pyarrow.LargeListViewType

class pyarrow.LargeListViewType

Bases: DataType

Concrete class for large list view data types (like ListViewType, but with 64-bit offsets).

Examples

Create an instance of LargeListViewType:

>>> import pyarrow as pa
>>> pa.large_list_view(pa.string())
LargeListViewType(large_list_view<item: string>)

__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.

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
num_buffers	Number of data buffers required to construct Array type excluding children.
num_fields	The number of child fields.
value_field	The field for large list view values.
value_type	The data type of large list view values.

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

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

to_pandas_dtype(self)

Return the equivalent NumPy / Pandas dtype.

Examples

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

value_field

The field for large list view values.

Examples

>>> import pyarrow as pa
>>> pa.large_list_view(pa.string()).value_field
pyarrow.Field<item: string>

value_type

The data type of large list view values.

Examples

>>> import pyarrow as pa
>>> pa.large_list_view(pa.string()).value_type
DataType(string)