zarr
====

.. py:module:: zarr


Submodules
----------

.. toctree::
   :maxdepth: 1

   /api/zarr/abc/index
   /api/zarr/api/index
   /api/zarr/codecs/index
   /api/zarr/convenience/index
   /api/zarr/creation/index
   /api/zarr/errors/index
   /api/zarr/registry/index
   /api/zarr/storage/index
   /api/zarr/testing/index


Attributes
----------

.. autoapisummary::

   zarr.config


Classes
-------

.. autoapisummary::

   zarr.Array
   zarr.AsyncArray
   zarr.AsyncGroup
   zarr.Group


Functions
---------

.. autoapisummary::

   zarr.array
   zarr.consolidate_metadata
   zarr.copy
   zarr.copy_all
   zarr.copy_store
   zarr.create
   zarr.create_array
   zarr.create_group
   zarr.create_hierarchy
   zarr.empty
   zarr.empty_like
   zarr.full
   zarr.full_like
   zarr.group
   zarr.load
   zarr.ones
   zarr.ones_like
   zarr.open
   zarr.open_array
   zarr.open_consolidated
   zarr.open_group
   zarr.open_like
   zarr.save
   zarr.save_array
   zarr.save_group
   zarr.tree
   zarr.zeros
   zarr.zeros_like


Package Contents
----------------

.. py:class:: Array

   
   Instantiate an array from an initialized store.
















   ..
       !! processed by numpydoc !!

   .. py:method:: append(data: numpy.typing.ArrayLike, axis: int = 0) -> zarr.core.common.ChunkCoords

      
      Append `data` to `axis`.


      :Parameters:

          **data** : array-like
              Data to be appended.

          **axis** : int
              Axis along which to append.



      :Returns:

          **new_shape** : tuple
              ..








      .. rubric:: Notes

      The size of all dimensions other than `axis` must match between this
      array and `data`.


      .. rubric:: Examples

      >>> import numpy as np
      >>> import zarr
      >>> a = np.arange(10000000, dtype='i4').reshape(10000, 1000)
      >>> z = zarr.array(a, chunks=(1000, 100))
      >>> z.shape
      (10000, 1000)
      >>> z.append(a)
      (20000, 1000)
      >>> z.append(np.vstack([a, a]), axis=1)
      (20000, 2000)
      >>> z.shape
      (20000, 2000)

      ..
          !! processed by numpydoc !!


   .. py:method:: create(store: zarr.storage.StoreLike, *, shape: zarr.core.common.ChunkCoords, dtype: numpy.typing.DTypeLike, zarr_format: zarr.core.common.ZarrFormat = 3, fill_value: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_shape: zarr.core.common.ChunkCoords | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | tuple[Literal['default'], Literal['.', '/']] | tuple[Literal['v2'], Literal['.', '/']] | None = None, codecs: collections.abc.Iterable[zarr.abc.codec.Codec | dict[str, zarr.core.common.JSON]] | None = None, dimension_names: collections.abc.Iterable[str] | None = None, chunks: zarr.core.common.ChunkCoords | None = None, dimension_separator: Literal['.', '/'] | None = None, order: zarr.core.common.MemoryOrder | None = None, filters: list[dict[str, zarr.core.common.JSON]] | None = None, compressor: dict[str, zarr.core.common.JSON] | None = None, overwrite: bool = False, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> Array
      :classmethod:


      
      Creates a new Array instance from an initialized store.

      .. deprecated:: 3.0.0
          Deprecated in favor of :func:`zarr.create_array`.

      :Parameters:

          **store** : StoreLike
              The array store that has already been initialized.

          **shape** : ChunkCoords
              The shape of the array.

          **dtype** : npt.DTypeLike
              The data type of the array.

          **chunk_shape** : ChunkCoords, optional
              The shape of the Array's chunks.
              Zarr format 3 only. Zarr format 2 arrays should use `chunks` instead.
              If not specified, default are guessed based on the shape and dtype.

          **chunk_key_encoding** : ChunkKeyEncodingLike, optional
              A specification of how the chunk keys are represented in storage.
              Zarr format 3 only. Zarr format 2 arrays should use `dimension_separator` instead.
              Default is ``("default", "/")``.

          **codecs** : Sequence of Codecs or dicts, optional
              An iterable of Codec or dict serializations of Codecs. The elements of
              this collection specify the transformation from array values to stored bytes.
              Zarr format 3 only. Zarr format 2 arrays should use ``filters`` and ``compressor`` instead.
              
              If no codecs are provided, default codecs will be used:
              
              - For numeric arrays, the default is ``BytesCodec`` and ``ZstdCodec``.
              - For Unicode strings, the default is ``VLenUTF8Codec`` and ``ZstdCodec``.
              - For bytes or objects, the default is ``VLenBytesCodec`` and ``ZstdCodec``.
              
              These defaults can be changed by modifying the value of ``array.v3_default_filters``,
              ``array.v3_default_serializer`` and ``array.v3_default_compressors`` in :mod:`zarr.core.config`.

          **dimension_names** : Iterable[str], optional
              The names of the dimensions (default is None).
              Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

          **chunks** : ChunkCoords, optional
              The shape of the array's chunks.
              Zarr format 2 only. Zarr format 3 arrays should use ``chunk_shape`` instead.
              If not specified, default are guessed based on the shape and dtype.

          **dimension_separator** : Literal[".", "/"], optional
              The dimension separator (default is ".").
              Zarr format 2 only. Zarr format 3 arrays should use ``chunk_key_encoding`` instead.

          **order** : Literal["C", "F"], optional
              The memory of the array (default is "C").
              If ``zarr_format`` is 2, this parameter sets the memory order of the array.
              If `zarr_format`` is 3, then this parameter is deprecated, because memory order
              is a runtime parameter for Zarr 3 arrays. The recommended way to specify the memory
              order for Zarr 3 arrays is via the ``config`` parameter, e.g. ``{'order': 'C'}``.

          **filters** : list[dict[str, JSON]], optional
              Sequence of filters to use to encode chunk data prior to compression.
              Zarr format 2 only. Zarr format 3 arrays should use ``codecs`` instead. If no ``filters``
              are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v2_default_filters`` in :mod:`zarr.core.config`.

          **compressor** : dict[str, JSON], optional
              Primary compressor to compress chunk data.
              Zarr format 2 only. Zarr format 3 arrays should use ``codecs`` instead.
              
              If no ``compressor`` is provided, a default compressor will be used:
              
              - For numeric arrays, the default is ``ZstdCodec``.
              - For Unicode strings, the default is ``VLenUTF8Codec``.
              - For bytes or objects, the default is ``VLenBytesCodec``.
              
              These defaults can be changed by modifying the value of ``array.v2_default_compressor`` in :mod:`zarr.core.config`.

          **overwrite** : bool, optional
              Whether to raise an error if the store already exists (default is False).



      :Returns:

          Array
              Array created from the store.











      ..
          !! processed by numpydoc !!


   .. py:method:: from_dict(store_path: zarr.storage._common.StorePath, data: dict[str, zarr.core.common.JSON]) -> Array
      :classmethod:


      
      Create a Zarr array from a dictionary.


      :Parameters:

          **store_path** : StorePath
              The path within the store where the array should be created.

          **data** : dict
              A dictionary representing the array data. This dictionary should include necessary metadata
              for the array, such as shape, dtype, fill value, and attributes.



      :Returns:

          Array
              The created Zarr array.




      :Raises:

          ValueError
              If the dictionary data is invalid or missing required fields for array creation.







      ..
          !! processed by numpydoc !!


   .. py:method:: get_basic_selection(selection: zarr.core.indexing.BasicSelection = Ellipsis, *, out: zarr.core.buffer.NDBuffer | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None, fields: zarr.core.indexing.Fields | None = None) -> zarr.core.buffer.NDArrayLike

      
      Retrieve data for an item or region of the array.


      :Parameters:

          **selection** : tuple
              A tuple specifying the requested item or region for each dimension of the
              array. May be any combination of int and/or slice or ellipsis for multidimensional arrays.

          **out** : NDBuffer, optional
              If given, load the selected data directly into this buffer.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to
              extract data for.



      :Returns:

          NDArrayLike
              An array-like containing the data for the requested region.







      .. seealso::

          
          :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_coordinate_selection`, :obj:`set_coordinate_selection`, :obj:`get_orthogonal_selection`
              ..
          :obj:`set_orthogonal_selection`, :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Slices with step > 1 are supported, but slices with negative step are not.

      For arrays with a structured dtype, see Zarr format 2 for examples of how to use
      the `fields` parameter.

      This method provides the implementation for accessing data via the
      square bracket notation (__getitem__). See :func:`__getitem__` for examples
      using the alternative notation.


      .. rubric:: Examples

      Setup a 1-dimensional array::

          >>> import zarr
          >>> import numpy as np
          >>> data = np.arange(100, dtype="uint16")
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=(3,),
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve a single item::

          >>> z.get_basic_selection(5)
          5

      Retrieve a region via slicing::

          >>> z.get_basic_selection(slice(5))
          array([0, 1, 2, 3, 4])
          >>> z.get_basic_selection(slice(-5, None))
          array([95, 96, 97, 98, 99])
          >>> z.get_basic_selection(slice(5, 10))
          array([5, 6, 7, 8, 9])
          >>> z.get_basic_selection(slice(5, 10, 2))
          array([5, 7, 9])
          >>> z.get_basic_selection(slice(None, None, 2))
          array([  0,  2,  4, ..., 94, 96, 98])

      Setup a 3-dimensional array::

          >>> data = np.arange(1000).reshape(10, 10, 10)
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=(5, 5, 5),
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve an item::

          >>> z.get_basic_selection((1, 2, 3))
          123

      Retrieve a region via slicing and Ellipsis::

          >>> z.get_basic_selection((slice(1, 3), slice(1, 3), 0))
          array([[110, 120],
                 [210, 220]])
          >>> z.get_basic_selection(0, (slice(1, 3), slice(None)))
          array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                 [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])
          >>> z.get_basic_selection((..., 5))
          array([[  2  12  22  32  42  52  62  72  82  92]
                 [102 112 122 132 142 152 162 172 182 192]
                 ...
                 [802 812 822 832 842 852 862 872 882 892]
                 [902 912 922 932 942 952 962 972 982 992]]

      ..
          !! processed by numpydoc !!


   .. py:method:: get_block_selection(selection: zarr.core.indexing.BasicSelection, *, out: zarr.core.buffer.NDBuffer | None = None, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> zarr.core.buffer.NDArrayLike

      
      Retrieve a selection of individual items, by providing the indices
      (coordinates) for each selected item.


      :Parameters:

          **selection** : int or slice or tuple of int or slice
              An integer (coordinate) or slice for each dimension of the array.

          **out** : NDBuffer, optional
              If given, load the selected data directly into this buffer.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to
              extract data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.



      :Returns:

          NDArrayLike
              An array-like containing the data for the requested block selection.







      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`get_coordinate_selection`
              ..
          :obj:`set_coordinate_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Block indexing is a convenience indexing method to work on individual chunks
      with chunk index slicing. It has the same concept as Dask's `Array.blocks`
      indexing.

      Slices are supported. However, only with a step size of one.

      Block index arrays may be multidimensional to index multidimensional arrays.
      For example::

          >>> z.blocks[0, 1:3]
          array([[ 3,  4,  5,  6,  7,  8],
                 [13, 14, 15, 16, 17, 18],
                 [23, 24, 25, 26, 27, 28]])


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> import numpy as np
          >>> data = np.arange(0, 100, dtype="uint16").reshape((10, 10))
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=(3, 3),
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve items by specifying their block coordinates::

          >>> z.get_block_selection((1, slice(None)))
          array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                 [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                 [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])

      Which is equivalent to::

          >>> z[3:6, :]
          array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                 [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                 [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])

      For convenience, the block selection functionality is also available via the
      `blocks` property, e.g.::

          >>> z.blocks[1]
          array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                 [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                 [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])

      ..
          !! processed by numpydoc !!


   .. py:method:: get_coordinate_selection(selection: zarr.core.indexing.CoordinateSelection, *, out: zarr.core.buffer.NDBuffer | None = None, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> zarr.core.buffer.NDArrayLike

      
      Retrieve a selection of individual items, by providing the indices
      (coordinates) for each selected item.


      :Parameters:

          **selection** : tuple
              An integer (coordinate) array for each dimension of the array.

          **out** : NDBuffer, optional
              If given, load the selected data directly into this buffer.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to
              extract data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.



      :Returns:

          NDArrayLike
              An array-like containing the data for the requested coordinate selection.







      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`set_coordinate_selection`
              ..
          :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Coordinate indexing is also known as point selection, and is a form of vectorized
      or inner indexing.

      Slices are not supported. Coordinate arrays must be provided for all dimensions
      of the array.

      Coordinate arrays may be multidimensional, in which case the output array will
      also be multidimensional. Coordinate arrays are broadcast against each other
      before being applied. The shape of the output will be the same as the shape of
      each coordinate array after broadcasting.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> import numpy as np
          >>> data = np.arange(0, 100, dtype="uint16").reshape((10, 10))
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=(3, 3),
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve items by specifying their coordinates::

          >>> z.get_coordinate_selection(([1, 4], [1, 4]))
          array([11, 44])

      For convenience, the coordinate selection functionality is also available via the
      `vindex` property, e.g.::

          >>> z.vindex[[1, 4], [1, 4]]
          array([11, 44])

      ..
          !! processed by numpydoc !!


   .. py:method:: get_mask_selection(mask: zarr.core.indexing.MaskSelection, *, out: zarr.core.buffer.NDBuffer | None = None, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> zarr.core.buffer.NDArrayLike

      
      Retrieve a selection of individual items, by providing a Boolean array of the
      same shape as the array against which the selection is being made, where True
      values indicate a selected item.


      :Parameters:

          **mask** : ndarray, bool
              A Boolean array of the same shape as the array against which the selection is
              being made.

          **out** : NDBuffer, optional
              If given, load the selected data directly into this buffer.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to
              extract data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.



      :Returns:

          NDArrayLike
              An array-like containing the data for the requested selection.







      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`get_coordinate_selection`
              ..
          :obj:`set_coordinate_selection`, :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Mask indexing is a form of vectorized or inner indexing, and is equivalent to
      coordinate indexing. Internally the mask array is converted to coordinate
      arrays by calling `np.nonzero`.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> import numpy as np
          >>> data = np.arange(100).reshape(10, 10)
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=data.shape,
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve items by specifying a mask::

          >>> sel = np.zeros_like(z, dtype=bool)
          >>> sel[1, 1] = True
          >>> sel[4, 4] = True
          >>> z.get_mask_selection(sel)
          array([11, 44])

      For convenience, the mask selection functionality is also available via the
      `vindex` property, e.g.::

          >>> z.vindex[sel]
          array([11, 44])

      ..
          !! processed by numpydoc !!


   .. py:method:: get_orthogonal_selection(selection: zarr.core.indexing.OrthogonalSelection, *, out: zarr.core.buffer.NDBuffer | None = None, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> zarr.core.buffer.NDArrayLike

      
      Retrieve data by making a selection for each dimension of the array. For
      example, if an array has 2 dimensions, allows selecting specific rows and/or
      columns. The selection for each dimension can be either an integer (indexing a
      single item), a slice, an array of integers, or a Boolean array where True
      values indicate a selection.


      :Parameters:

          **selection** : tuple
              A selection for each dimension of the array. May be any combination of int,
              slice, integer array or Boolean array.

          **out** : NDBuffer, optional
              If given, load the selected data directly into this buffer.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to
              extract data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.



      :Returns:

          NDArrayLike
              An array-like containing the data for the requested selection.







      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_coordinate_selection`, :obj:`set_coordinate_selection`, :obj:`set_orthogonal_selection`
              ..
          :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Orthogonal indexing is also known as outer indexing.

      Slices with step > 1 are supported, but slices with negative step are not.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> import numpy as np
          >>> data = np.arange(100).reshape(10, 10)
          >>> z = zarr.create_array(
          >>>        StorePath(MemoryStore(mode="w")),
          >>>        shape=data.shape,
          >>>        chunks=data.shape,
          >>>        dtype=data.dtype,
          >>>        )
          >>> z[:] = data

      Retrieve rows and columns via any combination of int, slice, integer array and/or
      Boolean array::

          >>> z.get_orthogonal_selection(([1, 4], slice(None)))
          array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                 [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])
          >>> z.get_orthogonal_selection((slice(None), [1, 4]))
          array([[ 1,  4],
                 [11, 14],
                 [21, 24],
                 [31, 34],
                 [41, 44],
                 [51, 54],
                 [61, 64],
                 [71, 74],
                 [81, 84],
                 [91, 94]])
          >>> z.get_orthogonal_selection(([1, 4], [1, 4]))
          array([[11, 14],
                 [41, 44]])
          >>> sel = np.zeros(z.shape[0], dtype=bool)
          >>> sel[1] = True
          >>> sel[4] = True
          >>> z.get_orthogonal_selection((sel, sel))
          array([[11, 14],
                 [41, 44]])

      For convenience, the orthogonal selection functionality is also available via the
      `oindex` property, e.g.::

          >>> z.oindex[[1, 4], :]
          array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                 [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])
          >>> z.oindex[:, [1, 4]]
          array([[ 1,  4],
                 [11, 14],
                 [21, 24],
                 [31, 34],
                 [41, 44],
                 [51, 54],
                 [61, 64],
                 [71, 74],
                 [81, 84],
                 [91, 94]])
          >>> z.oindex[[1, 4], [1, 4]]
          array([[11, 14],
                 [41, 44]])
          >>> sel = np.zeros(z.shape[0], dtype=bool)
          >>> sel[1] = True
          >>> sel[4] = True
          >>> z.oindex[sel, sel]
          array([[11, 14],
                 [41, 44]])

      ..
          !! processed by numpydoc !!


   .. py:method:: info_complete() -> Any

      
      Returns all the information about an array, including information from the Store.

      In addition to the statically known information like ``name`` and ``zarr_format``,
      this includes additional information like the size of the array in bytes and
      the number of chunks written.

      Note that this method will need to read metadata from the store.




      :Returns:

          ArrayInfo
              ..







      .. seealso::

          
          :obj:`Array.info`
              The statically known subset of metadata about an array.
          
          



      ..
          !! processed by numpydoc !!


   .. py:method:: nbytes_stored() -> int

      
      Determine the size, in bytes, of the array actually written to the store.





      :Returns:

          **size** : int
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: open(store: zarr.storage.StoreLike) -> Array
      :classmethod:


      
      Opens an existing Array from a store.


      :Parameters:

          **store** : Store
              Store containing the Array.



      :Returns:

          Array
              Array opened from the store.











      ..
          !! processed by numpydoc !!


   .. py:method:: resize(new_shape: zarr.core.common.ShapeLike) -> None

      
      Change the shape of the array by growing or shrinking one or more
      dimensions.


      :Parameters:

          **new_shape** : tuple
              New shape of the array.











      .. rubric:: Notes

      If one or more dimensions are shrunk, any chunks falling outside the
      new array shape will be deleted from the underlying store.
      However, it is noteworthy that the chunks partially falling inside the new array
      (i.e. boundary chunks) will remain intact, and therefore,
      the data falling outside the new array but inside the boundary chunks
      would be restored by a subsequent resize operation that grows the array size.


      .. rubric:: Examples

      >>> import zarr
      >>> z = zarr.zeros(shape=(10000, 10000),
      >>>                chunk_shape=(1000, 1000),
      >>>                dtype="i4",)
      >>> z.shape
      (10000, 10000)
      >>> z = z.resize(20000, 1000)
      >>> z.shape
      (20000, 1000)
      >>> z2 = z.resize(50, 50)
      >>> z.shape
      (20000, 1000)
      >>> z2.shape
      (50, 50)

      ..
          !! processed by numpydoc !!


   .. py:method:: set_basic_selection(selection: zarr.core.indexing.BasicSelection, value: numpy.typing.ArrayLike, *, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None

      
      Modify data for an item or region of the array.


      :Parameters:

          **selection** : tuple
              A tuple specifying the requested item or region for each dimension of the
              array. May be any combination of int and/or slice or ellipsis for multidimensional arrays.

          **value** : npt.ArrayLike
              An array-like containing values to be stored into the array.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to set
              data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer used for setting the data. If not provided, the
              default buffer prototype is used.










      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_coordinate_selection`, :obj:`set_coordinate_selection`, :obj:`get_orthogonal_selection`
              ..
          :obj:`set_orthogonal_selection`, :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      For arrays with a structured dtype, see Zarr format 2 for examples of how to use
      the `fields` parameter.

      This method provides the underlying implementation for modifying data via square
      bracket notation, see :func:`__setitem__` for equivalent examples using the
      alternative notation.


      .. rubric:: Examples

      Setup a 1-dimensional array::

          >>> import zarr
          >>> z = zarr.zeros(
          >>>        shape=(100,),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(100,),
          >>>        dtype="i4",
          >>>       )

      Set all array elements to the same scalar value::

          >>> z.set_basic_selection(..., 42)
          >>> z[...]
          array([42, 42, 42, ..., 42, 42, 42])

      Set a portion of the array::

          >>> z.set_basic_selection(slice(10), np.arange(10))
          >>> z.set_basic_selection(slice(-10, None), np.arange(10)[::-1])
          >>> z[...]
          array([ 0, 1, 2, ..., 2, 1, 0])

      Setup a 2-dimensional array::

          >>> z = zarr.zeros(
          >>>        shape=(5, 5),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(5, 5),
          >>>        dtype="i4",
          >>>       )

      Set all array elements to the same scalar value::

          >>> z.set_basic_selection(..., 42)

      Set a portion of the array::

          >>> z.set_basic_selection((0, slice(None)), np.arange(z.shape[1]))
          >>> z.set_basic_selection((slice(None), 0), np.arange(z.shape[0]))
          >>> z[...]
          array([[ 0,  1,  2,  3,  4],
                 [ 1, 42, 42, 42, 42],
                 [ 2, 42, 42, 42, 42],
                 [ 3, 42, 42, 42, 42],
                 [ 4, 42, 42, 42, 42]])

      ..
          !! processed by numpydoc !!


   .. py:method:: set_block_selection(selection: zarr.core.indexing.BasicSelection, value: numpy.typing.ArrayLike, *, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None

      
      Modify a selection of individual blocks, by providing the chunk indices
      (coordinates) for each block to be modified.


      :Parameters:

          **selection** : tuple
              An integer (coordinate) or slice for each dimension of the array.

          **value** : npt.ArrayLike
              An array-like containing the data to be stored in the block selection.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to set
              data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer used for setting the data. If not provided, the
              default buffer prototype is used.










      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`get_coordinate_selection`
              ..
          :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Block indexing is a convenience indexing method to work on individual chunks
      with chunk index slicing. It has the same concept as Dask's `Array.blocks`
      indexing.

      Slices are supported. However, only with a step size of one.


      .. rubric:: Examples

      Set up a 2-dimensional array::

          >>> import zarr
          >>> z = zarr.zeros(
          >>>        shape=(6, 6),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(2, 2),
          >>>        dtype="i4",
          >>>       )

      Set data for a selection of items::

          >>> z.set_block_selection((1, 0), 1)
          >>> z[...]
          array([[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 0, 0, 0, 0],
                 [1, 1, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0]])

      For convenience, this functionality is also available via the `blocks` property.
      E.g.::

          >>> z.blocks[2, 1] = 4
          >>> z[...]
          array([[0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [1, 1, 0, 0, 0, 0],
                 [1, 1, 0, 0, 0, 0],
                 [0, 0, 4, 4, 0, 0],
                 [0, 0, 4, 4, 0, 0]])

          >>> z.blocks[:, 2] = 7
          >>> z[...]
          array([[0, 0, 0, 0, 7, 7],
                 [0, 0, 0, 0, 7, 7],
                 [1, 1, 0, 0, 7, 7],
                 [1, 1, 0, 0, 7, 7],
                 [0, 0, 4, 4, 7, 7],
                 [0, 0, 4, 4, 7, 7]])

      ..
          !! processed by numpydoc !!


   .. py:method:: set_coordinate_selection(selection: zarr.core.indexing.CoordinateSelection, value: numpy.typing.ArrayLike, *, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None

      
      Modify a selection of individual items, by providing the indices (coordinates)
      for each item to be modified.


      :Parameters:

          **selection** : tuple
              An integer (coordinate) array for each dimension of the array.

          **value** : npt.ArrayLike
              An array-like containing values to be stored into the array.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to set
              data for.










      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`get_coordinate_selection`
              ..
          :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Coordinate indexing is also known as point selection, and is a form of vectorized
      or inner indexing.

      Slices are not supported. Coordinate arrays must be provided for all dimensions
      of the array.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> z = zarr.zeros(
          >>>        shape=(5, 5),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(5, 5),
          >>>        dtype="i4",
          >>>       )

      Set data for a selection of items::

          >>> z.set_coordinate_selection(([1, 4], [1, 4]), 1)
          >>> z[...]
          array([[0, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 1]])

      For convenience, this functionality is also available via the `vindex` property.
      E.g.::

          >>> z.vindex[[1, 4], [1, 4]] = 2
          >>> z[...]
          array([[0, 0, 0, 0, 0],
                 [0, 2, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 2]])

      ..
          !! processed by numpydoc !!


   .. py:method:: set_mask_selection(mask: zarr.core.indexing.MaskSelection, value: numpy.typing.ArrayLike, *, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None

      
      Modify a selection of individual items, by providing a Boolean array of the
      same shape as the array against which the selection is being made, where True
      values indicate a selected item.


      :Parameters:

          **mask** : ndarray, bool
              A Boolean array of the same shape as the array against which the selection is
              being made.

          **value** : npt.ArrayLike
              An array-like containing values to be stored into the array.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to set
              data for.










      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`
              ..
          :obj:`get_orthogonal_selection`, :obj:`set_orthogonal_selection`, :obj:`get_coordinate_selection`
              ..
          :obj:`set_coordinate_selection`, :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Mask indexing is a form of vectorized or inner indexing, and is equivalent to
      coordinate indexing. Internally the mask array is converted to coordinate
      arrays by calling `np.nonzero`.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> z = zarr.zeros(
          >>>        shape=(5, 5),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(5, 5),
          >>>        dtype="i4",
          >>>       )

      Set data for a selection of items::

          >>> sel = np.zeros_like(z, dtype=bool)
          >>> sel[1, 1] = True
          >>> sel[4, 4] = True
          >>> z.set_mask_selection(sel, 1)
          >>> z[...]
          array([[0, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 1]])

      For convenience, this functionality is also available via the `vindex` property.
      E.g.::

          >>> z.vindex[sel] = 2
          >>> z[...]
          array([[0, 0, 0, 0, 0],
                 [0, 2, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 2]])

      ..
          !! processed by numpydoc !!


   .. py:method:: set_orthogonal_selection(selection: zarr.core.indexing.OrthogonalSelection, value: numpy.typing.ArrayLike, *, fields: zarr.core.indexing.Fields | None = None, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None

      
      Modify data via a selection for each dimension of the array.


      :Parameters:

          **selection** : tuple
              A selection for each dimension of the array. May be any combination of int,
              slice, integer array or Boolean array.

          **value** : npt.ArrayLike
              An array-like array containing the data to be stored in the array.

          **fields** : str or sequence of str, optional
              For arrays with a structured dtype, one or more fields can be specified to set
              data for.

          **prototype** : BufferPrototype, optional
              The prototype of the buffer used for setting the data. If not provided, the
              default buffer prototype is used.










      .. seealso::

          
          :obj:`get_basic_selection`, :obj:`set_basic_selection`, :obj:`get_mask_selection`, :obj:`set_mask_selection`
              ..
          :obj:`get_coordinate_selection`, :obj:`set_coordinate_selection`, :obj:`get_orthogonal_selection`
              ..
          :obj:`get_block_selection`, :obj:`set_block_selection`
              ..
          :obj:`vindex`, :obj:`oindex`, :obj:`blocks`, :obj:`__getitem__`, :obj:`__setitem__`
              ..
          
      .. rubric:: Notes

      Orthogonal indexing is also known as outer indexing.

      Slices with step > 1 are supported, but slices with negative step are not.


      .. rubric:: Examples

      Setup a 2-dimensional array::

          >>> import zarr
          >>> z = zarr.zeros(
          >>>        shape=(5, 5),
          >>>        store=StorePath(MemoryStore(mode="w")),
          >>>        chunk_shape=(5, 5),
          >>>        dtype="i4",
          >>>       )

      Set data for a selection of rows::

          >>> z.set_orthogonal_selection(([1, 4], slice(None)), 1)
          >>> z[...]
          array([[0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1],
                 [0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1]])

      Set data for a selection of columns::

          >>> z.set_orthogonal_selection((slice(None), [1, 4]), 2)
          >>> z[...]
          array([[0, 2, 0, 0, 2],
                 [1, 2, 1, 1, 2],
                 [0, 2, 0, 0, 2],
                 [0, 2, 0, 0, 2],
                 [1, 2, 1, 1, 2]])

      Set data for a selection of rows and columns::

          >>> z.set_orthogonal_selection(([1, 4], [1, 4]), 3)
          >>> z[...]
          array([[0, 2, 0, 0, 2],
                 [1, 3, 1, 1, 3],
                 [0, 2, 0, 0, 2],
                 [0, 2, 0, 0, 2],
                 [1, 3, 1, 1, 3]])

      Set data from a 2D array::

          >>> values = np.arange(10).reshape(2, 5)
          >>> z.set_orthogonal_selection(([0, 3], ...), values)
          >>> z[...]
          array([[0, 1, 2, 3, 4],
                 [1, 3, 1, 1, 3],
                 [0, 2, 0, 0, 2],
                 [5, 6, 7, 8, 9],
                 [1, 3, 1, 1, 3]])

      For convenience, this functionality is also available via the `oindex` property.
      E.g.::

          >>> z.oindex[[1, 4], [1, 4]] = 4
          >>> z[...]
          array([[0, 1, 2, 3, 4],
                 [1, 4, 1, 1, 4],
                 [0, 2, 0, 0, 2],
                 [5, 6, 7, 8, 9],
                 [1, 4, 1, 1, 4]])

      ..
          !! processed by numpydoc !!


   .. py:method:: update_attributes(new_attributes: dict[str, zarr.core.common.JSON]) -> Array

      
      Update the array's attributes.


      :Parameters:

          **new_attributes** : dict
              A dictionary of new attributes to update or add to the array. The keys represent attribute
              names, and the values must be JSON-compatible.



      :Returns:

          Array
              The array with the updated attributes.




      :Raises:

          ValueError
              If the attributes are invalid or incompatible with the array's metadata.




      .. rubric:: Notes

      - The updated attributes will be merged with existing attributes, and any conflicts will be
        overwritten by the new values.



      ..
          !! processed by numpydoc !!


   .. py:property:: attrs
      :type: zarr.core.attributes.Attributes


      
      Returns a MutableMapping containing user-defined attributes.





      :Returns:

          **attrs** : MutableMapping
              A MutableMapping object containing user-defined attributes.








      .. rubric:: Notes

      Note that attribute values must be JSON serializable.



      ..
          !! processed by numpydoc !!


   .. py:property:: basename
      :type: str


      
      Final component of name.
















      ..
          !! processed by numpydoc !!


   .. py:property:: blocks
      :type: zarr.core.indexing.BlockIndex


      
      Shortcut for blocked chunked indexing, see :func:`get_block_selection` and
      :func:`set_block_selection` for documentation and examples.
















      ..
          !! processed by numpydoc !!


   .. py:property:: cdata_shape
      :type: zarr.core.common.ChunkCoords


      
      The shape of the chunk grid for this array.
















      ..
          !! processed by numpydoc !!


   .. py:property:: chunks
      :type: zarr.core.common.ChunkCoords


      
      Returns a tuple of integers describing the length of each dimension of a chunk of the array.
      If sharding is used the inner chunk shape is returned.

      Only defined for arrays using using `RegularChunkGrid`.
      If array doesn't use `RegularChunkGrid`, `NotImplementedError` is raised.




      :Returns:

          tuple
              A tuple of integers representing the length of each dimension of a chunk.











      ..
          !! processed by numpydoc !!


   .. py:property:: compressor
      :type: numcodecs.abc.Codec | None


      
      Compressor that is applied to each chunk of the array.

      .. deprecated:: 3.0.0
          `array.compressor` is deprecated and will be removed in a future release.
          Use `array.compressors` instead.















      ..
          !! processed by numpydoc !!


   .. py:property:: compressors
      :type: tuple[numcodecs.abc.Codec, Ellipsis] | tuple[zarr.abc.codec.BytesBytesCodec, Ellipsis]


      
      Compressors that are applied to each chunk of the array. Compressors are applied in order, and after any
      filters are applied (if any are specified) and the data is serialized into bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: dtype
      :type: numpy.dtype[Any]


      
      Returns the NumPy data type.





      :Returns:

          np.dtype
              The NumPy data type.











      ..
          !! processed by numpydoc !!


   .. py:property:: fill_value
      :type: Any



   .. py:property:: filters
      :type: tuple[numcodecs.abc.Codec, Ellipsis] | tuple[zarr.abc.codec.ArrayArrayCodec, Ellipsis]


      
      Filters that are applied to each chunk of the array, in order, before serializing that
      chunk to bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: info
      :type: Any


      
      Return the statically known information for an array.





      :Returns:

          ArrayInfo
              ..







      .. seealso::

          
          :obj:`Array.info_complete`
              All information about a group, including dynamic information like the number of bytes and chunks written.
          
          


      .. rubric:: Examples

      >>> arr = zarr.create(shape=(10,), chunks=(2,), dtype="float32")
      >>> arr.info
      Type               : Array
      Zarr format        : 3
      Data type          : DataType.float32
      Shape              : (10,)
      Chunk shape        : (2,)
      Order              : C
      Read-only          : False
      Store type         : MemoryStore
      Codecs             : [BytesCodec(endian=<Endian.little: 'little'>)]
      No. bytes          : 40

      ..
          !! processed by numpydoc !!


   .. py:property:: metadata
      :type: zarr.core.metadata.ArrayMetadata



   .. py:property:: name
      :type: str


      
      Array name following h5py convention.
















      ..
          !! processed by numpydoc !!


   .. py:property:: nbytes
      :type: int


      
      The total number of bytes that can be stored in the chunks of this array.













      .. rubric:: Notes

      This value is calculated by multiplying the number of elements in the array and the size
      of each element, the latter of which is determined by the dtype of the array.
      For this reason, ``nbytes`` will likely be inaccurate for arrays with variable-length
      dtypes. It is not possible to determine the size of an array with variable-length elements
      from the shape and dtype alone.



      ..
          !! processed by numpydoc !!


   .. py:property:: nchunks
      :type: int


      
      The number of chunks in the stored representation of this array.
















      ..
          !! processed by numpydoc !!


   .. py:property:: nchunks_initialized
      :type: int


      
      Calculate the number of chunks that have been initialized, i.e. the number of chunks that have
      been persisted to the storage backend.





      :Returns:

          **nchunks_initialized** : int
              The number of chunks that have been initialized.








      .. rubric:: Notes

      On :class:`Array` this is a (synchronous) property, unlike asynchronous function
      :meth:`AsyncArray.nchunks_initialized`.


      .. rubric:: Examples

      >>> arr = await zarr.create(shape=(10,), chunks=(2,))
      >>> arr.nchunks_initialized
      0
      >>> arr[:5] = 1
      >>> arr.nchunks_initialized
      3

      ..
          !! processed by numpydoc !!


   .. py:property:: ndim
      :type: int


      
      Returns the number of dimensions in the array.





      :Returns:

          int
              The number of dimensions in the array.











      ..
          !! processed by numpydoc !!


   .. py:property:: oindex
      :type: zarr.core.indexing.OIndex


      
      Shortcut for orthogonal (outer) indexing, see :func:`get_orthogonal_selection` and
      :func:`set_orthogonal_selection` for documentation and examples.
















      ..
          !! processed by numpydoc !!


   .. py:property:: order
      :type: zarr.core.common.MemoryOrder



   .. py:property:: path
      :type: str


      
      Storage path.
















      ..
          !! processed by numpydoc !!


   .. py:property:: read_only
      :type: bool



   .. py:property:: serializer
      :type: None | zarr.abc.codec.ArrayBytesCodec


      
      Array-to-bytes codec to use for serializing the chunks into bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: shape
      :type: zarr.core.common.ChunkCoords


      
      Returns the shape of the array.





      :Returns:

          ChunkCoords
              The shape of the array.











      ..
          !! processed by numpydoc !!


   .. py:property:: shards
      :type: zarr.core.common.ChunkCoords | None


      
      Returns a tuple of integers describing the length of each dimension of a shard of the array.
      Returns None if sharding is not used.

      Only defined for arrays using using `RegularChunkGrid`.
      If array doesn't use `RegularChunkGrid`, `NotImplementedError` is raised.




      :Returns:

          tuple | None
              A tuple of integers representing the length of each dimension of a shard or None if sharding is not used.











      ..
          !! processed by numpydoc !!


   .. py:property:: size
      :type: int


      
      Returns the total number of elements in the array.





      :Returns:

          int
              Total number of elements in the array.











      ..
          !! processed by numpydoc !!


   .. py:property:: store
      :type: zarr.abc.store.Store



   .. py:property:: store_path
      :type: zarr.storage._common.StorePath



   .. py:property:: vindex
      :type: zarr.core.indexing.VIndex


      
      Shortcut for vectorized (inner) indexing, see :func:`get_coordinate_selection`,
      :func:`set_coordinate_selection`, :func:`get_mask_selection` and
      :func:`set_mask_selection` for documentation and examples.
















      ..
          !! processed by numpydoc !!


.. py:class:: AsyncArray(metadata: zarr.core.metadata.ArrayV2Metadata | zarr.core.metadata.ArrayV2MetadataDict, store_path: zarr.storage._common.StorePath, config: zarr.core.array_spec.ArrayConfigLike | None = None)
              AsyncArray(metadata: zarr.core.metadata.ArrayV3Metadata | zarr.core.metadata.ArrayV3MetadataDict, store_path: zarr.storage._common.StorePath, config: zarr.core.array_spec.ArrayConfigLike | None = None)

   Bases: :py:obj:`Generic`\ [\ :py:obj:`zarr.core.metadata.T_ArrayMetadata`\ ]


   
   An asynchronous array class representing a chunked array stored in a Zarr store.


   :Parameters:

       **metadata** : ArrayMetadata
           The metadata of the array.

       **store_path** : StorePath
           The path to the Zarr store.

       **config** : ArrayConfigLike, optional
           The runtime configuration of the array, by default None.

   :Attributes:

       **metadata** : ArrayMetadata
           The metadata of the array.

       **store_path** : StorePath
           The path to the Zarr store.

       **codec_pipeline** : CodecPipeline
           The codec pipeline used for encoding and decoding chunks.

       **_config** : ArrayConfig
           The runtime configuration of the array.













   ..
       !! processed by numpydoc !!

   .. py:method:: append(data: numpy.typing.ArrayLike, axis: int = 0) -> zarr.core.common.ChunkCoords
      :async:


      
      Append `data` to `axis`.


      :Parameters:

          **data** : array-like
              Data to be appended.

          **axis** : int
              Axis along which to append.



      :Returns:

          **new_shape** : tuple
              ..








      .. rubric:: Notes

      The size of all dimensions other than `axis` must match between this
      array and `data`.



      ..
          !! processed by numpydoc !!


   .. py:method:: create(store: zarr.storage.StoreLike, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, zarr_format: Literal[2], fill_value: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunks: zarr.core.common.ShapeLike | None = None, dimension_separator: Literal['.', '/'] | None = None, order: zarr.core.common.MemoryOrder | None = None, filters: list[dict[str, zarr.core.common.JSON]] | None = None, compressor: dict[str, zarr.core.common.JSON] | None = None, overwrite: bool = False, data: numpy.typing.ArrayLike | None = None, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> AsyncArray[zarr.core.metadata.ArrayV2Metadata]
                  create(store: zarr.storage.StoreLike, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, zarr_format: Literal[3], fill_value: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_shape: zarr.core.common.ShapeLike | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | tuple[Literal['default'], Literal['.', '/']] | tuple[Literal['v2'], Literal['.', '/']] | None = None, codecs: collections.abc.Iterable[zarr.abc.codec.Codec | dict[str, zarr.core.common.JSON]] | None = None, dimension_names: collections.abc.Iterable[str] | None = None, overwrite: bool = False, data: numpy.typing.ArrayLike | None = None, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> AsyncArray[zarr.core.metadata.ArrayV3Metadata]
                  create(store: zarr.storage.StoreLike, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, zarr_format: Literal[3] = 3, fill_value: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_shape: zarr.core.common.ShapeLike | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | tuple[Literal['default'], Literal['.', '/']] | tuple[Literal['v2'], Literal['.', '/']] | None = None, codecs: collections.abc.Iterable[zarr.abc.codec.Codec | dict[str, zarr.core.common.JSON]] | None = None, dimension_names: collections.abc.Iterable[str] | None = None, overwrite: bool = False, data: numpy.typing.ArrayLike | None = None, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> AsyncArray[zarr.core.metadata.ArrayV3Metadata]
                  create(store: zarr.storage.StoreLike, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, zarr_format: zarr.core.common.ZarrFormat, fill_value: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_shape: zarr.core.common.ShapeLike | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | tuple[Literal['default'], Literal['.', '/']] | tuple[Literal['v2'], Literal['.', '/']] | None = None, codecs: collections.abc.Iterable[zarr.abc.codec.Codec | dict[str, zarr.core.common.JSON]] | None = None, dimension_names: collections.abc.Iterable[str] | None = None, chunks: zarr.core.common.ShapeLike | None = None, dimension_separator: Literal['.', '/'] | None = None, order: zarr.core.common.MemoryOrder | None = None, filters: list[dict[str, zarr.core.common.JSON]] | None = None, compressor: dict[str, zarr.core.common.JSON] | None = None, overwrite: bool = False, data: numpy.typing.ArrayLike | None = None, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> AsyncArray[zarr.core.metadata.ArrayV3Metadata] | AsyncArray[zarr.core.metadata.ArrayV2Metadata]
      :classmethod:

      :async:


      
      Method to create a new asynchronous array instance.

      .. deprecated:: 3.0.0
          Deprecated in favor of :func:`zarr.api.asynchronous.create_array`.

      :Parameters:

          **store** : StoreLike
              The store where the array will be created.

          **shape** : ShapeLike
              The shape of the array.

          **dtype** : npt.DTypeLike
              The data type of the array.

          **zarr_format** : ZarrFormat, optional
              The Zarr format version (default is 3).

          **fill_value** : Any, optional
              The fill value of the array (default is None).

          **attributes** : dict[str, JSON], optional
              The attributes of the array (default is None).

          **chunk_shape** : ChunkCoords, optional
              The shape of the array's chunks
              Zarr format 3 only. Zarr format 2 arrays should use `chunks` instead.
              If not specified, default are guessed based on the shape and dtype.

          **chunk_key_encoding** : ChunkKeyEncodingLike, optional
              A specification of how the chunk keys are represented in storage.
              Zarr format 3 only. Zarr format 2 arrays should use `dimension_separator` instead.
              Default is ``("default", "/")``.

          **codecs** : Sequence of Codecs or dicts, optional
              An iterable of Codec or dict serializations of Codecs. The elements of
              this collection specify the transformation from array values to stored bytes.
              Zarr format 3 only. Zarr format 2 arrays should use ``filters`` and ``compressor`` instead.
              
              If no codecs are provided, default codecs will be used:
              
              - For numeric arrays, the default is ``BytesCodec`` and ``ZstdCodec``.
              - For Unicode strings, the default is ``VLenUTF8Codec`` and ``ZstdCodec``.
              - For bytes or objects, the default is ``VLenBytesCodec`` and ``ZstdCodec``.
              
              These defaults can be changed by modifying the value of ``array.v3_default_filters``,
              ``array.v3_default_serializer`` and ``array.v3_default_compressors`` in :mod:`zarr.core.config`.

          **dimension_names** : Iterable[str], optional
              The names of the dimensions (default is None).
              Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

          **chunks** : ShapeLike, optional
              The shape of the array's chunks.
              Zarr format 2 only. Zarr format 3 arrays should use ``chunk_shape`` instead.
              If not specified, default are guessed based on the shape and dtype.

          **dimension_separator** : Literal[".", "/"], optional
              The dimension separator (default is ".").
              Zarr format 2 only. Zarr format 3 arrays should use ``chunk_key_encoding`` instead.

          **order** : Literal["C", "F"], optional
              The memory of the array (default is "C").
              If ``zarr_format`` is 2, this parameter sets the memory order of the array.
              If `zarr_format`` is 3, then this parameter is deprecated, because memory order
              is a runtime parameter for Zarr 3 arrays. The recommended way to specify the memory
              order for Zarr 3 arrays is via the ``config`` parameter, e.g. ``{'config': 'C'}``.

          **filters** : list[dict[str, JSON]], optional
              Sequence of filters to use to encode chunk data prior to compression.
              Zarr format 2 only. Zarr format 3 arrays should use ``codecs`` instead. If no ``filters``
              are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v2_default_filters`` in :mod:`zarr.core.config`.

          **compressor** : dict[str, JSON], optional
              The compressor used to compress the data (default is None).
              Zarr format 2 only. Zarr format 3 arrays should use ``codecs`` instead.
              
              If no ``compressor`` is provided, a default compressor will be used:
              
              - For numeric arrays, the default is ``ZstdCodec``.
              - For Unicode strings, the default is ``VLenUTF8Codec``.
              - For bytes or objects, the default is ``VLenBytesCodec``.
              
              These defaults can be changed by modifying the value of ``array.v2_default_compressor`` in :mod:`zarr.core.config`.

          **overwrite** : bool, optional
              Whether to raise an error if the store already exists (default is False).

          **data** : npt.ArrayLike, optional
              The data to be inserted into the array (default is None).

          **config** : ArrayConfigLike, optional
              Runtime configuration for the array.



      :Returns:

          AsyncArray
              The created asynchronous array instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: from_dict(store_path: zarr.storage._common.StorePath, data: dict[str, zarr.core.common.JSON]) -> AsyncArray[zarr.core.metadata.ArrayV3Metadata] | AsyncArray[zarr.core.metadata.ArrayV2Metadata]
      :classmethod:


      
      Create a Zarr array from a dictionary, with support for both Zarr format 2 and 3 metadata.


      :Parameters:

          **store_path** : StorePath
              The path within the store where the array should be created.

          **data** : dict
              A dictionary representing the array data. This dictionary should include necessary metadata
              for the array, such as shape, dtype, and other attributes. The format of the metadata
              will determine whether a Zarr format 2 or 3 array is created.



      :Returns:

          AsyncArray[ArrayV3Metadata] or AsyncArray[ArrayV2Metadata]
              The created Zarr array, either using Zarr format 2 or 3 metadata based on the provided data.




      :Raises:

          ValueError
              If the dictionary data is invalid or incompatible with either Zarr format 2 or 3 array creation.







      ..
          !! processed by numpydoc !!


   .. py:method:: getitem(selection: zarr.core.indexing.BasicSelection, *, prototype: zarr.core.buffer.BufferPrototype | None = None) -> zarr.core.buffer.NDArrayLike
      :async:


      
      Asynchronous function that retrieves a subset of the array's data based on the provided selection.


      :Parameters:

          **selection** : BasicSelection
              A selection object specifying the subset of data to retrieve.

          **prototype** : BufferPrototype, optional
              A buffer prototype to use for the retrieved data (default is None).



      :Returns:

          NDArrayLike
              The retrieved subset of the array's data.










      .. rubric:: Examples

      >>> import zarr
      >>>  store = zarr.storage.MemoryStore(mode='w')
      >>>  async_arr = await zarr.api.asynchronous.create_array(
      ...      store=store,
      ...      shape=(100,100),
      ...      chunks=(10,10),
      ...      dtype='i4',
      ...      fill_value=0)
      <AsyncArray memory://... shape=(100, 100) dtype=int32>
      >>> await async_arr.getitem((0,1)) # doctest: +ELLIPSIS
      array(0, dtype=int32)

      ..
          !! processed by numpydoc !!


   .. py:method:: info_complete() -> Any
      :async:


      
      Return all the information for an array, including dynamic information like a storage size.

      In addition to the static information, this provides

      - The count of chunks initialized
      - The sum of the bytes written




      :Returns:

          ArrayInfo
              ..







      .. seealso::

          
          :obj:`AsyncArray.info`
              A property giving just the statically known information about an array.
          
          



      ..
          !! processed by numpydoc !!


   .. py:method:: nbytes_stored() -> int
      :async:



   .. py:method:: nchunks_initialized() -> int
      :async:


      
      Calculate the number of chunks that have been initialized, i.e. the number of chunks that have
      been persisted to the storage backend.





      :Returns:

          **nchunks_initialized** : int
              The number of chunks that have been initialized.








      .. rubric:: Notes

      On :class:`AsyncArray` this is an asynchronous method, unlike the (synchronous)
      property :attr:`Array.nchunks_initialized`.


      .. rubric:: Examples

      >>> arr = await zarr.api.asynchronous.create(shape=(10,), chunks=(2,))
      >>> await arr.nchunks_initialized()
      0
      >>> await arr.setitem(slice(5), 1)
      >>> await arr.nchunks_initialized()
      3

      ..
          !! processed by numpydoc !!


   .. py:method:: open(store: zarr.storage.StoreLike, zarr_format: zarr.core.common.ZarrFormat | None = 3) -> AsyncArray[zarr.core.metadata.ArrayV3Metadata] | AsyncArray[zarr.core.metadata.ArrayV2Metadata]
      :classmethod:

      :async:


      
      Async method to open an existing Zarr array from a given store.


      :Parameters:

          **store** : StoreLike
              The store containing the Zarr array.

          **zarr_format** : ZarrFormat | None, optional
              The Zarr format version (default is 3).



      :Returns:

          AsyncArray
              The opened Zarr array.










      .. rubric:: Examples

      >>> import zarr
      >>>  store = zarr.storage.MemoryStore(mode='w')
      >>>  async_arr = await AsyncArray.open(store) # doctest: +ELLIPSIS
      <AsyncArray memory://... shape=(100, 100) dtype=int32>

      ..
          !! processed by numpydoc !!


   .. py:method:: resize(new_shape: zarr.core.common.ShapeLike, delete_outside_chunks: bool = True) -> None
      :async:


      
      Asynchronously resize the array to a new shape.


      :Parameters:

          **new_shape** : ChunkCoords
              The desired new shape of the array.

          **delete_outside_chunks** : bool, optional
              If True (default), chunks that fall outside the new shape will be deleted. If False,
              the data in those chunks will be preserved.



      :Returns:

          AsyncArray
              The resized array.




      :Raises:

          ValueError
              If the new shape is incompatible with the current array's chunking configuration.




      .. rubric:: Notes

      - This method is asynchronous and should be awaited.



      ..
          !! processed by numpydoc !!


   .. py:method:: setitem(selection: zarr.core.indexing.BasicSelection, value: numpy.typing.ArrayLike, prototype: zarr.core.buffer.BufferPrototype | None = None) -> None
      :async:


      
      Asynchronously set values in the array using basic indexing.


      :Parameters:

          **selection** : BasicSelection
              The selection defining the region of the array to set.

          **value** : numpy.typing.ArrayLike
              The values to be written into the selected region of the array.

          **prototype** : BufferPrototype or None, optional
              A prototype buffer that defines the structure and properties of the array chunks being modified.
              If None, the default buffer prototype is used. Default is None.



      :Returns:

          None
              This method does not return any value.




      :Raises:

          IndexError
              If the selection is out of bounds for the array.

          ValueError
              If the values are not compatible with the array's dtype or shape.




      .. rubric:: Notes

      - This method is asynchronous and should be awaited.
      - Supports basic indexing, where the selection is contiguous and does not involve advanced indexing.



      ..
          !! processed by numpydoc !!


   .. py:method:: update_attributes(new_attributes: dict[str, zarr.core.common.JSON]) -> Self
      :async:


      
      Asynchronously update the array's attributes.


      :Parameters:

          **new_attributes** : dict of str to JSON
              A dictionary of new attributes to update or add to the array. The keys represent attribute
              names, and the values must be JSON-compatible.



      :Returns:

          AsyncArray
              The array with the updated attributes.




      :Raises:

          ValueError
              If the attributes are invalid or incompatible with the array's metadata.




      .. rubric:: Notes

      - This method is asynchronous and should be awaited.
      - The updated attributes will be merged with existing attributes, and any conflicts will be
        overwritten by the new values.



      ..
          !! processed by numpydoc !!


   .. py:property:: attrs
      :type: dict[str, zarr.core.common.JSON]


      
      Returns the attributes of the array.





      :Returns:

          dict
              Attributes of the array











      ..
          !! processed by numpydoc !!


   .. py:property:: basename
      :type: str


      
      Final component of name.





      :Returns:

          str
              The basename or final component of the array name.











      ..
          !! processed by numpydoc !!


   .. py:property:: cdata_shape
      :type: zarr.core.common.ChunkCoords


      
      The shape of the chunk grid for this array.





      :Returns:

          Tuple[int]
              The shape of the chunk grid for this array.











      ..
          !! processed by numpydoc !!


   .. py:property:: chunks
      :type: zarr.core.common.ChunkCoords


      
      Returns the chunk shape of the Array.
      If sharding is used the inner chunk shape is returned.

      Only defined for arrays using using `RegularChunkGrid`.
      If array doesn't use `RegularChunkGrid`, `NotImplementedError` is raised.




      :Returns:

          ChunkCoords:
              The chunk shape of the Array.











      ..
          !! processed by numpydoc !!


   .. py:attribute:: codec_pipeline
      :type:  zarr.abc.codec.CodecPipeline


   .. py:property:: compressor
      :type: numcodecs.abc.Codec | None


      
      Compressor that is applied to each chunk of the array.

      .. deprecated:: 3.0.0
          `array.compressor` is deprecated and will be removed in a future release.
          Use `array.compressors` instead.















      ..
          !! processed by numpydoc !!


   .. py:property:: compressors
      :type: tuple[numcodecs.abc.Codec, Ellipsis] | tuple[zarr.abc.codec.BytesBytesCodec, Ellipsis]


      
      Compressors that are applied to each chunk of the array. Compressors are applied in order, and after any
      filters are applied (if any are specified) and the data is serialized into bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: dtype
      :type: numpy.dtype[Any]


      
      Returns the data type of the array.





      :Returns:

          np.dtype
              Data type of the array











      ..
          !! processed by numpydoc !!


   .. py:property:: filters
      :type: tuple[numcodecs.abc.Codec, Ellipsis] | tuple[zarr.abc.codec.ArrayArrayCodec, Ellipsis]


      
      Filters that are applied to each chunk of the array, in order, before serializing that
      chunk to bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: info
      :type: Any


      
      Return the statically known information for an array.





      :Returns:

          ArrayInfo
              ..







      .. seealso::

          
          :obj:`AsyncArray.info_complete`
              All information about a group, including dynamic information like the number of bytes and chunks written.
          
          


      .. rubric:: Examples

      >>> arr = await zarr.api.asynchronous.create(
      ...     path="array", shape=(3, 4, 5), chunks=(2, 2, 2))
      ... )
      >>> arr.info
      Type               : Array
      Zarr format        : 3
      Data type          : DataType.float64
      Shape              : (3, 4, 5)
      Chunk shape        : (2, 2, 2)
      Order              : C
      Read-only          : False
      Store type         : MemoryStore
      Codecs             : [{'endian': <Endian.little: 'little'>}]
      No. bytes          : 480

      ..
          !! processed by numpydoc !!


   .. py:attribute:: metadata
      :type:  zarr.core.metadata.T_ArrayMetadata


   .. py:property:: name
      :type: str


      
      Array name following h5py convention.





      :Returns:

          str
              The name of the array.











      ..
          !! processed by numpydoc !!


   .. py:property:: nbytes
      :type: int


      
      The total number of bytes that can be stored in the chunks of this array.













      .. rubric:: Notes

      This value is calculated by multiplying the number of elements in the array and the size
      of each element, the latter of which is determined by the dtype of the array.
      For this reason, ``nbytes`` will likely be inaccurate for arrays with variable-length
      dtypes. It is not possible to determine the size of an array with variable-length elements
      from the shape and dtype alone.



      ..
          !! processed by numpydoc !!


   .. py:property:: nchunks
      :type: int


      
      The number of chunks in the stored representation of this array.





      :Returns:

          int
              The total number of chunks in the array.











      ..
          !! processed by numpydoc !!


   .. py:property:: ndim
      :type: int


      
      Returns the number of dimensions in the Array.





      :Returns:

          int
              The number of dimensions in the Array.











      ..
          !! processed by numpydoc !!


   .. py:property:: order
      :type: zarr.core.common.MemoryOrder


      
      Returns the memory order of the array.





      :Returns:

          bool
              Memory order of the array











      ..
          !! processed by numpydoc !!


   .. py:property:: path
      :type: str


      
      Storage path.





      :Returns:

          str
              The path to the array in the Zarr store.











      ..
          !! processed by numpydoc !!


   .. py:property:: read_only
      :type: bool


      
      Returns True if the array is read-only.





      :Returns:

          bool
              True if the array is read-only











      ..
          !! processed by numpydoc !!


   .. py:property:: serializer
      :type: zarr.abc.codec.ArrayBytesCodec | None


      
      Array-to-bytes codec to use for serializing the chunks into bytes.
















      ..
          !! processed by numpydoc !!


   .. py:property:: shape
      :type: zarr.core.common.ChunkCoords


      
      Returns the shape of the Array.





      :Returns:

          tuple
              The shape of the Array.











      ..
          !! processed by numpydoc !!


   .. py:property:: shards
      :type: zarr.core.common.ChunkCoords | None


      
      Returns the shard shape of the Array.
      Returns None if sharding is not used.

      Only defined for arrays using using `RegularChunkGrid`.
      If array doesn't use `RegularChunkGrid`, `NotImplementedError` is raised.




      :Returns:

          ChunkCoords:
              The shard shape of the Array.











      ..
          !! processed by numpydoc !!


   .. py:property:: size
      :type: int


      
      Returns the total number of elements in the array





      :Returns:

          int
              Total number of elements in the array











      ..
          !! processed by numpydoc !!


   .. py:property:: store
      :type: zarr.abc.store.Store



   .. py:attribute:: store_path
      :type:  zarr.storage._common.StorePath


.. py:class:: AsyncGroup

   
   Asynchronous Group object.
















   ..
       !! processed by numpydoc !!

   .. py:method:: array_keys() -> collections.abc.AsyncGenerator[str, None]
      :async:


      
      Iterate over array names.
















      ..
          !! processed by numpydoc !!


   .. py:method:: array_values() -> collections.abc.AsyncGenerator[zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata], None]
      :async:


      
      Iterate over array values.
















      ..
          !! processed by numpydoc !!


   .. py:method:: arrays() -> collections.abc.AsyncGenerator[tuple[str, zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]], None]
      :async:


      
      Iterate over arrays.
















      ..
          !! processed by numpydoc !!


   .. py:method:: contains(member: str) -> bool
      :async:


      
      Check if a member exists in the group.


      :Parameters:

          **member** : str
              Member name.



      :Returns:

          bool
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_array(name: str, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, chunks: zarr.core.common.ChunkCoords | Literal['auto'] = 'auto', shards: zarr.core.array.ShardsLike | None = None, filters: zarr.core.array.FiltersLike = 'auto', compressors: zarr.core.array.CompressorsLike = 'auto', compressor: zarr.core.array.CompressorLike = 'auto', serializer: zarr.core.array.SerializerLike = 'auto', fill_value: Any | None = 0, order: zarr.core.common.MemoryOrder | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | zarr.core.chunk_key_encodings.ChunkKeyEncodingLike | None = None, dimension_names: collections.abc.Iterable[str] | None = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: zarr.core.array_spec.ArrayConfig | zarr.core.array_spec.ArrayConfigLike | None = None) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an array within this group.

      This method lightly wraps :func:`zarr.core.array.create_array`.

      :Parameters:

          **name** : str
              The name of the array relative to the group. If ``path`` is ``None``, the array will be located
              at the root of the store.

          **shape** : ChunkCoords
              Shape of the array.

          **dtype** : npt.DTypeLike
              Data type of the array.

          **chunks** : ChunkCoords, optional
              Chunk shape of the array.
              If not specified, default are guessed based on the shape and dtype.

          **shards** : ChunkCoords, optional
              Shard shape of the array. The default value of ``None`` results in no sharding at all.

          **filters** : Iterable[Codec], optional
              Iterable of filters to apply to each chunk of the array, in order, before serializing that
              chunk to bytes.
              
              For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
              and these values must be instances of ``ArrayArrayCodec``, or dict representations
              of ``ArrayArrayCodec``.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.
              
              For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
              the order if your filters is consistent with the behavior of each filter.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v2_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.

          **compressors** : Iterable[Codec], optional
              List of compressors to apply to the array. Compressors are applied in order, and after any
              filters are applied (if any are specified) and the data is serialized into bytes.
              
              For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
              returns another bytestream. Multiple compressors my be provided for Zarr format 3.
              If no ``compressors`` are provided, a default set of compressors will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_compressors``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default compressors.
              
              For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
              be provided for Zarr format 2.
              If no ``compressor`` is provided, a default compressor will be used.
              in :mod:`zarr.core.config`.
              Use ``None`` to omit the default compressor.

          **compressor** : Codec, optional
              Deprecated in favor of ``compressors``.

          **serializer** : dict[str, JSON] | ArrayBytesCodec, optional
              Array-to-bytes codec to use for encoding the array data.
              Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.
              If no ``serializer`` is provided, a default serializer will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_serializer``
              in :mod:`zarr.core.config`.

          **fill_value** : Any, optional
              Fill value for the array.

          **order** : {"C", "F"}, optional
              The memory of the array (default is "C").
              For Zarr format 2, this parameter sets the memory order of the array.
              For Zarr format 3, this parameter is deprecated, because memory order
              is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
              order for Zarr format 3 arrays is via the ``config`` parameter, e.g. ``{'config': 'C'}``.
              If no ``order`` is provided, a default order will be used.
              This default can be changed by modifying the value of ``array.order`` in :mod:`zarr.core.config`.

          **attributes** : dict, optional
              Attributes for the array.

          **chunk_key_encoding** : ChunkKeyEncoding, optional
              A specification of how the chunk keys are represented in storage.
              For Zarr format 3, the default is ``{"name": "default", "separator": "/"}}``.
              For Zarr format 2, the default is ``{"name": "v2", "separator": "."}}``.

          **dimension_names** : Iterable[str], optional
              The names of the dimensions (default is None).
              Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

          **storage_options** : dict, optional
              If using an fsspec URL to create the store, these will be passed to the backend implementation.
              Ignored otherwise.

          **overwrite** : bool, default False
              Whether to overwrite an array with the same name in the store, if one exists.

          **config** : ArrayConfig or ArrayConfigLike, optional
              Runtime configuration for the array.



      :Returns:

          AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_dataset(name: str, *, shape: zarr.core.common.ShapeLike, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an array.

      .. deprecated:: 3.0.0
          The h5py compatibility methods will be removed in 3.1.0. Use `AsyncGroup.create_array` instead.

      Arrays are known as "datasets" in HDF5 terminology. For compatibility
      with h5py, Zarr groups also implement the :func:`zarr.AsyncGroup.require_dataset` method.

      :Parameters:

          **name** : str
              Array name.

          **\*\*kwargs** : dict
              Additional arguments passed to :func:`zarr.AsyncGroup.create_array`.



      :Returns:

          **a** : AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_group(name: str, *, overwrite: bool = False, attributes: dict[str, Any] | None = None) -> AsyncGroup
      :async:


      
      Create a sub-group.


      :Parameters:

          **name** : str
              Group name.

          **overwrite** : bool, optional
              If True, do not raise an error if the group already exists.

          **attributes** : dict, optional
              Group attributes.



      :Returns:

          **g** : AsyncGroup
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_hierarchy(nodes: dict[str, zarr.core.metadata.ArrayV2Metadata | zarr.core.metadata.ArrayV3Metadata | GroupMetadata], *, overwrite: bool = False) -> collections.abc.AsyncIterator[tuple[str, AsyncGroup | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]]]
      :async:


      
      Create a hierarchy of arrays or groups rooted at this group.

      This function will parse its input to ensure that the hierarchy is complete. Any implicit groups
      will be inserted as needed. For example, an input like
      ```{'a/b': GroupMetadata}``` will be parsed to
      ```{'': GroupMetadata, 'a': GroupMetadata, 'b': Groupmetadata}```.

      Explicitly specifying a root group, e.g. with ``nodes = {'': GroupMetadata()}`` is an error
      because this group instance is the root group.

      After input parsing, this function then creates all the nodes in the hierarchy concurrently.

      Arrays and Groups are yielded in the order they are created. This order is not stable and
      should not be relied on.

      :Parameters:

          **nodes** : dict[str, GroupMetadata | ArrayV3Metadata | ArrayV2Metadata]
              A dictionary defining the hierarchy. The keys are the paths of the nodes in the hierarchy,
              relative to the path of the group. The values are instances of ``GroupMetadata`` or ``ArrayMetadata``. Note that
              all values must have the same ``zarr_format`` as the parent group -- it is an error to mix zarr versions in the
              same hierarchy.
              
              Leading "/" characters from keys will be removed.

          **overwrite** : bool
              Whether to overwrite existing nodes. Defaults to ``False``, in which case an error is
              raised instead of overwriting an existing array or group.
              
              This function will not erase an existing group unless that group is explicitly named in
              ``nodes``. If ``nodes`` defines implicit groups, e.g. ``{`'a/b/c': GroupMetadata}``, and a
              group already exists at path ``a``, then this function will leave the group at ``a`` as-is.




      :Yields:

          tuple[str, AsyncArray | AsyncGroup].
              ..










      ..
          !! processed by numpydoc !!


   .. py:method:: delitem(key: str) -> None
      :async:


      
      Delete a group member.


      :Parameters:

          **key** : str
              Array or group name














      ..
          !! processed by numpydoc !!


   .. py:method:: empty(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an empty array with the specified shape in this Group. The contents will
      be filled with the array's fill value or zeros if no fill value is provided.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.











      .. rubric:: Notes

      The contents of an empty Zarr array are not defined. On attempting to
      retrieve data from an empty Zarr array, any values may be returned,
      and these are not guaranteed to be stable from one access to the next.



      ..
          !! processed by numpydoc !!


   .. py:method:: empty_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an empty sub-array like `data`. The contents will be filled with
      the array's fill value or zeros if no fill value is provided.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create an empty array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: from_dict(store_path: zarr.storage.StorePath, data: dict[str, Any]) -> AsyncGroup
      :classmethod:



   .. py:method:: from_store(store: zarr.storage.StoreLike, *, attributes: dict[str, Any] | None = None, overwrite: bool = False, zarr_format: zarr.core.common.ZarrFormat = 3) -> AsyncGroup
      :classmethod:

      :async:



   .. py:method:: full(*, name: str, shape: zarr.core.common.ChunkCoords, fill_value: Any | None, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an array, with "fill_value" being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **fill_value** : scalar
              Value to fill the array with.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: full_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create a sub-array like `data` filled with the `fill_value` of `data` .


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: get(key: str, default: DefaultT | None = None) -> zarr.core.array.AsyncArray[Any] | AsyncGroup | DefaultT | None
      :async:


      
      Obtain a group member, returning default if not found.


      :Parameters:

          **key** : str
              Group member name.

          **default** : object
              Default value to return if key is not found (default: None).



      :Returns:

          object
              Group member (AsyncArray or AsyncGroup) or default if not found.











      ..
          !! processed by numpydoc !!


   .. py:method:: getitem(key: str) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata] | AsyncGroup
      :async:


      
      Get a subarray or subgroup from the group.


      :Parameters:

          **key** : str
              Array or group name



      :Returns:

          AsyncArray or AsyncGroup
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: group_keys() -> collections.abc.AsyncGenerator[str, None]
      :async:


      
      Iterate over group names.
















      ..
          !! processed by numpydoc !!


   .. py:method:: group_values() -> collections.abc.AsyncGenerator[AsyncGroup, None]
      :async:


      
      Iterate over group values.
















      ..
          !! processed by numpydoc !!


   .. py:method:: groups() -> collections.abc.AsyncGenerator[tuple[str, AsyncGroup], None]
      :async:


      
      Iterate over subgroups.
















      ..
          !! processed by numpydoc !!


   .. py:method:: info_complete() -> Any
      :async:


      
      Return all the information for a group.

      This includes dynamic information like the number
      of child Groups or Arrays. If this group doesn't contain consolidated
      metadata then this will need to read from the backing Store.




      :Returns:

          GroupInfo
              ..







      .. seealso::

          
          :obj:`AsyncGroup.info`
              ..
          



      ..
          !! processed by numpydoc !!


   .. py:method:: keys() -> collections.abc.AsyncGenerator[str, None]
      :async:


      
      Iterate over member names.
















      ..
          !! processed by numpydoc !!


   .. py:method:: members(max_depth: int | None = 0) -> collections.abc.AsyncGenerator[tuple[str, zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata] | AsyncGroup], None]
      :async:


      
      Returns an AsyncGenerator over the arrays and groups contained in this group.
      This method requires that `store_path.store` supports directory listing.

      The results are not guaranteed to be ordered.

      :Parameters:

          **max_depth** : int, default 0
              The maximum number of levels of the hierarchy to include. By
              default, (``max_depth=0``) only immediate children are included. Set
              ``max_depth=None`` to include all nodes, and some positive integer
              to consider children within that many levels of the root Group.



      :Returns:

          path:
              A string giving the path to the target, relative to the Group ``self``.

          value: AsyncArray or AsyncGroup
              The AsyncArray or AsyncGroup that is a child of ``self``.











      ..
          !! processed by numpydoc !!


   .. py:method:: move(source: str, dest: str) -> None
      :abstractmethod:

      :async:


      
      Move a sub-group or sub-array from one path to another.













      .. rubric:: Notes

      Not implemented



      ..
          !! processed by numpydoc !!


   .. py:method:: nmembers(max_depth: int | None = 0) -> int
      :async:


      
      Count the number of members in this group.


      :Parameters:

          **max_depth** : int, default 0
              The maximum number of levels of the hierarchy to include. By
              default, (``max_depth=0``) only immediate children are included. Set
              ``max_depth=None`` to include all nodes, and some positive integer
              to consider children within that many levels of the root Group.



      :Returns:

          **count** : int
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: ones(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an array, with one being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: ones_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create a sub-array of ones like `data`.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: open(store: zarr.storage.StoreLike, zarr_format: zarr.core.common.ZarrFormat | None = 3, use_consolidated: bool | str | None = None) -> AsyncGroup
      :classmethod:

      :async:


      
      Open a new AsyncGroup


      :Parameters:

          **store** : StoreLike
              ..

          **zarr_format** : {2, 3}, optional
              ..

          **use_consolidated** : bool or str, default None
              Whether to use consolidated metadata.
              
              By default, consolidated metadata is used if it's present in the
              store (in the ``zarr.json`` for Zarr format 3 and in the ``.zmetadata`` file
              for Zarr format 2).
              
              To explicitly require consolidated metadata, set ``use_consolidated=True``,
              which will raise an exception if consolidated metadata is not found.
              
              To explicitly *not* use consolidated metadata, set ``use_consolidated=False``,
              which will fall back to using the regular, non consolidated metadata.
              
              Zarr format 2 allowed configuring the key storing the consolidated metadata
              (``.zmetadata`` by default). Specify the custom key as ``use_consolidated``
              to load consolidated metadata from a non-default key.














      ..
          !! processed by numpydoc !!


   .. py:method:: require_array(name: str, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike = None, exact: bool = False, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Obtain an array, creating if it doesn't exist.

      Other `kwargs` are as per :func:`zarr.AsyncGroup.create_dataset`.

      :Parameters:

          **name** : str
              Array name.

          **shape** : int or tuple of ints
              Array shape.

          **dtype** : str or dtype, optional
              NumPy dtype.

          **exact** : bool, optional
              If True, require `dtype` to match exactly. If false, require
              `dtype` can be cast from array dtype.



      :Returns:

          **a** : AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_dataset(name: str, *, shape: zarr.core.common.ChunkCoords, dtype: numpy.typing.DTypeLike = None, exact: bool = False, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Obtain an array, creating if it doesn't exist.

      .. deprecated:: 3.0.0
          The h5py compatibility methods will be removed in 3.1.0. Use `AsyncGroup.require_dataset` instead.

      Arrays are known as "datasets" in HDF5 terminology. For compatibility
      with h5py, Zarr groups also implement the :func:`zarr.AsyncGroup.create_dataset` method.

      Other `kwargs` are as per :func:`zarr.AsyncGroup.create_dataset`.

      :Parameters:

          **name** : str
              Array name.

          **shape** : int or tuple of ints
              Array shape.

          **dtype** : str or dtype, optional
              NumPy dtype.

          **exact** : bool, optional
              If True, require `dtype` to match exactly. If false, require
              `dtype` can be cast from array dtype.



      :Returns:

          **a** : AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_group(name: str, overwrite: bool = False) -> AsyncGroup
      :async:


      
      Obtain a sub-group, creating one if it doesn't exist.


      :Parameters:

          **name** : str
              Group name.

          **overwrite** : bool, optional
              Overwrite any existing group with given `name` if present.



      :Returns:

          **g** : AsyncGroup
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_groups(*names: str) -> tuple[AsyncGroup, Ellipsis]
      :async:


      
      Convenience method to require multiple groups in a single call.


      :Parameters:

          **\*names** : str
              Group names.



      :Returns:

          Tuple[AsyncGroup, ...]
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: setitem(key: str, value: Any) -> None
      :async:


      
      Fastpath for creating a new array
      New arrays will be created with default array settings for the array type.


      :Parameters:

          **key** : str
              Array name

          **value** : array-like
              Array data














      ..
          !! processed by numpydoc !!


   .. py:method:: tree(expand: bool | None = None, level: int | None = None) -> Any
      :async:


      
      Return a tree-like representation of a hierarchy.

      This requires the optional ``rich`` dependency.

      :Parameters:

          **expand** : bool, optional
              This keyword is not yet supported. A NotImplementedError is raised if
              it's used.

          **level** : int, optional
              The maximum depth below this Group to display in the tree.



      :Returns:

          TreeRepr
              A pretty-printable object displaying the hierarchy.











      ..
          !! processed by numpydoc !!


   .. py:method:: update_attributes(new_attributes: dict[str, Any]) -> AsyncGroup
      :async:


      
      Update group attributes.


      :Parameters:

          **new_attributes** : dict
              New attributes to set on the group.



      :Returns:

          **self** : AsyncGroup
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: zeros(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create an array, with zero being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: zeros_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV2Metadata] | zarr.core.array.AsyncArray[zarr.core.metadata.ArrayV3Metadata]
      :async:


      
      Create a sub-array of zeros like `data`.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          AsyncArray
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:property:: attrs
      :type: dict[str, Any]



   .. py:property:: basename
      :type: str


      
      Final component of name.
















      ..
          !! processed by numpydoc !!


   .. py:property:: info
      :type: Any


      
      Return a visual representation of the statically known information about a group.

      Note that this doesn't include dynamic information, like the number of child
      Groups or Arrays.




      :Returns:

          GroupInfo
              ..







      .. seealso::

          
          :obj:`AsyncGroup.info_complete`
              All information about a group, including dynamic information
          
          



      ..
          !! processed by numpydoc !!


   .. py:attribute:: metadata
      :type:  GroupMetadata


   .. py:property:: name
      :type: str


      
      Group name following h5py convention.
















      ..
          !! processed by numpydoc !!


   .. py:property:: path
      :type: str


      
      Storage path.
















      ..
          !! processed by numpydoc !!


   .. py:property:: read_only
      :type: bool



   .. py:property:: store
      :type: zarr.abc.store.Store



   .. py:attribute:: store_path
      :type:  zarr.storage.StorePath


   .. py:property:: synchronizer
      :type: None



.. py:class:: Group

   Bases: :py:obj:`zarr.core.sync.SyncMixin`


   .. py:method:: array(name: str, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, chunks: zarr.core.common.ChunkCoords | Literal['auto'] = 'auto', shards: zarr.core.common.ChunkCoords | Literal['auto'] | None = None, filters: zarr.core.array.FiltersLike = 'auto', compressors: zarr.core.array.CompressorsLike = 'auto', compressor: zarr.core.array.CompressorLike = None, serializer: zarr.core.array.SerializerLike = 'auto', fill_value: Any | None = 0, order: zarr.core.common.MemoryOrder | None = 'C', attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | zarr.core.chunk_key_encodings.ChunkKeyEncodingLike | None = None, dimension_names: collections.abc.Iterable[str] | None = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: zarr.core.array_spec.ArrayConfig | zarr.core.array_spec.ArrayConfigLike | None = None, data: numpy.typing.ArrayLike | None = None) -> zarr.core.array.Array

      
      Create an array within this group.

      .. deprecated:: 3.0.0
          Use `Group.create_array` instead.

      This method lightly wraps :func:`zarr.core.array.create_array`.

      :Parameters:

          **name** : str
              The name of the array relative to the group. If ``path`` is ``None``, the array will be located
              at the root of the store.

          **shape** : ChunkCoords
              Shape of the array.

          **dtype** : npt.DTypeLike
              Data type of the array.

          **chunks** : ChunkCoords, optional
              Chunk shape of the array.
              If not specified, default are guessed based on the shape and dtype.

          **shards** : ChunkCoords, optional
              Shard shape of the array. The default value of ``None`` results in no sharding at all.

          **filters** : Iterable[Codec], optional
              Iterable of filters to apply to each chunk of the array, in order, before serializing that
              chunk to bytes.
              
              For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
              and these values must be instances of ``ArrayArrayCodec``, or dict representations
              of ``ArrayArrayCodec``.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.
              
              For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
              the order if your filters is consistent with the behavior of each filter.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v2_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.

          **compressors** : Iterable[Codec], optional
              List of compressors to apply to the array. Compressors are applied in order, and after any
              filters are applied (if any are specified) and the data is serialized into bytes.
              
              For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
              returns another bytestream. Multiple compressors my be provided for Zarr format 3.
              If no ``compressors`` are provided, a default set of compressors will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_compressors``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default compressors.
              
              For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
              be provided for Zarr format 2.
              If no ``compressor`` is provided, a default compressor will be used.
              in :mod:`zarr.core.config`.
              Use ``None`` to omit the default compressor.

          **compressor** : Codec, optional
              Deprecated in favor of ``compressors``.

          **serializer** : dict[str, JSON] | ArrayBytesCodec, optional
              Array-to-bytes codec to use for encoding the array data.
              Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.
              If no ``serializer`` is provided, a default serializer will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_serializer``
              in :mod:`zarr.core.config`.

          **fill_value** : Any, optional
              Fill value for the array.

          **order** : {"C", "F"}, optional
              The memory of the array (default is "C").
              For Zarr format 2, this parameter sets the memory order of the array.
              For Zarr format 3, this parameter is deprecated, because memory order
              is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
              order for Zarr format 3 arrays is via the ``config`` parameter, e.g. ``{'config': 'C'}``.
              If no ``order`` is provided, a default order will be used.
              This default can be changed by modifying the value of ``array.order`` in :mod:`zarr.core.config`.

          **attributes** : dict, optional
              Attributes for the array.

          **chunk_key_encoding** : ChunkKeyEncoding, optional
              A specification of how the chunk keys are represented in storage.
              For Zarr format 3, the default is ``{"name": "default", "separator": "/"}}``.
              For Zarr format 2, the default is ``{"name": "v2", "separator": "."}}``.

          **dimension_names** : Iterable[str], optional
              The names of the dimensions (default is None).
              Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

          **storage_options** : dict, optional
              If using an fsspec URL to create the store, these will be passed to the backend implementation.
              Ignored otherwise.

          **overwrite** : bool, default False
              Whether to overwrite an array with the same name in the store, if one exists.

          **config** : ArrayConfig or ArrayConfigLike, optional
              Runtime configuration for the array.

          **data** : array_like
              The data to fill the array with.



      :Returns:

          AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: array_keys() -> collections.abc.Generator[str, None]

      
      Return an iterator over group member names.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_array("subarray", shape=(10,), chunks=(10,))
      >>> for name in group.array_keys():
      ...     print(name)
      subarray

      ..
          !! processed by numpydoc !!


   .. py:method:: array_values() -> collections.abc.Generator[zarr.core.array.Array, None]

      
      Return an iterator over group members.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_array("subarray", shape=(10,), chunks=(10,))
      >>> for subarray in group.array_values():
      ...     print(subarray)
      <Array memory://140198565357056/subarray shape=(10,) dtype=float64>

      ..
          !! processed by numpydoc !!


   .. py:method:: arrays() -> collections.abc.Generator[tuple[str, zarr.core.array.Array], None]

      
      Return the sub-arrays of this group as a generator of (name, array) pairs















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_array("subarray", shape=(10,), chunks=(10,))
      >>> for name, subarray in group.arrays():
      ...     print(name, subarray)
      subarray <Array memory://140198565357056/subarray shape=(10,) dtype=float64>

      ..
          !! processed by numpydoc !!


   .. py:method:: create(*args: Any, **kwargs: Any) -> zarr.core.array.Array


   .. py:method:: create_array(name: str, *, shape: zarr.core.common.ShapeLike, dtype: numpy.typing.DTypeLike, chunks: zarr.core.common.ChunkCoords | Literal['auto'] = 'auto', shards: zarr.core.array.ShardsLike | None = None, filters: zarr.core.array.FiltersLike = 'auto', compressors: zarr.core.array.CompressorsLike = 'auto', compressor: zarr.core.array.CompressorLike = 'auto', serializer: zarr.core.array.SerializerLike = 'auto', fill_value: Any | None = 0, order: zarr.core.common.MemoryOrder | None = 'C', attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | zarr.core.chunk_key_encodings.ChunkKeyEncodingLike | None = None, dimension_names: collections.abc.Iterable[str] | None = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: zarr.core.array_spec.ArrayConfig | zarr.core.array_spec.ArrayConfigLike | None = None) -> zarr.core.array.Array

      
      Create an array within this group.

      This method lightly wraps :func:`zarr.core.array.create_array`.

      :Parameters:

          **name** : str
              The name of the array relative to the group. If ``path`` is ``None``, the array will be located
              at the root of the store.

          **shape** : ChunkCoords
              Shape of the array.

          **dtype** : npt.DTypeLike
              Data type of the array.

          **chunks** : ChunkCoords, optional
              Chunk shape of the array.
              If not specified, default are guessed based on the shape and dtype.

          **shards** : ChunkCoords, optional
              Shard shape of the array. The default value of ``None`` results in no sharding at all.

          **filters** : Iterable[Codec], optional
              Iterable of filters to apply to each chunk of the array, in order, before serializing that
              chunk to bytes.
              
              For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
              and these values must be instances of ``ArrayArrayCodec``, or dict representations
              of ``ArrayArrayCodec``.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.
              
              For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
              the order if your filters is consistent with the behavior of each filter.
              If no ``filters`` are provided, a default set of filters will be used.
              These defaults can be changed by modifying the value of ``array.v2_default_filters``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default filters.

          **compressors** : Iterable[Codec], optional
              List of compressors to apply to the array. Compressors are applied in order, and after any
              filters are applied (if any are specified) and the data is serialized into bytes.
              
              For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
              returns another bytestream. Multiple compressors my be provided for Zarr format 3.
              If no ``compressors`` are provided, a default set of compressors will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_compressors``
              in :mod:`zarr.core.config`.
              Use ``None`` to omit default compressors.
              
              For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
              be provided for Zarr format 2.
              If no ``compressor`` is provided, a default compressor will be used.
              in :mod:`zarr.core.config`.
              Use ``None`` to omit the default compressor.

          **compressor** : Codec, optional
              Deprecated in favor of ``compressors``.

          **serializer** : dict[str, JSON] | ArrayBytesCodec, optional
              Array-to-bytes codec to use for encoding the array data.
              Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.
              If no ``serializer`` is provided, a default serializer will be used.
              These defaults can be changed by modifying the value of ``array.v3_default_serializer``
              in :mod:`zarr.core.config`.

          **fill_value** : Any, optional
              Fill value for the array.

          **order** : {"C", "F"}, optional
              The memory of the array (default is "C").
              For Zarr format 2, this parameter sets the memory order of the array.
              For Zarr format 3, this parameter is deprecated, because memory order
              is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
              order for Zarr format 3 arrays is via the ``config`` parameter, e.g. ``{'config': 'C'}``.
              If no ``order`` is provided, a default order will be used.
              This default can be changed by modifying the value of ``array.order`` in :mod:`zarr.core.config`.

          **attributes** : dict, optional
              Attributes for the array.

          **chunk_key_encoding** : ChunkKeyEncoding, optional
              A specification of how the chunk keys are represented in storage.
              For Zarr format 3, the default is ``{"name": "default", "separator": "/"}}``.
              For Zarr format 2, the default is ``{"name": "v2", "separator": "."}}``.

          **dimension_names** : Iterable[str], optional
              The names of the dimensions (default is None).
              Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

          **storage_options** : dict, optional
              If using an fsspec URL to create the store, these will be passed to the backend implementation.
              Ignored otherwise.

          **overwrite** : bool, default False
              Whether to overwrite an array with the same name in the store, if one exists.

          **config** : ArrayConfig or ArrayConfigLike, optional
              Runtime configuration for the array.



      :Returns:

          AsyncArray
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_dataset(name: str, **kwargs: Any) -> zarr.core.array.Array

      
      Create an array.

      .. deprecated:: 3.0.0
          The h5py compatibility methods will be removed in 3.1.0. Use `Group.create_array` instead.

      Arrays are known as "datasets" in HDF5 terminology. For compatibility
      with h5py, Zarr groups also implement the :func:`zarr.Group.require_dataset` method.

      :Parameters:

          **name** : str
              Array name.

          **\*\*kwargs** : dict
              Additional arguments passed to :func:`zarr.Group.create_array`



      :Returns:

          **a** : Array
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: create_group(name: str, **kwargs: Any) -> Group

      
      Create a sub-group.


      :Parameters:

          **name** : str
              Name of the new subgroup.



      :Returns:

          Group
              ..










      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> subgroup = group.create_group("subgroup")
      >>> subgroup
      <Group memory://132270269438272/subgroup>

      ..
          !! processed by numpydoc !!


   .. py:method:: create_hierarchy(nodes: dict[str, zarr.core.metadata.ArrayV2Metadata | zarr.core.metadata.ArrayV3Metadata | GroupMetadata], *, overwrite: bool = False) -> collections.abc.Iterator[tuple[str, Group | zarr.core.array.Array]]

      
      Create a hierarchy of arrays or groups rooted at this group.

      This function will parse its input to ensure that the hierarchy is complete. Any implicit groups
      will be inserted as needed. For example, an input like
      ```{'a/b': GroupMetadata}``` will be parsed to
      ```{'': GroupMetadata, 'a': GroupMetadata, 'b': Groupmetadata}```.

      Explicitly specifying a root group, e.g. with ``nodes = {'': GroupMetadata()}`` is an error
      because this group instance is the root group.

      After input parsing, this function then creates all the nodes in the hierarchy concurrently.

      Arrays and Groups are yielded in the order they are created. This order is not stable and
      should not be relied on.

      :Parameters:

          **nodes** : dict[str, GroupMetadata | ArrayV3Metadata | ArrayV2Metadata]
              A dictionary defining the hierarchy. The keys are the paths of the nodes in the hierarchy,
              relative to the path of the group. The values are instances of ``GroupMetadata`` or ``ArrayMetadata``. Note that
              all values must have the same ``zarr_format`` as the parent group -- it is an error to mix zarr versions in the
              same hierarchy.
              
              Leading "/" characters from keys will be removed.

          **overwrite** : bool
              Whether to overwrite existing nodes. Defaults to ``False``, in which case an error is
              raised instead of overwriting an existing array or group.
              
              This function will not erase an existing group unless that group is explicitly named in
              ``nodes``. If ``nodes`` defines implicit groups, e.g. ``{`'a/b/c': GroupMetadata}``, and a
              group already exists at path ``a``, then this function will leave the group at ``a`` as-is.




      :Yields:

          tuple[str, Array | Group].
              ..









      .. rubric:: Examples

      >>> import zarr
      >>> from zarr.core.group import GroupMetadata
      >>> root = zarr.create_group(store={})
      >>> for key, val in root.create_hierarchy({'a/b/c': GroupMetadata()}):
      ...   print(key, val)
      ...
      <AsyncGroup memory://123209880766144/a>
      <AsyncGroup memory://123209880766144/a/b/c>
      <AsyncGroup memory://123209880766144/a/b>

      ..
          !! processed by numpydoc !!


   .. py:method:: empty(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

      
      Create an empty array with the specified shape in this Group. The contents will be filled with
      the array's fill value or zeros if no fill value is provided.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.











      .. rubric:: Notes

      The contents of an empty Zarr array are not defined. On attempting to
      retrieve data from an empty Zarr array, any values may be returned,
      and these are not guaranteed to be stable from one access to the next.



      ..
          !! processed by numpydoc !!


   .. py:method:: empty_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

      
      Create an empty sub-array like `data`. The contents will be filled
      with the array's fill value or zeros if no fill value is provided.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create an empty array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.








      .. rubric:: Notes

      The contents of an empty Zarr array are not defined. On attempting to
      retrieve data from an empty Zarr array, any values may be returned,
      and these are not guaranteed to be stable from one access to the next.



      ..
          !! processed by numpydoc !!


   .. py:method:: from_store(store: zarr.storage.StoreLike, *, attributes: dict[str, Any] | None = None, zarr_format: zarr.core.common.ZarrFormat = 3, overwrite: bool = False) -> Group
      :classmethod:


      
      Instantiate a group from an initialized store.


      :Parameters:

          **store** : StoreLike
              StoreLike containing the Group.

          **attributes** : dict, optional
              A dictionary of JSON-serializable values with user-defined attributes.

          **zarr_format** : {2, 3}, optional
              Zarr storage format version.

          **overwrite** : bool, optional
              If True, do not raise an error if the group already exists.



      :Returns:

          Group
              Group instantiated from the store.




      :Raises:

          ContainsArrayError, ContainsGroupError, ContainsArrayAndGroupError
              ..







      ..
          !! processed by numpydoc !!


   .. py:method:: full(*, name: str, shape: zarr.core.common.ChunkCoords, fill_value: Any | None, **kwargs: Any) -> zarr.core.array.Array

      
      Create an array, with "fill_value" being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **fill_value** : scalar
              Value to fill the array with.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: full_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

      
      Create a sub-array like `data` filled with the `fill_value` of `data` .


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: get(path: str, default: DefaultT | None = None) -> zarr.core.array.Array | Group | DefaultT | None

      
      Obtain a group member, returning default if not found.


      :Parameters:

          **path** : str
              Group member name.

          **default** : object
              Default value to return if key is not found (default: None).



      :Returns:

          object
              Group member (Array or Group) or default if not found.










      .. rubric:: Examples

      >>> import zarr
      >>> group = Group.from_store(zarr.storage.MemoryStore()
      >>> group.create_array(name="subarray", shape=(10,), chunks=(10,))
      >>> group.create_group(name="subgroup")
      >>> group.get("subarray")
      <Array memory://132270269438272/subarray shape=(10,) dtype=float64>
      >>> group.get("subgroup")
      <Group memory://132270269438272/subgroup>
      >>> group.get("nonexistent", None)

      ..
          !! processed by numpydoc !!


   .. py:method:: group_keys() -> collections.abc.Generator[str, None]

      
      Return an iterator over group member names.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_group("subgroup")
      >>> for name in group.group_keys():
      ...     print(name)
      subgroup

      ..
          !! processed by numpydoc !!


   .. py:method:: group_values() -> collections.abc.Generator[Group, None]

      
      Return an iterator over group members.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_group("subgroup")
      >>> for subgroup in group.group_values():
      ...     print(subgroup)
      <Group memory://132270269438272/subgroup>

      ..
          !! processed by numpydoc !!


   .. py:method:: groups() -> collections.abc.Generator[tuple[str, Group], None]

      
      Return the sub-groups of this group as a generator of (name, group) pairs.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.create_group("subgroup")
      >>> for name, subgroup in group.groups():
      ...     print(name, subgroup)
      subgroup <Group memory://132270269438272/subgroup>

      ..
          !! processed by numpydoc !!


   .. py:method:: info_complete() -> Any

      
      Return information for a group.

      If this group doesn't contain consolidated metadata then
      this will need to read from the backing Store.




      :Returns:

          GroupInfo
              ..







      .. seealso::

          
          :obj:`Group.info`
              ..
          



      ..
          !! processed by numpydoc !!


   .. py:method:: keys() -> collections.abc.Generator[str, None]

      
      Return an iterator over group member names.















      .. rubric:: Examples

      >>> import zarr
      >>> g1 = zarr.group()
      >>> g2 = g1.create_group('foo')
      >>> g3 = g1.create_group('bar')
      >>> d1 = g1.create_array('baz', shape=(10,), chunks=(10,))
      >>> d2 = g1.create_array('quux', shape=(10,), chunks=(10,))
      >>> for name in g1.keys():
      ...     print(name)
      baz
      bar
      foo
      quux

      ..
          !! processed by numpydoc !!


   .. py:method:: members(max_depth: int | None = 0) -> tuple[tuple[str, zarr.core.array.Array | Group], Ellipsis]

      
      Return the sub-arrays and sub-groups of this group as a tuple of (name, array | group)
      pairs
















      ..
          !! processed by numpydoc !!


   .. py:method:: move(source: str, dest: str) -> None

      
      Move a sub-group or sub-array from one path to another.













      .. rubric:: Notes

      Not implemented



      ..
          !! processed by numpydoc !!


   .. py:method:: nmembers(max_depth: int | None = 0) -> int

      
      Count the number of members in this group.


      :Parameters:

          **max_depth** : int, default 0
              The maximum number of levels of the hierarchy to include. By
              default, (``max_depth=0``) only immediate children are included. Set
              ``max_depth=None`` to include all nodes, and some positive integer
              to consider children within that many levels of the root Group.



      :Returns:

          **count** : int
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: ones(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

      
      Create an array, with one being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: ones_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

      
      Create a sub-array of ones like `data`.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: open(store: zarr.storage.StoreLike, zarr_format: zarr.core.common.ZarrFormat | None = 3) -> Group
      :classmethod:


      
      Open a group from an initialized store.


      :Parameters:

          **store** : StoreLike
              Store containing the Group.

          **zarr_format** : {2, 3, None}, optional
              Zarr storage format version.



      :Returns:

          Group
              Group instantiated from the store.











      ..
          !! processed by numpydoc !!


   .. py:method:: require_array(name: str, *, shape: zarr.core.common.ShapeLike, **kwargs: Any) -> zarr.core.array.Array

      
      Obtain an array, creating if it doesn't exist.

      Other `kwargs` are as per :func:`zarr.Group.create_array`.

      :Parameters:

          **name** : str
              Array name.

          **\*\*kwargs**
              See :func:`zarr.Group.create_array`.



      :Returns:

          **a** : Array
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_dataset(name: str, *, shape: zarr.core.common.ShapeLike, **kwargs: Any) -> zarr.core.array.Array

      
      Obtain an array, creating if it doesn't exist.

      .. deprecated:: 3.0.0
          The h5py compatibility methods will be removed in 3.1.0. Use `Group.require_array` instead.

      Arrays are known as "datasets" in HDF5 terminology. For compatibility
      with h5py, Zarr groups also implement the :func:`zarr.Group.create_dataset` method.

      Other `kwargs` are as per :func:`zarr.Group.create_dataset`.

      :Parameters:

          **name** : str
              Array name.

          **\*\*kwargs**
              See :func:`zarr.Group.create_dataset`.



      :Returns:

          **a** : Array
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_group(name: str, **kwargs: Any) -> Group

      
      Obtain a sub-group, creating one if it doesn't exist.


      :Parameters:

          **name** : str
              Group name.



      :Returns:

          **g** : Group
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: require_groups(*names: str) -> tuple[Group, Ellipsis]

      
      Convenience method to require multiple groups in a single call.


      :Parameters:

          **\*names** : str
              Group names.



      :Returns:

          **groups** : tuple of Groups
              ..











      ..
          !! processed by numpydoc !!


   .. py:method:: tree(expand: bool | None = None, level: int | None = None) -> Any

      
      Return a tree-like representation of a hierarchy.

      This requires the optional ``rich`` dependency.

      :Parameters:

          **expand** : bool, optional
              This keyword is not yet supported. A NotImplementedError is raised if
              it's used.

          **level** : int, optional
              The maximum depth below this Group to display in the tree.



      :Returns:

          TreeRepr
              A pretty-printable object displaying the hierarchy.











      ..
          !! processed by numpydoc !!


   .. py:method:: update_attributes(new_attributes: dict[str, Any]) -> Group

      
      Update the attributes of this group.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> group.update_attributes({"foo": "bar"})
      >>> group.attrs.asdict()
      {'foo': 'bar'}

      ..
          !! processed by numpydoc !!


   .. py:method:: update_attributes_async(new_attributes: dict[str, Any]) -> Group
      :async:


      
      Update the attributes of this group.















      .. rubric:: Examples

      >>> import zarr
      >>> group = zarr.group()
      >>> await group.update_attributes_async({"foo": "bar"})
      >>> group.attrs.asdict()
      {'foo': 'bar'}

      ..
          !! processed by numpydoc !!


   .. py:method:: zeros(*, name: str, shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

      
      Create an array, with zero being used as the default value for uninitialized portions of the array.


      :Parameters:

          **name** : str
              Name of the array.

          **shape** : int or tuple of int
              Shape of the empty array.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:method:: zeros_like(*, name: str, data: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

      
      Create a sub-array of zeros like `data`.


      :Parameters:

          **name** : str
              Name of the array.

          **data** : array-like
              The array to create the new array like.

          **\*\*kwargs**
              Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



      :Returns:

          Array
              The new array.











      ..
          !! processed by numpydoc !!


   .. py:property:: attrs
      :type: zarr.core.attributes.Attributes


      
      Attributes of this Group
















      ..
          !! processed by numpydoc !!


   .. py:property:: basename
      :type: str


      
      Final component of name.
















      ..
          !! processed by numpydoc !!


   .. py:property:: info
      :type: Any


      
      Return the statically known information for a group.





      :Returns:

          GroupInfo
              ..







      .. seealso::

          
          :obj:`Group.info_complete`
              All information about a group, including dynamic information like the children members.
          
          



      ..
          !! processed by numpydoc !!


   .. py:property:: metadata
      :type: GroupMetadata


      
      Group metadata.
















      ..
          !! processed by numpydoc !!


   .. py:property:: name
      :type: str


      
      Group name following h5py convention.
















      ..
          !! processed by numpydoc !!


   .. py:property:: path
      :type: str


      
      Storage path.
















      ..
          !! processed by numpydoc !!


   .. py:property:: read_only
      :type: bool



   .. py:property:: store
      :type: zarr.abc.store.Store



   .. py:property:: store_path
      :type: zarr.storage.StorePath


      
      Path-like interface for the Store.
















      ..
          !! processed by numpydoc !!


   .. py:property:: synchronizer
      :type: None



.. py:function:: array(data: numpy.typing.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array filled with `data`.


   :Parameters:

       **data** : array_like
           The data to fill the array with.

       **\*\*kwargs**
           Passed through to :func:`create`.



   :Returns:

       **array** : Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: consolidate_metadata(store: zarr.storage.StoreLike, path: str | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None) -> zarr.core.group.Group

   
   Consolidate the metadata of all nodes in a hierarchy.

   Upon completion, the metadata of the root node in the Zarr hierarchy will be
   updated to include all the metadata of child nodes.

   :Parameters:

       **store** : StoreLike
           The store-like object whose metadata you wish to consolidate.

       **path** : str, optional
           A path to a group in the store to consolidate at. Only children
           below that group will be consolidated.
           
           By default, the root node is used so all the metadata in the
           store is consolidated.

       **zarr_format** : {2, 3, None}, optional
           The zarr format of the hierarchy. By default the zarr format
           is inferred.



   :Returns:

       group: Group
           The group, with the ``consolidated_metadata`` field set to include
           the metadata of each child node.











   ..
       !! processed by numpydoc !!

.. py:function:: copy(*args: Any, **kwargs: Any) -> tuple[int, int, int]

.. py:function:: copy_all(*args: Any, **kwargs: Any) -> tuple[int, int, int]

.. py:function:: copy_store(*args: Any, **kwargs: Any) -> tuple[int, int, int]

.. py:function:: create(shape: zarr.core.common.ChunkCoords | int, *, chunks: zarr.core.common.ChunkCoords | int | bool | None = None, dtype: numpy.typing.DTypeLike | None = None, compressor: dict[str, zarr.core.common.JSON] | None = None, fill_value: Any | None = 0, order: zarr.core.common.MemoryOrder | None = None, store: str | zarr.storage.StoreLike | None = None, synchronizer: Any | None = None, overwrite: bool = False, path: zarr.api.asynchronous.PathLike | None = None, chunk_store: zarr.storage.StoreLike | None = None, filters: list[dict[str, zarr.core.common.JSON]] | None = None, cache_metadata: bool | None = None, cache_attrs: bool | None = None, read_only: bool | None = None, object_codec: zarr.abc.codec.Codec | None = None, dimension_separator: Literal['.', '/'] | None = None, write_empty_chunks: bool | None = None, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, meta_array: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_shape: zarr.core.common.ChunkCoords | int | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | tuple[Literal['default'], Literal['.', '/']] | tuple[Literal['v2'], Literal['.', '/']] | None = None, codecs: collections.abc.Iterable[zarr.abc.codec.Codec | dict[str, zarr.core.common.JSON]] | None = None, dimension_names: collections.abc.Iterable[str] | None = None, storage_options: dict[str, Any] | None = None, config: zarr.core.array_spec.ArrayConfigLike | None = None, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array.


   :Parameters:

       **shape** : int or tuple of ints
           Array shape.

       **chunks** : int or tuple of ints, optional
           Chunk shape. If True, will be guessed from `shape` and `dtype`. If
           False, will be set to `shape`, i.e., single chunk for the whole array.
           If an int, the chunk size in each dimension will be given by the value
           of `chunks`. Default is True.

       **dtype** : str or dtype, optional
           NumPy dtype.

       **compressor** : Codec, optional
           Primary compressor.

       **fill_value** : object
           Default value to use for uninitialized portions of the array.

       **order** : {'C', 'F'}, optional
           Deprecated in favor of the ``config`` keyword argument.
           Pass ``{'order': <value>}`` to ``create`` instead of using this parameter.
           Memory layout to be used within each chunk.
           If not specified, the ``array.order`` parameter in the global config will be used.

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **synchronizer** : object, optional
           Array synchronizer.

       **overwrite** : bool, optional
           If True, delete all pre-existing data in `store` at `path` before
           creating the array.

       **path** : str, optional
           Path under which array is stored.

       **chunk_store** : MutableMapping, optional
           Separate storage for chunks. If not provided, `store` will be used
           for storage of both chunks and metadata.

       **filters** : sequence of Codecs, optional
           Sequence of filters to use to encode chunk data prior to compression.

       **cache_metadata** : bool, optional
           If True, array configuration metadata will be cached for the
           lifetime of the object. If False, array metadata will be reloaded
           prior to all data access and modification operations (may incur
           overhead depending on storage and data access pattern).

       **cache_attrs** : bool, optional
           If True (default), user attributes will be cached for attribute read
           operations. If False, user attributes are reloaded from the store prior
           to all attribute read operations.

       **read_only** : bool, optional
           True if array should be protected against modification.

       **object_codec** : Codec, optional
           A codec to encode object arrays, only needed if dtype=object.

       **dimension_separator** : {'.', '/'}, optional
           Separator placed between the dimensions of a chunk.

       **write_empty_chunks** : bool, optional
           Deprecated in favor of the ``config`` keyword argument.
           Pass ``{'write_empty_chunks': <value>}`` to ``create`` instead of using this parameter.
           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.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **meta_array** : array-like, optional
           An array instance to use for determining arrays to create and return
           to users. Use `numpy.empty(())` by default.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **config** : ArrayConfigLike, optional
           Runtime configuration of the array. If provided, will override the
           default values from `zarr.config.array`.



   :Returns:

       **z** : Array
           The array.











   ..
       !! processed by numpydoc !!

.. py:function:: create_array(store: str | zarr.storage.StoreLike, *, name: str | None = None, shape: zarr.core.common.ShapeLike | None = None, dtype: numpy.typing.DTypeLike | None = None, data: numpy.ndarray[Any, numpy.dtype[Any]] | None = None, chunks: zarr.core.common.ChunkCoords | Literal['auto'] = 'auto', shards: zarr.core.array.ShardsLike | None = None, filters: zarr.core.array.FiltersLike = 'auto', compressors: zarr.core.array.CompressorsLike = 'auto', serializer: zarr.core.array.SerializerLike = 'auto', fill_value: Any | None = None, order: zarr.core.common.MemoryOrder | None = None, zarr_format: zarr.core.common.ZarrFormat | None = 3, attributes: dict[str, zarr.core.common.JSON] | None = None, chunk_key_encoding: zarr.core.chunk_key_encodings.ChunkKeyEncoding | zarr.core.chunk_key_encodings.ChunkKeyEncodingLike | None = None, dimension_names: collections.abc.Iterable[str] | None = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: zarr.core.array_spec.ArrayConfigLike | None = None) -> zarr.core.array.Array

   
   Create an array.

   This function wraps :func:`zarr.core.array.create_array`.

   :Parameters:

       **store** : str or Store
           Store or path to directory in file system or name of zip file.

       **name** : str or None, optional
           The name of the array within the store. If ``name`` is ``None``, the array will be located
           at the root of the store.

       **shape** : ChunkCoords, optional
           Shape of the array. Can be ``None`` if ``data`` is provided.

       **dtype** : npt.DTypeLike, optional
           Data type of the array. Can be ``None`` if ``data`` is provided.

       **data** : np.ndarray, optional
           Array-like data to use for initializing the array. If this parameter is provided, the
           ``shape`` and ``dtype`` parameters must be identical to ``data.shape`` and ``data.dtype``,
           or ``None``.

       **chunks** : ChunkCoords, optional
           Chunk shape of the array.
           If not specified, default are guessed based on the shape and dtype.

       **shards** : ChunkCoords, optional
           Shard shape of the array. The default value of ``None`` results in no sharding at all.

       **filters** : Iterable[Codec], optional
           Iterable of filters to apply to each chunk of the array, in order, before serializing that
           chunk to bytes.
           
           For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
           and these values must be instances of ``ArrayArrayCodec``, or dict representations
           of ``ArrayArrayCodec``.
           If no ``filters`` are provided, a default set of filters will be used.
           These defaults can be changed by modifying the value of ``array.v3_default_filters``
           in :mod:`zarr.core.config`.
           Use ``None`` to omit default filters.
           
           For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
           the order if your filters is consistent with the behavior of each filter.
           If no ``filters`` are provided, a default set of filters will be used.
           These defaults can be changed by modifying the value of ``array.v2_default_filters``
           in :mod:`zarr.core.config`.
           Use ``None`` to omit default filters.

       **compressors** : Iterable[Codec], optional
           List of compressors to apply to the array. Compressors are applied in order, and after any
           filters are applied (if any are specified) and the data is serialized into bytes.
           
           For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
           returns another bytestream. Multiple compressors my be provided for Zarr format 3.
           If no ``compressors`` are provided, a default set of compressors will be used.
           These defaults can be changed by modifying the value of ``array.v3_default_compressors``
           in :mod:`zarr.core.config`.
           Use ``None`` to omit default compressors.
           
           For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
           be provided for Zarr format 2.
           If no ``compressor`` is provided, a default compressor will be used.
           in :mod:`zarr.core.config`.
           Use ``None`` to omit the default compressor.

       **serializer** : dict[str, JSON] | ArrayBytesCodec, optional
           Array-to-bytes codec to use for encoding the array data.
           Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.
           If no ``serializer`` is provided, a default serializer will be used.
           These defaults can be changed by modifying the value of ``array.v3_default_serializer``
           in :mod:`zarr.core.config`.

       **fill_value** : Any, optional
           Fill value for the array.

       **order** : {"C", "F"}, optional
           The memory of the array (default is "C").
           For Zarr format 2, this parameter sets the memory order of the array.
           For Zarr format 3, this parameter is deprecated, because memory order
           is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
           order for Zarr format 3 arrays is via the ``config`` parameter, e.g. ``{'config': 'C'}``.
           If no ``order`` is provided, a default order will be used.
           This default can be changed by modifying the value of ``array.order`` in :mod:`zarr.core.config`.

       **zarr_format** : {2, 3}, optional
           The zarr format to use when saving.

       **attributes** : dict, optional
           Attributes for the array.

       **chunk_key_encoding** : ChunkKeyEncoding, optional
           A specification of how the chunk keys are represented in storage.
           For Zarr format 3, the default is ``{"name": "default", "separator": "/"}}``.
           For Zarr format 2, the default is ``{"name": "v2", "separator": "."}}``.

       **dimension_names** : Iterable[str], optional
           The names of the dimensions (default is None).
           Zarr format 3 only. Zarr format 2 arrays should not use this parameter.

       **storage_options** : dict, optional
           If using an fsspec URL to create the store, these will be passed to the backend implementation.
           Ignored otherwise.

       **overwrite** : bool, default False
           Whether to overwrite an array with the same name in the store, if one exists.

       **config** : ArrayConfigLike, optional
           Runtime configuration for the array.



   :Returns:

       Array
           The array.










   .. rubric:: Examples

   >>> import zarr
   >>> store = zarr.storage.MemoryStore(mode='w')
   >>> arr = await zarr.create_array(
   >>>     store=store,
   >>>     shape=(100,100),
   >>>     chunks=(10,10),
   >>>     dtype='i4',
   >>>     fill_value=0)
   <Array memory://140349042942400 shape=(100, 100) dtype=int32>

   ..
       !! processed by numpydoc !!

.. py:function:: create_group(store: zarr.storage.StoreLike, *, path: str | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, overwrite: bool = False, attributes: dict[str, Any] | None = None, storage_options: dict[str, Any] | None = None) -> zarr.core.group.Group

   
   Create a group.


   :Parameters:

       **store** : Store or str
           Store or path to directory in file system.

       **path** : str, optional
           Group path within store.

       **overwrite** : bool, optional
           If True, pre-existing data at ``path`` will be deleted before
           creating the group.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.
           If no ``zarr_format`` is provided, the default format will be used.
           This default can be changed by modifying the value of ``default_zarr_format``
           in :mod:`zarr.core.config`.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.



   :Returns:

       Group
           The new group.











   ..
       !! processed by numpydoc !!

.. py:function:: create_hierarchy(*, store: zarr.abc.store.Store, nodes: dict[str, zarr.core.group.GroupMetadata | zarr.core.metadata.ArrayV2Metadata | zarr.core.metadata.ArrayV3Metadata], overwrite: bool = False) -> collections.abc.Iterator[tuple[str, zarr.core.group.Group | zarr.core.array.Array]]

   
   Create a complete zarr hierarchy from a collection of metadata objects.

   This function will parse its input to ensure that the hierarchy is complete. Any implicit groups
   will be inserted as needed. For example, an input like
   ```{'a/b': GroupMetadata}``` will be parsed to
   ```{'': GroupMetadata, 'a': GroupMetadata, 'b': Groupmetadata}```

   After input parsing, this function then creates all the nodes in the hierarchy concurrently.

   Arrays and Groups are yielded in the order they are created. This order is not stable and
   should not be relied on.

   :Parameters:

       **store** : Store
           The storage backend to use.

       **nodes** : dict[str, GroupMetadata | ArrayV3Metadata | ArrayV2Metadata]
           A dictionary defining the hierarchy. The keys are the paths of the nodes in the hierarchy,
           relative to the root of the ``Store``. The root of the store can be specified with the empty
           string ``''``. The values are instances of ``GroupMetadata`` or ``ArrayMetadata``. Note that
           all values must have the same ``zarr_format`` -- it is an error to mix zarr versions in the
           same hierarchy.
           
           Leading "/" characters from keys will be removed.

       **overwrite** : bool
           Whether to overwrite existing nodes. Defaults to ``False``, in which case an error is
           raised instead of overwriting an existing array or group.
           
           This function will not erase an existing group unless that group is explicitly named in
           ``nodes``. If ``nodes`` defines implicit groups, e.g. ``{`'a/b/c': GroupMetadata}``, and a
           group already exists at path ``a``, then this function will leave the group at ``a`` as-is.




   :Yields:

       tuple[str, Group | Array]
           This function yields (path, node) pairs, in the order the nodes were created.









   .. rubric:: Examples

   >>> from zarr import create_hierarchy
   >>> from zarr.storage import MemoryStore
   >>> from zarr.core.group import GroupMetadata

   >>> store = MemoryStore()
   >>> nodes = {'a': GroupMetadata(attributes={'name': 'leaf'})}
   >>> nodes_created = dict(create_hierarchy(store=store, nodes=nodes))
   >>> print(nodes)
   # {'a': GroupMetadata(attributes={'name': 'leaf'}, zarr_format=3, consolidated_metadata=None, node_type='group')}

   ..
       !! processed by numpydoc !!

.. py:function:: empty(shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

   
   Create an empty array with the specified shape. The contents will be filled with the
   array's fill value or zeros if no fill value is provided.


   :Parameters:

       **shape** : int or tuple of int
           Shape of the empty array.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.








   .. rubric:: Notes

   The contents of an empty Zarr array are not defined. On attempting to
   retrieve data from an empty Zarr array, any values may be returned,
   and these are not guaranteed to be stable from one access to the next.



   ..
       !! processed by numpydoc !!

.. py:function:: empty_like(a: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

   
   Create an empty array like another array. The contents will be filled with the
   array's fill value or zeros if no fill value is provided.


   :Parameters:

       **a** : array-like
           The array to create an empty array like.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.








   .. rubric:: Notes

   The contents of an empty Zarr array are not defined. On attempting to
   retrieve data from an empty Zarr array, any values may be returned,
   and these are not guaranteed to be stable from one access to the next.



   ..
       !! processed by numpydoc !!

.. py:function:: full(shape: zarr.core.common.ChunkCoords, fill_value: Any, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array with a default fill value.


   :Parameters:

       **shape** : int or tuple of int
           Shape of the empty array.

       **fill_value** : scalar
           Fill value.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: full_like(a: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

   
   Create a filled array like another array.


   :Parameters:

       **a** : array-like
           The array to create an empty array like.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: group(store: zarr.storage.StoreLike | None = None, *, overwrite: bool = False, chunk_store: zarr.storage.StoreLike | None = None, cache_attrs: bool | None = None, synchronizer: Any | None = None, path: str | None = None, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, meta_array: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, storage_options: dict[str, Any] | None = None) -> zarr.core.group.Group

   
   Create a group.


   :Parameters:

       **store** : Store or str, optional
           Store or path to directory in file system.

       **overwrite** : bool, optional
           If True, delete any pre-existing data in `store` at `path` before
           creating the group.

       **chunk_store** : Store, optional
           Separate storage for chunks. If not provided, `store` will be used
           for storage of both chunks and metadata.

       **cache_attrs** : bool, optional
           If True (default), user attributes will be cached for attribute read
           operations. If False, user attributes are reloaded from the store prior
           to all attribute read operations.

       **synchronizer** : object, optional
           Array synchronizer.

       **path** : str, optional
           Group path within store.

       **meta_array** : array-like, optional
           An array instance to use for determining arrays to create and return
           to users. Use `numpy.empty(())` by default.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.



   :Returns:

       **g** : Group
           The new group.











   ..
       !! processed by numpydoc !!

.. py:function:: load(store: zarr.storage.StoreLike, path: str | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, zarr_version: zarr.core.common.ZarrFormat | None = None) -> zarr.core.buffer.NDArrayLike | dict[str, zarr.core.buffer.NDArrayLike]

   
   Load data from an array or group into memory.


   :Parameters:

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **path** : str or None, optional
           The path within the store from which to load.



   :Returns:

       out
           If the path contains an array, out will be a numpy array. If the path contains
           a group, out will be a dict-like object where keys are array names and values
           are numpy arrays.







   .. seealso::

       
       :obj:`save`, :obj:`savez`
           ..
       
   .. rubric:: Notes

   If loading data from a group of arrays, data will not be immediately loaded into
   memory. Rather, arrays will be loaded into memory as they are requested.



   ..
       !! processed by numpydoc !!

.. py:function:: ones(shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array with a fill value of one.


   :Parameters:

       **shape** : int or tuple of int
           Shape of the empty array.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: ones_like(a: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array of ones like another array.


   :Parameters:

       **a** : array-like
           The array to create an empty array like.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: open(store: zarr.storage.StoreLike | None = None, *, mode: zarr.core.common.AccessModeLiteral = 'a', zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, path: str | None = None, storage_options: dict[str, Any] | None = None, **kwargs: Any) -> zarr.core.array.Array | zarr.core.group.Group

   
   Open a group or array using file-mode-like semantics.


   :Parameters:

       **store** : Store or str, optional
           Store or path to directory in file system or name of zip file.

       **mode** : {'r', 'r+', 'a', 'w', 'w-'}, optional
           Persistence mode: 'r' means read only (must exist); 'r+' means
           read/write (must exist); 'a' means read/write (create if doesn't
           exist); 'w' means create (overwrite if exists); 'w-' means create
           (fail if exists).

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **path** : str or None, optional
           The path within the store to open.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **\*\*kwargs**
           Additional parameters are passed through to :func:`zarr.api.asynchronous.open_array` or
           :func:`zarr.api.asynchronous.open_group`.



   :Returns:

       **z** : array or group
           Return type depends on what exists in the given store.











   ..
       !! processed by numpydoc !!

.. py:function:: open_array(store: zarr.storage.StoreLike | None = None, *, zarr_version: zarr.core.common.ZarrFormat | None = None, path: zarr.api.asynchronous.PathLike = '', storage_options: dict[str, Any] | None = None, **kwargs: Any) -> zarr.core.array.Array

   
   Open an array using file-mode-like semantics.


   :Parameters:

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **zarr_version** : {2, 3, None}, optional
           The zarr format to use when saving.

       **path** : str, optional
           Path in store to array.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **\*\*kwargs**
           Any keyword arguments to pass to ``create``.



   :Returns:

       AsyncArray
           The opened array.











   ..
       !! processed by numpydoc !!

.. py:function:: open_consolidated(*args: Any, use_consolidated: Literal[True] = True, **kwargs: Any) -> zarr.core.group.Group

   
   Alias for :func:`open_group` with ``use_consolidated=True``.
















   ..
       !! processed by numpydoc !!

.. py:function:: open_group(store: zarr.storage.StoreLike | None = None, *, mode: zarr.core.common.AccessModeLiteral = 'a', cache_attrs: bool | None = None, synchronizer: Any = None, path: str | None = None, chunk_store: zarr.storage.StoreLike | None = None, storage_options: dict[str, Any] | None = None, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, meta_array: Any | None = None, attributes: dict[str, zarr.core.common.JSON] | None = None, use_consolidated: bool | str | None = None) -> zarr.core.group.Group

   
   Open a group using file-mode-like semantics.


   :Parameters:

       **store** : Store, str, or mapping, optional
           Store or path to directory in file system or name of zip file.
           
           Strings are interpreted as paths on the local file system
           and used as the ``root`` argument to :class:`zarr.storage.LocalStore`.
           
           Dictionaries are used as the ``store_dict`` argument in
           :class:`zarr.storage.MemoryStore``.
           
           By default (``store=None``) a new :class:`zarr.storage.MemoryStore`
           is created.

       **mode** : {'r', 'r+', 'a', 'w', 'w-'}, optional
           Persistence mode: 'r' means read only (must exist); 'r+' means
           read/write (must exist); 'a' means read/write (create if doesn't
           exist); 'w' means create (overwrite if exists); 'w-' means create
           (fail if exists).

       **cache_attrs** : bool, optional
           If True (default), user attributes will be cached for attribute read
           operations. If False, user attributes are reloaded from the store prior
           to all attribute read operations.

       **synchronizer** : object, optional
           Array synchronizer.

       **path** : str, optional
           Group path within store.

       **chunk_store** : Store or str, optional
           Store or path to directory in file system or name of zip file.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **meta_array** : array-like, optional
           An array instance to use for determining arrays to create and return
           to users. Use `numpy.empty(())` by default.

       **attributes** : dict
           A dictionary of JSON-serializable values with user-defined attributes.

       **use_consolidated** : bool or str, default None
           Whether to use consolidated metadata.
           
           By default, consolidated metadata is used if it's present in the
           store (in the ``zarr.json`` for Zarr format 3 and in the ``.zmetadata`` file
           for Zarr format 2).
           
           To explicitly require consolidated metadata, set ``use_consolidated=True``,
           which will raise an exception if consolidated metadata is not found.
           
           To explicitly *not* use consolidated metadata, set ``use_consolidated=False``,
           which will fall back to using the regular, non consolidated metadata.
           
           Zarr format 2 allows configuring the key storing the consolidated metadata
           (``.zmetadata`` by default). Specify the custom key as ``use_consolidated``
           to load consolidated metadata from a non-default key.



   :Returns:

       **g** : Group
           The new group.











   ..
       !! processed by numpydoc !!

.. py:function:: open_like(a: zarr.api.asynchronous.ArrayLike, path: str, **kwargs: Any) -> zarr.core.array.Array

   
   Open a persistent array like another array.


   :Parameters:

       **a** : Array
           The shape and data-type of a define these same attributes of the returned array.

       **path** : str
           The path to the new array.

       **\*\*kwargs**
           Any keyword arguments to pass to the array constructor.



   :Returns:

       AsyncArray
           The opened array.











   ..
       !! processed by numpydoc !!

.. py:function:: save(store: zarr.storage.StoreLike, *args: zarr.core.buffer.NDArrayLike, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, path: str | None = None, **kwargs: Any) -> None

   
   Save an array or group of arrays to the local file system.


   :Parameters:

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **\*args** : ndarray
           NumPy arrays with data to save.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **path** : str or None, optional
           The path within the group where the arrays will be saved.

       **\*\*kwargs**
           NumPy arrays with data to save.














   ..
       !! processed by numpydoc !!

.. py:function:: save_array(store: zarr.storage.StoreLike, arr: zarr.core.buffer.NDArrayLike, *, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, path: str | None = None, storage_options: dict[str, Any] | None = None, **kwargs: Any) -> None

   
   Save a NumPy array to the local file system.

   Follows a similar API to the NumPy save() function.

   :Parameters:

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **arr** : ndarray
           NumPy array with data to save.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **path** : str or None, optional
           The path within the store where the array will be saved.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **\*\*kwargs**
           Passed through to :func:`create`, e.g., compressor.














   ..
       !! processed by numpydoc !!

.. py:function:: save_group(store: zarr.storage.StoreLike, *args: zarr.core.buffer.NDArrayLike, zarr_version: zarr.core.common.ZarrFormat | None = None, zarr_format: zarr.core.common.ZarrFormat | None = None, path: str | None = None, storage_options: dict[str, Any] | None = None, **kwargs: zarr.core.buffer.NDArrayLike) -> None

   
   Save several NumPy arrays to the local file system.

   Follows a similar API to the NumPy savez()/savez_compressed() functions.

   :Parameters:

       **store** : Store or str
           Store or path to directory in file system or name of zip file.

       **\*args** : ndarray
           NumPy arrays with data to save.

       **zarr_format** : {2, 3, None}, optional
           The zarr format to use when saving.

       **path** : str or None, optional
           Path within the store where the group will be saved.

       **storage_options** : dict
           If using an fsspec URL to create the store, these will be passed to
           the backend implementation. Ignored otherwise.

       **\*\*kwargs**
           NumPy arrays with data to save.














   ..
       !! processed by numpydoc !!

.. py:function:: tree(grp: zarr.core.group.Group, expand: bool | None = None, level: int | None = None) -> Any

   
   Provide a rich display of the hierarchy.

   .. deprecated:: 3.0.0
       `zarr.tree()` is deprecated and will be removed in a future release.
       Use `group.tree()` instead.

   :Parameters:

       **grp** : Group
           Zarr or h5py group.

       **expand** : bool, optional
           Only relevant for HTML representation. If True, tree will be fully expanded.

       **level** : int, optional
           Maximum depth to descend into hierarchy.



   :Returns:

       TreeRepr
           A pretty-printable object displaying the hierarchy.











   ..
       !! processed by numpydoc !!

.. py:function:: zeros(shape: zarr.core.common.ChunkCoords, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array with a fill value of zero.


   :Parameters:

       **shape** : int or tuple of int
           Shape of the empty array.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:function:: zeros_like(a: zarr.api.asynchronous.ArrayLike, **kwargs: Any) -> zarr.core.array.Array

   
   Create an array of zeros like another array.


   :Parameters:

       **a** : array-like
           The array to create an empty array like.

       **\*\*kwargs**
           Keyword arguments passed to :func:`zarr.api.asynchronous.create`.



   :Returns:

       Array
           The new array.











   ..
       !! processed by numpydoc !!

.. py:data:: config

