel operations with knowledge of xarray, its indices, and its methods like .groupby().

>>> def calculate_anomaly(da, groupby_type="time.month"):
...     gb = da.groupby(groupby_type)
...     clim = gb.mean(dim="time")
...     return gb - clim
...
>>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
>>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
>>> np.random.seed(123)
>>> array = xr.DataArray(
...     np.random.rand(len(time)),
...     dims=["time"],
...     coords={"time": time, "month": month},
... ).chunk()
>>> array.map_blocks(calculate_anomaly, template=array).compute()
<xarray.DataArray (time: 24)>
array([ 0.12894847,  0.11323072, -0.0855964 , -0.09334032,  0.26848862,
        0.12382735,  0.22460641,  0.07650108, -0.07673453, -0.22865714,
       -0.19063865,  0.0590131 , -0.12894847, -0.11323072,  0.0855964 ,
        0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108,
        0.07673453,  0.22865714,  0.19063865, -0.0590131 ])
Coordinates:
  * time     (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

Note that one must explicitly use args=[] and kwargs={} to pass arguments to the function being applied in xr.map_blocks():

>>> array.map_blocks(
...     calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array
... )  
<xarray.DataArray (time: 24)>
dask.array<<this-array>-calculate_anomaly, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray>
Coordinates:
  * time     (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
    month    (time) int64 dask.array<chunksize=(24,), meta=np.ndarray>
max(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 – New DataArray with max applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.max, dask.array.max, Dataset.max

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.max()
<xarray.DataArray ()>
array(3.)

Use skipna to control whether NaNs are ignored.

>>> da.max(skipna=False)
<xarray.DataArray ()>
array(nan)
mean(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 – New DataArray with mean applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.mean, dask.array.mean, Dataset.mean

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.mean()
<xarray.DataArray ()>
array(1.8)

Use skipna to control whether NaNs are ignored.

>>> da.mean(skipna=False)
<xarray.DataArray ()>
array(nan)
median(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 – New DataArray with median applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.median, dask.array.median, Dataset.median

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.median()
<xarray.DataArray ()>
array(2.)

Use skipna to control whether NaNs are ignored.

>>> da.median(skipna=False)
<xarray.DataArray ()>
array(nan)
min(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 – New DataArray with min applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.min, dask.array.min, Dataset.min

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.min()
<xarray.DataArray ()>
array(1.)

Use skipna to control whether NaNs are ignored.

>>> da.min(skipna=False)
<xarray.DataArray ()>
array(nan)
property name: collections.abc.Hashable | None#

The name of this array.

property nbytes: int#

Total bytes consumed by the elements of this DataArray’s data.

If the underlying data array does not include nbytes, estimates the bytes consumed based on the size and dtype.

property ndim: int#

Number of array dimensions.

See also

numpy.ndarray.ndim

notnull(keep_attrs: bool | None = None) T_DataWithCoords#

Test each value in the array for whether it is not a missing value.

Parameters

keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

Returns

notnull – Same type and shape as object, but the dtype of the data is bool.

Return type

DataArray or Dataset

See also

pandas.notnull

Examples

>>> array = xr.DataArray([1, np.nan, 3], dims="x")
>>> array
<xarray.DataArray (x: 3)>
array([ 1., nan,  3.])
Dimensions without coordinates: x
>>> array.notnull()
<xarray.DataArray (x: 3)>
array([ True, False,  True])
Dimensions without coordinates: x
pad(pad_width: Mapping[Any, int | tuple[int, int]] | None = None, mode: PadModeOptions = 'constant', stat_length: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None, constant_values: float | tuple[float, float] | Mapping[Any, tuple[float, float]] | None = None, end_values: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None, reflect_type: PadReflectOptions = None, keep_attrs: bool | None = None, **pad_width_kwargs: Any) T_DataArray#

Pad this array along one or more dimensions.

Warning

This function is experimental and its behaviour is likely to change especially regarding padding of dimension coordinates (or IndexVariables).

When using one of the modes (“edge”, “reflect”, “symmetric”, “wrap”), coordinates will be padded with the same mode, otherwise coordinates are padded using the “constant” mode with fill_value dtypes.NA.

Parameters
  • pad_width (mapping of Hashable to tuple of int) – Mapping with the form of {dim: (pad_before, pad_after)} describing the number of values padded along each dimension. {dim: pad} is a shortcut for pad_before = pad_after = pad

  • mode ({"constant", "edge", "linear_ramp", "maximum", "mean", "median", "minimum", "reflect", "symmetric", "wrap"}, default: "constant") –

    How to pad the DataArray (taken from numpy docs):

    • ”constant”: Pads with a constant value.

    • ”edge”: Pads with the edge values of array.

    • ”linear_ramp”: Pads with the linear ramp between end_value and the array edge value.

    • ”maximum”: Pads with the maximum value of all or part of the vector along each axis.

    • ”mean”: Pads with the mean value of all or part of the vector along each axis.

    • ”median”: Pads with the median value of all or part of the vector along each axis.

    • ”minimum”: Pads with the minimum value of all or part of the vector along each axis.

    • ”reflect”: Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.

    • ”symmetric”: Pads with the reflection of the vector mirrored along the edge of the array.

    • ”wrap”: Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.

  • stat_length (int, tuple or mapping of Hashable to tuple, default: None) – Used in ‘maximum’, ‘mean’, ‘median’, and ‘minimum’. Number of values at edge of each axis used to calculate the statistic value. {dim_1: (before_1, after_1), … dim_N: (before_N, after_N)} unique statistic lengths along each dimension. ((before, after),) yields same before and after statistic lengths for each dimension. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default is None, to use the entire axis.

  • constant_values (scalar, tuple or mapping of Hashable to tuple, default: 0) – Used in ‘constant’. The values to set the padded values for each axis. {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)} unique pad constants along each dimension. ((before, after),) yields same before and after constants for each dimension. (constant,) or constant is a shortcut for before = after = constant for all dimensions. Default is 0.

  • end_values (scalar, tuple or mapping of Hashable to tuple, default: 0) – Used in ‘linear_ramp’. The values used for the ending value of the linear_ramp and that will form the edge of the padded array. {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)} unique end values along each dimension. ((before, after),) yields same before and after end values for each axis. (constant,) or constant is a shortcut for before = after = constant for all axes. Default is 0.

  • reflect_type ({"even", "odd", None}, optional) – Used in “reflect”, and “symmetric”. The “even” style is the default with an unaltered reflection around the edge value. For the “odd” style, the extended part of the array is created by subtracting the reflected values from two times the edge value.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.

  • **pad_width_kwargs – The keyword arguments form of pad_width. One of pad_width or pad_width_kwargs must be provided.

Returns

padded – DataArray with the padded coordinates and data.

Return type

DataArray

See also

DataArray.shift, DataArray.roll, DataArray.bfill, DataArray.ffill, numpy.pad, dask.array.pad

Notes

For mode="constant" and constant_values=None, integer types will be promoted to float and padded with np.nan.

Padding coordinates will drop their corresponding index (if any) and will reset default indexes for dimension coordinates.

Examples

>>> arr = xr.DataArray([5, 6, 7], coords=[("x", [0, 1, 2])])
>>> arr.pad(x=(1, 2), constant_values=0)
<xarray.DataArray (x: 6)>
array([0, 5, 6, 7, 0, 0])
Coordinates:
  * x        (x) float64 nan 0.0 1.0 2.0 nan nan
>>> da = xr.DataArray(
...     [[0, 1, 2, 3], [10, 11, 12, 13]],
...     dims=["x", "y"],
...     coords={"x": [0, 1], "y": [10, 20, 30, 40], "z": ("x", [100, 200])},
... )
>>> da.pad(x=1)
<xarray.DataArray (x: 4, y: 4)>
array([[nan, nan, nan, nan],
       [ 0.,  1.,  2.,  3.],
       [10., 11., 12., 13.],
       [nan, nan, nan, nan]])
Coordinates:
  * x        (x) float64 nan 0.0 1.0 nan
  * y        (y) int64 10 20 30 40
    z        (x) float64 nan 100.0 200.0 nan

Careful, constant_values are coerced to the data type of the array which may lead to a loss of precision:

>>> da.pad(x=1, constant_values=1.23456789)
<xarray.DataArray (x: 4, y: 4)>
array([[ 1,  1,  1,  1],
       [ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [ 1,  1,  1,  1]])
Coordinates:
  * x        (x) float64 nan 0.0 1.0 nan
  * y        (y) int64 10 20 30 40
    z        (x) float64 nan 100.0 200.0 nan
persist(**kwargs) T_DataArray#

Trigger computation in constituent dask arrays

This keeps them as dask arrays but encourages them to keep data in memory. This is particularly useful when on a distributed machine. When on a single machine consider using .compute() instead.

Parameters

**kwargs (dict) – Additional keyword arguments passed on to dask.persist.

See also

dask.persist

pipe(func: Union[Callable[[...], xarray.core.common.T], tuple[Callable[..., T], str]], *args: Any, **kwargs: Any) xarray.core.common.T#

Apply func(self, *args, **kwargs)

This method replicates the pandas method of the same name.

Parameters
  • func (callable) – function to apply to this xarray object (Dataset/DataArray). args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the xarray object.

  • *args – positional arguments passed into func.

  • **kwargs – a dictionary of keyword arguments passed into func.

Returns

object – the return type of func.

Return type

Any

Notes

Use .pipe when chaining together functions that expect xarray or pandas objects, e.g., instead of writing

f(g(h(ds), arg1=a), arg2=b, arg3=c)

You can write

(ds.pipe(h).pipe(g, arg1=a).pipe(f, arg2=b, arg3=c))

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

(ds.pipe(h).pipe(g, arg1=a).pipe((f, "arg2"), arg1=a, arg3=c))

Examples

>>> x = xr.Dataset(
...     {
...         "temperature_c": (
...             ("lat", "lon"),
...             20 * np.random.rand(4).reshape(2, 2),
...         ),
...         "precipitation": (("lat", "lon"), np.random.rand(4).reshape(2, 2)),
...     },
...     coords={"lat": [10, 20], "lon": [150, 160]},
... )
>>> x
<xarray.Dataset>
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 10 20
  * lon            (lon) int64 150 160
Data variables:
    temperature_c  (lat, lon) float64 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 0.4237 0.6459 0.4376 0.8918
>>> def adder(data, arg):
...     return data + arg
...
>>> def div(data, arg):
...     return data / arg
...
>>> def sub_mult(data, sub_arg, mult_arg):
...     return (data * mult_arg) - sub_arg
...
>>> x.pipe(adder, 2)
<xarray.Dataset>
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 10 20
  * lon            (lon) int64 150 160
Data variables:
    temperature_c  (lat, lon) float64 12.98 16.3 14.06 12.9
    precipitation  (lat, lon) float64 2.424 2.646 2.438 2.892
>>> x.pipe(adder, arg=2)
<xarray.Dataset>
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 10 20
  * lon            (lon) int64 150 160
Data variables:
    temperature_c  (lat, lon) float64 12.98 16.3 14.06 12.9
    precipitation  (lat, lon) float64 2.424 2.646 2.438 2.892
>>> (
...     x.pipe(adder, arg=2)
...     .pipe(div, arg=2)
...     .pipe(sub_mult, sub_arg=2, mult_arg=2)
... )
<xarray.Dataset>
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 10 20
  * lon            (lon) int64 150 160
Data variables:
    temperature_c  (lat, lon) float64 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 0.4237 0.6459 0.4376 0.8918

See also

pandas.DataFrame.pipe

plot#

alias of xarray.plot.accessor.DataArrayPlotAccessor

polyfit(dim: collections.abc.Hashable, deg: int, skipna: Optional[bool] = None, rcond: Optional[float] = None, w: Optional[Union[collections.abc.Hashable, Any]] = None, full: bool = False, cov: Union[bool, Literal['unscaled']] = False) xarray.core.dataset.Dataset#

Least squares polynomial fit.

This replicates the behaviour of numpy.polyfit but differs by skipping invalid values when skipna = True.

Parameters
  • dim (Hashable) – Coordinate along which to fit the polynomials.

  • deg (int) – Degree of the fitting polynomial.

  • skipna (bool or None, optional) – If True, removes all invalid values before fitting each 1D slices of the array. Default is True if data is stored in a dask.array or if there is any invalid values, False otherwise.

  • rcond (float or None, optional) – Relative condition number to the fit.

  • w (Hashable, array-like or None, optional) – Weights to apply to the y-coordinate of the sample points. Can be an array-like object or the name of a coordinate in the dataset.

  • full (bool, default: False) – Whether to return the residuals, matrix rank and singular values in addition to the coefficients.

  • cov (bool or "unscaled", default: False) – Whether to return to the covariance matrix in addition to the coefficients. The matrix is not scaled if cov=’unscaled’.

Returns

polyfit_results – A single dataset which contains:

polyfit_coefficients

The coefficients of the best fit.

polyfit_residuals

The residuals of the least-square computation (only included if full=True). When the matrix rank is deficient, np.nan is returned.

[dim]_matrix_rank

The effective rank of the scaled Vandermonde coefficient matrix (only included if full=True)

[dim]_singular_value

The singular values of the scaled Vandermonde coefficient matrix (only included if full=True)

polyfit_covariance

The covariance matrix of the polynomial coefficient estimates (only included if full=False and cov=True)

Return type

Dataset

See also

numpy.polyfit, numpy.polyval, xarray.polyval

prod(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 – New DataArray with prod applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.prod, dask.array.prod, Dataset.prod

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.prod()
<xarray.DataArray ()>
array(12.)

Use skipna to control whether NaNs are ignored.

>>> da.prod(skipna=False)
<xarray.DataArray ()>
array(nan)

Specify min_count for finer control over when NaNs are ignored.

>>> da.prod(skipna=True, min_count=2)
<xarray.DataArray ()>
array(12.)
quantile(q: ArrayLike, dim: Dims = None, method: QuantileMethods = 'linear', keep_attrs: bool | None = None, skipna: bool | None = None, interpolation: QuantileMethods | None = None) T_DataArray#

Compute the qth quantile of the data along the specified dimension.

Returns the qth quantiles(s) of the array elements.

Parameters
  • q (float or array-like of float) – Quantile to compute, which must be between 0 and 1 inclusive.

  • dim (str or Iterable of Hashable, optional) – Dimension(s) over which to apply quantile.

  • method (str, default: "linear") –

    This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points. The options sorted by their R type as summarized in the H&F paper [1]_ are:

    1. ”inverted_cdf” (*)

    2. ”averaged_inverted_cdf” (*)

    3. ”closest_observation” (*)

    4. ”interpolated_inverted_cdf” (*)

    5. ”hazen” (*)

    6. ”weibull” (*)

    7. ”linear” (default)

    8. ”median_unbiased” (*)

    9. ”normal_unbiased” (*)

    The first three methods are discontiuous. The following discontinuous variations of the default “linear” (7.) option are also available:

    • ”lower”

    • ”higher”

    • ”midpoint”

    • ”nearest”

    See numpy.quantile() or [1]_ for details. The “method” argument was previously called “interpolation”, renamed in accordance with numpy version 1.22.0.

    (*) These methods require numpy version 1.22 or newer.

  • keep_attrs (bool or None, optional) – If True, the dataset’s attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.

  • 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).

Returns

quantiles – If q is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array.

Return type

DataArray

See also

numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile

Examples

>>> da = xr.DataArray(
...     data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
...     coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]},
...     dims=("x", "y"),
... )
>>> da.quantile(0)  # or da.quantile(0, dim=...)
<xarray.DataArray ()>
array(0.7)
Coordinates:
    quantile  float64 0.0
>>> da.quantile(0, dim="x")
<xarray.DataArray (y: 4)>
array([0.7, 4.2, 2.6, 1.5])
Coordinates:
  * y         (y) float64 1.0 1.5 2.0 2.5
    quantile  float64 0.0
>>> da.quantile([0, 0.5, 1])
<xarray.DataArray (quantile: 3)>
array([0.7, 3.4, 9.4])
Coordinates:
  * quantile  (quantile) float64 0.0 0.5 1.0
>>> da.quantile([0, 0.5, 1], dim="x")
<xarray.DataArray (quantile: 3, y: 4)>
array([[0.7 , 4.2 , 2.6 , 1.5 ],
       [3.6 , 5.75, 6.  , 1.7 ],
       [6.5 , 7.3 , 9.4 , 1.9 ]])
Coordinates:
  * y         (y) float64 1.0 1.5 2.0 2.5
  * quantile  (quantile) float64 0.0 0.5 1.0

References

1

R. J. Hyndman and Y. Fan, “Sample quantiles in statistical packages,” The American Statistician, 50(4), pp. 361-365, 1996

query(queries: Mapping[Any, Any] | None = None, parser: QueryParserOptions = 'pandas', engine: QueryEngineOptions = None, missing_dims: ErrorOptionsWithWarn = 'raise', **queries_kwargs: Any) DataArray#

Return a new data array indexed along the specified dimension(s), where the indexers are given as strings containing Python expressions to be evaluated against the values in the array.

Parameters
  • queries (dict-like or None, optional) – A dict-like with keys matching dimensions and values given by strings containing Python expressions to be evaluated against the data variables in the dataset. The expressions will be evaluated using the pandas eval() function, and can contain any valid Python expressions but cannot contain any Python statements.

  • parser ({"pandas", "python"}, default: "pandas") – The parser to use to construct the syntax tree from the expression. The default of ‘pandas’ parses code slightly different than standard Python. Alternatively, you can parse an expression using the ‘python’ parser to retain strict Python semantics.

  • engine ({"python", "numexpr", None}, default: None) –

    The engine used to evaluate the expression. Supported engines are:

    • None: tries to use numexpr, falls back to python

    • ”numexpr”: evaluates expressions using numexpr

    • ”python”: performs operations as if you had eval’d in top level python

  • missing_dims ({"raise", "warn", "ignore"}, default: "raise") –

    What to do if dimensions that should be selected from are not present in the DataArray:

    • ”raise”: raise an exception

    • ”warn”: raise a warning, and ignore the missing dimensions

    • ”ignore”: ignore the missing dimensions

  • **queries_kwargs ({dim: query, ...}, optional) – The keyword arguments form of queries. One of queries or queries_kwargs must be provided.

Returns

obj – A new DataArray with the same contents as this dataset, indexed by the results of the appropriate queries.

Return type

DataArray

See also

DataArray.isel, Dataset.query, pandas.eval

Examples

>>> da = xr.DataArray(np.arange(0, 5, 1), dims="x", name="a")
>>> da
<xarray.DataArray 'a' (x: 5)>
array([0, 1, 2, 3, 4])
Dimensions without coordinates: x
>>> da.query(x="a > 2")
<xarray.DataArray 'a' (x: 2)>
array([3, 4])
Dimensions without coordinates: x
rank(dim: Hashable, pct: bool = False, keep_attrs: bool | None = None) T_DataArray#

Ranks the data.

Equal values are assigned a rank that is the average of the ranks that would have been otherwise assigned to all of the values within that set. Ranks begin at 1, not 0. If pct, computes percentage ranks.

NaNs in the input array are returned as NaNs.

The bottleneck library is required.

Parameters
  • dim (Hashable) – Dimension over which to compute rank.

  • pct (bool, default: False) – If True, compute percentage ranks, otherwise compute integer ranks.

  • keep_attrs (bool or None, optional) – If True, the dataset’s attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.

Returns

ranked – DataArray with the same coordinates and dtype ‘float64’.

Return type

DataArray

Examples

>>> arr = xr.DataArray([5, 6, 7], dims="x")
>>> arr.rank("x")
<xarray.DataArray (x: 3)>
array([1., 2., 3.])
Dimensions without coordinates: x
property real: T_DataArray#

The real part of the array.

See also

numpy.ndarray.real

reduce(func: Callable[..., Any], dim: Dims = None, *, axis: int | Sequence[int] | None = None, keep_attrs: bool | None = None, keepdims: bool = False, **kwargs: Any) T_DataArray#

Reduce this array by applying func along some dimension(s).

Parameters
  • func (callable) – Function which can be called in the form f(x, axis=axis, **kwargs) to return the result of reducing an np.ndarray over an integer valued axis.

  • dim ("...", str, Iterable of Hashable or None, optional) – Dimension(s) over which to apply func. By default func is applied over all dimensions.

  • axis (int or sequence of int, optional) – Axis(es) over which to repeatedly apply func. Only one of the ‘dim’ and ‘axis’ arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling f(x) without an axis argument).

  • keep_attrs (bool or None, optional) – If True, the variable’s attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.

  • keepdims (bool, default: False) – If True, the dimensions which are reduced are left in the result as dimensions of size one. Coordinates that use these dimensions are removed.

  • **kwargs (dict) – Additional keyword arguments passed on to func.

Returns

reduced – DataArray with this object’s array replaced with an array with summarized data and the indicated dimension(s) removed.

Return type

DataArray

reindex(indexers: Mapping[Any, Any] | None = None, method: ReindexMethodOptions = None, tolerance: float | Iterable[float] | None = None, copy: bool = True, fill_value=<NA>, **indexers_kwargs: Any) T_DataArray#

Conform this object onto the indexes of another object, filling in missing values with fill_value. The default fill value is NaN.

Parameters
  • indexers (dict, optional) – Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided.

  • copy (bool, optional) – If copy=True, data in the return value is always copied. If copy=False and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned.

  • method ({None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional) –

    Method to use for filling index values in indexers not found on this data array:

    • None (default): don’t fill gaps

    • pad / ffill: propagate last valid index value forward

    • backfill / bfill: propagate next valid index value backward

    • nearest: use nearest valid index value

  • tolerance (float | Iterable[float] | None, default: None) – Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like must be the same size as the index and its dtype must exactly match the index’s type.

  • fill_value (scalar or dict-like, optional) – Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array’s name to refer to the data array’s values.

  • **indexers_kwargs ({dim: indexer, ...}, optional) – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Returns

reindexed – Another dataset array, with this array’s data but replaced coordinates.

Return type

DataArray

Examples

Reverse latitude:

>>> da = xr.DataArray(
...     np.arange(4),
...     coords=[np.array([90, 89, 88, 87])],
...     dims="lat",
... )
>>> da
<xarray.DataArray (lat: 4)>
array([0, 1, 2, 3])
Coordinates:
  * lat      (lat) int64 90 89 88 87
>>> da.reindex(lat=da.lat[::-1])
<xarray.DataArray (lat: 4)>
array([3, 2, 1, 0])
Coordinates:
  * lat      (lat) int64 87 88 89 90

See also

DataArray.reindex_like, align

reindex_like(other: DataArray | Dataset, method: ReindexMethodOptions = None, tolerance: int | float | Iterable[int | float] | None = None, copy: bool = True, fill_value=<NA>) T_DataArray#

Conform this object onto the indexes of another object, filling in missing values with fill_value. The default fill value is NaN.

Parameters
  • other (Dataset or DataArray) – Object with an ‘indexes’ attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored.

  • method ({None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional) –

    Method to use for filling index values from other not found on this data array:

    • None (default): don’t fill gaps

    • pad / ffill: propagate last valid index value forward

    • backfill / bfill: propagate next valid index value backward

    • nearest: use nearest valid index value

  • tolerance (optional) – Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like must be the same size as the index and its dtype must exactly match the index’s type.

  • copy (bool, default: True) – If copy=True, data in the return value is always copied. If copy=False and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned.

  • fill_value (scalar or dict-like, optional) – Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array’s name to refer to the data array’s values.

Returns

reindexed – Another dataset array, with this array’s data but coordinates from the other object.

Return type

DataArray

Examples

>>> data = np.arange(12).reshape(4, 3)
>>> da1 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
... )
>>> da1
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) int64 10 20 30 40
  * y        (y) int64 70 80 90
>>> da2 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [40, 30, 20, 10], "y": [90, 80, 70]},
... )
>>> da2
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
Coordinates:
  * x        (x) int64 40 30 20 10
  * y        (y) int64 90 80 70

Reindexing with both DataArrays having the same coordinates set, but in different order:

>>> da1.reindex_like(da2)
<xarray.DataArray (x: 4, y: 3)>
array([[11, 10,  9],
       [ 8,  7,  6],
       [ 5,  4,  3],
       [ 2,  1,  0]])
Coordinates:
  * x        (x) int64 40 30 20 10
  * y        (y) int64 90 80 70

Reindexing with the other array having coordinates which the source array doesn’t have:

>>> data = np.arange(12).reshape(4, 3)
>>> da1 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
... )
>>> da2 = xr.DataArray(
...     data=data,
...     dims=["x", "y"],
...     coords={"x": [20, 10, 29, 39], "y": [70, 80, 90]},
... )
>>> da1.reindex_like(da2)
<xarray.DataArray (x: 4, y: 3)>
array([[ 3.,  4.,  5.],
       [ 0.,  1.,  2.],
       [nan, nan, nan],
       [nan, nan, nan]])
Coordinates:
  * x        (x) int64 20 10 29 39
  * y        (y) int64 70 80 90

Filling missing values with the previous valid index with respect to the coordinates’ value:

>>> da1.reindex_like(da2, method="ffill")
<xarray.DataArray (x: 4, y: 3)>
array([[3, 4, 5],
       [0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
Coordinates:
  * x        (x) int64 20 10 29 39
  * y        (y) int64 70 80 90

Filling missing values while tolerating specified error for inexact matches:

>>> da1.reindex_like(da2, method="ffill", tolerance=5)
<xarray.DataArray (x: 4, y: 3)>
array([[ 3.,  4.,  5.],
       [ 0.,  1.,  2.],
       [nan, nan, nan],
       [nan, nan, nan]])
Coordinates:
  * x        (x) int64 20 10 29 39
  * y        (y) int64 70 80 90

Filling missing values with manually specified values:

>>> da1.reindex_like(da2, fill_value=19)
<xarray.DataArray (x: 4, y: 3)>
array([[ 3,  4,  5],
       [ 0,  1,  2],
       [19, 19, 19],
       [19, 19, 19]])
Coordinates:
  * x        (x) int64 20 10 29 39
  * y        (y) int64 70 80 90

See also

DataArray.reindex, align

rename(new_name_or_name_dict: Optional[Union[collections.abc.Hashable, collections.abc.Mapping[Any, collections.abc.Hashable]]] = None, **names: collections.abc.Hashable) xarray.core.dataarray.DataArray#

Returns a new DataArray with renamed coordinates, dimensions or a new name.

Parameters
  • new_name_or_name_dict (str or dict-like, optional) – If the argument is dict-like, it used as a mapping from old names to new names for coordinates or dimensions. Otherwise, use the argument as the new name for this array.

  • **names (Hashable, optional) – The keyword arguments form of a mapping from old names to new names for coordinates or dimensions. One of new_name_or_name_dict or names must be provided.

Returns

renamed – Renamed array or array with renamed coordinates.

Return type

DataArray

See also

Dataset.rename, DataArray.swap_dims

reorder_levels(dim_order: Mapping[Any, Sequence[int | Hashable]] | None = None, **dim_order_kwargs: Sequence[int | Hashable]) T_DataArray#

Rearrange index levels using input order.

Parameters
  • Hashable (dim_order dict-like of Hashable to int or) – Mapping from names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index.

  • **dim_order_kwargs (optional) – The keyword arguments form of dim_order. One of dim_order or dim_order_kwargs must be provided.

Returns

obj – Another dataarray, with this dataarray’s data but replaced coordinates.

Return type

DataArray

resample(indexer: Mapping[Any, str] | None = None, skipna: bool | None = None, closed: SideOptions | None = None, label: SideOptions | None = None, base: int | None = None, offset: pd.Timedelta | datetime.timedelta | str | None = None, origin: str | DatetimeLike = 'start_day', keep_attrs: bool | None = None, loffset: datetime.timedelta | str | None = None, restore_coord_dims: bool | None = None, **indexer_kwargs: str) DataArrayResample#

Returns a Resample object for performing resampling operations.

Handles both downsampling and upsampling. The resampled dimension must be a datetime-like coordinate. If any intervals contain no values from the original object, they will be given the value NaN.

Parameters
  • indexer (Mapping of Hashable to str, optional) – Mapping from the dimension name to resample frequency [1]_. The dimension must be datetime-like.

  • skipna (bool, optional) – Whether to skip missing values when aggregating in downsampling.

  • closed ({"left", "right"}, optional) – Side of each interval to treat as closed.

  • label ({"left", "right"}, optional) – Side of each interval to use for labeling.

  • base (int, optional) – For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for “24H” frequency, base could range from 0 through 23.

  • origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, pd.Timestamp, datetime.datetime, np.datetime64, or cftime.datetime, default 'start_day') –

    The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index.

    If a datetime is not used, these values are also supported: - ‘epoch’: origin is 1970-01-01 - ‘start’: origin is the first value of the timeseries - ‘start_day’: origin is the first day at midnight of the timeseries - ‘end’: origin is the last value of the timeseries - ‘end_day’: origin is the ceiling midnight of the last day

  • offset (pd.Timedelta, datetime.timedelta, or str, default is None) – An offset timedelta added to the origin.

  • loffset (timedelta or str, optional) – Offset used to adjust the resampled time labels. Some pandas date offset strings are supported.

  • restore_coord_dims (bool, optional) – If True, also restore the dimension order of multi-dimensional coordinates.

  • **indexer_kwargs (str) – The keyword arguments form of indexer. One of indexer or indexer_kwargs must be provided.

Returns

resampled – This object resampled.

Return type

core.resample.DataArrayResample

Examples

Downsample monthly time-series data to seasonal data:

>>> da = xr.DataArray(
...     np.linspace(0, 11, num=12),
...     coords=[
...         pd.date_range(
...             "1999-12-15",
...             periods=12,
...             freq=pd.DateOffset(months=1),
...         )
...     ],
...     dims="time",
... )
>>> da
<xarray.DataArray (time: 12)>
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.resample(time="QS-DEC").mean()
<xarray.DataArray (time: 4)>
array([ 1.,  4.,  7., 10.])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01

Upsample monthly time-series data to daily data:

>>> da.resample(time="1D").interpolate("linear")  # +doctest: ELLIPSIS
<xarray.DataArray (time: 337)>
array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,
        0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,
        0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,
...
       10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,
       10.96774194, 11.        ])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

Limit scope of upsampling method

>>> da.resample(time="1D").nearest(tolerance="1D")
<xarray.DataArray (time: 337)>
array([ 0.,  0., nan, ..., nan, 11., 11.])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

See also

Dataset.resample, pandas.Series.resample, pandas.DataFrame.resample

References

1

http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases

reset_coords(names: Dims = None, drop: bool = False) T_DataArray | Dataset#

Given names of coordinates, reset them to become variables.

Parameters
  • names (str, Iterable of Hashable or None, optional) – Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset.

  • drop (bool, default: False) – If True, remove coordinates instead of converting them into variables.

Return type

Dataset, or DataArray if drop == True

Examples

>>> temperature = np.arange(25).reshape(5, 5)
>>> pressure = np.arange(50, 75).reshape(5, 5)
>>> da = xr.DataArray(
...     data=temperature,
...     dims=["x", "y"],
...     coords=dict(
...         lon=("x", np.arange(10, 15)),
...         lat=("y", np.arange(20, 25)),
...         Pressure=(["x", "y"], pressure),
...     ),
...     name="Temperature",
... )
>>> da
<xarray.DataArray 'Temperature' (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
    lon       (x) int64 10 11 12 13 14
    lat       (y) int64 20 21 22 23 24
    Pressure  (x, y) int64 50 51 52 53 54 55 56 57 ... 67 68 69 70 71 72 73 74
Dimensions without coordinates: x, y

Return Dataset with target coordinate as a data variable rather than a coordinate variable:

>>> da.reset_coords(names="Pressure")
<xarray.Dataset>
Dimensions:      (x: 5, y: 5)
Coordinates:
    lon          (x) int64 10 11 12 13 14
    lat          (y) int64 20 21 22 23 24
Dimensions without coordinates: x, y
Data variables:
    Pressure     (x, y) int64 50 51 52 53 54 55 56 57 ... 68 69 70 71 72 73 74
    Temperature  (x, y) int64 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 23 24

Return DataArray without targeted coordinate:

>>> da.reset_coords(names="Pressure", drop=True)
<xarray.DataArray 'Temperature' (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
    lon      (x) int64 10 11 12 13 14
    lat      (y) int64 20 21 22 23 24
Dimensions without coordinates: x, y
reset_encoding() T_DataArray#

Return a new DataArray without encoding on the array or any attached coords.

reset_index(dims_or_levels: collections.abc.Hashable | collections.abc.Sequence[collections.abc.Hashable], drop: bool = False) xarray.core.dataarray.DataArray#

Reset the specified index(es) or multi-index level(s).

This legacy method is specific to pandas (multi-)indexes and 1-dimensional “dimension” coordinates. See the more generic drop_indexes() and set_xindex() method to respectively drop and set pandas or custom indexes for arbitrary coordinates.

Parameters
  • dims_or_levels (Hashable or sequence of Hashable) – Name(s) of the dimension(s) and/or multi-index level(s) that will be reset.

  • drop (bool, default: False) – If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False).

Returns

obj – Another dataarray, with this dataarray’s data but replaced coordinates.

Return type

DataArray

See also

DataArray.set_index, DataArray.set_xindex, DataArray.drop_indexes

roll(shifts: Mapping[Hashable, int] | None = None, roll_coords: bool = False, **shifts_kwargs: int) T_DataArray#

Roll this array by an offset along one or more dimensions.

Unlike shift, roll treats the given dimensions as periodic, so will not create any missing values to be filled.

Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with numpy.roll().

Parameters
  • shifts (mapping of Hashable to int, optional) – Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left.

  • roll_coords (bool, default: False) – Indicates whether to roll the coordinates by the offset too.

  • **shifts_kwargs ({dim: offset, ...}, optional) – The keyword arguments form of shifts. One of shifts or shifts_kwargs must be provided.

Returns

rolled – DataArray with the same attributes but rolled data and coordinates.

Return type

DataArray

See also

shift

Examples

>>> arr = xr.DataArray([5, 6, 7], dims="x")
>>> arr.roll(x=1)
<xarray.DataArray (x: 3)>
array([7, 5, 6])
Dimensions without coordinates: x
rolling(dim: Mapping[Any, int] | None = None, min_periods: int | None = None, center: bool | Mapping[Any, bool] = False, **window_kwargs: int) DataArrayRolling#

Rolling window object for DataArrays.

Parameters
  • dim (dict, optional) – Mapping from the dimension name to create the rolling iterator along (e.g. time) to its moving window size.

  • min_periods (int or None, default: None) – Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window.

  • center (bool or Mapping to int, default: False) – Set the labels at the center of the window.

  • **window_kwargs (optional) – The keyword arguments form of dim. One of dim or window_kwargs must be provided.

Return type

core.rolling.DataArrayRolling

Examples

Create rolling seasonal average of monthly data e.g. DJF, JFM, …, SON:

>>> da = xr.DataArray(
...     np.linspace(0, 11, num=12),
...     coords=[
...         pd.date_range(
...             "1999-12-15",
...             periods=12,
...             freq=pd.DateOffset(months=1),
...         )
...     ],
...     dims="time",
... )
>>> da
<xarray.DataArray (time: 12)>
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.rolling(time=3, center=True).mean()
<xarray.DataArray (time: 12)>
array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., nan])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15

Remove the NaNs using dropna():

>>> da.rolling(time=3, center=True).mean().dropna("time")
<xarray.DataArray (time: 10)>
array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-15 2000-02-15 ... 2000-10-15

See also

core.rolling.DataArrayRolling, Dataset.rolling

rolling_exp(window: Mapping[Any, int] | None = None, window_type: str = 'span', **window_kwargs) RollingExp[T_DataWithCoords]#

Exponentially-weighted moving window. Similar to EWM in pandas

Requires the optional Numbagg dependency.

Parameters
  • window (mapping of hashable to int, optional) – A mapping from the name of the dimension to create the rolling exponential window along (e.g. time) to the size of the moving window.

  • window_type ({"span", "com", "halflife", "alpha"}, default: "span") – The format of the previously supplied window. Each is a simple numerical transformation of the others. Described in detail: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html

  • **window_kwargs (optional) – The keyword arguments form of window. One of window or window_kwargs must be provided.

See also

core.rolling_exp.RollingExp

round(*args, **kwargs)#

Evenly round to the given number of decimals.

Parameters
  • a (array_like) – Input data.

  • decimals (int, optional) – Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.

  • out (ndarray, optional) – Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. See ufuncs-output-type for more details.

Returns

rounded_array – An array of the same type as a, containing the rounded values. Unless out was specified, a new array is created. A reference to the result is returned.

The real and imaginary parts of complex numbers are rounded separately. The result of rounding a float is a float.

Return type

ndarray

See also

ndarray.round

equivalent method

ceil, fix, floor, rint, trunc

Notes

~numpy.round is often used as an alias for ~numpy.around.

For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc.

np.around uses a fast but sometimes inexact algorithm to round floating-point datatypes. For positive decimals it is equivalent to np.true_divide(np.rint(a * 10**decimals), 10**decimals), which has error due to the inexact representation of decimal fractions in the IEEE floating point standard [1]_ and errors introduced when scaling by powers of ten. For instance, note the extra “1” in the following:

>>> np.round(56294995342131.5, 3)
56294995342131.51

If your goal is to print such values with a fixed number of decimals, it is preferable to use numpy’s float printing routines to limit the number of printed decimals:

>>> np.format_float_positional(56294995342131.5, precision=3)
'56294995342131.5'

The float printing routines use an accurate but much more computationally demanding algorithm to compute the number of digits after the decimal point.

Alternatively, Python’s builtin round function uses a more accurate but slower algorithm for 64-bit floating point values:

>>> round(56294995342131.5, 3)
56294995342131.5
>>> np.round(16.055, 2), round(16.055, 2)  # equals 16.0549999999999997
(16.06, 16.05)

References

1

“Lecture Notes on the Status of IEEE 754”, William Kahan, https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF

Examples

>>> np.around([0.37, 1.64])
array([0., 2.])
>>> np.around([0.37, 1.64], decimals=1)
array([0.4, 1.6])
>>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
array([0., 2., 2., 4., 4.])
>>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
array([ 1,  2,  3, 11])
>>> np.around([1,2,3,11], decimals=-1)
array([ 0,  0,  0, 10])
searchsorted(v, side='left', sorter=None)#

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

See also

numpy.searchsorted

equivalent function

sel(indexers: Mapping[Any, Any] | None = None, method: str | None = None, tolerance=None, drop: bool = False, **indexers_kwargs: Any) T_DataArray#

Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).

In contrast to DataArray.isel, indexers for this method should use labels instead of integers.

Under the hood, this method is powered by using pandas’s powerful Index objects. This makes label based indexing essentially just as fast as using integer indexing.

It also means this method uses pandas’s (well documented) logic for indexing. This means you can use string shortcuts for datetime indexes (e.g., ‘2000-01’ to select all values in January 2000). It also means that slices are treated as inclusive of both the start and stop values, unlike normal Python indexing.

Warning

Do not try to assign values when using any of the indexing methods isel or sel:

da = xr.DataArray([0, 1, 2, 3], dims=['x'])
# DO NOT do this
da.isel(x=[0, 1, 2])[1] = -1

Assigning values with the chained indexing using .sel or .isel fails silently.

Parameters
  • indexers (dict, optional) – A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See indexing for the details. One of indexers or indexers_kwargs must be provided.

  • method ({None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional) –

    Method to use for inexact matches:

    • None (default): only exact matches

    • pad / ffill: propagate last valid index value forward

    • backfill / bfill: propagate next valid index value backward

    • nearest: use nearest valid index value

  • tolerance (optional) – Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

  • drop (bool, optional) – If drop=True, drop coordinates variables in indexers instead of making them scalar.

  • **indexers_kwargs ({dim: indexer, ...}, optional) – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Returns

obj – A new DataArray with the same contents as this DataArray, except the data and each dimension is indexed by the appropriate indexers. If indexer DataArrays have coordinates that do not conflict with this object, then these coordinates will be attached. In general, each array’s data will be a view of the array’s data in this DataArray, unless vectorized indexing was triggered by using an array indexer, in which case the data will be a copy.

Return type

DataArray

See also

Dataset.sel, DataArray.isel

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     coords={"x": np.arange(5), "y": np.arange(5)},
...     dims=("x", "y"),
... )
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 0 1 2 3 4
  * y        (y) int64 0 1 2 3 4
>>> tgt_x = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
>>> tgt_y = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
>>> da = da.sel(x=tgt_x, y=tgt_y, method="nearest")
>>> da
<xarray.DataArray (points: 5)>
array([ 0,  6, 12, 18, 24])
Coordinates:
    x        (points) int64 0 1 2 3 4
    y        (points) int64 0 1 2 3 4
Dimensions without coordinates: points
set_close(close: Optional[Callable[[], None]]) None#

Register the function that releases any resources linked to this object.

This method controls how xarray cleans up resources associated with this object when the .close() method is called. It is mostly intended for backend developers and it is rarely needed by regular end-users.

Parameters

close (callable) – The function that when called like close() releases any resources linked to this object.

set_index(indexes: Optional[collections.abc.Mapping[Any, collections.abc.Hashable | collections.abc.Sequence[collections.abc.Hashable]]] = None, append: bool = False, **indexes_kwargs: collections.abc.Hashable | collections.abc.Sequence[collections.abc.Hashable]) xarray.core.dataarray.DataArray#

Set DataArray (multi-)indexes using one or more existing coordinates.

This legacy method is limited to pandas (multi-)indexes and 1-dimensional “dimension” coordinates. See set_xindex() for setting a pandas or a custom Xarray-compatible index from one or more arbitrary coordinates.

Parameters
  • indexes ({dim: index, ...}) – Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index.

  • append (bool, default: False) – If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es).

  • **indexes_kwargs (optional) – The keyword arguments form of indexes. One of indexes or indexes_kwargs must be provided.

Returns

obj – Another DataArray, with this data but replaced coordinates.

Return type

DataArray

Examples

>>> arr = xr.DataArray(
...     data=np.ones((2, 3)),
...     dims=["x", "y"],
...     coords={"x": range(2), "y": range(3), "a": ("x", [3, 4])},
... )
>>> arr
<xarray.DataArray (x: 2, y: 3)>
array([[1., 1., 1.],
       [1., 1., 1.]])
Coordinates:
  * x        (x) int64 0 1
  * y        (y) int64 0 1 2
    a        (x) int64 3 4
>>> arr.set_index(x="a")
<xarray.DataArray (x: 2, y: 3)>
array([[1., 1., 1.],
       [1., 1., 1.]])
Coordinates:
  * x        (x) int64 3 4
  * y        (y) int64 0 1 2

See also

DataArray.reset_index, DataArray.set_xindex

set_xindex(coord_names: str | Sequence[Hashable], index_cls: type[Index] | None = None, **options) T_DataArray#

Set a new, Xarray-compatible index from one or more existing coordinate(s).

Parameters
  • coord_names (str or list) – Name(s) of the coordinate(s) used to build the index. If several names are given, their order matters.

  • index_cls (subclass of Index) – The type of index to create. By default, try setting a pandas (multi-)index from the supplied coordinates.

  • **options – Options passed to the index constructor.

Returns

obj – Another dataarray, with this dataarray’s data and with a new index.

Return type

DataArray

property shape: tuple[int, ...]#

Tuple of array dimensions.

See also

numpy.ndarray.shape

shift(shifts: Mapping[Any, int] | None = None, fill_value: Any = <NA>, **shifts_kwargs: int) T_DataArray#

Shift this DataArray by an offset along one or more dimensions.

Only the data is moved; coordinates stay in place. This is consistent with the behavior of shift in pandas.

Values shifted from beyond array bounds will appear at one end of each dimension, which are filled according to fill_value. For periodic offsets instead see roll.

Parameters
  • shifts (mapping of Hashable to int or None, optional) – Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left.

  • fill_value (scalar, optional) – Value to use for newly missing values

  • **shifts_kwargs – The keyword arguments form of shifts. One of shifts or shifts_kwargs must be provided.

Returns

shifted – DataArray with the same coordinates and attributes but shifted data.

Return type

DataArray

See also

roll

Examples

>>> arr = xr.DataArray([5, 6, 7], dims="x")
>>> arr.shift(x=1)
<xarray.DataArray (x: 3)>
array([nan,  5.,  6.])
Dimensions without coordinates: x
property size: int#

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

See also

numpy.ndarray.size

property sizes: xarray.core.utils.Frozen[collections.abc.Hashable, int]#

Ordered mapping from dimension names to lengths.

Immutable.

See also

Dataset.sizes

sortby(variables: collections.abc.Hashable | xarray.core.dataarray.DataArray | collections.abc.Sequence[collections.abc.Hashable | xarray.core.dataarray.DataArray], ascending: bool = True) xarray.core.dataarray.DataArray#

Sort object by labels or values (along an axis).

Sorts the dataarray, either along specified dimensions, or according to values of 1-D dataarrays that share dimension with calling object.

If the input variables are dataarrays, then the dataarrays are aligned (via left-join) to the calling object prior to sorting by cell values. NaNs are sorted to the end, following Numpy convention.

If multiple sorts along the same dimension is given, numpy’s lexsort is performed along that dimension: https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html and the FIRST key in the sequence is used as the primary sort key, followed by the 2nd key, etc.

Parameters
  • variables (Hashable, DataArray, or sequence of Hashable or DataArray) – 1D DataArray objects or name(s) of 1D variable(s) in coords whose values are used to sort this array.

  • ascending (bool, default: True) – Whether to sort by ascending or descending order.

Returns

sorted – A new dataarray where all the specified dims are sorted by dim labels.

Return type

DataArray

See also

Dataset.sortby, numpy.sort, pandas.sort_values, pandas.sort_index

Examples

>>> da = xr.DataArray(
...     np.random.rand(5),
...     coords=[pd.date_range("1/1/2000", periods=5)],
...     dims="time",
... )
>>> da
<xarray.DataArray (time: 5)>
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
>>> da.sortby(da)
<xarray.DataArray (time: 5)>
array([0.4236548 , 0.54488318, 0.5488135 , 0.60276338, 0.71518937])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-05 2000-01-04 ... 2000-01-02
squeeze(dim: Hashable | Iterable[Hashable] | None = None, drop: bool = False, axis: int | Iterable[int] | None = None) T_DataWithCoords#

Return a new object with squeezed data.

Parameters
  • dim (None or Hashable or iterable of Hashable, optional) – Selects a subset of the length one dimensions. If a dimension is selected with length greater than one, an error is raised. If None, all length one dimensions are squeezed.

  • drop (bool, default: False) – If drop=True, drop squeezed coordinates instead of making them scalar.

  • axis (None or int or iterable of int, optional) – Like dim, but positional.

Returns

squeezed – This object, but with with all or a subset of the dimensions of length 1 removed.

Return type

same type as caller

See also

numpy.squeeze

stack(dimensions: Mapping[Any, Sequence[Hashable]] | None = None, create_index: bool | None = True, index_cls: type[Index] = <class 'xarray.core.indexes.PandasMultiIndex'>, **dimensions_kwargs: Sequence[Hashable]) T_DataArray#

Stack any number of existing dimensions into a single new dimension.

New dimensions will be added at the end, and the corresponding coordinate variables will be combined into a MultiIndex.

Parameters
  • dimensions (mapping of Hashable to sequence of Hashable) – Mapping of the form new_name=(dim1, dim2, …). Names of new dimensions, and the existing dimensions that they replace. An ellipsis () will be replaced by all unlisted dimensions. Passing a list containing an ellipsis (stacked_dim=[…]) will stack over all dimensions.

  • create_index (bool or None, default: True) – If True, create a multi-index for each of the stacked dimensions. If False, don’t create any index. If None, create a multi-index only if exactly one single (1-d) coordinate index is found for every dimension to stack.

  • index_cls (class, optional) – Can be used to pass a custom multi-index type. Must be an Xarray index that implements .stack(). By default, a pandas multi-index wrapper is used.

  • **dimensions_kwargs – The keyword arguments form of dimensions. One of dimensions or dimensions_kwargs must be provided.

Returns

stacked – DataArray with stacked data.

Return type

DataArray

Examples

>>> arr = xr.DataArray(
...     np.arange(6).reshape(2, 3),
...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
... )
>>> arr
<xarray.DataArray (x: 2, y: 3)>
array([[0, 1, 2],
       [3, 4, 5]])
Coordinates:
  * x        (x) <U1 'a' 'b'
  * y        (y) int64 0 1 2
>>> stacked = arr.stack(z=("x", "y"))
>>> stacked.indexes["z"]
MultiIndex([('a', 0),
            ('a', 1),
            ('a', 2),
            ('b', 0),
            ('b', 1),
            ('b', 2)],
           name='z')

See also

DataArray.unstack

std(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 – New DataArray with std applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.std, dask.array.std, Dataset.std

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.std()
<xarray.DataArray ()>
array(0.74833148)

Use skipna to control whether NaNs are ignored.

>>> da.std(skipna=False)
<xarray.DataArray ()>
array(nan)

Specify ddof=1 for an unbiased estimate.

>>> da.std(skipna=True, ddof=1)
<xarray.DataArray ()>
array(0.83666003)
sum(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 – New DataArray with sum applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.sum, dask.array.sum, Dataset.sum

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.sum()
<xarray.DataArray ()>
array(9.)

Use skipna to control whether NaNs are ignored.

>>> da.sum(skipna=False)
<xarray.DataArray ()>
array(nan)

Specify min_count for finer control over when NaNs are ignored.

>>> da.sum(skipna=True, min_count=2)
<xarray.DataArray ()>
array(9.)
swap_dims(dims_dict: Mapping[Any, Hashable] | None = None, **dims_kwargs) T_DataArray#

Returns a new DataArray with swapped dimensions.

Parameters
  • dims_dict (dict-like) – Dictionary whose keys are current dimension names and whose values are new names.

  • **dims_kwargs ({existing_dim: new_dim, ...}, optional) – The keyword arguments form of dims_dict. One of dims_dict or dims_kwargs must be provided.

Returns

swapped – DataArray with swapped dimensions.

Return type

DataArray

Examples

>>> arr = xr.DataArray(
...     data=[0, 1],
...     dims="x",
...     coords={"x": ["a", "b"], "y": ("x", [0, 1])},
... )
>>> arr
<xarray.DataArray (x: 2)>
array([0, 1])
Coordinates:
  * x        (x) <U1 'a' 'b'
    y        (x) int64 0 1
>>> arr.swap_dims({"x": "y"})
<xarray.DataArray (y: 2)>
array([0, 1])
Coordinates:
    x        (y) <U1 'a' 'b'
  * y        (y) int64 0 1
>>> arr.swap_dims({"x": "z"})
<xarray.DataArray (z: 2)>
array([0, 1])
Coordinates:
    x        (z) <U1 'a' 'b'
    y        (z) int64 0 1
Dimensions without coordinates: z

See also

DataArray.rename, Dataset.swap_dims

tail(indexers: Mapping[Any, int] | int | None = None, **indexers_kwargs: Any) T_DataArray#

Return a new DataArray whose data is given by the the last n values along the specified dimension(s). Default n = 5

See also

Dataset.tail, DataArray.head, DataArray.thin

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     dims=("x", "y"),
... )
>>> da
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y
>>> da.tail(y=1)
<xarray.DataArray (x: 5, y: 1)>
array([[ 4],
       [ 9],
       [14],
       [19],
       [24]])
Dimensions without coordinates: x, y
>>> da.tail({"x": 2, "y": 2})
<xarray.DataArray (x: 2, y: 2)>
array([[18, 19],
       [23, 24]])
Dimensions without coordinates: x, y
thin(indexers: Mapping[Any, int] | int | None = None, **indexers_kwargs: Any) T_DataArray#

Return a new DataArray whose data is given by each n value along the specified dimension(s).

Examples

>>> x_arr = np.arange(0, 26)
>>> x_arr
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25])
>>> x = xr.DataArray(
...     np.reshape(x_arr, (2, 13)),
...     dims=("x", "y"),
...     coords={"x": [0, 1], "y": np.arange(0, 13)},
... )
>>> x
<xarray.DataArray (x: 2, y: 13)>
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
       [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]])
Coordinates:
  * x        (x) int64 0 1
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12
>>>
>>> x.thin(3)
<xarray.DataArray (x: 1, y: 5)>
array([[ 0,  3,  6,  9, 12]])
Coordinates:
  * x        (x) int64 0
  * y        (y) int64 0 3 6 9 12
>>> x.thin({"x": 2, "y": 5})
<xarray.DataArray (x: 1, y: 3)>
array([[ 0,  5, 10]])
Coordinates:
  * x        (x) int64 0
  * y        (y) int64 0 5 10

See also

Dataset.thin, DataArray.head, DataArray.tail

to_cdms2() cdms2_Variable#

Convert this array into a cdms2.Variable

to_dask_dataframe(dim_order: Sequence[Hashable] | None = None, set_index: bool = False) DaskDataFrame#

Convert this array into a dask.dataframe.DataFrame.

Parameters
  • dim_order (Sequence of Hashable or None , optional) – Hierarchical dimension order for the resulting dataframe. Array content is transposed to this order and then written out as flat vectors in contiguous order, so the last dimension in this list will be contiguous in the resulting DataFrame. This has a major influence on which operations are efficient on the resulting dask dataframe.

  • set_index (bool, default: False) – If set_index=True, the dask DataFrame is indexed by this dataset’s coordinate. Since dask DataFrames do not support multi-indexes, set_index only works if the dataset only contains one dimension.

Return type

dask.dataframe.DataFrame

Examples

>>> da = xr.DataArray(
...     np.arange(4 * 2 * 2).reshape(4, 2, 2),
...     dims=("time", "lat", "lon"),
...     coords={
...         "time": np.arange(4),
...         "lat": [-30, -20],
...         "lon": [120, 130],
...     },
...     name="eg_dataarray",
...     attrs={"units": "Celsius", "description": "Random temperature data"},
... )
>>> da.to_dask_dataframe(["lat", "lon", "time"]).compute()
    lat  lon  time  eg_dataarray
0   -30  120     0             0
1   -30  120     1             4
2   -30  120     2             8
3   -30  120     3            12
4   -30  130     0             1
5   -30  130     1             5
6   -30  130     2             9
7   -30  130     3            13
8   -20  120     0             2
9   -20  120     1             6
10  -20  120     2            10
11  -20  120     3            14
12  -20  130     0             3
13  -20  130     1             7
14  -20  130     2            11
15  -20  130     3            15
to_dataframe(name: Optional[collections.abc.Hashable] = None, dim_order: Optional[collections.abc.Sequence[collections.abc.Hashable]] = None) pandas.core.frame.DataFrame#

Convert this array and its coordinates into a tidy pandas.DataFrame.

The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a pandas.MultiIndex). Other coordinates are included as columns in the DataFrame.

For 1D and 2D DataArrays, see also DataArray.to_pandas() which doesn’t rely on a MultiIndex to build the DataFrame.

Parameters
  • name (Hashable or None, optional) – Name to give to this array (required if unnamed).

  • dim_order (Sequence of Hashable or None, optional) –

    Hierarchical dimension order for the resulting dataframe. Array content is transposed to this order and then written out as flat vectors in contiguous order, so the last dimension in this list will be contiguous in the resulting DataFrame. This has a major influence on which operations are efficient on the resulting dataframe.

    If provided, must include all dimensions of this DataArray. By default, dimensions are sorted according to the DataArray dimensions order.

Returns

result – DataArray as a pandas DataFrame.

Return type

DataFrame

See also

DataArray.to_pandas, DataArray.to_series

to_dataset(dim: Optional[collections.abc.Hashable] = None, *, name: Optional[collections.abc.Hashable] = None, promote_attrs: bool = False) xarray.core.dataset.Dataset#

Convert a DataArray to a Dataset.

Parameters
  • dim (Hashable, optional) – Name of the dimension on this array along which to split this array into separate variables. If not provided, this array is converted into a Dataset of one variable.

  • name (Hashable, optional) – Name to substitute for this array’s name. Only valid if dim is not provided.

  • promote_attrs (bool, default: False) – Set to True to shallow copy attrs of DataArray to returned Dataset.

Returns

dataset

Return type

Dataset

to_dict(data: Union[bool, Literal['list', 'array']] = 'list', encoding: bool = False) dict[str, Any]#

Convert this xarray.DataArray into a dictionary following xarray naming conventions.

Converts all variables and attributes to native Python objects. Useful for converting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarray.open_dataset.

Parameters
  • data (bool or {"list", "array"}, default: "list") – Whether to include the actual data in the dictionary. When set to False, returns just the schema. If set to “array”, returns data as underlying array type. If set to “list” (or True for backwards compatibility), returns data in lists of Python data types. Note that for obtaining the “list” output efficiently, use da.compute().to_dict(data=”list”).

  • encoding (bool, default: False) – Whether to include the Dataset’s encoding in the dictionary.

Returns

dict

Return type

dict

See also

DataArray.from_dict, Dataset.to_dict

to_hdf5(fname: str, group_path: str) None#

Save an xr.DataArray to the hdf5 file with a given path to the group.

to_index() pandas.core.indexes.base.Index#

Convert this variable to a pandas.Index. Only possible for 1D arrays.

to_iris() iris_Cube#

Convert this array into a iris.cube.Cube

to_masked_array(copy: bool = True) numpy.ma.core.MaskedArray#

Convert this array into a numpy.ma.MaskedArray

Parameters

copy (bool, default: True) – If True make a copy of the array in the result. If False, a MaskedArray view of DataArray.values is returned.

Returns

result – Masked where invalid values (nan or inf) occur.

Return type

MaskedArray

to_netcdf(path: str | PathLike | None = None, mode: Literal['w', 'a'] = 'w', format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, compute: bool = True, invalid_netcdf: bool = False) bytes | Delayed | None#

Write DataArray contents to a netCDF file.

Parameters
  • path (str, path-like or None, optional) – Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resulting netCDF file as bytes; in this case, we need to use scipy, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT).

  • mode ({"w", "a"}, default: "w") – Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten. If mode=’a’, existing variables will be overwritten.

  • format ({"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC"}, optional) –

    File format for the resulting netCDF file:

    • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.

    • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.

    • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.

    • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.

    All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.

    The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).

  • group (str, optional) – Path to the netCDF4 group in the given file to open (only works for format=’NETCDF4’). The group(s) will be created if necessary.

  • engine ({"netcdf4", "scipy", "h5netcdf"}, optional) – Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for ‘netcdf4’ if writing to a file on disk.

  • encoding (dict, optional) –

    Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., {"my_variable": {"dtype": "int16", "scale_factor": 0.1, "zlib": True}, ...}

    The h5netcdf engine supports both the NetCDF4-style compression encoding parameters {"zlib": True, "complevel": 9} and the h5py ones {"compression": "gzip", "compression_opts": 9}. This allows using any compression plugin installed in the HDF5 library, e.g. LZF.

  • unlimited_dims (iterable of Hashable, optional) – Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set via dataset.encoding["unlimited_dims"].

  • compute (bool, default: True) – If true compute immediately, otherwise return a dask.delayed.Delayed object that can be computed later.

  • invalid_netcdf (bool, default: False) – Only valid along with engine="h5netcdf". If True, allow writing hdf5 files which are invalid netcdf as described in https://github.com/h5netcdf/h5netcdf.

Returns

store

  • bytes if path is None

  • dask.delayed.Delayed if compute is False

  • None otherwise

Return type

bytes or Delayed or None

Notes

Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a coordinate name, then it is given the name "__xarray_dataarray_variable__".

See also

Dataset.to_netcdf

to_numpy() numpy.ndarray#

Coerces wrapped data to numpy and returns a numpy.ndarray.

See also

DataArray.as_numpy

Same but returns the surrounding DataArray instead.

Dataset.as_numpy, DataArray.values, DataArray.data

to_pandas() xarray.core.dataarray.DataArray | pandas.core.series.Series | pandas.core.frame.DataFrame#

Convert this array into a pandas object with the same shape.

The type of the returned object depends on the number of DataArray dimensions:

  • 0D -> xarray.DataArray

  • 1D -> pandas.Series

  • 2D -> pandas.DataFrame

Only works for arrays with 2 or fewer dimensions.

The DataArray constructor performs the inverse transformation.

Returns

result – DataArray, pandas Series or pandas DataFrame.

Return type

DataArray | Series | DataFrame

to_series() pandas.core.series.Series#

Convert this array into a pandas.Series.

The Series is indexed by the Cartesian product of index coordinates (in the form of a pandas.MultiIndex).

Returns

result – DataArray as a pandas Series.

Return type

Series

See also

DataArray.to_pandas, DataArray.to_dataframe

to_unstacked_dataset(dim: collections.abc.Hashable, level: int | collections.abc.Hashable = 0) xarray.core.dataset.Dataset#

Unstack DataArray expanding to Dataset along a given level of a stacked coordinate.

This is the inverse operation of Dataset.to_stacked_array.

Parameters
  • dim (Hashable) – Name of existing dimension to unstack

  • level (int or Hashable, default: 0) – The MultiIndex level to expand to a dataset along. Can either be the integer index of the level or its name.

Returns

unstacked

Return type

Dataset

Examples

>>> arr = xr.DataArray(
...     np.arange(6).reshape(2, 3),
...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
... )
>>> data = xr.Dataset({"a": arr, "b": arr.isel(y=0)})
>>> data
<xarray.Dataset>
Dimensions:  (x: 2, y: 3)
Coordinates:
  * x        (x) <U1 'a' 'b'
  * y        (y) int64 0 1 2
Data variables:
    a        (x, y) int64 0 1 2 3 4 5
    b        (x) int64 0 3
>>> stacked = data.to_stacked_array("z", ["x"])
>>> stacked.indexes["z"]
MultiIndex([('a', 0.0),
            ('a', 1.0),
            ('a', 2.0),
            ('b', nan)],
           name='z')
>>> roundtripped = stacked.to_unstacked_dataset(dim="z")
>>> data.identical(roundtripped)
True

See also

Dataset.to_stacked_array

to_zarr(store: MutableMapping | str | PathLike[str] | None = None, chunk_store: MutableMapping | str | PathLike | None = None, mode: Literal['w', 'w-', 'a', 'r+', None] = None, synchronizer=None, group: str | None = None, encoding: Mapping | None = None, compute: bool = True, consolidated: bool | None = None, append_dim: Hashable | None = None, region: Mapping[str, slice] | None = None, safe_chunks: bool = True, storage_options: dict[str, str] | None = None, zarr_version: int | None = None) ZarrStore | Delayed#

Write DataArray contents to a Zarr store

Zarr chunks are determined in the following way:

  • From the chunks attribute in each variable’s encoding (can be set via DataArray.chunk).

  • If the variable is a Dask array, from the dask chunks

  • If neither Dask chunks nor encoding chunks are present, chunks will be determined automatically by Zarr

  • If both Dask chunks and encoding chunks are present, encoding chunks will be used, provided that there is a many-to-one relationship between encoding chunks and dask chunks (i.e. Dask chunks are bigger than and evenly divide encoding chunks); otherwise raise a ValueError. This restriction ensures that no synchronization / locks are required when writing. To disable this restriction, use safe_chunks=False.

Parameters
  • store (MutableMapping, str or path-like, optional) – Store or path to directory in local or remote file system.

  • chunk_store (MutableMapping, str or path-like, optional) – Store or path to directory in local or remote file system only for Zarr array chunks. Requires zarr-python v2.4.0 or later.

  • mode ({"w", "w-", "a", "r+", None}, optional) – Persistence mode: “w” means create (overwrite if exists); “w-” means create (fail if exists); “a” means override existing variables (create if does not exist); “r+” means modify existing array values only (raise an error if any metadata or shapes would change). The default mode is “a” if append_dim is set. Otherwise, it is “r+” if region is set and w- otherwise.

  • synchronizer (object, optional) – Zarr array synchronizer.

  • group (str, optional) – Group path. (a.k.a. path in zarr terminology.)

  • encoding (dict, optional) – Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., {"my_variable": {"dtype": "int16", "scale_factor": 0.1,}, ...}

  • compute (bool, default: True) – If True write array data immediately, otherwise return a dask.delayed.Delayed object that can be computed to write array data later. Metadata is always updated eagerly.

  • consolidated (bool, optional) –

    If True, apply zarr’s consolidate_metadata function to the store after writing metadata and read existing stores with consolidated metadata; if False, do not. The default (consolidated=None) means write consolidated metadata and attempt to read consolidated metadata for existing stores (falling back to non-consolidated).

    When the experimental zarr_version=3, consolidated must be either be None or False.

  • append_dim (hashable, optional) – If set, the dimension along which the data will be appended. All other dimensions on overridden variables must remain the same size.

  • region (dict, optional) –

    Optional mapping from dimension names to integer slices along dataarray dimensions to indicate the region of existing zarr array(s) in which to write this datarray’s data. For example, {'x': slice(0, 1000), 'y': slice(10000, 11000)} would indicate that values should be written to the region 0:1000 along x and 10000:11000 along y.

    Two restrictions apply to the use of region:

    • If region is set, _all_ variables in a dataarray must have at least one dimension in common with the region. Other variables should be written in a separate call to to_zarr().

    • Dimensions cannot be included in both region and append_dim at the same time. To create empty arrays to fill in with region, use a separate call to to_zarr() with compute=False. See “Appending to existing Zarr stores” in the reference documentation for full details.

  • safe_chunks (bool, default: True) – If True, only allow writes to when there is a many-to-one relationship between Zarr chunks (specified in encoding) and Dask chunks. Set False to override this restriction; however, data may become corrupted if Zarr arrays are written in parallel. This option may be useful in combination with compute=False to initialize a Zarr store from an existing DataArray with arbitrary chunk structure.

  • storage_options (dict, optional) – Any additional parameters for the storage backend (ignored for local paths).

  • zarr_version (int or None, optional) – The desired zarr spec version to target (currently 2 or 3). The default of None will attempt to determine the zarr version from store when possible, otherwise defaulting to 2.

Returns

  • * dask.delayed.Delayed if compute is False

  • * ZarrStore otherwise

References

https://zarr.readthedocs.io/

Notes

Zarr chunking behavior:

If chunks are found in the encoding argument or attribute corresponding to any DataArray, those chunks are used. If a DataArray is a dask array, it is written with those chunks. If not other chunks are found, Zarr uses its own heuristics to choose automatic chunk sizes.

encoding:

The encoding attribute (if exists) of the DataArray(s) will be used. Override any existing encodings by providing the encoding kwarg.

See also

Dataset.to_zarr

io.zarr

The I/O user guide, with more details and examples.

transpose(*dims: Hashable, transpose_coords: bool = True, missing_dims: ErrorOptionsWithWarn = 'raise') T_DataArray#

Return a new DataArray object with transposed dimensions.

Parameters
  • *dims (Hashable, optional) – By default, reverse the dimensions. Otherwise, reorder the dimensions to this order.

  • transpose_coords (bool, default: True) – If True, also transpose the coordinates of this DataArray.

  • missing_dims ({"raise", "warn", "ignore"}, default: "raise") – What to do if dimensions that should be selected from are not present in the DataArray: - “raise”: raise an exception - “warn”: raise a warning, and ignore the missing dimensions - “ignore”: ignore the missing dimensions

Returns

transposed – The returned DataArray’s array is transposed.

Return type

DataArray

Notes

This operation returns a view of this array’s data. It is lazy for dask-backed DataArrays but not for numpy-backed DataArrays – the data will be fully loaded.

See also

numpy.transpose, Dataset.transpose

unify_chunks() xarray.core.dataarray.DataArray#

Unify chunk size along all chunked dimensions of this DataArray.

Return type

DataArray with consistent chunk sizes for all dask-array variables

See also

dask.array.core.unify_chunks

unstack(dim: Dims = None, fill_value: Any = <NA>, sparse: bool = False) DataArray#

Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.

New dimensions will be added at the end.

Parameters
  • dim (str, Iterable of Hashable or None, optional) – Dimension(s) over which to unstack. By default unstacks all MultiIndexes.

  • fill_value (scalar or dict-like, default: nan) – Value to be filled. If a dict-like, maps variable names to fill values. Use the data array’s name to refer to its name. If not provided or if the dict-like does not contain all variables, the dtype’s NA value will be used.

  • sparse (bool, default: False) – Use sparse-array if True

Returns

unstacked – Array with unstacked data.

Return type

DataArray

Examples

>>> arr = xr.DataArray(
...     np.arange(6).reshape(2, 3),
...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
... )
>>> arr
<xarray.DataArray (x: 2, y: 3)>
array([[0, 1, 2],
       [3, 4, 5]])
Coordinates:
  * x        (x) <U1 'a' 'b'
  * y        (y) int64 0 1 2
>>> stacked = arr.stack(z=("x", "y"))
>>> stacked.indexes["z"]
MultiIndex([('a', 0),
            ('a', 1),
            ('a', 2),
            ('b', 0),
            ('b', 1),
            ('b', 2)],
           name='z')
>>> roundtripped = stacked.unstack()
>>> arr.identical(roundtripped)
True

See also

DataArray.stack

classmethod validate_dims(val)#

Make sure the dims are the same as _dims, then put them in the correct order.

property values: numpy.ndarray#

The array’s data as a numpy.ndarray.

If the array’s data is not a numpy.ndarray this will attempt to convert it naively using np.array(), which will raise an error if the array type does not support coercion like this (e.g. cupy).

var(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 – New DataArray with var applied to its data and the indicated dimension(s) removed

Return type

DataArray

See also

numpy.var, dask.array.var, Dataset.var

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("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.var()
<xarray.DataArray ()>
array(0.56)

Use skipna to control whether NaNs are ignored.

>>> da.var(skipna=False)
<xarray.DataArray ()>
array(nan)

Specify ddof=1 for an unbiased estimate.

>>> da.var(skipna=True, ddof=1)
<xarray.DataArray ()>
array(0.7)
property variable: xarray.core.variable.Variable#

Low level interface to the Variable object for this DataArray.

weighted(weights: DataArray) DataArrayWeighted#

Weighted DataArray operations.

Parameters

weights (DataArray) – An array of weights associated with the values in this Dataset. Each value in the data contributes to the reduction operation according to its associated weight.

Notes

weights must be a DataArray and cannot contain missing values. Missing values can be replaced by weights.fillna(0).

Return type

core.weighted.DataArrayWeighted

See also

Dataset.weighted

where(cond: Any, other: Any = <NA>, drop: bool = False) T_DataWithCoords#

Filter elements from this object according to a condition.

This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic.

Parameters
  • cond (DataArray, Dataset, or callable) – Locations at which to preserve this object’s values. dtype must be bool. If a callable, it must expect this object as its only parameter.

  • other (scalar, DataArray or Dataset, optional) – Value to use for locations in this object where cond is False. By default, these locations filled with NA.

  • drop (bool, default: False) – If True, coordinate labels that only correspond to False values of the condition are dropped from the result.

Returns

Same xarray type as caller, with dtype float64.

Return type

DataArray or Dataset

Examples

>>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
>>> a
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4)
<xarray.DataArray (x: 5, y: 5)>
array([[ 0.,  1.,  2.,  3., nan],
       [ 5.,  6.,  7., nan, nan],
       [10., 11., nan, nan, nan],
       [15., nan, nan, nan, nan],
       [nan, nan, nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 5, -1)
<xarray.DataArray (x: 5, y: 5)>
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8, -1],
       [10, 11, 12, -1, -1],
       [15, 16, -1, -1, -1],
       [20, -1, -1, -1, -1]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4, drop=True)
<xarray.DataArray (x: 4, y: 4)>
array([[ 0.,  1.,  2.,  3.],
       [ 5.,  6.,  7., nan],
       [10., 11., nan, nan],
       [15., nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(lambda x: x.x + x.y < 4, drop=True)
<xarray.DataArray (x: 4, y: 4)>
array([[ 0.,  1.,  2.,  3.],
       [ 5.,  6.,  7., nan],
       [10., 11., nan, nan],
       [15., nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4, -1, drop=True)
<xarray.DataArray (x: 4, y: 4)>
array([[ 0,  1,  2,  3],
       [ 5,  6,  7, -1],
       [10, 11, -1, -1],
       [15, -1, -1, -1]])
Dimensions without coordinates: x, y

See also

numpy.where

corresponding numpy function

where

equivalent function

property xindexes: xarray.core.indexes.Indexes#

Mapping of xarray Index objects used for label based indexing.