Table Classes

Each Dataset object is backed by a PyArrow Table. A Table can be loaded from either the disk (memory mapped) or in memory. Several Table types are available, and they all inherit from [table.Table].

Table

[[autodoc]] datasets.table.Table - validate - equals - to_batches - to_pydict - to_pandas - to_string - field - column - itercolumns - schema - columns - num_columns - num_rows - shape - nbytes

InMemoryTable

[[autodoc]] datasets.table.InMemoryTable - validate - equals - to_batches - to_pydict - to_pandas - to_string - field - column - itercolumns - schema - columns - num_columns - num_rows - shape - nbytes - column_names - slice - filter - flatten - combine_chunks - cast - replace_schema_metadata - add_column - append_column - remove_column - set_column - rename_columns - select - drop - from_file - from_buffer - from_pandas - from_arrays - from_pydict - from_batches

MemoryMappedTable

[[autodoc]] datasets.table.MemoryMappedTable - validate - equals - to_batches - to_pydict - to_pandas - to_string - field - column - itercolumns - schema - columns - num_columns - num_rows - shape - nbytes - column_names - slice - filter - flatten - combine_chunks - cast - replace_schema_metadata - add_column - append_column - remove_column - set_column - rename_columns - select - drop - from_file

ConcatenationTable

[[autodoc]] datasets.table.ConcatenationTable - validate - equals - to_batches - to_pydict - to_pandas - to_string - field - column - itercolumns - schema - columns - num_columns - num_rows - shape - nbytes - column_names - slice - filter - flatten - combine_chunks - cast - replace_schema_metadata - add_column - append_column - remove_column - set_column - rename_columns - select - drop - from_blocks - from_tables

Utils

[[autodoc]] datasets.table.concat_tables

[[autodoc]] datasets.table.list_table_cache_files