pyarrow.DenseUnionType#

class pyarrow.DenseUnionType#

Bases: UnionType

Concrete class for dense union types.

Examples

Create an instance of a dense UnionType using pa.union:

>>> import pyarrow as pa
>>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
...          mode=pa.lib.UnionMode_DENSE),
(DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>),)

Create an instance of a dense UnionType using pa.dense_union:

>>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>)

__init__(*args, **kwargs)#

Methods

__init__(*args,**kwargs)
equals(self,other,*[,check_metadata])	Return true if type is equivalent to passed value.
field(self,i)	Return a child field by its numeric index.
to_pandas_dtype(self)	Return the equivalent NumPy / Pandas dtype.

Attributes

bit_width	Bit width for fixed width type.
byte_width	Byte width for fixed width type.
has_variadic_buffers	If True, the number of expected buffers is only lower-bounded by num_buffers.
id
mode	The mode of the union ("dense" or "sparse").
num_buffers	Number of data buffers required to construct Array type excluding children.
num_fields	The number of child fields.
type_codes	The type code to indicate each data type in this union.

bit_width#

Bit width for fixed width type.

Examples

>>> import pyarrow as pa
>>> pa.int64()
DataType(int64)
>>> pa.int64().bit_width
64

byte_width#

Byte width for fixed width type.

Examples

>>> import pyarrow as pa
>>> pa.int64()
DataType(int64)
>>> pa.int64().byte_width
8

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

field(self, i) → Field#

Return a child field by its numeric index.

Parameters:

iint

Returns:

pyarrow.Field

Examples

>>> import pyarrow as pa
>>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
>>> union[0]
pyarrow.Field<a: fixed_size_binary[10]>

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#

mode#

The mode of the union (dense or sparse).

Examples

>>> import pyarrow as pa
>>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
>>> union.mode
'sparse'

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

to_pandas_dtype(self)#

Return the equivalent NumPy / Pandas dtype.

Examples

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

type_codes#

The type code to indicate each data type in this union.

Examples

>>> import pyarrow as pa
>>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
>>> union.type_codes
[0, 1]