pycoupler.LPJmLDataSet

class pycoupler.LPJmLDataSet(*args, **kwargs)

Bases: Dataset

Class for LPJmL data sets.

Parameters:

• *args (tuple) – Arguments for the xarray.Dataset constructor.

• **kwargs (dict) – Keyword arguments for the xarray.Dataset constructor.

Variables:

• data_vars (dict) – Data variables of the dataset.

• attrs (dict) – Attributes of the dataset.

to_numpy()

Return data as numpy array.

Returns:

Dictionary with data variables as keys and corresponding numpy arrays as values.

Return type:

dict

to_dict(data='list', encoding=False)

Convert this dataset to 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 the decode_times=False argument in xarray.open_dataset.

Parameters:

• data (bool or {“list”, “array”, “lpjmldata”}, optional, default: “list”) – Whether to include the actual data in the dictionary. - If set to False, returns just the schema. - If set to "array", returns data as the underlying array type. - If set to "list" (or True for backwards compatibility),

  returns data in lists of Python data types. For efficient “list” output, use ds.compute().to_dict(data="list").

• encoding (bool, optional, default: False) – Whether to include the Dataset’s encoding in the dictionary.

Returns:

A dictionary with keys: "coords", "attrs", "dims", "data_vars", and optionally "encoding".

Return type:

dict

See also

xarray.Dataset.from_dict, xarray.DataArray.to_dict

__getitem__()

Access variables or coordinates of this dataset as a DataArray or a subset of variables or a indexed dataset.

Indexing with a list of names will return a new Dataset object.

all()

Reduce this Dataset’s data by applying all along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply all. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating all on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

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

Return type:

Dataset

See also

numpy.all, dask.array.all, DataArray.all

Aggregation

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="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 78B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) bool 6B True True True True True False

>>> ds.all()
<xarray.Dataset> Size: 1B
Dimensions:  ()
Data variables:
    da       bool 1B False

any()

Reduce this Dataset’s data by applying any along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply any. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating any on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with any applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.any, dask.array.any, DataArray.any

Aggregation

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="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 78B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) bool 6B True True True True True False

>>> ds.any()
<xarray.Dataset> Size: 1B
Dimensions:  ()
Data variables:
    da       bool 1B True

apply()

Backward compatible implementation of map

See also

Dataset.map

argmax()

Indices of the maxima of the member variables.

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

Parameters:

• dim (str, optional) – The dimension 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 be an error, since DataArray.argmax will return a dict with indices for all dimensions, which does not make sense for a Dataset.

• keep_attrs (bool, optional) – If True, the 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, 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:

Dataset

Examples

>>> dataset = xr.Dataset(
...     {
...         "math_scores": (
...             ["student", "test"],
...             [[90, 85, 92], [78, 80, 85], [95, 92, 98]],
...         ),
...         "english_scores": (
...             ["student", "test"],
...             [[88, 90, 92], [75, 82, 79], [93, 96, 91]],
...         ),
...     },
...     coords={
...         "student": ["Alice", "Bob", "Charlie"],
...         "test": ["Test 1", "Test 2", "Test 3"],
...     },
... )

# Indices of the maximum values along the ‘student’ dimension are calculated

>>> argmax_indices = dataset.argmax(dim="test")

>>> argmax_indices
<xarray.Dataset> Size: 132B
Dimensions:         (student: 3)
Coordinates:
  * student         (student) <U7 84B 'Alice' 'Bob' 'Charlie'
Data variables:
    math_scores     (student) int64 24B 2 2 2
    english_scores  (student) int64 24B 2 1 1

See also

DataArray.argmax

argmin()

Indices of the minima of the member variables.

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

Parameters:

• dim (Hashable, optional) – The dimension 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 be an error, since DataArray.argmin will return a dict with indices for all dimensions, which does not make sense for a Dataset.

• keep_attrs (bool, optional) – If True, the 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, 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:

Dataset

Examples

>>> dataset = xr.Dataset(
...     {
...         "math_scores": (
...             ["student", "test"],
...             [[90, 85, 79], [78, 80, 85], [95, 92, 98]],
...         ),
...         "english_scores": (
...             ["student", "test"],
...             [[88, 90, 92], [75, 82, 79], [39, 96, 78]],
...         ),
...     },
...     coords={
...         "student": ["Alice", "Bob", "Charlie"],
...         "test": ["Test 1", "Test 2", "Test 3"],
...     },
... )

# Indices of the minimum values along the ‘student’ dimension are calculated

>>> argmin_indices = dataset.argmin(dim="student")

>>> min_score_in_math = dataset["student"].isel(
...     student=argmin_indices["math_scores"]
... )
>>> min_score_in_math
<xarray.DataArray 'student' (test: 3)> Size: 84B
array(['Bob', 'Bob', 'Alice'], dtype='<U7')
Coordinates:
    student  (test) <U7 84B 'Bob' 'Bob' 'Alice'
  * test     (test) <U6 72B 'Test 1' 'Test 2' 'Test 3'

>>> min_score_in_english = dataset["student"].isel(
...     student=argmin_indices["english_scores"]
... )
>>> min_score_in_english
<xarray.DataArray 'student' (test: 3)> Size: 84B
array(['Charlie', 'Bob', 'Charlie'], dtype='<U7')
Coordinates:
    student  (test) <U7 84B 'Charlie' 'Bob' 'Charlie'
  * test     (test) <U6 72B 'Test 1' 'Test 2' 'Test 3'

See also

Dataset.idxmin, DataArray.argmin

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()

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

See also

DataArray.as_numpy

DataArray.to_numpy

Returns only the data as a numpy.ndarray object.

assign()

Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones.

Parameters:

• variables (mapping of hashable to Any) – Mapping from variables names to the new values. If the new values are callable, they are computed on the Dataset and assigned to new data variables. If the values are not callable, (e.g. a DataArray, scalar, or array), they are simply assigned.

• **variables_kwargs – The keyword arguments form of variables. One of variables or variables_kwargs must be provided.

Returns:

ds – A new Dataset with the new variables in addition to all the existing variables.

Return type:

Dataset

Notes

Since 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 is possible, but you cannot reference other variables created within the same assign call.

The new assigned variables that replace existing coordinates in the original dataset are still listed as coordinates in the returned Dataset.

See also

pandas.DataFrame.assign

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> Size: 96B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918

Where the value is a callable, evaluated on dataset:

>>> x.assign(temperature_f=lambda x: x.temperature_c * 9 / 5 + 32)
<xarray.Dataset> Size: 128B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
    temperature_f  (lat, lon) float64 32B 51.76 57.75 53.7 51.62

Alternatively, the same behavior can be achieved by directly referencing an existing dataarray:

>>> x.assign(temperature_f=x["temperature_c"] * 9 / 5 + 32)
<xarray.Dataset> Size: 128B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
    temperature_f  (lat, lon) float64 32B 51.76 57.75 53.7 51.62

assign_attrs()

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.

Examples

>>> dataset = xr.Dataset({"temperature": [25, 30, 27]})
>>> dataset
<xarray.Dataset> Size: 24B
Dimensions:      (temperature: 3)
Coordinates:
  * temperature  (temperature) int64 24B 25 30 27
Data variables:
    *empty*

>>> new_dataset = dataset.assign_attrs(
...     units="Celsius", description="Temperature data"
... )
>>> new_dataset
<xarray.Dataset> Size: 24B
Dimensions:      (temperature: 3)
Coordinates:
  * temperature  (temperature) int64 24B 25 30 27
Data variables:
    *empty*
:ivar units: Celsius
:ivar description: Temperature data

# Attributes of the new dataset

>>> new_dataset.attrs
{'units': 'Celsius', 'description': 'Temperature data'}

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

assign_coords()

Assign new coordinates to this object.

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

Parameters:

• coords (mapping of dim to coord, optional) – A mapping whose keys are the names of the coordinates and values are the coordinates to assign. The mapping will generally be a dict or Coordinates.

  • If a value is a standard data value — for example, a DataArray, scalar, or array — the data is simply assigned as a coordinate.

  • If a value is callable, it is called with this object as the only parameter, and the return value is used as new coordinate variables.

  • A 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)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 32B 358 359 0 1
>>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 32B -2 -1 0 1

The function also accepts dictionary arguments:

>>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)})
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 32B -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)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
  * lon      (lon) int64 32B 358 359 0 1
    lon_2    (lon) int64 32B 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> Size: 360B
Dimensions:         (x: 2, y: 2, time: 4)
Coordinates:
    lon             (x, y) float64 32B 260.2 260.7 260.2 260.8
    lat             (x, y) float64 32B 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 32B 2014-09-06 ... 2014-09-09
    reference_time  datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 128B 20.0 20.8 21.6 ... 30.4 31.2 32.0
    precipitation   (x, y, time) float64 128B 2.0 0.0 0.0 0.0 ... 0.0 0.0 2.0
:ivar description: Weather-related data

>>> ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180))
<xarray.Dataset> Size: 360B
Dimensions:         (x: 2, y: 2, time: 4)
Coordinates:
    lon             (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
    lat             (x, y) float64 32B 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 32B 2014-09-06 ... 2014-09-09
    reference_time  datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 128B 20.0 20.8 21.6 ... 30.4 31.2 32.0
    precipitation   (x, y, time) float64 128B 2.0 0.0 0.0 0.0 ... 0.0 0.0 2.0
:ivar description: Weather-related data

See also

Dataset.assign, Dataset.swap_dims, Dataset.set_coords

astype()

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

Dictionary of global attributes on this dataset

bfill()

Fill NaN values by propagating values backward

Requires bottleneck.

Parameters:

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

• limit (int or None, optional) – 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).

Examples

>>> time = pd.date_range("2023-01-01", periods=10, freq="D")
>>> data = np.array(
...     [1, np.nan, np.nan, np.nan, 5, np.nan, np.nan, 8, np.nan, 10]
... )
>>> dataset = xr.Dataset({"data": (("time",), data)}, coords={"time": time})
>>> dataset
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 nan nan nan 5.0 nan nan 8.0 nan 10.0

# filled dataset, fills NaN values by propagating values backward

>>> dataset.bfill(dim="time")
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 5.0 5.0 5.0 5.0 8.0 8.0 8.0 10.0 10.0

# Limit the backward filling to a maximum of 2 consecutive NaN values

>>> dataset.bfill(dim="time", limit=2)
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 nan 5.0 5.0 5.0 8.0 8.0 8.0 10.0 10.0

Return type:

Dataset

See also

Dataset.ffill

broadcast_equals()

Two Datasets are broadcast equal if they are equal after broadcasting all variables against each other.

For example, variables that are scalar in one dataset but non-scalar in the other dataset can still be broadcast equal if the the non-scalar variable is a constant.

Examples

# 2D array with shape (1, 3)

>>> data = np.array([[1, 2, 3]])
>>> a = xr.Dataset(
...     {"variable_name": (("space", "time"), data)},
...     coords={"space": [0], "time": [0, 1, 2]},
... )
>>> a
<xarray.Dataset> Size: 56B
Dimensions:        (space: 1, time: 3)
Coordinates:
  * space          (space) int64 8B 0
  * time           (time) int64 24B 0 1 2
Data variables:
    variable_name  (space, time) int64 24B 1 2 3

# 2D array with shape (3, 1)

>>> data = np.array([[1], [2], [3]])
>>> b = xr.Dataset(
...     {"variable_name": (("time", "space"), data)},
...     coords={"time": [0, 1, 2], "space": [0]},
... )
>>> b
<xarray.Dataset> Size: 56B
Dimensions:        (time: 3, space: 1)
Coordinates:
  * time           (time) int64 24B 0 1 2
  * space          (space) int64 8B 0
Data variables:
    variable_name  (time, space) int64 24B 1 2 3

.equals returns True if two Datasets have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two Datasets against each other have the same values, dimensions, and coordinates.

>>> a.equals(b)
False

>>> a.broadcast_equals(b)
True

>>> a2, b2 = xr.broadcast(a, b)
>>> a2.equals(b2)
True

See also

Dataset.equals, Dataset.identical, Dataset.broadcast

broadcast_like()

Broadcast this DataArray against another Dataset or DataArray. This is equivalent to xr.broadcast(other, self)[1]

Parameters:

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

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

chunk()

Coerce all arrays in this dataset into dask arrays with the given chunks.

Non-dask arrays in this dataset will be converted to dask arrays. Dask arrays 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.

Along datetime-like dimensions, a Resampler object (e.g. groupers.TimeResampler or groupers.SeasonResampler) is also accepted.

Parameters:

• chunks (int, tuple of int, “auto” or mapping of hashable to int or a Resampler, optional) – Chunk sizes along each dimension, e.g., 5, "auto", or {"x": 5, "y": 5} or {"x": 5, "time": TimeResampler(freq="YE")} or {"time": SeasonResampler(["DJF", "MAM", "JJA", "SON"])}.

• name_prefix (str, default: “xarray-”) – Prefix for the name of any new dask arrays.

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

• 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 this datasets’ arrays 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.Dataset

See also

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

property chunks

Mapping from dimension names to block lengths for this dataset’s data.

If this dataset does not contain chunked arrays, the mapping will be empty.

Cannot be modified directly, but can be modified by calling .chunk().

Same as Dataset.chunksizes, but maintained for backwards compatibility.

See also

Dataset.chunk, Dataset.chunksizes, xarray.unify_chunks

property chunksizes

Mapping from dimension names to block lengths for this dataset’s data.

If this dataset does not contain chunked arrays, the mapping will be empty.

Cannot be modified directly, but can be modified by calling .chunk().

Same as Dataset.chunks.

See also

Dataset.chunk, Dataset.chunks, xarray.unify_chunks

clip()

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()

Release any resources linked to this object.

combine_first()

Combine two Datasets, default to data_vars of self.

The new coordinates follow the normal broadcasting and alignment rules of join='outer'. Vacant cells in the expanded coordinates are filled with np.nan.

Parameters:

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

Return type:

Dataset

compute()

Trigger loading data into memory and return a new dataset.

Data will be computed and/or loaded from disk or a remote source.

Unlike .load, the original dataset 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.

Returns:

object – New object with lazy data variables and coordinates as in-memory arrays.

Return type:

Dataset

See also

dask.compute, Dataset.load, Dataset.load_async, DataArray.compute, Variable.compute

conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

conjugate()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

convert_calendar()

Convert the Dataset 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 (Hashable, default: “time”) – Name of the time coordinate.

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

• missing (Any or None, optional) – 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 (bool or None, 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:

Dataset

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

Mapping of DataArray objects corresponding to coordinate variables.

See also

Coordinates

copy()

Returns a copy of this dataset.

If deep=True, a deep copy is made of each of the component variables. Otherwise, a shallow copy of each of the component variable is made, so that the underlying memory region of the new dataset is the same as in the original dataset.

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

Parameters:

• deep (bool, default: False) – Whether each component variable is loaded into memory and copied onto the new object. Default is False.

• data (dict-like or None, optional) – Data to use in the new object. Each item in data must have same shape as corresponding data variable in original. When data is used, deep is ignored for the data variables and only used for coords.

Returns:

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

Return type:

Dataset

Examples

Shallow copy versus deep copy

>>> da = xr.DataArray(np.random.randn(2, 3))
>>> ds = xr.Dataset(
...     {"foo": da, "bar": ("x", [-1, 2])},
...     coords={"x": ["one", "two"]},
... )
>>> ds.copy()
<xarray.Dataset> Size: 88B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Coordinates:
  * x        (x) <U3 24B 'one' 'two'
Dimensions without coordinates: dim_0, dim_1
Data variables:
    foo      (dim_0, dim_1) float64 48B 1.764 0.4002 0.9787 2.241 1.868 -0.9773
    bar      (x) int64 16B -1 2

>>> ds_0 = ds.copy(deep=False)
>>> ds_0["foo"][0, 0] = 7
>>> ds_0
<xarray.Dataset> Size: 88B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Coordinates:
  * x        (x) <U3 24B 'one' 'two'
Dimensions without coordinates: dim_0, dim_1
Data variables:
    foo      (dim_0, dim_1) float64 48B 7.0 0.4002 0.9787 2.241 1.868 -0.9773
    bar      (x) int64 16B -1 2

>>> ds
<xarray.Dataset> Size: 88B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Coordinates:
  * x        (x) <U3 24B 'one' 'two'
Dimensions without coordinates: dim_0, dim_1
Data variables:
    foo      (dim_0, dim_1) float64 48B 7.0 0.4002 0.9787 2.241 1.868 -0.9773
    bar      (x) int64 16B -1 2

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

>>> ds.copy(data={"foo": np.arange(6).reshape(2, 3), "bar": ["a", "b"]})
<xarray.Dataset> Size: 80B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Coordinates:
  * x        (x) <U3 24B 'one' 'two'
Dimensions without coordinates: dim_0, dim_1
Data variables:
    foo      (dim_0, dim_1) int64 48B 0 1 2 3 4 5
    bar      (x) <U1 8B 'a' 'b'

>>> ds
<xarray.Dataset> Size: 88B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Coordinates:
  * x        (x) <U3 24B 'one' 'two'
Dimensions without coordinates: dim_0, dim_1
Data variables:
    foo      (dim_0, dim_1) float64 48B 7.0 0.4002 0.9787 2.241 1.868 -0.9773
    bar      (x) int64 16B -1 2

See also

pandas.DataFrame.copy

count()

Reduce this Dataset’s data by applying count along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply count. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

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

Return type:

Dataset

See also

pandas.DataFrame.count, dask.dataframe.DataFrame.count, DataArray.count

Aggregation

User guide on reduction or aggregation operations.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.count()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       int64 8B 5

cumprod()

Reduce this Dataset’s data by applying cumprod along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply cumprod. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating cumprod on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with cumprod applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.cumprod, dask.array.cumprod, DataArray.cumprod, Dataset.cumulative

Aggregation

User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing.

Note that the methods on the cumulative method are more performant (with numbagg installed) and better supported. cumsum and cumprod may be deprecated in the future.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.cumprod()
<xarray.Dataset> Size: 48B
Dimensions:  (time: 6)
Dimensions without coordinates: time
Data variables:
    da       (time) float64 48B 1.0 2.0 6.0 0.0 0.0 0.0

Use skipna to control whether NaNs are ignored.

>>> ds.cumprod(skipna=False)
<xarray.Dataset> Size: 48B
Dimensions:  (time: 6)
Dimensions without coordinates: time
Data variables:
    da       (time) float64 48B 1.0 2.0 6.0 0.0 0.0 nan

cumsum()

Reduce this Dataset’s data by applying cumsum along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply cumsum. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating cumsum on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with cumsum applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.cumsum, dask.array.cumsum, DataArray.cumsum, Dataset.cumulative

Aggregation

User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing.

Note that the methods on the cumulative method are more performant (with numbagg installed) and better supported. cumsum and cumprod may be deprecated in the future.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.cumsum()
<xarray.Dataset> Size: 48B
Dimensions:  (time: 6)
Dimensions without coordinates: time
Data variables:
    da       (time) float64 48B 1.0 3.0 6.0 6.0 8.0 8.0

Use skipna to control whether NaNs are ignored.

>>> ds.cumsum(skipna=False)
<xarray.Dataset> Size: 48B
Dimensions:  (time: 6)
Dimensions without coordinates: time
Data variables:
    da       (time) float64 48B 1.0 3.0 6.0 6.0 8.0 nan

cumulative()

Accumulating object for Datasets

Parameters:

• dims (iterable of hashable) – The name(s) of the dimensions to create the cumulative window along

• min_periods (int, default: 1) – Minimum number of observations in window required to have a value (otherwise result is NA). The default is 1 (note this is different from Rolling, whose default is the size of the window).

Return type:

computation.rolling.DatasetRolling

See also

DataArray.cumulative, Dataset.rolling, Dataset.rolling_exp

cumulative_integrate()

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.

The first entry of the cumulative integral of each variable 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 ({‘W’, ‘D’, ‘h’, ‘m’, ‘s’, ‘ms’, ‘us’, ‘ns’, ‘ps’, ‘fs’, ‘as’, None}, optional) – Specify the unit if datetime coordinate is used.

Returns:

integrated

Return type:

Dataset

See also

DataArray.cumulative_integrate

scipy.integrate.cumulative_trapezoid

corresponding scipy function

Examples

>>> ds = xr.Dataset(
...     data_vars={"a": ("x", [5, 5, 6, 6]), "b": ("x", [1, 2, 1, 0])},
...     coords={"x": [0, 1, 2, 3], "y": ("x", [1, 7, 3, 5])},
... )
>>> ds
<xarray.Dataset> Size: 128B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
    y        (x) int64 32B 1 7 3 5
Data variables:
    a        (x) int64 32B 5 5 6 6
    b        (x) int64 32B 1 2 1 0
>>> ds.cumulative_integrate("x")
<xarray.Dataset> Size: 128B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
    y        (x) int64 32B 1 7 3 5
Data variables:
    a        (x) float64 32B 0.0 5.0 10.5 16.5
    b        (x) float64 32B 0.0 1.5 3.0 3.5
>>> ds.cumulative_integrate("y")
<xarray.Dataset> Size: 128B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
    y        (x) int64 32B 1 7 3 5
Data variables:
    a        (x) float64 32B 0.0 30.0 8.0 20.0
    b        (x) float64 32B 0.0 9.0 3.0 4.0

curvefit()

Curve fitting optimization for arbitrary functions.

Wraps scipy.optimize.curve_fit() with apply_ufunc().

Parameters:

• coords (hashable, DataArray, or sequence of hashable or DataArray) – 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, optional) – Optional dictionary of parameter names to initial guesses passed to the curve_fit p0 arg. If the values are DataArrays, they will be appropriately broadcast to the coordinates of the array. If none or only some parameters are passed, the rest will be assigned initial values following the default scipy behavior.

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

• param_names (sequence of hashable, 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.

• errors ({“raise”, “ignore”}, default: “raise”) – If ‘raise’, any errors from the scipy.optimize_curve_fit optimization will raise an exception. If ‘ignore’, the coefficients and covariances for the coordinates where the fitting failed will be NaN.

• **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

Dataset.polyfit, scipy.optimize.curve_fit

xarray.Dataset.xlm.modelfit

External method from xarray-lmfit with more curve fitting functionality.

property data_vars

Dictionary of DataArray objects corresponding to data variables

diff()

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:

Dataset

Notes

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

Examples

>>> ds = xr.Dataset({"foo": ("x", [5, 5, 6, 6])})
>>> ds.diff("x")
<xarray.Dataset> Size: 24B
Dimensions:  (x: 3)
Dimensions without coordinates: x
Data variables:
    foo      (x) int64 24B 0 1 0
>>> ds.diff("x", 2)
<xarray.Dataset> Size: 16B
Dimensions:  (x: 2)
Dimensions without coordinates: x
Data variables:
    foo      (x) int64 16B 1 -1

See also

Dataset.differentiate

differentiate()

Differentiate 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 (None or {“W”, “D”, “h”, “m”, “s”, “ms”, “us”, “ns”, “ps”, “fs”, “as”, None}, default: None) – Unit to compute gradient. Only valid for datetime coordinate.

Returns:

differentiated

Return type:

Dataset

See also

numpy.gradient

corresponding numpy function

property dims

Mapping from dimension names to lengths.

Cannot be modified directly, but is updated when adding new variables.

Note that type of this object differs from DataArray.dims. See Dataset.sizes and DataArray.sizes for consistently named properties. This property will be changed to return a type more consistent with DataArray.dims in the future, i.e. a set of dimension names.

See also

Dataset.sizes, DataArray.dims

drop()

Backward compatible method based on drop_vars and drop_sel

Using either drop_vars or drop_sel is encouraged

See also

Dataset.drop_vars, Dataset.drop_sel

drop_attrs()

Removes all attributes from the Dataset and its variables.

Parameters:

deep (bool, default True) – Removes attributes from all variables.

Return type:

Dataset

drop_dims()

Drop dimensions and associated variables from this dataset.

Parameters:

• drop_dims (str or Iterable of Hashable) – Dimension or dimensions to drop.

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

Returns:

obj – The dataset without the given dimensions (or any variables containing those dimensions).

Return type:

Dataset

drop_duplicates()

Returns a new Dataset 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:

Dataset

See also

DataArray.drop_duplicates

drop_encoding()

Return a new Dataset without encoding on the dataset or any of its variables/coords.

drop_indexes()

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 dataset with dropped indexes.

Return type:

Dataset

drop_isel()

Drop index positions from this Dataset.

Parameters:

• indexers (mapping of hashable to Any) – Index locations to drop

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

Returns:

dropped

Return type:

Dataset

Raises:

IndexError –

Examples

>>> data = np.arange(6).reshape(2, 3)
>>> labels = ["a", "b", "c"]
>>> ds = xr.Dataset({"A": (["x", "y"], data), "y": labels})
>>> ds
<xarray.Dataset> Size: 60B
Dimensions:  (x: 2, y: 3)
Coordinates:
  * y        (y) <U1 12B 'a' 'b' 'c'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 48B 0 1 2 3 4 5
>>> ds.drop_isel(y=[0, 2])
<xarray.Dataset> Size: 20B
Dimensions:  (x: 2, y: 1)
Coordinates:
  * y        (y) <U1 4B 'b'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 16B 1 4
>>> ds.drop_isel(y=1)
<xarray.Dataset> Size: 40B
Dimensions:  (x: 2, y: 2)
Coordinates:
  * y        (y) <U1 8B 'a' 'c'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 32B 0 2 3 5

drop_sel()

Drop index labels from this dataset.

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:

Dataset

Examples

>>> data = np.arange(6).reshape(2, 3)
>>> labels = ["a", "b", "c"]
>>> ds = xr.Dataset({"A": (["x", "y"], data), "y": labels})
>>> ds
<xarray.Dataset> Size: 60B
Dimensions:  (x: 2, y: 3)
Coordinates:
  * y        (y) <U1 12B 'a' 'b' 'c'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 48B 0 1 2 3 4 5
>>> ds.drop_sel(y=["a", "c"])
<xarray.Dataset> Size: 20B
Dimensions:  (x: 2, y: 1)
Coordinates:
  * y        (y) <U1 4B 'b'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 16B 1 4
>>> ds.drop_sel(y="b")
<xarray.Dataset> Size: 40B
Dimensions:  (x: 2, y: 2)
Coordinates:
  * y        (y) <U1 8B 'a' 'c'
Dimensions without coordinates: x
Data variables:
    A        (x, y) int64 32B 0 2 3 5

drop_vars()

Drop variables from this dataset.

Parameters:

• names (Hashable or iterable of Hashable or Callable) – Name(s) of variables to drop. If a Callable, this object is passed as its only argument and its result is used.

• 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 dataset are dropped and no error is raised.

Examples

>>> dataset = xr.Dataset(
...     {
...         "temperature": (
...             ["time", "latitude", "longitude"],
...             [[[25.5, 26.3], [27.1, 28.0]]],
...         ),
...         "humidity": (
...             ["time", "latitude", "longitude"],
...             [[[65.0, 63.8], [58.2, 59.6]]],
...         ),
...         "wind_speed": (
...             ["time", "latitude", "longitude"],
...             [[[10.2, 8.5], [12.1, 9.8]]],
...         ),
...     },
...     coords={
...         "time": pd.date_range("2023-07-01", periods=1),
...         "latitude": [40.0, 40.2],
...         "longitude": [-75.0, -74.8],
...     },
... )
>>> dataset
<xarray.Dataset> Size: 136B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[ns] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop the ‘humidity’ variable

>>> dataset.drop_vars(["humidity"])
<xarray.Dataset> Size: 104B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[ns] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop the ‘humidity’, ‘temperature’ variables

>>> dataset.drop_vars(["humidity", "temperature"])
<xarray.Dataset> Size: 72B
Dimensions:     (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time        (time) datetime64[ns] 8B 2023-07-01
  * latitude    (latitude) float64 16B 40.0 40.2
  * longitude   (longitude) float64 16B -75.0 -74.8
Data variables:
    wind_speed  (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Drop all indexes

>>> dataset.drop_vars(lambda x: x.indexes)
<xarray.Dataset> Size: 96B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Dimensions without coordinates: time, latitude, longitude
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Attempt to drop non-existent variable with errors=”ignore”

>>> dataset.drop_vars(["pressure"], errors="ignore")
<xarray.Dataset> Size: 136B
Dimensions:      (time: 1, latitude: 2, longitude: 2)
Coordinates:
  * time         (time) datetime64[ns] 8B 2023-07-01
  * latitude     (latitude) float64 16B 40.0 40.2
  * longitude    (longitude) float64 16B -75.0 -74.8
Data variables:
    temperature  (time, latitude, longitude) float64 32B 25.5 26.3 27.1 28.0
    humidity     (time, latitude, longitude) float64 32B 65.0 63.8 58.2 59.6
    wind_speed   (time, latitude, longitude) float64 32B 10.2 8.5 12.1 9.8

Attempt to drop non-existent variable with errors=”raise”

>>> dataset.drop_vars(["pressure"], errors="raise")
Traceback (most recent call last):
ValueError: These variables cannot be found in this dataset: ['pressure']

Raises:

ValueError – Raised if you attempt to drop a variable which is not present, and the kwarg errors='raise'.

Returns:

dropped

Return type:

Dataset

See also

DataArray.drop_vars

dropna()

Returns a new dataset 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, optional) – If supplied, require this many non-NA values (summed over all the subset variables).

• subset (iterable of hashable or None, optional) – Which variables to check for missing values. By default, all variables in the dataset are checked.

Examples

>>> dataset = xr.Dataset(
...     {
...         "temperature": (
...             ["time", "location"],
...             [[23.4, 24.1], [np.nan, 22.1], [21.8, 24.2], [20.5, 25.3]],
...         )
...     },
...     coords={"time": [1, 2, 3, 4], "location": ["A", "B"]},
... )
>>> dataset
<xarray.Dataset> Size: 104B
Dimensions:      (time: 4, location: 2)
Coordinates:
  * time         (time) int64 32B 1 2 3 4
  * location     (location) <U1 8B 'A' 'B'
Data variables:
    temperature  (time, location) float64 64B 23.4 24.1 nan ... 24.2 20.5 25.3

Drop NaN values from the dataset

>>> dataset.dropna(dim="time")
<xarray.Dataset> Size: 80B
Dimensions:      (time: 3, location: 2)
Coordinates:
  * time         (time) int64 24B 1 3 4
  * location     (location) <U1 8B 'A' 'B'
Data variables:
    temperature  (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3

Drop labels with any NaN values

>>> dataset.dropna(dim="time", how="any")
<xarray.Dataset> Size: 80B
Dimensions:      (time: 3, location: 2)
Coordinates:
  * time         (time) int64 24B 1 3 4
  * location     (location) <U1 8B 'A' 'B'
Data variables:
    temperature  (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3

Drop labels with all NAN values

>>> dataset.dropna(dim="time", how="all")
<xarray.Dataset> Size: 104B
Dimensions:      (time: 4, location: 2)
Coordinates:
  * time         (time) int64 32B 1 2 3 4
  * location     (location) <U1 8B 'A' 'B'
Data variables:
    temperature  (time, location) float64 64B 23.4 24.1 nan ... 24.2 20.5 25.3

Drop labels with less than 2 non-NA values

>>> dataset.dropna(dim="time", thresh=2)
<xarray.Dataset> Size: 80B
Dimensions:      (time: 3, location: 2)
Coordinates:
  * time         (time) int64 24B 1 3 4
  * location     (location) <U1 8B 'A' 'B'
Data variables:
    temperature  (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3

Return type:

Dataset

See also

DataArray.dropna, pandas.DataFrame.dropna

property dtypes

Mapping from data variable names to dtypes.

Cannot be modified directly, but is updated when adding new variables.

See also

DataArray.dtype

dump_to_store()

Store dataset contents to a backends.*DataStore object.

property encoding

Dictionary of global encoding attributes on this dataset

equals()

Two Datasets are equal if they have matching variables and coordinates, all of which are equal.

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

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

Examples

# 2D array with shape (1, 3)

>>> data = np.array([[1, 2, 3]])
>>> dataset1 = xr.Dataset(
...     {"variable_name": (("space", "time"), data)},
...     coords={"space": [0], "time": [0, 1, 2]},
... )
>>> dataset1
<xarray.Dataset> Size: 56B
Dimensions:        (space: 1, time: 3)
Coordinates:
  * space          (space) int64 8B 0
  * time           (time) int64 24B 0 1 2
Data variables:
    variable_name  (space, time) int64 24B 1 2 3

# 2D array with shape (3, 1)

>>> data = np.array([[1], [2], [3]])
>>> dataset2 = xr.Dataset(
...     {"variable_name": (("time", "space"), data)},
...     coords={"time": [0, 1, 2], "space": [0]},
... )
>>> dataset2
<xarray.Dataset> Size: 56B
Dimensions:        (time: 3, space: 1)
Coordinates:
  * time           (time) int64 24B 0 1 2
  * space          (space) int64 8B 0
Data variables:
    variable_name  (time, space) int64 24B 1 2 3
>>> dataset1.equals(dataset2)
False

>>> dataset1.broadcast_equals(dataset2)
True

.equals returns True if two Datasets have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two Datasets against each other have the same values, dimensions, and coordinates.

Similar for missing values too:

>>> ds1 = xr.Dataset(
...     {
...         "temperature": (["x", "y"], [[1, np.nan], [3, 4]]),
...     },
...     coords={"x": [0, 1], "y": [0, 1]},
... )

>>> ds2 = xr.Dataset(
...     {
...         "temperature": (["x", "y"], [[1, np.nan], [3, 4]]),
...     },
...     coords={"x": [0, 1], "y": [0, 1]},
... )
>>> ds1.equals(ds2)
True

See also

Dataset.broadcast_equals, Dataset.identical

eval()

Calculate an expression supplied as a string in the context of the dataset.

This is currently experimental; the API may change particularly around assignments, which currently return a Dataset with the additional variable. Currently only the python engine is supported, which has the same performance as executing in python.

Parameters:

statement (str) – String containing the Python-like expression to evaluate.

Returns:

• result (Dataset or DataArray, depending on whether statement contains an)

• assignment.

Examples

>>> ds = xr.Dataset(
...     {"a": ("x", np.arange(0, 5, 1)), "b": ("x", np.linspace(0, 1, 5))}
... )
>>> ds
<xarray.Dataset> Size: 80B
Dimensions:  (x: 5)
Dimensions without coordinates: x
Data variables:
    a        (x) int64 40B 0 1 2 3 4
    b        (x) float64 40B 0.0 0.25 0.5 0.75 1.0

>>> ds.eval("a + b")
<xarray.DataArray (x: 5)> Size: 40B
array([0.  , 1.25, 2.5 , 3.75, 5.  ])
Dimensions without coordinates: x

>>> ds.eval("c = a + b")
<xarray.Dataset> Size: 120B
Dimensions:  (x: 5)
Dimensions without coordinates: x
Data variables:
    a        (x) int64 40B 0 1 2 3 4
    b        (x) float64 40B 0.0 0.25 0.5 0.75 1.0
    c        (x) float64 40B 0.0 1.25 2.5 3.75 5.0

expand_dims()

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.

The automatic creation of indexes to back new 1D coordinate variables controlled by the create_index_for_new_dim kwarg.

Parameters:

• dim (hashable, sequence of hashable, mapping, or None) – Dimensions to include on the new variable. If provided as hashable or sequence of hashable, then dimensions are inserted with length 1. If provided as a mapping, then the keys are the new dimensions and the values are either integers (giving the length of the new dimensions) or array-like (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.

• create_index_for_new_dim (bool, default: True) – Whether to create new PandasIndex objects when the object being expanded contains scalar variables with names in dim.

• **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:

Dataset

Examples

>>> dataset = xr.Dataset({"temperature": ([], 25.0)})
>>> dataset
<xarray.Dataset> Size: 8B
Dimensions:      ()
Data variables:
    temperature  float64 8B 25.0

# Expand the dataset with a new dimension called “time”

>>> dataset.expand_dims(dim="time")
<xarray.Dataset> Size: 8B
Dimensions:      (time: 1)
Dimensions without coordinates: time
Data variables:
    temperature  (time) float64 8B 25.0

# 1D data

>>> temperature_1d = xr.DataArray([25.0, 26.5, 24.8], dims="x")
>>> dataset_1d = xr.Dataset({"temperature": temperature_1d})
>>> dataset_1d
<xarray.Dataset> Size: 24B
Dimensions:      (x: 3)
Dimensions without coordinates: x
Data variables:
    temperature  (x) float64 24B 25.0 26.5 24.8

# Expand the dataset with a new dimension called “time” using axis argument

>>> dataset_1d.expand_dims(dim="time", axis=0)
<xarray.Dataset> Size: 24B
Dimensions:      (time: 1, x: 3)
Dimensions without coordinates: time, x
Data variables:
    temperature  (time, x) float64 24B 25.0 26.5 24.8

# 2D data

>>> temperature_2d = xr.DataArray(np.random.rand(3, 4), dims=("y", "x"))
>>> dataset_2d = xr.Dataset({"temperature": temperature_2d})
>>> dataset_2d
<xarray.Dataset> Size: 96B
Dimensions:      (y: 3, x: 4)
Dimensions without coordinates: y, x
Data variables:
    temperature  (y, x) float64 96B 0.5488 0.7152 0.6028 ... 0.7917 0.5289

# Expand the dataset with a new dimension called “time” using axis argument

>>> dataset_2d.expand_dims(dim="time", axis=2)
<xarray.Dataset> Size: 96B
Dimensions:      (y: 3, x: 4, time: 1)
Dimensions without coordinates: y, x, time
Data variables:
    temperature  (y, x, time) float64 96B 0.5488 0.7152 0.6028 ... 0.7917 0.5289

# Expand a scalar variable along a new dimension of the same name with and without creating a new index

>>> ds = xr.Dataset(coords={"x": 0})
>>> ds
<xarray.Dataset> Size: 8B
Dimensions:  ()
Coordinates:
    x        int64 8B 0
Data variables:
    *empty*

>>> ds.expand_dims("x")
<xarray.Dataset> Size: 8B
Dimensions:  (x: 1)
Coordinates:
  * x        (x) int64 8B 0
Data variables:
    *empty*

>>> ds.expand_dims("x").indexes
Indexes:
    x        Index([0], dtype='int64', name='x')

>>> ds.expand_dims("x", create_index_for_new_dim=False).indexes
Indexes:
    *empty*

See also

DataArray.expand_dims

ffill()

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, optional) – 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).

Examples

>>> time = pd.date_range("2023-01-01", periods=10, freq="D")
>>> data = np.array(
...     [1, np.nan, np.nan, np.nan, 5, np.nan, np.nan, 8, np.nan, 10]
... )
>>> dataset = xr.Dataset({"data": (("time",), data)}, coords={"time": time})
>>> dataset
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 nan nan nan 5.0 nan nan 8.0 nan 10.0

# Perform forward fill (ffill) on the dataset

>>> dataset.ffill(dim="time")
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 1.0 1.0 1.0 5.0 5.0 5.0 8.0 8.0 10.0

# Limit the forward filling to a maximum of 2 consecutive NaN values

>>> dataset.ffill(dim="time", limit=2)
<xarray.Dataset> Size: 160B
Dimensions:  (time: 10)
Coordinates:
  * time     (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10
Data variables:
    data     (time) float64 80B 1.0 1.0 1.0 nan 5.0 5.0 5.0 8.0 8.0 10.0

Return type:

Dataset

See also

Dataset.bfill

fillna()

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, DataArray, dict or Dataset) – Used to fill all matching missing values in this dataset’s data variables. Scalars, ndarrays or DataArrays arguments are used to fill all data with aligned coordinates (for DataArrays). Dictionaries or datasets match data variables and then align coordinates if necessary.

Return type:

Dataset

Examples

>>> ds = xr.Dataset(
...     {
...         "A": ("x", [np.nan, 2, np.nan, 0]),
...         "B": ("x", [3, 4, np.nan, 1]),
...         "C": ("x", [np.nan, np.nan, np.nan, 5]),
...         "D": ("x", [np.nan, 3, np.nan, 4]),
...     },
...     coords={"x": [0, 1, 2, 3]},
... )
>>> ds
<xarray.Dataset> Size: 160B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
Data variables:
    A        (x) float64 32B nan 2.0 nan 0.0
    B        (x) float64 32B 3.0 4.0 nan 1.0
    C        (x) float64 32B nan nan nan 5.0
    D        (x) float64 32B nan 3.0 nan 4.0

Replace all NaN values with 0s.

>>> ds.fillna(0)
<xarray.Dataset> Size: 160B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
Data variables:
    A        (x) float64 32B 0.0 2.0 0.0 0.0
    B        (x) float64 32B 3.0 4.0 0.0 1.0
    C        (x) float64 32B 0.0 0.0 0.0 5.0
    D        (x) float64 32B 0.0 3.0 0.0 4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> ds.fillna(value=values)
<xarray.Dataset> Size: 160B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
Data variables:
    A        (x) float64 32B 0.0 2.0 0.0 0.0
    B        (x) float64 32B 3.0 4.0 1.0 1.0
    C        (x) float64 32B 2.0 2.0 2.0 5.0
    D        (x) float64 32B 3.0 3.0 3.0 4.0

filter_by_attrs()

Returns a Dataset with variables that match specific conditions.

Can pass in key=value or key=callable. A Dataset is returned containing only the variables for which all the filter tests pass. These tests are either key=value for which the attribute key has the exact value value or the callable passed into key=callable returns True. The callable will be passed a single value, either the value of the attribute key or None if the DataArray does not have an attribute with the name key.

Parameters:

**kwargs –

keystr

Attribute name.

valuecallable or obj

If value is a callable, it should return a boolean in the form of bool = func(attr) where attr is da.attrs[key]. Otherwise, value will be compared to the each DataArray’s attrs[key].

Returns:

new – New dataset with variables filtered by attribute.

Return type:

Dataset

Examples

>>> temp = 15 + 8 * np.random.randn(2, 2, 3)
>>> precip = 10 * np.random.rand(2, 2, 3)
>>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
>>> lat = [[42.25, 42.21], [42.63, 42.59]]
>>> dims = ["x", "y", "time"]
>>> temp_attr = dict(standard_name="air_potential_temperature")
>>> precip_attr = dict(standard_name="convective_precipitation_flux")

>>> ds = xr.Dataset(
...     dict(
...         temperature=(dims, temp, temp_attr),
...         precipitation=(dims, precip, precip_attr),
...     ),
...     coords=dict(
...         lon=(["x", "y"], lon),
...         lat=(["x", "y"], lat),
...         time=pd.date_range("2014-09-06", periods=3),
...         reference_time=pd.Timestamp("2014-09-05"),
...     ),
... )

Get variables matching a specific standard_name:

>>> ds.filter_by_attrs(standard_name="convective_precipitation_flux")
<xarray.Dataset> Size: 192B
Dimensions:         (x: 2, y: 2, time: 3)
Coordinates:
    lon             (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
    lat             (x, y) float64 32B 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 24B 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    precipitation   (x, y, time) float64 96B 5.68 9.256 0.7104 ... 4.615 7.805

Get all variables that have a standard_name attribute:

>>> standard_name = lambda v: v is not None
>>> ds.filter_by_attrs(standard_name=standard_name)
<xarray.Dataset> Size: 288B
Dimensions:         (x: 2, y: 2, time: 3)
Coordinates:
    lon             (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
    lat             (x, y) float64 32B 42.25 42.21 42.63 42.59
  * time            (time) datetime64[ns] 24B 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 96B 29.11 18.2 22.83 ... 16.15 26.63
    precipitation   (x, y, time) float64 96B 5.68 9.256 0.7104 ... 4.615 7.805

classmethod from_dataframe()

Convert a pandas.DataFrame into an xarray.Dataset

Each column will be converted into an independent variable in the Dataset. If the dataframe’s index is a MultiIndex, it will be expanded into a tensor product of one-dimensional indices (filling in missing values with NaN). If you rather preserve the MultiIndex use xr.Dataset(df). This method will produce a Dataset very similar to that on which the ‘to_dataframe’ method was called, except with possibly redundant dimensions (since all dataset variables will have the same dimensionality).

Parameters:

• dataframe (DataFrame) – DataFrame from which to copy data and indices.

• sparse (bool, default: False) – If true, create a sparse arrays instead of dense numpy arrays. This can potentially save a large amount of memory if the DataFrame has a MultiIndex. Requires the sparse package (sparse.pydata.org).

Return type:

New Dataset.

See also

xarray.DataArray.from_series, pandas.DataFrame.to_xarray

classmethod from_dict()

Convert a dictionary into an xarray.Dataset.

Parameters:

d (dict-like) –

Mapping with a minimum structure of

{"var_0": {"dims": [..], "data": [..]},                             ...}

Returns:

obj

Return type:

Dataset

See also

Dataset.to_dict, DataArray.from_dict

Examples

>>> d = {
...     "t": {"dims": ("t"), "data": [0, 1, 2]},
...     "a": {"dims": ("t"), "data": ["a", "b", "c"]},
...     "b": {"dims": ("t"), "data": [10, 20, 30]},
... }
>>> ds = xr.Dataset.from_dict(d)
>>> ds
<xarray.Dataset> Size: 60B
Dimensions:  (t: 3)
Coordinates:
  * t        (t) int64 24B 0 1 2
Data variables:
    a        (t) <U1 12B 'a' 'b' 'c'
    b        (t) int64 24B 10 20 30

>>> d = {
...     "coords": {
...         "t": {"dims": "t", "data": [0, 1, 2], "attrs": {"units": "s"}}
...     },
...     "attrs": {"title": "air temperature"},
...     "dims": "t",
...     "data_vars": {
...         "a": {"dims": "t", "data": [10, 20, 30]},
...         "b": {"dims": "t", "data": ["a", "b", "c"]},
...     },
... }
>>> ds = xr.Dataset.from_dict(d)
>>> ds
<xarray.Dataset> Size: 60B
Dimensions:  (t: 3)
Coordinates:
  * t        (t) int64 24B 0 1 2
Data variables:
    a        (t) int64 24B 10 20 30
    b        (t) <U1 12B 'a' 'b' 'c'
:ivar title: air temperature

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

get_index()

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

groupby_bins()

Returns a DatasetGroupBy 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 string, must be the name of a variable contained in this dataset.

• 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 or bool, 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 (False) – This argument is deprecated.

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

• duplicates ({“raise”, “drop”}, default: “raise”) – If bin edges are not unique, raise ValueError or drop non-uniques.

• eagerly_compute_group (False, optional) – This argument is deprecated.

Returns:

grouped – A DatasetGroupBy 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:

DatasetGroupBy

See also

GroupBy: Group and Bin Data

Users guide explanation of how to group and bin data.

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

References

[1]

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html

head()

Returns a new dataset with the first n values of each array for the specified dimension(s).

Parameters:

• indexers (dict or int, default: 5) – A dict with keys matching dimensions and integer values n or a single integer n applied over all dimensions. One of indexers or indexers_kwargs must be provided.

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

Examples

>>> dates = pd.date_range(start="2023-01-01", periods=5)
>>> pageviews = [1200, 1500, 900, 1800, 2000]
>>> visitors = [800, 1000, 600, 1200, 1500]
>>> dataset = xr.Dataset(
...     {
...         "pageviews": (("date"), pageviews),
...         "visitors": (("date"), visitors),
...     },
...     coords={"date": dates},
... )
>>> busiest_days = dataset.sortby("pageviews", ascending=False)
>>> busiest_days.head()
<xarray.Dataset> Size: 120B
Dimensions:    (date: 5)
Coordinates:
  * date       (date) datetime64[ns] 40B 2023-01-05 2023-01-04 ... 2023-01-03
Data variables:
    pageviews  (date) int64 40B 2000 1800 1500 1200 900
    visitors   (date) int64 40B 1500 1200 1000 800 600

# Retrieve the 3 most busiest days in terms of pageviews

>>> busiest_days.head(3)
<xarray.Dataset> Size: 72B
Dimensions:    (date: 3)
Coordinates:
  * date       (date) datetime64[ns] 24B 2023-01-05 2023-01-04 2023-01-02
Data variables:
    pageviews  (date) int64 24B 2000 1800 1500
    visitors   (date) int64 24B 1500 1200 1000

# Using a dictionary to specify the number of elements for specific dimensions

>>> busiest_days.head({"date": 3})
<xarray.Dataset> Size: 72B
Dimensions:    (date: 3)
Coordinates:
  * date       (date) datetime64[ns] 24B 2023-01-05 2023-01-04 2023-01-02
Data variables:
    pageviews  (date) int64 24B 2000 1800 1500
    visitors   (date) int64 24B 1500 1200 1000

See also

Dataset.tail, Dataset.thin, DataArray.head

identical()

Like equals, but also checks all dataset attributes and the attributes on all variables and coordinates.

Example

>>> a = xr.Dataset(
...     {"Width": ("X", [1, 2, 3])},
...     coords={"X": [1, 2, 3]},
...     attrs={"units": "m"},
... )
>>> b = xr.Dataset(
...     {"Width": ("X", [1, 2, 3])},
...     coords={"X": [1, 2, 3]},
...     attrs={"units": "m"},
... )
>>> c = xr.Dataset(
...     {"Width": ("X", [1, 2, 3])},
...     coords={"X": [1, 2, 3]},
...     attrs={"units": "ft"},
... )
>>> a
<xarray.Dataset> Size: 48B
Dimensions:  (X: 3)
Coordinates:
  * X        (X) int64 24B 1 2 3
Data variables:
    Width    (X) int64 24B 1 2 3
:ivar units: m

>>> b
<xarray.Dataset> Size: 48B
Dimensions:  (X: 3)
Coordinates:
  * X        (X) int64 24B 1 2 3
Data variables:
    Width    (X) int64 24B 1 2 3
:ivar units: m

>>> c
<xarray.Dataset> Size: 48B
Dimensions:  (X: 3)
Coordinates:
  * X        (X) int64 24B 1 2 3
Data variables:
    Width    (X) int64 24B 1 2 3
:ivar units: ft

>>> a.equals(b)
True

>>> a.identical(b)
True

>>> a.equals(c)
True

>>> a.identical(c)
False

See also

Dataset.broadcast_equals, Dataset.equals

idxmax()

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

Returns a new Dataset 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 (str, optional) – Dimension over which to apply idxmax. This is optional for 1D variables, but required for variables with 2 or more dimensions.

• skipna (bool or None, optional) – 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 Dataset object with idxmax applied to its data and the indicated dimension removed.

Return type:

Dataset

See also

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

Examples

>>> array1 = xr.DataArray(
...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
... )
>>> array2 = 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": ["a", "b", "c", "d", "e"]},
... )
>>> ds = xr.Dataset({"int": array1, "float": array2})
>>> ds.max(dim="x")
<xarray.Dataset> Size: 56B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      int64 8B 2
    float    (y) float64 24B 2.0 2.0 1.0
>>> ds.argmax(dim="x")
<xarray.Dataset> Size: 56B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      int64 8B 1
    float    (y) int64 24B 0 2 2
>>> ds.idxmax(dim="x")
<xarray.Dataset> Size: 52B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      <U1 4B 'b'
    float    (y) object 24B 'a' 'c' 'c'

idxmin()

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

Returns a new Dataset 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 (Hashable, optional) – Dimension over which to apply idxmin. This is optional for 1D variables, but required for variables with 2 or more dimensions.

• skipna (bool or None, optional) – 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 Dataset object with idxmin applied to its data and the indicated dimension removed.

Return type:

Dataset

See also

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

Examples

>>> array1 = xr.DataArray(
...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
... )
>>> array2 = 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": ["a", "b", "c", "d", "e"]},
... )
>>> ds = xr.Dataset({"int": array1, "float": array2})
>>> ds.min(dim="x")
<xarray.Dataset> Size: 56B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      int64 8B -2
    float    (y) float64 24B -2.0 -4.0 1.0
>>> ds.argmin(dim="x")
<xarray.Dataset> Size: 56B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      int64 8B 4
    float    (y) int64 24B 4 0 2
>>> ds.idxmin(dim="x")
<xarray.Dataset> Size: 52B
Dimensions:  (y: 3)
Coordinates:
  * y        (y) int64 24B -1 0 1
Data variables:
    int      <U1 4B 'e'
    float    (y) object 24B 'e' 'a' 'c'

property imag

The imaginary part of each data variable.

See also

numpy.ndarray.imag

property 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

Dataset.xindexes

info()

Concise summary of a Dataset variables and attributes.

Parameters:

buf (file-like, default: sys.stdout) – writable buffer

See also

pandas.DataFrame.assign

ncdump

netCDF’s ncdump

integrate()

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 ({‘W’, ‘D’, ‘h’, ‘m’, ‘s’, ‘ms’, ‘us’, ‘ns’, ‘ps’, ‘fs’, ‘as’, None}, optional) – Specify the unit if datetime coordinate is used.

Returns:

integrated

Return type:

Dataset

See also

DataArray.integrate

numpy.trapz

corresponding numpy function

Examples

>>> ds = xr.Dataset(
...     data_vars={"a": ("x", [5, 5, 6, 6]), "b": ("x", [1, 2, 1, 0])},
...     coords={"x": [0, 1, 2, 3], "y": ("x", [1, 7, 3, 5])},
... )
>>> ds
<xarray.Dataset> Size: 128B
Dimensions:  (x: 4)
Coordinates:
  * x        (x) int64 32B 0 1 2 3
    y        (x) int64 32B 1 7 3 5
Data variables:
    a        (x) int64 32B 5 5 6 6
    b        (x) int64 32B 1 2 1 0
>>> ds.integrate("x")
<xarray.Dataset> Size: 16B
Dimensions:  ()
Data variables:
    a        float64 8B 16.5
    b        float64 8B 3.5
>>> ds.integrate("y")
<xarray.Dataset> Size: 16B
Dimensions:  ()
Data variables:
    a        float64 8B 20.0
    b        float64 8B 4.0

interp_calendar()

Interpolates the Dataset 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 (Hashable, default: “time”) – The time coordinate name.

Returns:

The source interpolated on the decimal years of target,

Return type:

DataArray

interp_like()

Interpolate this object onto the coordinates of another object.

Performs univariate or multivariate interpolation of a Dataset onto new coordinates, utilizing either NumPy or SciPy interpolation routines.

Out-of-range values are filled with NaN, unless specified otherwise via kwargs to the numpy/scipy interpolant.

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”, “quintic”, “polynomial”, “pchip”, “barycentric”, “krogh”, “akima”, “makima” }) – Interpolation method to use (see descriptions above).

• 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 arguments passed to the interpolator. Valid options and their behavior depend which interpolant is use

• method_non_numeric ({“nearest”, “pad”, “ffill”, “backfill”, “bfill”}, optional) – Method for non-numeric types. Passed on to Dataset.reindex(). "nearest" is used by default.

Returns:

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

Return type:

Dataset

Notes

• scipy is required.

• If the dataset has object-type coordinates, reindex is used for these

  coordinates instead of the interpolation.

• When interpolating along multiple dimensions with methods linear and nearest,

  the process attempts to decompose the interpolation into independent interpolations along one dimension at a time.

• The specific interpolation method and dimensionality determine which

  interpolant is used:

  1. Interpolation along one dimension of 1D data (`method=’linear’`)

     • Uses numpy.interp(), unless fill_value=’extrapolate’ is provided via kwargs.

  2. Interpolation along one dimension of N-dimensional data (N ≥ 1)

     • Methods {“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”, “quintic”, “polynomial”}

       use scipy.interpolate.interp1d(), unless conditions permit the use of numpy.interp() (as in the case of method=’linear’ for 1D data).

     • If method=’polynomial’, the order keyword argument must also be provided.

  3. Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)

     • Depending on the method, the following interpolants from scipy.interpolate are used:

       • “pchip”: scipy.interpolate.PchipInterpolator

       • “barycentric”: scipy.interpolate.BarycentricInterpolator

       • “krogh”: scipy.interpolate.KroghInterpolator

       • “akima” or “makima”: scipy.interpolate.Akima1dInterpolator

         (makima is handled by passing the makima flag).

  4. Interpolation along multiple dimensions of multi-dimensional data

     • Uses scipy.interpolate.interpn() for methods {“linear”, “nearest”, “slinear”,

       “cubic”, “quintic”, “pchip”}.

See also

Dataset.interp(), Dataset.reindex_like(), scipy.interpolate

interpolate_na()

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”, “krogh”, “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’, ‘krogh’, ‘pchip’, ‘spline’, ‘akima’: use their respective scipy.interpolate classes.

• use_coordinate (bool or Hashable, 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, 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 or None, 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

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

Returns:

interpolated – Filled in Dataset.

Return type:

Dataset

Warning

When passing fill_value as a keyword argument with method=”linear”, it does not use numpy.interp but it uses scipy.interpolate.interp1d, which provides the fill_value parameter.

See also

numpy.interp, scipy.interpolate

Examples

>>> ds = xr.Dataset(
...     {
...         "A": ("x", [np.nan, 2, 3, np.nan, 0]),
...         "B": ("x", [3, 4, np.nan, 1, 7]),
...         "C": ("x", [np.nan, np.nan, np.nan, 5, 0]),
...         "D": ("x", [np.nan, 3, np.nan, -1, 4]),
...     },
...     coords={"x": [0, 1, 2, 3, 4]},
... )
>>> ds
<xarray.Dataset> Size: 200B
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 40B 0 1 2 3 4
Data variables:
    A        (x) float64 40B nan 2.0 3.0 nan 0.0
    B        (x) float64 40B 3.0 4.0 nan 1.0 7.0
    C        (x) float64 40B nan nan nan 5.0 0.0
    D        (x) float64 40B nan 3.0 nan -1.0 4.0

>>> ds.interpolate_na(dim="x", method="linear")
<xarray.Dataset> Size: 200B
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 40B 0 1 2 3 4
Data variables:
    A        (x) float64 40B nan 2.0 3.0 1.5 0.0
    B        (x) float64 40B 3.0 4.0 2.5 1.0 7.0
    C        (x) float64 40B nan nan nan 5.0 0.0
    D        (x) float64 40B nan 3.0 1.0 -1.0 4.0

>>> ds.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
<xarray.Dataset> Size: 200B
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 40B 0 1 2 3 4
Data variables:
    A        (x) float64 40B 1.0 2.0 3.0 1.5 0.0
    B        (x) float64 40B 3.0 4.0 2.5 1.0 7.0
    C        (x) float64 40B 20.0 15.0 10.0 5.0 0.0
    D        (x) float64 40B 5.0 3.0 1.0 -1.0 4.0

isin()

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)> Size: 3B
array([ True, False,  True])
Dimensions without coordinates: x

See also

numpy.isin

isnull()

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)> Size: 24B
array([ 1., nan,  3.])
Dimensions without coordinates: x
>>> array.isnull()
<xarray.DataArray (x: 3)> Size: 3B
array([False,  True, False])
Dimensions without coordinates: x

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

load()

Trigger loading data into memory and return this dataset.

Data will be computed and/or loaded from disk or a remote source.

Unlike .compute, the original dataset is modified and returned.

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.

Returns:

object – Same object but with lazy data variables and coordinates as in-memory arrays.

Return type:

Dataset

See also

dask.compute, Dataset.compute, Dataset.load_async, DataArray.load, Variable.load

async load_async()

Trigger and await asynchronous loading of data into memory and return this dataset.

Data will be computed and/or loaded from disk or a remote source.

Unlike .compute, the original dataset is modified and returned.

Only works when opening data lazily from IO storage backends which support lazy asynchronous loading. Otherwise will raise a NotImplementedError.

Note users are expected to limit concurrency themselves - xarray does not internally limit concurrency in any way.

Parameters:

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

Returns:

object – Same object but with lazy data variables and coordinates as in-memory arrays.

Return type:

Dataset

See also

dask.compute, Dataset.compute, Dataset.load, DataArray.load_async, Variable.load_async

classmethod load_store()

Create a new dataset from the contents of a backends.*DataStore object

property loc

Attribute for location based indexing. Only supports __getitem__, and only when the key is a dict of the form {dim: labels}.

map()

Apply a function to each data variable in this dataset

Parameters:

• func (callable) – Function which can be called in the form func(x, *args, **kwargs) to transform each DataArray x in this dataset into another DataArray.

• keep_attrs (bool or None, optional) – If True, both the dataset’s and variables’ attributes (attrs) will be copied from the original objects to the new ones. If False, the new dataset and variables will be returned without copying the attributes.

• args (iterable, optional) – Positional arguments passed on to func.

• **kwargs (Any) – Keyword arguments passed on to func.

Returns:

applied – Resulting dataset from applying func to each data variable.

Return type:

Dataset

Examples

>>> da = xr.DataArray(np.random.randn(2, 3))
>>> ds = xr.Dataset({"foo": da, "bar": ("x", [-1, 2])})
>>> ds
<xarray.Dataset> Size: 64B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Dimensions without coordinates: dim_0, dim_1, x
Data variables:
    foo      (dim_0, dim_1) float64 48B 1.764 0.4002 0.9787 2.241 1.868 -0.9773
    bar      (x) int64 16B -1 2
>>> ds.map(np.fabs)
<xarray.Dataset> Size: 64B
Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
Dimensions without coordinates: dim_0, dim_1, x
Data variables:
    foo      (dim_0, dim_1) float64 48B 1.764 0.4002 0.9787 2.241 1.868 0.9773
    bar      (x) float64 16B 1.0 2.0

max()

Reduce this Dataset’s data by applying max along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply max. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating max on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with max applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.max, dask.array.max, DataArray.max

Aggregation

User guide on reduction or aggregation operations.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.max()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 3.0

Use skipna to control whether NaNs are ignored.

>>> ds.max(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

mean()

Reduce this Dataset’s data by applying mean along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply mean. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with mean applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.mean, dask.array.mean, DataArray.mean

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.mean()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 1.6

Use skipna to control whether NaNs are ignored.

>>> ds.mean(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

median()

Reduce this Dataset’s data by applying median along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply median. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating median on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with median applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.median, dask.array.median, DataArray.median

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.median()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 2.0

Use skipna to control whether NaNs are ignored.

>>> ds.median(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

merge()

Merge the arrays of two datasets into a single dataset.

This method generally does not allow for overriding data, with the exception of attributes, which are ignored on the second dataset. Variables with the same name are checked for conflicts via the equals or identical methods.

Parameters:

• other (Dataset or mapping) – Dataset or variables to merge with this dataset.

• overwrite_vars (hashable or iterable of hashable, optional) – If provided, update variables of these name(s) without checking for conflicts in this dataset.

• compat ({“identical”, “equals”, “broadcast_equals”, “no_conflicts”, “override”, “minimal”}, default: “no_conflicts”) – String indicating how to compare variables of the same name for potential conflicts:

  • ‘identical’: all values, dimensions and attributes must be the same.

  • ‘equals’: all values and dimensions must be the same.

  • ‘broadcast_equals’: all values must be equal when variables are broadcast against each other to ensure common dimensions.

  • ‘no_conflicts’: only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values.

  • ‘override’: skip comparing and pick variable from first dataset

  • ‘minimal’: drop conflicting coordinates

• join ({“outer”, “inner”, “left”, “right”, “exact”, “override”}, default: “outer”) – Method for joining self and other along shared dimensions:

  • ‘outer’: use the union of the indexes

  • ‘inner’: use the intersection of the indexes

  • ‘left’: use indexes from self

  • ‘right’: use indexes from other

  • ‘exact’: error instead of aligning non-equal indexes

  • ‘override’: use indexes from self that are the same size as those of other in that dimension

• 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.

• combine_attrs ({“drop”, “identical”, “no_conflicts”, “drop_conflicts”, “override”} or callable, default: “override”) – A callable or a string indicating how to combine attrs of the objects being merged:

  • “drop”: empty attrs on returned Dataset.

  • “identical”: all attrs must be the same on every object.

  • “no_conflicts”: attrs from all objects are combined, any that have the same name must also have the same value.

  • “drop_conflicts”: attrs from all objects are combined, any that have the same name but different values are dropped.

  • “override”: skip comparing and copy attrs from the first dataset to the result.

  If a callable, it must expect a sequence of attrs dicts and a context object as its only parameters.

Returns:

merged – Merged dataset.

Return type:

Dataset

Raises:

MergeError – If any variables conflict (see compat).

See also

Dataset.update

min()

Reduce this Dataset’s data by applying min along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply min. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating min on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with min applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.min, dask.array.min, DataArray.min

Aggregation

User guide on reduction or aggregation operations.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.min()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 0.0

Use skipna to control whether NaNs are ignored.

>>> ds.min(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

property nbytes

Total bytes consumed by the data arrays of all variables in this dataset.

If the backend array for any variable does not include nbytes, estimates the total bytes for that array based on the size and dtype.

notnull()

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)> Size: 24B
array([ 1., nan,  3.])
Dimensions without coordinates: x
>>> array.notnull()
<xarray.DataArray (x: 3)> Size: 3B
array([ True, False,  True])
Dimensions without coordinates: x

pad()

Pad this dataset 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, mapping of dim name to scalar or tuple, or mapping of var name to scalar, tuple or to mapping of dim name to scalar or tuple, default: None) – Used in ‘constant’. The values to set the padded values for each data variable / axis. {var_1: {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}, ... var_M: (before, after)} unique pad constants per data variable. {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 None, pads with np.nan.

• end_values (scalar, tuple or mapping of hashable to tuple, default: None) – 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 None.

• 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 – Dataset with the padded coordinates and data.

Return type:

Dataset

See also

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

Notes

By default when mode="constant" and constant_values=None, integer types will be promoted to float and padded with np.nan. To avoid type promotion specify constant_values=np.nan

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

Examples

>>> ds = xr.Dataset({"foo": ("x", range(5))})
>>> ds.pad(x=(1, 2))
<xarray.Dataset> Size: 64B
Dimensions:  (x: 8)
Dimensions without coordinates: x
Data variables:
    foo      (x) float64 64B nan 0.0 1.0 2.0 3.0 4.0 nan nan

persist()

Trigger computation, keeping data as chunked arrays.

This operation can be used to trigger computation on underlying dask arrays, similar to .compute() or .load(). However this operation keeps the data as dask arrays. This is particularly useful when using the dask.distributed scheduler and you want to load a large amount of data into distributed memory. Like compute (but unlike load), the original dataset is left unaltered.

Parameters:

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

Returns:

object – New object with all dask-backed coordinates and data variables as persisted dask arrays.

Return type:

Dataset

See also

dask.persist

pipe()

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> Size: 96B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 32B 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> Size: 96B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 12.98 16.3 14.06 12.9
    precipitation  (lat, lon) float64 32B 2.424 2.646 2.438 2.892

>>> x.pipe(adder, arg=2)
<xarray.Dataset> Size: 96B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 12.98 16.3 14.06 12.9
    precipitation  (lat, lon) float64 32B 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> Size: 96B
Dimensions:        (lat: 2, lon: 2)
Coordinates:
  * lat            (lat) int64 16B 10 20
  * lon            (lon) int64 16B 150 160
Data variables:
    temperature_c  (lat, lon) float64 32B 10.98 14.3 12.06 10.9
    precipitation  (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918

See also

pandas.DataFrame.pipe

plot

alias of DatasetPlotAccessor

polyfit()

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 or Any, 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 (for each “var” in the input dataset):

[var]_polyfit_coefficients

The coefficients of the best fit for each variable in this dataset.

[var]_polyfit_residuals

The residuals of the least-square computation for each variable (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) The rank is computed ignoring the NaN values that might be skipped.

[dim]_singular_values

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

[var]_polyfit_covariance

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

Return type:

Dataset

Warns:

RankWarning – The rank of the coefficient matrix in the least-squares fit is deficient. The warning is not raised with in-memory (not dask) data and full=True.

See also

numpy.polyfit, numpy.polyval, xarray.polyval

prod()

Reduce this Dataset’s data by applying prod along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply prod. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• min_count (int or None, optional) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating prod on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

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

Return type:

Dataset

See also

numpy.prod, dask.array.prod, DataArray.prod

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.prod()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 0.0

Use skipna to control whether NaNs are ignored.

>>> ds.prod(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

Specify min_count for finer control over when NaNs are ignored.

>>> ds.prod(skipna=True, min_count=2)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 0.0

quantile()

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

Returns the qth quantiles(s) of the array elements for each variable in the Dataset.

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.

• keep_attrs (bool, 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.

• numeric_only (bool, optional) – If True, only apply func to variables with a numeric dtype.

• skipna (bool, 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 for each variable in data_vars. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return Dataset. The other dimensions are the dimensions that remain after the reduction of the array.

Return type:

Dataset

See also

numpy.nanquantile, numpy.quantile, pandas.Series.quantile, DataArray.quantile

Examples

>>> ds = xr.Dataset(
...     {"a": (("x", "y"), [[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]},
... )
>>> ds.quantile(0)  # or ds.quantile(0, dim=...)
<xarray.Dataset> Size: 16B
Dimensions:   ()
Coordinates:
    quantile  float64 8B 0.0
Data variables:
    a         float64 8B 0.7
>>> ds.quantile(0, dim="x")
<xarray.Dataset> Size: 72B
Dimensions:   (y: 4)
Coordinates:
  * y         (y) float64 32B 1.0 1.5 2.0 2.5
    quantile  float64 8B 0.0
Data variables:
    a         (y) float64 32B 0.7 4.2 2.6 1.5
>>> ds.quantile([0, 0.5, 1])
<xarray.Dataset> Size: 48B
Dimensions:   (quantile: 3)
Coordinates:
  * quantile  (quantile) float64 24B 0.0 0.5 1.0
Data variables:
    a         (quantile) float64 24B 0.7 3.4 9.4
>>> ds.quantile([0, 0.5, 1], dim="x")
<xarray.Dataset> Size: 152B
Dimensions:   (quantile: 3, y: 4)
Coordinates:
  * y         (y) float64 32B 1.0 1.5 2.0 2.5
  * quantile  (quantile) float64 24B 0.0 0.5 1.0
Data variables:
    a         (quantile, y) float64 96B 0.7 4.2 2.6 1.5 3.6 ... 6.5 7.3 9.4 1.9

References

[1] (1,2)

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

query()

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

Parameters:

• queries (dict-like, 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 Dataset:

  • “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 Dataset with the same contents as this dataset, except each array and dimension is indexed by the results of the appropriate queries.

Return type:

Dataset

See also

Dataset.isel, pandas.eval

Examples

>>> a = np.arange(0, 5, 1)
>>> b = np.linspace(0, 1, 5)
>>> ds = xr.Dataset({"a": ("x", a), "b": ("x", b)})
>>> ds
<xarray.Dataset> Size: 80B
Dimensions:  (x: 5)
Dimensions without coordinates: x
Data variables:
    a        (x) int64 40B 0 1 2 3 4
    b        (x) float64 40B 0.0 0.25 0.5 0.75 1.0
>>> ds.query(x="a > 2")
<xarray.Dataset> Size: 32B
Dimensions:  (x: 2)
Dimensions without coordinates: x
Data variables:
    a        (x) int64 16B 3 4
    b        (x) float64 16B 0.75 1.0

rank()

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 is True, 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, the new object will be returned without attributes.

Returns:

ranked – Variables that do not depend on dim are dropped.

Return type:

Dataset

property real

The real part of each data variable.

See also

numpy.ndarray.real

reduce()

Reduce this dataset 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.

• 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.

• 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.

• numeric_only (bool, default: False) – If True, only apply func to variables with a numeric dtype.

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

Returns:

reduced – Dataset with this object’s DataArrays replaced with new DataArrays of summarized data and the indicated dimension(s) removed.

Return type:

Dataset

Examples

>>> dataset = xr.Dataset(
...     {
...         "math_scores": (
...             ["student", "test"],
...             [[90, 85, 92], [78, 80, 85], [95, 92, 98]],
...         ),
...         "english_scores": (
...             ["student", "test"],
...             [[88, 90, 92], [75, 82, 79], [93, 96, 91]],
...         ),
...     },
...     coords={
...         "student": ["Alice", "Bob", "Charlie"],
...         "test": ["Test 1", "Test 2", "Test 3"],
...     },
... )

# Calculate the 75th percentile of math scores for each student using np.percentile

>>> percentile_scores = dataset.reduce(np.percentile, q=75, dim="test")
>>> percentile_scores
<xarray.Dataset> Size: 132B
Dimensions:         (student: 3)
Coordinates:
  * student         (student) <U7 84B 'Alice' 'Bob' 'Charlie'
Data variables:
    math_scores     (student) float64 24B 91.0 82.5 96.5
    english_scores  (student) float64 24B 91.0 80.5 94.5

reindex()

Conform this object onto a new set of indexes, 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 mismatched coordinate values will be filled in with NaN, and any mismatched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided.

• method ({None, “nearest”, “pad”, “ffill”, “backfill”, “bfill”, None}, optional) – Method to use for filling index values in indexers not found in this dataset:

  • 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] | str | 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.

• 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.

• sparse (bool, default: False) – use sparse-array.

• **indexers_kwargs ({dim: indexer, …}, optional) – Keyword arguments in the same form as indexers. One of indexers or indexers_kwargs must be provided.

Returns:

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

Return type:

Dataset

See also

Dataset.reindex_like, align, pandas.Index.get_indexer

Examples

Create a dataset with some fictional data.

>>> x = xr.Dataset(
...     {
...         "temperature": ("station", 20 * np.random.rand(4)),
...         "pressure": ("station", 500 * np.random.rand(4)),
...     },
...     coords={"station": ["boston", "nyc", "seattle", "denver"]},
... )
>>> x
<xarray.Dataset> Size: 176B
Dimensions:      (station: 4)
Coordinates:
  * station      (station) <U7 112B 'boston' 'nyc' 'seattle' 'denver'
Data variables:
    temperature  (station) float64 32B 10.98 14.3 12.06 10.9
    pressure     (station) float64 32B 211.8 322.9 218.8 445.9
>>> x.indexes
Indexes:
    station  Index(['boston', 'nyc', 'seattle', 'denver'], dtype='object', name='station')

Create a new index and reindex the dataset. By default values in the new index that do not have corresponding records in the dataset are assigned NaN.

>>> new_index = ["boston", "austin", "seattle", "lincoln"]
>>> x.reindex({"station": new_index})
<xarray.Dataset> Size: 176B
Dimensions:      (station: 4)
Coordinates:
  * station      (station) <U7 112B 'boston' 'austin' 'seattle' 'lincoln'
Data variables:
    temperature  (station) float64 32B 10.98 nan 12.06 nan
    pressure     (station) float64 32B 211.8 nan 218.8 nan

We can fill in the missing values by passing a value to the keyword fill_value.

>>> x.reindex({"station": new_index}, fill_value=0)
<xarray.Dataset> Size: 176B
Dimensions:      (station: 4)
Coordinates:
  * station      (station) <U7 112B 'boston' 'austin' 'seattle' 'lincoln'
Data variables:
    temperature  (station) float64 32B 10.98 0.0 12.06 0.0
    pressure     (station) float64 32B 211.8 0.0 218.8 0.0

We can also use different fill values for each variable.

>>> x.reindex(
...     {"station": new_index}, fill_value={"temperature": 0, "pressure": 100}
... )
<xarray.Dataset> Size: 176B
Dimensions:      (station: 4)
Coordinates:
  * station      (station) <U7 112B 'boston' 'austin' 'seattle' 'lincoln'
Data variables:
    temperature  (station) float64 32B 10.98 0.0 12.06 0.0
    pressure     (station) float64 32B 211.8 100.0 218.8 100.0

Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> x.reindex({"station": new_index}, method="nearest")
Traceback (most recent call last):
...
    raise ValueError('index must be monotonic increasing or decreasing')
ValueError: index must be monotonic increasing or decreasing

To further illustrate the filling functionality in reindex, we will create a dataset with a monotonically increasing index (for example, a sequence of dates).

>>> x2 = xr.Dataset(
...     {
...         "temperature": (
...             "time",
...             [15.57, 12.77, np.nan, 0.3081, 16.59, 15.12],
...         ),
...         "pressure": ("time", 500 * np.random.rand(6)),
...     },
...     coords={"time": pd.date_range("01/01/2019", periods=6, freq="D")},
... )
>>> x2
<xarray.Dataset> Size: 144B
Dimensions:      (time: 6)
Coordinates:
  * time         (time) datetime64[ns] 48B 2019-01-01 2019-01-02 ... 2019-01-06
Data variables:
    temperature  (time) float64 48B 15.57 12.77 nan 0.3081 16.59 15.12
    pressure     (time) float64 48B 481.8 191.7 395.9 264.4 284.0 462.8

Suppose we decide to expand the dataset to cover a wider date range.

>>> time_index2 = pd.date_range("12/29/2018", periods=10, freq="D")
>>> x2.reindex({"time": time_index2})
<xarray.Dataset> Size: 240B
Dimensions:      (time: 10)
Coordinates:
  * time         (time) datetime64[ns] 80B 2018-12-29 2018-12-30 ... 2019-01-07
Data variables:
    temperature  (time) float64 80B nan nan nan 15.57 ... 0.3081 16.59 15.12 nan
    pressure     (time) float64 80B nan nan nan 481.8 ... 264.4 284.0 462.8 nan

The index entries that did not have a value in the original data frame (for example, 2018-12-29) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> x3 = x2.reindex({"time": time_index2}, method="bfill")
>>> x3
<xarray.Dataset> Size: 240B
Dimensions:      (time: 10)
Coordinates:
  * time         (time) datetime64[ns] 80B 2018-12-29 2018-12-30 ... 2019-01-07
Data variables:
    temperature  (time) float64 80B 15.57 15.57 15.57 15.57 ... 16.59 15.12 nan
    pressure     (time) float64 80B 481.8 481.8 481.8 481.8 ... 284.0 462.8 nan

Please note that the NaN value present in the original dataset (at index value 2019-01-03) will not be filled by any of the value propagation schemes.

>>> x2.where(x2.temperature.isnull(), drop=True)
<xarray.Dataset> Size: 24B
Dimensions:      (time: 1)
Coordinates:
  * time         (time) datetime64[ns] 8B 2019-01-03
Data variables:
    temperature  (time) float64 8B nan
    pressure     (time) float64 8B 395.9
>>> x3.where(x3.temperature.isnull(), drop=True)
<xarray.Dataset> Size: 48B
Dimensions:      (time: 2)
Coordinates:
  * time         (time) datetime64[ns] 16B 2019-01-03 2019-01-07
Data variables:
    temperature  (time) float64 16B nan nan
    pressure     (time) float64 16B 395.9 nan

This is because filling while reindexing does not look at dataset values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataset, use the fillna() method.

reindex_like()

Conform this object onto the indexes of another object, for indexes which the objects share. Missing values are filled 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 mismatched index values will be filled in with NaN, and any mismatched dimension names will simply be ignored.

• method ({None, “nearest”, “pad”, “ffill”, “backfill”, “bfill”, None}, optional) – Method to use for filling index values from other not found in this dataset:

  • 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] | str | 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.

• 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 to fill values.

Returns:

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

Return type:

Dataset

See also

Dataset.reindex, DataArray.reindex_like, align

rename()

Returns a new object with renamed variables, coordinates and dimensions.

Parameters:

• name_dict (dict-like, optional) – Dictionary whose keys are current variable, coordinate or dimension names and whose values are the desired names.

• **names (optional) – Keyword form of name_dict. One of name_dict or names must be provided.

Returns:

renamed – Dataset with renamed variables, coordinates and dimensions.

Return type:

Dataset

See also

Dataset.swap_dims, Dataset.rename_vars, Dataset.rename_dims, DataArray.rename

rename_dims()

Returns a new object with renamed dimensions only.

Parameters:

• dims_dict (dict-like, optional) – Dictionary whose keys are current dimension names and whose values are the desired names. The desired names must not be the name of an existing dimension or Variable in the Dataset.

• **dims (optional) – Keyword form of dims_dict. One of dims_dict or dims must be provided.

Returns:

renamed – Dataset with renamed dimensions.

Return type:

Dataset

See also

Dataset.swap_dims, Dataset.rename, Dataset.rename_vars, DataArray.rename

rename_vars()

Returns a new object with renamed variables including coordinates

Parameters:

• name_dict (dict-like, optional) – Dictionary whose keys are current variable or coordinate names and whose values are the desired names.

• **names (optional) – Keyword form of name_dict. One of name_dict or names must be provided.

Returns:

renamed – Dataset with renamed variables including coordinates

Return type:

Dataset

See also

Dataset.swap_dims, Dataset.rename, Dataset.rename_dims, DataArray.rename

reorder_levels()

Rearrange index levels using input order.

Parameters:

• dim_order (dict-like of Hashable to Sequence of int or Hashable, optional) – 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 (Sequence of int or Hashable, optional) – The keyword arguments form of dim_order. One of dim_order or dim_order_kwargs must be provided.

Returns:

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

Return type:

Dataset

resample()

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, datetime.timedelta, pd.Timedelta, pd.DateOffset, or Resampler, 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.

• 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.

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

• **indexer_kwargs (str, datetime.timedelta, pd.Timedelta, pd.DateOffset, or Resampler) – 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

See also

DataArray.resample, pandas.Series.resample, pandas.DataFrame.resample, Dataset.groupby, DataArray.groupby

References

[1]

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

reset_coords()

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.

Examples

>>> dataset = xr.Dataset(
...     {
...         "temperature": (
...             ["time", "lat", "lon"],
...             [[[25, 26], [27, 28]], [[29, 30], [31, 32]]],
...         ),
...         "precipitation": (
...             ["time", "lat", "lon"],
...             [[[0.5, 0.8], [0.2, 0.4]], [[0.3, 0.6], [0.7, 0.9]]],
...         ),
...     },
...     coords={
...         "time": pd.date_range(start="2023-01-01", periods=2),
...         "lat": [40, 41],
...         "lon": [-80, -79],
...         "altitude": 1000,
...     },
... )

# Dataset before resetting coordinates

>>> dataset
<xarray.Dataset> Size: 184B
Dimensions:        (time: 2, lat: 2, lon: 2)
Coordinates:
  * time           (time) datetime64[ns] 16B 2023-01-01 2023-01-02
  * lat            (lat) int64 16B 40 41
  * lon            (lon) int64 16B -80 -79
    altitude       int64 8B 1000
Data variables:
    temperature    (time, lat, lon) int64 64B 25 26 27 28 29 30 31 32
    precipitation  (time, lat, lon) float64 64B 0.5 0.8 0.2 0.4 0.3 0.6 0.7 0.9

# Reset the ‘altitude’ coordinate

>>> dataset_reset = dataset.reset_coords("altitude")

# Dataset after resetting coordinates

>>> dataset_reset
<xarray.Dataset> Size: 184B
Dimensions:        (time: 2, lat: 2, lon: 2)
Coordinates:
  * time           (time) datetime64[ns] 16B 2023-01-01 2023-01-02
  * lat            (lat) int64 16B 40 41
  * lon            (lon) int64 16B -80 -79
Data variables:
    temperature    (time, lat, lon) int64 64B 25 26 27 28 29 30 31 32
    precipitation  (time, lat, lon) float64 64B 0.5 0.8 0.2 0.4 0.3 0.6 0.7 0.9
    altitude       int64 8B 1000

Return type:

Dataset

See also

Dataset.set_coords

reset_encoding()

reset_index()

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 dataset, with this dataset’s data but replaced coordinates.

Return type:

Dataset

See also

Dataset.set_index, Dataset.set_xindex, Dataset.drop_indexes

roll()

Roll this dataset 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.

Also 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) – A dict with keys matching dimensions and values given by integers 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 – Dataset with the same attributes but rolled data and coordinates.

Return type:

Dataset

See also

shift

Examples

>>> ds = xr.Dataset({"foo": ("x", list("abcde"))}, coords={"x": np.arange(5)})
>>> ds.roll(x=2)
<xarray.Dataset> Size: 60B
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 40B 0 1 2 3 4
Data variables:
    foo      (x) <U1 20B 'd' 'e' 'a' 'b' 'c'

>>> ds.roll(x=2, roll_coords=True)
<xarray.Dataset> Size: 60B
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 40B 3 4 0 1 2
Data variables:
    foo      (x) <U1 20B 'd' 'e' 'a' 'b' 'c'

rolling()

Rolling window object for Datasets.

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. The default, False, sets the labels at the right edge of the window.

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

Return type:

computation.rolling.DatasetRolling

See also

Dataset.cumulative, DataArray.rolling, DataArray.rolling_exp

rolling_exp()

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()

set_close()

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_coords()

Given names of one or more variables, set them as coordinates

Parameters:

names (hashable or iterable of hashable) – Name(s) of variables in this dataset to convert into coordinates.

Examples

>>> dataset = xr.Dataset(
...     {
...         "pressure": ("time", [1.013, 1.2, 3.5]),
...         "time": pd.date_range("2023-01-01", periods=3),
...     }
... )
>>> dataset
<xarray.Dataset> Size: 48B
Dimensions:   (time: 3)
Coordinates:
  * time      (time) datetime64[ns] 24B 2023-01-01 2023-01-02 2023-01-03
Data variables:
    pressure  (time) float64 24B 1.013 1.2 3.5

>>> dataset.set_coords("pressure")
<xarray.Dataset> Size: 48B
Dimensions:   (time: 3)
Coordinates:
    pressure  (time) float64 24B 1.013 1.2 3.5
  * time      (time) datetime64[ns] 24B 2023-01-01 2023-01-02 2023-01-03
Data variables:
    *empty*

On calling set_coords , these data variables are converted to coordinates, as shown in the final dataset.

Return type:

Dataset

See also

Dataset.swap_dims, Dataset.assign_coords

set_index()

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

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) (default).

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

Returns:

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

Return type:

Dataset

Examples

>>> arr = xr.DataArray(
...     data=np.ones((2, 3)),
...     dims=["x", "y"],
...     coords={"x": range(2), "y": range(3), "a": ("x", [3, 4])},
... )
>>> ds = xr.Dataset({"v": arr})
>>> ds
<xarray.Dataset> Size: 104B
Dimensions:  (x: 2, y: 3)
Coordinates:
  * x        (x) int64 16B 0 1
  * y        (y) int64 24B 0 1 2
    a        (x) int64 16B 3 4
Data variables:
    v        (x, y) float64 48B 1.0 1.0 1.0 1.0 1.0 1.0
>>> ds.set_index(x="a")
<xarray.Dataset> Size: 88B
Dimensions:  (x: 2, y: 3)
Coordinates:
  * x        (x) int64 16B 3 4
  * y        (y) int64 24B 0 1 2
Data variables:
    v        (x, y) float64 48B 1.0 1.0 1.0 1.0 1.0 1.0

See also

Dataset.reset_index, Dataset.set_xindex, Dataset.swap_dims

set_xindex()

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, optional) – The type of index to create. By default, try setting a PandasIndex if len(coord_names) == 1, otherwise a PandasMultiIndex.

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

Returns:

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

Return type:

Dataset

shift()

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

Only data variables are 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) – 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 or dict-like, optional) – Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values.

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

Returns:

shifted – Dataset with the same coordinates and attributes but shifted data variables.

Return type:

Dataset

See also

roll

Examples

>>> ds = xr.Dataset({"foo": ("x", list("abcde"))})
>>> ds.shift(x=2)
<xarray.Dataset> Size: 40B
Dimensions:  (x: 5)
Dimensions without coordinates: x
Data variables:
    foo      (x) object 40B nan nan 'a' 'b' 'c'

property sizes

Mapping from dimension names to lengths.

Cannot be modified directly, but is updated when adding new variables.

This is an alias for Dataset.dims provided for the benefit of consistency with DataArray.sizes.

See also

DataArray.sizes

sortby()

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

Sorts the dataset, 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, sequence of Hashable or DataArray, or Callable) – 1D DataArray objects or name(s) of 1D variable(s) in coords whose values are used to sort this array. If a callable, the callable is passed this object, and the result is used as the value for cond.

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

Returns:

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

Return type:

Dataset

See also

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

Examples

>>> ds = xr.Dataset(
...     {
...         "A": (("x", "y"), [[1, 2], [3, 4]]),
...         "B": (("x", "y"), [[5, 6], [7, 8]]),
...     },
...     coords={"x": ["b", "a"], "y": [1, 0]},
... )
>>> ds.sortby("x")
<xarray.Dataset> Size: 88B
Dimensions:  (x: 2, y: 2)
Coordinates:
  * x        (x) <U1 8B 'a' 'b'
  * y        (y) int64 16B 1 0
Data variables:
    A        (x, y) int64 32B 3 4 1 2
    B        (x, y) int64 32B 7 8 5 6
>>> ds.sortby(lambda x: -x["y"])
<xarray.Dataset> Size: 88B
Dimensions:  (x: 2, y: 2)
Coordinates:
  * x        (x) <U1 8B 'b' 'a'
  * y        (y) int64 16B 1 0
Data variables:
    A        (x, y) int64 32B 1 2 3 4
    B        (x, y) int64 32B 5 6 7 8

squeeze()

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()

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

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

Parameters:

• dim (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) –

  • True: create a multi-index for each of the stacked dimensions.

  • False: don’t create any index.

  • None. create a multi-index only if exactly one single (1-d) coordinate index is found for every dimension to stack.

• index_cls (Index-class, default: PandasMultiIndex) – 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.

• **dim_kwargs – The keyword arguments form of dim. One of dim or dim_kwargs must be provided.

Returns:

stacked – Dataset with stacked data.

Return type:

Dataset

See also

Dataset.unstack

std()

Reduce this Dataset’s data by applying std along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply std. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• ddof (int, default: 0) – “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating std on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with std applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.std, dask.array.std, DataArray.std

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.std()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 1.02

Use skipna to control whether NaNs are ignored.

>>> ds.std(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

Specify ddof=1 for an unbiased estimate.

>>> ds.std(skipna=True, ddof=1)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 1.14

sum()

Reduce this Dataset’s data by applying sum along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply sum. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• min_count (int or None, optional) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating sum on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with sum applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.sum, dask.array.sum, DataArray.sum

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.sum()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 8.0

Use skipna to control whether NaNs are ignored.

>>> ds.sum(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

Specify min_count for finer control over when NaNs are ignored.

>>> ds.sum(skipna=True, min_count=2)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 8.0

swap_dims()

Returns a new object 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 – Dataset with swapped dimensions.

Return type:

Dataset

Examples

>>> ds = xr.Dataset(
...     data_vars={"a": ("x", [5, 7]), "b": ("x", [0.1, 2.4])},
...     coords={"x": ["a", "b"], "y": ("x", [0, 1])},
... )
>>> ds
<xarray.Dataset> Size: 56B
Dimensions:  (x: 2)
Coordinates:
  * x        (x) <U1 8B 'a' 'b'
    y        (x) int64 16B 0 1
Data variables:
    a        (x) int64 16B 5 7
    b        (x) float64 16B 0.1 2.4

>>> ds.swap_dims({"x": "y"})
<xarray.Dataset> Size: 56B
Dimensions:  (y: 2)
Coordinates:
    x        (y) <U1 8B 'a' 'b'
  * y        (y) int64 16B 0 1
Data variables:
    a        (y) int64 16B 5 7
    b        (y) float64 16B 0.1 2.4

>>> ds.swap_dims({"x": "z"})
<xarray.Dataset> Size: 56B
Dimensions:  (z: 2)
Coordinates:
    x        (z) <U1 8B 'a' 'b'
    y        (z) int64 16B 0 1
Dimensions without coordinates: z
Data variables:
    a        (z) int64 16B 5 7
    b        (z) float64 16B 0.1 2.4

See also

Dataset.rename, DataArray.swap_dims

tail()

Returns a new dataset with the last n values of each array for the specified dimension(s).

Parameters:

• indexers (dict or int, default: 5) – A dict with keys matching dimensions and integer values n or a single integer n applied over all dimensions. One of indexers or indexers_kwargs must be provided.

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

Examples

>>> activity_names = ["Walking", "Running", "Cycling", "Swimming", "Yoga"]
>>> durations = [30, 45, 60, 45, 60]  # in minutes
>>> energies = [150, 300, 250, 400, 100]  # in calories
>>> dataset = xr.Dataset(
...     {
...         "duration": (["activity"], durations),
...         "energy_expenditure": (["activity"], energies),
...     },
...     coords={"activity": activity_names},
... )
>>> sorted_dataset = dataset.sortby("energy_expenditure", ascending=False)
>>> sorted_dataset
<xarray.Dataset> Size: 240B
Dimensions:             (activity: 5)
Coordinates:
  * activity            (activity) <U8 160B 'Swimming' 'Running' ... 'Yoga'
Data variables:
    duration            (activity) int64 40B 45 45 60 30 60
    energy_expenditure  (activity) int64 40B 400 300 250 150 100

# Activities with the least energy expenditures using tail()

>>> sorted_dataset.tail(3)
<xarray.Dataset> Size: 144B
Dimensions:             (activity: 3)
Coordinates:
  * activity            (activity) <U8 96B 'Cycling' 'Walking' 'Yoga'
Data variables:
    duration            (activity) int64 24B 60 30 60
    energy_expenditure  (activity) int64 24B 250 150 100

>>> sorted_dataset.tail({"activity": 3})
<xarray.Dataset> Size: 144B
Dimensions:             (activity: 3)
Coordinates:
  * activity            (activity) <U8 96B 'Cycling' 'Walking' 'Yoga'
Data variables:
    duration            (activity) int64 24B 60 30 60
    energy_expenditure  (activity) int64 24B 250 150 100

See also

Dataset.head, Dataset.thin, DataArray.tail

thin()

Returns a new dataset with each array indexed along every n-th value for the specified dimension(s)

Parameters:

• indexers (dict or int) – A dict with keys matching dimensions and integer values n or a single integer n applied over all dimensions. One of indexers or indexers_kwargs must be provided.

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

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_ds = xr.Dataset({"foo": x})
>>> x_ds
<xarray.Dataset> Size: 328B
Dimensions:  (x: 2, y: 13)
Coordinates:
  * x        (x) int64 16B 0 1
  * y        (y) int64 104B 0 1 2 3 4 5 6 7 8 9 10 11 12
Data variables:
    foo      (x, y) int64 208B 0 1 2 3 4 5 6 7 8 ... 17 18 19 20 21 22 23 24 25

>>> x_ds.thin(3)
<xarray.Dataset> Size: 88B
Dimensions:  (x: 1, y: 5)
Coordinates:
  * x        (x) int64 8B 0
  * y        (y) int64 40B 0 3 6 9 12
Data variables:
    foo      (x, y) int64 40B 0 3 6 9 12
>>> x.thin({"x": 2, "y": 5})
<xarray.DataArray (x: 1, y: 3)> Size: 24B
array([[ 0,  5, 10]])
Coordinates:
  * x        (x) int64 8B 0
  * y        (y) int64 24B 0 5 10

See also

Dataset.head, Dataset.tail, DataArray.thin

to_array()

Deprecated version of to_dataarray

to_dask_dataframe()

Convert this dataset into a dask.dataframe.DataFrame.

The dimensions, coordinates and data variables in this dataset form the columns of the DataFrame.

Parameters:

• dim_order (list, optional) – Hierarchical dimension order for the resulting dataframe. All arrays are 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.

  If provided, must include all dimensions of this dataset. By default, dimensions are sorted alphabetically.

• 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

to_dataarray()

Convert this dataset into an xarray.DataArray

The data variables of this dataset will be broadcast against each other and stacked along the first axis of the new array. All coordinates of this dataset will remain coordinates.

Parameters:

• dim (Hashable, default: “variable”) – Name of the new dimension.

• name (Hashable or None, optional) – Name of the new data array.

Returns:

array

Return type:

xarray.DataArray

to_dataframe()

Convert this dataset into a pandas.DataFrame.

Non-index variables in this dataset form the columns of the DataFrame. The DataFrame is indexed by the Cartesian product of this dataset’s indices.

Parameters:

dim_order (Sequence of Hashable or None, optional) – Hierarchical dimension order for the resulting dataframe. All arrays are 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 dataset. By default, dimensions are in the same order as in Dataset.sizes.

Returns:

result – Dataset as a pandas DataFrame.

Return type:

DataFrame

to_netcdf()

Write dataset contents to a netCDF file.

Parameters:

• path (str, path-like, file-like or None, optional) – Path to which to save this datatree, or a file-like object to write it to (which must support read and write and be seekable) or None (default) to return in-memory bytes as a memoryview.

• 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}, ...}. If encoding is specified the original encoding of the variables of the dataset is ignored.

  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 h5netcdf/h5netcdf.

Returns:

• * memoryview if path is None

• * dask.delayed.Delayed if compute is False

• * None otherwise

See also

DataArray.to_netcdf

to_pandas()

Convert this dataset into a pandas object without changing the number of dimensions.

The type of the returned object depends on the number of Dataset dimensions:

• 0D -> pandas.Series

• 1D -> pandas.DataFrame

Only works for Datasets with 1 or fewer dimensions.

to_stacked_array()

Combine variables of differing dimensionality into a DataArray without broadcasting.

This method is similar to Dataset.to_dataarray but does not broadcast the variables.

Parameters:

• new_dim (hashable) – Name of the new stacked coordinate

• sample_dims (Collection of hashables) – List of dimensions that will not be stacked. Each array in the dataset must share these dimensions. For machine learning applications, these define the dimensions over which samples are drawn.

• variable_dim (hashable, default: “variable”) – Name of the level in the stacked coordinate which corresponds to the variables.

• name (hashable, optional) – Name of the new data array.

Returns:

stacked – DataArray with the specified dimensions and data variables stacked together. The stacked coordinate is named new_dim and represented by a MultiIndex object with a level containing the data variable names. The name of this level is controlled using the variable_dim argument.

Return type:

DataArray

See also

Dataset.to_dataarray, Dataset.stack, DataArray.to_unstacked_dataset

Examples

>>> data = xr.Dataset(
...     data_vars={
...         "a": (("x", "y"), [[0, 1, 2], [3, 4, 5]]),
...         "b": ("x", [6, 7]),
...     },
...     coords={"y": ["u", "v", "w"]},
... )

>>> data
<xarray.Dataset> Size: 76B
Dimensions:  (x: 2, y: 3)
Coordinates:
  * y        (y) <U1 12B 'u' 'v' 'w'
Dimensions without coordinates: x
Data variables:
    a        (x, y) int64 48B 0 1 2 3 4 5
    b        (x) int64 16B 6 7

>>> data.to_stacked_array("z", sample_dims=["x"])
<xarray.DataArray 'a' (x: 2, z: 4)> Size: 64B
array([[0, 1, 2, 6],
       [3, 4, 5, 7]])
Coordinates:
  * z         (z) object 32B MultiIndex
  * variable  (z) <U1 16B 'a' 'a' 'a' 'b'
  * y         (z) object 32B 'u' 'v' 'w' nan
Dimensions without coordinates: x

to_zarr()

Write dataset contents to a zarr group.

Zarr chunks are determined in the following way:

• From the chunks attribute in each variable’s encoding (can be set via Dataset.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”, “a-”, r+”, None}, optional) – Persistence mode: “w” means create (overwrite if exists); “w-” means create (fail if exists); “a” means override all existing variables including dimension coordinates (create if does not exist); “a-” means only append those variables that have append_dim. “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.convenience.consolidate_metadata() 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 or “auto”, optional) – Optional mapping from dimension names to either a) "auto", or b) integer slices, indicating the region of existing zarr array(s) in which to write this dataset’s data.

  If "auto" is provided the existing store will be opened and the region inferred by matching indexes. "auto" can be used as a single string, which will automatically infer the region for all dimensions, or as dictionary values for specific dimensions mixed together with explicit slices for other dimensions.

  Alternatively integer slices can be provided; 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 dataset must have at least one dimension in common with the region. Other variables should be written in a separate single 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 “Modifying existing Zarr stores” in the reference documentation for full details.

  Users are expected to ensure that the specified region aligns with Zarr chunk boundaries, and that dask chunks are also aligned. Xarray makes limited checks that these multiple chunk boundaries line up. It is possible to write incomplete chunks and corrupt the data with this option if you are not careful.

• 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 from an existing Dataset with arbitrary chunk structure. In addition to the many-to-one relationship validation, it also detects partial chunks writes when using the region parameter, these partial chunks are considered unsafe in the mode “r+” but safe in the mode “a”. Note: Even with these validations it can still be unsafe to write two or more chunked arrays in the same location in parallel if they are not writing in independent regions, for those cases it is better to use a synchronizer.

• align_chunks (bool, default False) – If True, rechunks the Dask array to align with Zarr chunks before writing. This ensures each Dask chunk maps to one or more contiguous Zarr chunks, which avoids race conditions. Internally, the process sets safe_chunks=False and tries to preserve the original Dask chunking as much as possible. Note: While this alignment avoids write conflicts stemming from chunk boundary misalignment, it does not protect against race conditions if multiple uncoordinated processes write to the same Zarr array concurrently.

• storage_options (dict, optional) – Any additional parameters for the storage backend (ignored for local paths).

• zarr_version (int or None, optional) – .. deprecated:: 2024.9.1 Use zarr_format instead.

• zarr_format (int or None, optional) – The desired zarr format to target (currently 2 or 3). The default of None will attempt to determine the zarr version from store when possible, otherwise defaulting to the default version used by the zarr-python library installed.

• write_empty_chunks (bool or None, optional) – If True, all chunks will be stored regardless of their contents. If False, each chunk is compared to the array’s fill value prior to storing. If a chunk is uniformly equal to the fill value, then that chunk is not be stored, and the store entry for that chunk’s key is deleted. This setting enables sparser storage, as only chunks with non-fill-value data are stored, at the expense of overhead associated with checking the data of each chunk. If None (default) fall back to specification(s) in encoding or Zarr defaults. A ValueError will be raised if the value of this (if not None) differs with encoding.

• chunkmanager_store_kwargs (dict, optional) – Additional keyword arguments passed on to the ChunkManager.store method used to store chunked arrays. For example for a dask array additional kwargs will be passed eventually to dask.array.store(). Experimental API that should not be relied upon.

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.

fill_value handling:

There exists a subtlety in interpreting zarr’s fill_value property. For zarr v2 format arrays, fill_value is always interpreted as an invalid value similar to the _FillValue attribute in CF/netCDF. For Zarr v3 format arrays, only an explicit _FillValue attribute will be used to mask the data if requested using mask_and_scale=True. See this Github issue for more.

See also

Zarr

The I/O user guide, with more details and examples.

transpose()

Return a new Dataset object with all array dimensions transposed.

Although the order of dimensions on each array will change, the dataset dimensions themselves will remain in fixed (sorted) order.

Parameters:

• *dim (hashable, optional) – By default, reverse the dimensions on each array. Otherwise, reorder the dimensions to this order.

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

Returns:

transposed – Each array in the dataset (including) coordinates will be transposed to the given order.

Return type:

Dataset

Notes

This operation returns a view of each array’s data. It is lazy for dask-backed DataArrays but not for numpy-backed DataArrays – the data will be fully loaded into memory.

See also

numpy.transpose, DataArray.transpose

unify_chunks()

Unify chunk size along all chunked dimensions of this Dataset.

Return type:

Dataset with consistent chunk sizes for all dask-array variables

See also

dask.array.core.unify_chunks

unstack()

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. 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 – Dataset with unstacked data.

Return type:

Dataset

See also

Dataset.stack

update()

Update this dataset’s variables with those from another dataset.

Just like dict.update() this is a in-place operation. For a non-inplace version, see Dataset.merge().

Parameters:

other (Dataset or mapping) – Variables with which to update this dataset. One of:

• Dataset

• mapping {var name: DataArray}

• mapping {var name: Variable}

• mapping {var name: (dimension name, array-like)}

• mapping {var name: (tuple of dimension names, array-like)}

Returns:

updated – Updated dataset. Note that since the update is in-place this is the input dataset.

It is deprecated since version 0.17 and scheduled to be removed in 0.21.

Return type:

Dataset

Raises:

ValueError – If any dimensions would have inconsistent sizes in the updated dataset.

See also

Dataset.assign, Dataset.merge

values() → an object providing a view on D's values

var()

Reduce this Dataset’s data by applying var along some dimension(s).

Parameters:

• dim (str, Iterable of Hashable, “…” or None, default: None) – Name of dimension[s] along which to apply var. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

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

• ddof (int, default: 0) – “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements.

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

• **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating var on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced – New Dataset with var applied to its data and the indicated dimension(s) removed

Return type:

Dataset

See also

numpy.var, dask.array.var, DataArray.var

Aggregation

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, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.var()
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 1.04

Use skipna to control whether NaNs are ignored.

>>> ds.var(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B nan

Specify ddof=1 for an unbiased estimate.

>>> ds.var(skipna=True, ddof=1)
<xarray.Dataset> Size: 8B
Dimensions:  ()
Data variables:
    da       float64 8B 1.3

property variables

Low level interface to Dataset contents as dict of Variable objects.

This ordered dictionary is frozen to prevent mutation that could violate Dataset invariants. It contains all variable objects constituting the Dataset, including both data variables and coordinates.

where()

Filter elements from this object according to a condition.

Returns elements from ‘DataArray’, where ‘cond’ is True, otherwise fill in ‘other’.

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, the callable is passed this object, and the result is used as the value for cond.

• other (scalar, DataArray, Dataset, or callable, optional) – Value to use for locations in this object where cond is False. By default, these locations are filled with NA. If a callable, it must expect this object as its only parameter.

• 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)> Size: 200B
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)> Size: 200B
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)> Size: 200B
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)> Size: 128B
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, lambda x: -x)
<xarray.DataArray (x: 5, y: 5)> Size: 200B
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, drop=True)
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[ 0.,  1.,  2.,  3.],
       [ 5.,  6.,  7., nan],
       [10., 11., nan, nan],
       [15., nan, nan, nan]])
Dimensions without coordinates: x, y

See also

numpy.where

corresponding numpy function

where

equivalent function

property xindexes

Mapping of Index objects used for label based indexing.