pyarrow.JsonType#

class pyarrow.JsonType#

Bases: BaseExtensionType

Concrete class for JSON extension type.

Examples

Define the extension type for JSON array

>>> import pyarrow as pa
>>> json_type = pa.json_(pa.large_utf8())

Create an extension array

>>> arr = [None, '{ "id":30, "values":["a", "b"] }']
>>> storage = pa.array(arr, pa.large_utf8())
>>> pa.ExtensionArray.from_storage(json_type, storage)
<pyarrow.lib.JsonArray object at ...>
[
  null,
  "{ "id":30, "values":["a", "b"] }"
]

__init__(*args, **kwargs)#

Methods

`__init__`(args, *kwargs)
`equals`(self, other, *[, check_metadata])	Return true if type is equivalent to passed value.
`field`(self, i)	Parameters:
`to_pandas_dtype`(self)	Return the equivalent NumPy / Pandas dtype.
`wrap_array`(self, storage)	Wrap the given storage array as an extension array.

Attributes

`bit_width`	The bit width of the extension type.
`byte_width`	The byte width of the extension type.
`extension_name`	The extension type name.
`has_variadic_buffers`	If True, the number of expected buffers is only lower-bounded by num_buffers.
`id`
`num_buffers`	Number of data buffers required to construct Array type excluding children.
`num_fields`	The number of child fields.
`storage_type`	The underlying storage type.

bit_width#: The bit width of the extension type.

byte_width#: The byte width of the extension type.

equals(self, other, *, check_metadata=False)#

Return true if type is equivalent to passed value.

Parameters:

otherDataType or str convertible to DataType
check_metadatabool: Whether nested Field metadata equality should be checked as well.

Returns:

is_equalbool

Examples

>>> import pyarrow as pa
>>> pa.int64().equals(pa.string())
False
>>> pa.int64().equals(pa.int64())
True

extension_name#: The extension type name.

field(self, i) → Field#

Parameters:

iint

Returns:

pyarrow.Field

has_variadic_buffers#

If True, the number of expected buffers is only lower-bounded by num_buffers.

Examples

>>> import pyarrow as pa
>>> pa.int64().has_variadic_buffers
False
>>> pa.string_view().has_variadic_buffers
True

id#

num_buffers#

Number of data buffers required to construct Array type excluding children.

Examples

>>> import pyarrow as pa
>>> pa.int64().num_buffers
2
>>> pa.string().num_buffers
3

num_fields#

The number of child fields.

Examples

>>> import pyarrow as pa
>>> pa.int64()
DataType(int64)
>>> pa.int64().num_fields
0
>>> pa.list_(pa.string())
ListType(list<item: string>)
>>> pa.list_(pa.string()).num_fields
1
>>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
>>> struct.num_fields
2

storage_type#: The underlying storage type.

to_pandas_dtype(self)#

Return the equivalent NumPy / Pandas dtype.

Examples

>>> import pyarrow as pa
>>> pa.int64().to_pandas_dtype()
<class 'numpy.int64'>

wrap_array(self, storage)#

Wrap the given storage array as an extension array.

Parameters:

storageArray or ChunkedArray

Returns:

arrayArray or ChunkedArray: Extension array wrapping the storage array