pyarrow.MapType#

class pyarrow.MapType#

Bases: DataType

Concrete class for map data types.

Examples

Create an instance of MapType:

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32())
MapType(map<string, int32>)
>>> pa.map_(pa.string(), pa.int32(), keys_sorted=True)
MapType(map<string, int32, keys_sorted>)

__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`
`item_field`	The field for items in the map entries.
`item_type`	The data type of items in the map entries.
`key_field`	The field for keys in the map entries.
`key_type`	The data type of keys in the map entries.
`keys_sorted`	Should the entries be sorted according to keys.
`num_buffers`	Number of data buffers required to construct Array type excluding children.
`num_fields`	The number of child fields.

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#

item_field#

The field for items in the map entries.

Examples

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32()).item_field
pyarrow.Field<value: int32>

item_type#

The data type of items in the map entries.

Examples

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32()).item_type
DataType(int32)

key_field#

The field for keys in the map entries.

Examples

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32()).key_field
pyarrow.Field<key: string not null>

key_type#

The data type of keys in the map entries.

Examples

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32()).key_type
DataType(string)

keys_sorted#

Should the entries be sorted according to keys.

Examples

>>> import pyarrow as pa
>>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted
True

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'>