pyarrow.ListViewType#
- class pyarrow.ListViewType#
- Bases: - DataType- Concrete class for list view data types. - Examples - Create an instance of ListViewType: - >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view<item: 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)- 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. - Number of data buffers required to construct Array type excluding children. - The number of child fields. - The field for list view values. - The data type of list view values. - 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#
 - 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'> 
 - value_field#
- The field for list view values. - Examples - >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_field pyarrow.Field<item: string> 
 - value_type#
- The data type of list view values. - Examples - >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_type DataType(string) 
 
 
    