pyarrow.StructType#

classpyarrow.StructType#

Bases:DataType

Concrete class for struct data types.

StructType supports direct indexing using[...] (implemented via__getitem__) to access its fields.It will return the struct field with the given index or name.

Examples

>>>importpyarrowaspa

Accessing fields using direct indexing:

>>>struct_type=pa.struct({'x':pa.int32(),'y':pa.string()})>>>struct_type[0]pyarrow.Field<x: int32>>>>struct_type['y']pyarrow.Field<y: string>

Accessing fields usingfield():

>>>struct_type.field(1)pyarrow.Field<y: string>>>>struct_type.field('x')pyarrow.Field<x: int32>

# Creating a schema from the struct type’s fields:>>> pa.schema(list(struct_type))x: int32y: 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)	Select a field by its column name or numeric index.
get_all_field_indices(self, name)	Return sorted list of indices for the fields with the given name.
get_field_index(self, name)	Return index of the unique field with the given name.
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.
fields	Lists all fields within the StructType.
has_variadic_buffers	If True, the number of expected buffers is only lower-bounded by num_buffers.
id
names	Lists the field names.
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#

Select a field by its column name or numeric index.

Parameters:

iint orstr

Returns:

pyarrow.Field

Examples

>>>importpyarrowaspa>>>struct_type=pa.struct({'x':pa.int32(),'y':pa.string()})

Select the second field:

>>>struct_type.field(1)pyarrow.Field<y: string>

Select the field named ‘x’:

>>>struct_type.field('x')pyarrow.Field<x: int32>

fields#

Lists all fields within the StructType.

Examples

>>>importpyarrowaspa>>>struct_type=pa.struct([('a',pa.int64()),('b',pa.float64()),('c',pa.string())])>>>struct_type.fields[pyarrow.Field<a: int64>, pyarrow.Field<b: double>, pyarrow.Field<c: string>]

get_all_field_indices(self,name)#

Return sorted list of indices for the fields with the given name.

Parameters:

namestr

The name of the field to look up.

Returns:

indicesList[int]

Examples

>>>importpyarrowaspa>>>struct_type=pa.struct({'x':pa.int32(),'y':pa.string()})>>>struct_type.get_all_field_indices('x')[0]

get_field_index(self,name)#

Return index of the unique field with the given name.

Parameters:

namestr

The name of the field to look up.

Returns:

indexint

The index of the field with the given name; -1 if thename isn’t found or there are several fields with the givenname.

Examples

>>>importpyarrowaspa>>>struct_type=pa.struct({'x':pa.int32(),'y':pa.string()})

Index of the field with a name ‘y’:

>>>struct_type.get_field_index('y')1

Index of the field that does not exist:

>>>struct_type.get_field_index('z')-1

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#

names#

Lists the field names.

Examples

>>>importpyarrowaspa>>>struct_type=pa.struct([('a',pa.int64()),('b',pa.float64()),('c',pa.string())])>>>struct_type.names['a', 'b', 'c']

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'>