pyarrow.DataType#

classpyarrow.DataType#

Bases:_Weakrefable

Base class of all Arrow data types.

Each data type is aninstance of this class.

Examples

Instance of int64 type:

>>>importpyarrowaspa>>>pa.int64()DataType(int64)

__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.

bit_width#

Bit width for fixed width type.

Examples

>>>importpyarrowaspa>>>pa.int64()DataType(int64)>>>pa.int64().bit_width64

byte_width#

Byte width for fixed width type.

Examples

>>>importpyarrowaspa>>>pa.int64()DataType(int64)>>>pa.int64().byte_width8

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

Return true if type is equivalent to passed value.

Parameters:

otherDataType orstrconvertible toDataType

check_metadatabool

Whether nested Field metadata equality should be checked as well.

Returns:

is_equalbool

Examples

>>>importpyarrowaspa>>>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 onlylower-bounded by num_buffers.

Examples

>>>importpyarrowaspa>>>pa.int64().has_variadic_buffersFalse>>>pa.string_view().has_variadic_buffersTrue

id#

num_buffers#

Number of data buffers required to construct Array typeexcluding children.

Examples

>>>importpyarrowaspa>>>pa.int64().num_buffers2>>>pa.string().num_buffers3

num_fields#

The number of child fields.

Examples

>>>importpyarrowaspa>>>pa.int64()DataType(int64)>>>pa.int64().num_fields0>>>pa.list_(pa.string())ListType(list<item: string>)>>>pa.list_(pa.string()).num_fields1>>>struct=pa.struct({'x':pa.int32(),'y':pa.string()})>>>struct.num_fields2

to_pandas_dtype(self)#

Return the equivalent NumPy / Pandas dtype.

Examples

>>>importpyarrowaspa>>>pa.int64().to_pandas_dtype()<class 'numpy.int64'>