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
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 using field():
>>> struct_type.field(1)
pyarrow.Field<y: string>
>>> struct_type.field('x')
pyarrow.Field<x: int32>
# Creating a schema from the struct typeâs fields: >>> pa.schema(list(struct_type)) x: int32 y: string
Methods
Attributes
Bit width for fixed width type.
Examples
>>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64
Byte width for fixed width type.
Examples
>>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().byte_width 8
Return true if type is equivalent to passed value.
DataType or str convertible to DataType
Whether nested Field metadata equality should be checked as well.
Examples
>>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True
Select a field by its column name or numeric index.
int or str
pyarrow.Field
Examples
>>> import pyarrow as pa
>>> 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 âxâ:
>>> struct_type.field('x')
pyarrow.Field<x: int32>
Lists all fields within the StructType.
Examples
>>> import pyarrow as pa
>>> 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>]
Return sorted list of indices for the fields with the given name.
str
The name of the field to look up.
List[int]
Examples
>>> import pyarrow as pa
>>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
>>> struct_type.get_all_field_indices('x')
[0]
Return index of the unique field with the given name.
str
The name of the field to look up.
int
The index of the field with the given name; -1 if the name isnât found or there are several fields with the given name.
Examples
>>> import pyarrow as pa
>>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
Index of the field with a name âyâ:
>>> struct_type.get_field_index('y')
1
Index of the field that does not exist:
>>> struct_type.get_field_index('z')
-1
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
Lists the field names.
Examples
>>> import pyarrow as pa
>>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())])
>>> struct_type.names
['a', 'b', 'c']
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
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
Return the equivalent NumPy / Pandas dtype.
Examples
>>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() <class 'numpy.int64'>
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