Add docstrings for array module (#2281)

* Add docstrings for array module

* style: pre-commit fixes

* Add more docstrings

* Remove docstrings for internal functions

* Fix pre-commit

* Minor fix

* Precommit fix

* Update src/zarr/core/array.py

Co-authored-by: Joe Hamman <[email protected]>

* Update src/zarr/core/array.py

Co-authored-by: Joe Hamman <[email protected]>

* Update src/zarr/core/array.py

Co-authored-by: Joe Hamman <[email protected]>

* Update src/zarr/core/array.py

Co-authored-by: Joe Hamman <[email protected]>

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Joe Hamman <[email protected]>
Co-authored-by: Joe Hamman <[email protected]>

Author: 3 people

src/zarr/core/array.py

@@ -645,6 +645,29 @@ def from_dict(
         store_path: StorePath,
         data: dict[str, JSON],
     ) -> AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata]:
+        """
+        Create a Zarr array from a dictionary, with support for both Zarr v2 and v3 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 v2 or v3 array is created.
+
+        Returns
+        -------
+        AsyncArray[ArrayV3Metadata] or AsyncArray[ArrayV2Metadata]
+            The created Zarr array, either using v2 or v3 metadata based on the provided data.
+
+        Raises
+        ------
+        ValueError
+            If the dictionary data is invalid or incompatible with either Zarr v2 or v3 array creation.
+        """
         metadata = parse_array_metadata(data)
         return cls(metadata=metadata, store_path=store_path)
 
@@ -1040,6 +1063,9 @@ async def getitem(
         return await self._get_selection(indexer, prototype=prototype)
 
     async def _save_metadata(self, metadata: ArrayMetadata, ensure_parents: bool = False) -> None:
+        """
+        Asynchronously save the array metadata.
+        """
         to_save = metadata.to_buffer_dict(default_buffer_prototype())
         awaitables = [set_or_delete(self.store_path / key, value) for key, value in to_save.items()]
 
@@ -1118,6 +1144,39 @@ async def setitem(
         value: npt.ArrayLike,
         prototype: BufferPrototype | None = None,
     ) -> None:
+        """
+        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.
+
+        Notes
+        -----
+        - This method is asynchronous and should be awaited.
+        - Supports basic indexing, where the selection is contiguous and does not involve advanced indexing.
+        """
         if prototype is None:
             prototype = default_buffer_prototype()
         indexer = BasicIndexer(
@@ -1128,6 +1187,32 @@ async def setitem(
         return await self._set_selection(indexer, value, prototype=prototype)
 
     async def resize(self, new_shape: ShapeLike, delete_outside_chunks: bool = True) -> None:
+        """
+        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.
+
+        Notes
+        -----
+        - This method is asynchronous and should be awaited.
+        """
         new_shape = parse_shapelike(new_shape)
         assert len(new_shape) == len(self.metadata.shape)
         new_metadata = self.metadata.update_shape(new_shape)
@@ -1210,6 +1295,31 @@ async def append(self, data: npt.ArrayLike, axis: int = 0) -> ChunkCoords:
         return new_shape
 
     async def update_attributes(self, new_attributes: dict[str, JSON]) -> Self:
+        """
+        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.
+
+        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.
+        """
         # metadata.attributes is "frozen" so we simply clear and update the dict
         self.metadata.attributes.clear()
         self.metadata.attributes.update(new_attributes)
@@ -1328,6 +1438,28 @@ def from_dict(
         store_path: StorePath,
         data: dict[str, JSON],
     ) -> Array:
+        """
+        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.
+        """
         async_array = AsyncArray.from_dict(store_path=store_path, data=data)
         return cls(async_array)
 
@@ -2934,6 +3066,30 @@ def append(self, data: npt.ArrayLike, axis: int = 0) -> ChunkCoords:
         return sync(self._async_array.append(data, axis=axis))
 
     def update_attributes(self, new_attributes: dict[str, 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.
+
+        Notes
+        -----
+        - The updated attributes will be merged with existing attributes, and any conflicts will be
+          overwritten by the new values.
+        """
         # TODO: remove this cast when type inference improves
         new_array = sync(self._async_array.update_attributes(new_attributes))
         # TODO: remove this cast when type inference improves