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'>