pyarrow.DictionaryType#
- class pyarrow.DictionaryType#
Bases:
DataType
Concrete class for dictionary data types.
Examples
Create an instance of dictionary type:
>>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary<values=string, indices=int64, ordered=0>)
- __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.
The data type of dictionary indices (a signed integer type).
Number of data buffers required to construct Array type excluding children.
The number of child fields.
Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important.
The dictionary value type.
- bit_width#
Bit width for fixed width type.
Examples
>>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64
- 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
- id#
- index_type#
The data type of dictionary indices (a signed integer type).
Examples
>>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).index_type DataType(int16)
- 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
- ordered#
Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important.
Examples
>>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()).ordered False
- to_pandas_dtype(self)#
Return the equivalent NumPy / Pandas dtype.
Examples
>>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() <class 'numpy.int64'>
- value_type#
The dictionary value type.
The dictionary values are found in an instance of DictionaryArray.
Examples
>>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).value_type DataType(string)