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 for fixed width type.
Byte width for fixed width type.
If True, the number of expected buffers is only lower-bounded by num_buffers.
The field for items in the map entries.
The data type of items in the map entries.
The field for keys in the map entries.
The data type of keys in the map entries.
Should the entries be sorted according to keys.
Number of data buffers required to construct Array type excluding children.
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:
- Returns:
- is_equalbool
Examples
>>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True
- 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'>