datasets/pr_8113/en/package_reference/table_classes.md

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[[datasets.table.Table]]

datasets.table.Table[[datasets.table.Table]]

Source

Wraps a pyarrow Table by using composition. This is the base class for InMemoryTable, MemoryMappedTable and ConcatenationTable.

It implements all the basic attributes/methods of the pyarrow Table class except the Table transforms: slice, filter, flatten, combine_chunks, cast, add_column, append_column, remove_column, set_column, rename_columns and drop.

The implementation of these methods differs for the subclasses.

validatedatasets.table.Table.validatehttps://github.com/huggingface/datasets/blob/r_8113/src/datasets/table.py#L187[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]- full (bool, defaults to False) -- If True, run expensive checks, otherwise cheap checks only.0- pa.lib.ArrowInvalid -- if validation failspa.lib.ArrowInvalid

Perform validation checks. An exception is raised if validation fails.

By default only cheap validation checks are run. Pass full=True for thorough validation checks (potentially O(n)).

Parameters:

full (bool, defaults to False) : If True, run expensive checks, otherwise cheap checks only.

equals[[datasets.table.Table.equals]]

Source

Check if contents of two tables are equal.

Parameters:

other (Table) : Table to compare against.

check_metadata bool, defaults to False) : Whether schema metadata equality should be checked as well.

Returns:

bool

to_batches[[datasets.table.Table.to_batches]]

Source

Convert Table to list of (contiguous) RecordBatch objects.

Parameters:

max_chunksize (int, defaults to None) : Maximum size for RecordBatch chunks. Individual chunks may be smaller depending on the chunk layout of individual columns.

Returns:

List[pyarrow.RecordBatch]

to_pydict[[datasets.table.Table.to_pydict]]

Source

Convert the Table to a dict or OrderedDict.

Returns:

dict

to_pandas[[datasets.table.Table.to_pandas]]

Source

Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.

Parameters:

memory_pool (MemoryPool, defaults to None) : Arrow MemoryPool to use for allocations. Uses the default memory pool is not passed.

strings_to_categorical (bool, defaults to False) : Encode string (UTF8) and binary types to pandas.Categorical.

categories (list, defaults to empty) : List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.

zero_copy_only (bool, defaults to False) : Raise an ArrowException if this function call would require copying the underlying data.

integer_object_nulls (bool, defaults to False) : Cast integers with nulls to objects.

date_as_object (bool, defaults to True) : Cast dates to objects. If False, convert to datetime64[ns] dtype.

timestamp_as_object (bool, defaults to False) : Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). If False, all timestamps are converted to datetime64[ns] dtype.

use_threads (bool, defaults to True) : Whether to parallelize the conversion using multiple threads.

deduplicate_objects (bool, defaults to False) : Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.

ignore_metadata (bool, defaults to False) : If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present.

safe (bool, defaults to True) : For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not.

split_blocks (bool, defaults to False) : If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use.

self_destruct (bool, defaults to False) : EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program.

types_mapper (function, defaults to None) : A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. If you have a dictionary mapping, you can pass dict.get as function.

Returns:

pandas.Series` or `pandas.DataFrame

pandas.Series or pandas.DataFrame depending on type of object

to_string[[datasets.table.Table.to_string]]

Source

field[[datasets.table.Table.field]]

Source

Select a schema field by its column name or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the field to retrieve.

Returns:

pyarrow.Field

column[[datasets.table.Table.column]]

Source

Select a column by its column name, or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the column to retrieve.

Returns:

pyarrow.ChunkedArray

itercolumns[[datasets.table.Table.itercolumns]]

Source

Iterator over all columns in their numerical order.

schema[[datasets.table.Table.schema]]

Source

Schema of the table and its columns.

Returns:

pyarrow.Schema

columns[[datasets.table.Table.columns]]

Source

List of all columns in numerical order.

Returns:

List[pa.ChunkedArray]

num_columns[[datasets.table.Table.num_columns]]

Source

Number of columns in this table.

Returns:

int

num_rows[[datasets.table.Table.num_rows]]

Source

Number of rows in this table.

Due to the definition of a table, all columns have the same number of rows.

Returns:

int

shape[[datasets.table.Table.shape]]

Source

Dimensions of the table: (#rows, #columns).

Returns:

(int, int)

Number of rows and number of columns.

nbytes[[datasets.table.Table.nbytes]]

Source

Total number of bytes consumed by the elements of the table.

InMemoryTable[[datasets.table.InMemoryTable]]

datasets.table.InMemoryTable[[datasets.table.InMemoryTable]]

Source

The table is said in-memory when it is loaded into the user's RAM.

Pickling it does copy all the data using memory. Its implementation is simple and uses the underlying pyarrow Table methods directly.

This is different from the MemoryMapped table, for which pickling doesn't copy all the data in memory. For a MemoryMapped, unpickling instead reloads the table from the disk.

InMemoryTable must be used when data fit in memory, while MemoryMapped are reserved for data bigger than memory or when you want the memory footprint of your application to stay low.

validatedatasets.table.InMemoryTable.validatehttps://github.com/huggingface/datasets/blob/r_8113/src/datasets/table.py#L187[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]- full (bool, defaults to False) -- If True, run expensive checks, otherwise cheap checks only.0- pa.lib.ArrowInvalid -- if validation failspa.lib.ArrowInvalid

Perform validation checks. An exception is raised if validation fails.

By default only cheap validation checks are run. Pass full=True for thorough validation checks (potentially O(n)).

Parameters:

full (bool, defaults to False) : If True, run expensive checks, otherwise cheap checks only.

equals[[datasets.table.InMemoryTable.equals]]

Source

Check if contents of two tables are equal.

Parameters:

other (Table) : Table to compare against.

check_metadata bool, defaults to False) : Whether schema metadata equality should be checked as well.

Returns:

bool

to_batches[[datasets.table.InMemoryTable.to_batches]]

Source

Convert Table to list of (contiguous) RecordBatch objects.

Parameters:

max_chunksize (int, defaults to None) : Maximum size for RecordBatch chunks. Individual chunks may be smaller depending on the chunk layout of individual columns.

Returns:

List[pyarrow.RecordBatch]

to_pydict[[datasets.table.InMemoryTable.to_pydict]]

Source

Convert the Table to a dict or OrderedDict.

Returns:

dict

to_pandas[[datasets.table.InMemoryTable.to_pandas]]

Source

Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.

Parameters:

memory_pool (MemoryPool, defaults to None) : Arrow MemoryPool to use for allocations. Uses the default memory pool is not passed.

strings_to_categorical (bool, defaults to False) : Encode string (UTF8) and binary types to pandas.Categorical.

categories (list, defaults to empty) : List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.

zero_copy_only (bool, defaults to False) : Raise an ArrowException if this function call would require copying the underlying data.

integer_object_nulls (bool, defaults to False) : Cast integers with nulls to objects.

date_as_object (bool, defaults to True) : Cast dates to objects. If False, convert to datetime64[ns] dtype.

timestamp_as_object (bool, defaults to False) : Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). If False, all timestamps are converted to datetime64[ns] dtype.

use_threads (bool, defaults to True) : Whether to parallelize the conversion using multiple threads.

deduplicate_objects (bool, defaults to False) : Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.

ignore_metadata (bool, defaults to False) : If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present.

safe (bool, defaults to True) : For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not.

split_blocks (bool, defaults to False) : If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use.

self_destruct (bool, defaults to False) : EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program.

types_mapper (function, defaults to None) : A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. If you have a dictionary mapping, you can pass dict.get as function.

Returns:

pandas.Series` or `pandas.DataFrame

pandas.Series or pandas.DataFrame depending on type of object

to_string[[datasets.table.InMemoryTable.to_string]]

Source

field[[datasets.table.InMemoryTable.field]]

Source

Select a schema field by its column name or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the field to retrieve.

Returns:

pyarrow.Field

column[[datasets.table.InMemoryTable.column]]

Source

Select a column by its column name, or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the column to retrieve.

Returns:

pyarrow.ChunkedArray

itercolumns[[datasets.table.InMemoryTable.itercolumns]]

Source

Iterator over all columns in their numerical order.

schema[[datasets.table.InMemoryTable.schema]]

Source

Schema of the table and its columns.

Returns:

pyarrow.Schema

columns[[datasets.table.InMemoryTable.columns]]

Source

List of all columns in numerical order.

Returns:

List[pa.ChunkedArray]

num_columns[[datasets.table.InMemoryTable.num_columns]]

Source

Number of columns in this table.

Returns:

int

num_rows[[datasets.table.InMemoryTable.num_rows]]

Source

Number of rows in this table.

Due to the definition of a table, all columns have the same number of rows.

Returns:

int

shape[[datasets.table.InMemoryTable.shape]]

Source

Dimensions of the table: (#rows, #columns).

Returns:

(int, int)

Number of rows and number of columns.

nbytes[[datasets.table.InMemoryTable.nbytes]]

Source

Total number of bytes consumed by the elements of the table.

column_names[[datasets.table.InMemoryTable.column_names]]

Source

Names of the table's columns.

slice[[datasets.table.InMemoryTable.slice]]

Source

Compute zero-copy slice of this Table.

Parameters:

offset (int, defaults to 0) : Offset from start of table to slice.

length (int, defaults to None) : Length of slice (default is until end of table starting from offset).

Returns:

datasets.table.Table

filter[[datasets.table.InMemoryTable.filter]]

Source

Select records from a Table. See pyarrow.compute.filter for full usage.

flatten[[datasets.table.InMemoryTable.flatten]]

Source

Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

combine_chunks[[datasets.table.InMemoryTable.combine_chunks]]

Source

Make a new table by combining the chunks this table has.

All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

cast[[datasets.table.InMemoryTable.cast]]

Source

Cast table values to another schema.

Parameters:

target_schema (Schema) : Schema to cast to, the names and order of fields must match.

safe (bool, defaults to True) : Check for overflows or other unsafe conversions.

Returns:

datasets.table.Table

replace_schema_metadata[[datasets.table.InMemoryTable.replace_schema_metadata]]

Source

EXPERIMENTAL: Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata).

Parameters:

metadata (dict, defaults to None) --

Returns:

datasets.table.Table

shallow_copy

add_column[[datasets.table.InMemoryTable.add_column]]

Source

Add column to Table at position.

A new table is returned with the column added, the original table object is left unchanged.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

append_column[[datasets.table.InMemoryTable.append_column]]

Source

Append column at end of columns.

Parameters:

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

remove_column[[datasets.table.InMemoryTable.remove_column]]

Source

Create new Table with the indicated column removed.

Parameters:

i (int) : Index of column to remove.

Returns:

datasets.table.Table

New table without the column.

set_column[[datasets.table.InMemoryTable.set_column]]

Source

Replace column in Table at position.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column set.

rename_columns[[datasets.table.InMemoryTable.rename_columns]]

Source

Create new table with columns renamed to provided names.

select[[datasets.table.InMemoryTable.select]]

Source

Select columns of the table.

Returns a new table with the specified columns, and metadata preserved.

Parameters:

columns (Union[List[str], List[int]]) : The column names or integer indices to select.

Returns:

[datasets.table.Table](/docs/datasets/pr_8113/en/package_reference/table_classes#datasets.table.Table)

New table with the specified columns, and metadata preserved.

drop[[datasets.table.InMemoryTable.drop]]

Source

Drop one or more columns and return a new table.

Parameters:

columns (List[str]) : List of field names referencing existing columns.

Returns:

datasets.table.Table

New table without the columns.

from_file[[datasets.table.InMemoryTable.from_file]]

Source

from_buffer[[datasets.table.InMemoryTable.from_buffer]]

Source

from_pandas[[datasets.table.InMemoryTable.from_pandas]]

Source

Convert pandas.DataFrame to an Arrow Table.

The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of object, we need to guess the datatype by looking at the Python objects in this Series.

Be aware that Series of the object dtype don't carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function.

Examples:

>>> import pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({
    ...     'int': [1, 2],
    ...     'str': ['a', 'b']
    ... })
>>> pa.Table.from_pandas(df)

Parameters:

df (pandas.DataFrame) --

schema (pyarrow.Schema, optional) : The expected schema of the Arrow Table. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored.

preserve_index (bool, optional) : Whether to store the index as an additional column in the resulting Table. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use preserve_index=True to force it to be stored as a column.

nthreads (int, defaults to None (may use up to system CPU count threads)) : If greater than 1, convert columns to Arrow in parallel using indicated number of threads.

columns (List[str], optional) : List of column to be converted. If None, use all columns.

safe (bool, defaults to True) : Check for overflows or other unsafe conversions,

Returns:

datasets.table.Table

from_arrays[[datasets.table.InMemoryTable.from_arrays]]

Source

Construct a Table from Arrow arrays.

Parameters:

arrays (List[Union[pyarrow.Array, pyarrow.ChunkedArray]]) : Equal-length arrays that should form the table.

names (List[str], optional) : Names for the table columns. If not passed, schema must be passed.

schema (Schema, defaults to None) : Schema for the created table. If not passed, names must be passed.

metadata (Union[dict, Mapping], defaults to None) : Optional metadata for the schema (if inferred).

Returns:

datasets.table.Table

from_pydict[[datasets.table.InMemoryTable.from_pydict]]

Source

Construct a Table from Arrow arrays or columns.

Parameters:

mapping (Union[dict, Mapping]) : A mapping of strings to Arrays or Python lists.

schema (Schema, defaults to None) : If not passed, will be inferred from the Mapping values

metadata (Union[dict, Mapping], defaults to None) : Optional metadata for the schema (if inferred).

Returns:

datasets.table.Table

from_batches[[datasets.table.InMemoryTable.from_batches]]

Source

Construct a Table from a sequence or iterator of Arrow RecordBatches.

Parameters:

batches (Union[Sequence[pyarrow.RecordBatch], Iterator[pyarrow.RecordBatch]]) : Sequence of RecordBatch to be converted, all schemas must be equal.

schema (Schema, defaults to None) : If not passed, will be inferred from the first RecordBatch.

Returns:

datasets.table.Table

MemoryMappedTable[[datasets.table.MemoryMappedTable]]

datasets.table.MemoryMappedTable[[datasets.table.MemoryMappedTable]]

Source

The table is said memory mapped when it doesn't use the user's RAM but loads the data from the disk instead.

Pickling it doesn't copy the data into memory. Instead, only the path to the memory mapped arrow file is pickled, as well as the list of transforms to "replay" when reloading the table from the disk.

Its implementation requires to store an history of all the transforms that were applied to the underlying pyarrow Table, so that they can be "replayed" when reloading the Table from the disk.

This is different from the InMemoryTable table, for which pickling does copy all the data in memory.

InMemoryTable must be used when data fit in memory, while MemoryMapped are reserved for data bigger than memory or when you want the memory footprint of your application to stay low.

validatedatasets.table.MemoryMappedTable.validatehttps://github.com/huggingface/datasets/blob/r_8113/src/datasets/table.py#L187[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]- full (bool, defaults to False) -- If True, run expensive checks, otherwise cheap checks only.0- pa.lib.ArrowInvalid -- if validation failspa.lib.ArrowInvalid

Perform validation checks. An exception is raised if validation fails.

By default only cheap validation checks are run. Pass full=True for thorough validation checks (potentially O(n)).

Parameters:

full (bool, defaults to False) : If True, run expensive checks, otherwise cheap checks only.

equals[[datasets.table.MemoryMappedTable.equals]]

Source

Check if contents of two tables are equal.

Parameters:

other (Table) : Table to compare against.

check_metadata bool, defaults to False) : Whether schema metadata equality should be checked as well.

Returns:

bool

to_batches[[datasets.table.MemoryMappedTable.to_batches]]

Source

Convert Table to list of (contiguous) RecordBatch objects.

Parameters:

max_chunksize (int, defaults to None) : Maximum size for RecordBatch chunks. Individual chunks may be smaller depending on the chunk layout of individual columns.

Returns:

List[pyarrow.RecordBatch]

to_pydict[[datasets.table.MemoryMappedTable.to_pydict]]

Source

Convert the Table to a dict or OrderedDict.

Returns:

dict

to_pandas[[datasets.table.MemoryMappedTable.to_pandas]]

Source

Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.

Parameters:

memory_pool (MemoryPool, defaults to None) : Arrow MemoryPool to use for allocations. Uses the default memory pool is not passed.

strings_to_categorical (bool, defaults to False) : Encode string (UTF8) and binary types to pandas.Categorical.

categories (list, defaults to empty) : List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.

zero_copy_only (bool, defaults to False) : Raise an ArrowException if this function call would require copying the underlying data.

integer_object_nulls (bool, defaults to False) : Cast integers with nulls to objects.

date_as_object (bool, defaults to True) : Cast dates to objects. If False, convert to datetime64[ns] dtype.

timestamp_as_object (bool, defaults to False) : Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). If False, all timestamps are converted to datetime64[ns] dtype.

use_threads (bool, defaults to True) : Whether to parallelize the conversion using multiple threads.

deduplicate_objects (bool, defaults to False) : Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.

ignore_metadata (bool, defaults to False) : If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present.

safe (bool, defaults to True) : For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not.

split_blocks (bool, defaults to False) : If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use.

self_destruct (bool, defaults to False) : EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program.

types_mapper (function, defaults to None) : A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. If you have a dictionary mapping, you can pass dict.get as function.

Returns:

pandas.Series` or `pandas.DataFrame

pandas.Series or pandas.DataFrame depending on type of object

to_string[[datasets.table.MemoryMappedTable.to_string]]

Source

field[[datasets.table.MemoryMappedTable.field]]

Source

Select a schema field by its column name or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the field to retrieve.

Returns:

pyarrow.Field

column[[datasets.table.MemoryMappedTable.column]]

Source

Select a column by its column name, or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the column to retrieve.

Returns:

pyarrow.ChunkedArray

itercolumns[[datasets.table.MemoryMappedTable.itercolumns]]

Source

Iterator over all columns in their numerical order.

schema[[datasets.table.MemoryMappedTable.schema]]

Source

Schema of the table and its columns.

Returns:

pyarrow.Schema

columns[[datasets.table.MemoryMappedTable.columns]]

Source

List of all columns in numerical order.

Returns:

List[pa.ChunkedArray]

num_columns[[datasets.table.MemoryMappedTable.num_columns]]

Source

Number of columns in this table.

Returns:

int

num_rows[[datasets.table.MemoryMappedTable.num_rows]]

Source

Number of rows in this table.

Due to the definition of a table, all columns have the same number of rows.

Returns:

int

shape[[datasets.table.MemoryMappedTable.shape]]

Source

Dimensions of the table: (#rows, #columns).

Returns:

(int, int)

Number of rows and number of columns.

nbytes[[datasets.table.MemoryMappedTable.nbytes]]

Source

Total number of bytes consumed by the elements of the table.

column_names[[datasets.table.MemoryMappedTable.column_names]]

Source

Names of the table's columns.

slice[[datasets.table.MemoryMappedTable.slice]]

Source

Compute zero-copy slice of this Table.

Parameters:

offset (int, defaults to 0) : Offset from start of table to slice.

length (int, defaults to None) : Length of slice (default is until end of table starting from offset).

Returns:

datasets.table.Table

filter[[datasets.table.MemoryMappedTable.filter]]

Source

Select records from a Table. See pyarrow.compute.filter for full usage.

flatten[[datasets.table.MemoryMappedTable.flatten]]

Source

Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

combine_chunks[[datasets.table.MemoryMappedTable.combine_chunks]]

Source

Make a new table by combining the chunks this table has.

All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

cast[[datasets.table.MemoryMappedTable.cast]]

Source

Cast table values to another schema

Parameters:

target_schema (Schema) : Schema to cast to, the names and order of fields must match.

safe (bool, defaults to True) : Check for overflows or other unsafe conversions.

Returns:

datasets.table.Table

replace_schema_metadata[[datasets.table.MemoryMappedTable.replace_schema_metadata]]

Source

EXPERIMENTAL: Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata.

Parameters:

metadata (dict, defaults to None) --

Returns:

datasets.table.Table

shallow_copy

add_column[[datasets.table.MemoryMappedTable.add_column]]

Source

Add column to Table at position.

A new table is returned with the column added, the original table object is left unchanged.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

append_column[[datasets.table.MemoryMappedTable.append_column]]

Source

Append column at end of columns.

Parameters:

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

remove_column[[datasets.table.MemoryMappedTable.remove_column]]

Source

Create new Table with the indicated column removed.

Parameters:

i (int) : Index of column to remove.

Returns:

datasets.table.Table

New table without the column.

set_column[[datasets.table.MemoryMappedTable.set_column]]

Source

Replace column in Table at position.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column set.

rename_columns[[datasets.table.MemoryMappedTable.rename_columns]]

Source

Create new table with columns renamed to provided names.

select[[datasets.table.MemoryMappedTable.select]]

Source

Select columns of the table.

Returns a new table with the specified columns, and metadata preserved.

Parameters:

columns (Union[List[str], List[int]]) : The column names or integer indices to select.

Returns:

[datasets.table.Table](/docs/datasets/pr_8113/en/package_reference/table_classes#datasets.table.Table)

New table with the specified columns, and metadata preserved.

drop[[datasets.table.MemoryMappedTable.drop]]

Source

Drop one or more columns and return a new table.

Parameters:

columns (List[str]) : List of field names referencing existing columns.

Returns:

datasets.table.Table

New table without the columns.

from_file[[datasets.table.MemoryMappedTable.from_file]]

Source

ConcatenationTable[[datasets.table.ConcatenationTable]]

datasets.table.ConcatenationTable[[datasets.table.ConcatenationTable]]

Source

The table comes from the concatenation of several tables called blocks. It enables concatenation on both axis 0 (append rows) and axis 1 (append columns).

The underlying tables are called "blocks" and can be either InMemoryTable or MemoryMappedTable objects. This allows to combine tables that come from memory or that are memory mapped. When a ConcatenationTable is pickled, then each block is pickled:

• the InMemoryTable objects are pickled by copying all the data in memory.
• the MemoryMappedTable objects are pickled without copying the data into memory. Instead, only the path to the memory mapped arrow file is pickled, as well as the list of transforms to "replays" when reloading the table from the disk.

Its implementation requires to store each block separately. The blocks attributes stores a list of list of blocks. The first axis concatenates the tables along the axis 0 (it appends rows), while the second axis concatenates tables along the axis 1 (it appends columns).

If some columns are missing when concatenating on axis 0, they are filled with null values. This is done using pyarrow.concat_tables(tables, promote=True).

You can access the fully combined table by accessing the ConcatenationTable.table attribute, and the blocks by accessing the ConcatenationTable.blocks attribute.

validatedatasets.table.ConcatenationTable.validatehttps://github.com/huggingface/datasets/blob/r_8113/src/datasets/table.py#L187[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]- full (bool, defaults to False) -- If True, run expensive checks, otherwise cheap checks only.0- pa.lib.ArrowInvalid -- if validation failspa.lib.ArrowInvalid

Perform validation checks. An exception is raised if validation fails.

By default only cheap validation checks are run. Pass full=True for thorough validation checks (potentially O(n)).

Parameters:

full (bool, defaults to False) : If True, run expensive checks, otherwise cheap checks only.

equals[[datasets.table.ConcatenationTable.equals]]

Source

Check if contents of two tables are equal.

Parameters:

other (Table) : Table to compare against.

check_metadata bool, defaults to False) : Whether schema metadata equality should be checked as well.

Returns:

bool

to_batches[[datasets.table.ConcatenationTable.to_batches]]

Source

Convert Table to list of (contiguous) RecordBatch objects.

Parameters:

max_chunksize (int, defaults to None) : Maximum size for RecordBatch chunks. Individual chunks may be smaller depending on the chunk layout of individual columns.

Returns:

List[pyarrow.RecordBatch]

to_pydict[[datasets.table.ConcatenationTable.to_pydict]]

Source

Convert the Table to a dict or OrderedDict.

Returns:

dict

to_pandas[[datasets.table.ConcatenationTable.to_pandas]]

Source

Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.

Parameters:

memory_pool (MemoryPool, defaults to None) : Arrow MemoryPool to use for allocations. Uses the default memory pool is not passed.

strings_to_categorical (bool, defaults to False) : Encode string (UTF8) and binary types to pandas.Categorical.

categories (list, defaults to empty) : List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.

zero_copy_only (bool, defaults to False) : Raise an ArrowException if this function call would require copying the underlying data.

integer_object_nulls (bool, defaults to False) : Cast integers with nulls to objects.

date_as_object (bool, defaults to True) : Cast dates to objects. If False, convert to datetime64[ns] dtype.

timestamp_as_object (bool, defaults to False) : Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). If False, all timestamps are converted to datetime64[ns] dtype.

use_threads (bool, defaults to True) : Whether to parallelize the conversion using multiple threads.

deduplicate_objects (bool, defaults to False) : Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.

ignore_metadata (bool, defaults to False) : If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present.

safe (bool, defaults to True) : For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not.

split_blocks (bool, defaults to False) : If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use.

self_destruct (bool, defaults to False) : EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program.

types_mapper (function, defaults to None) : A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. If you have a dictionary mapping, you can pass dict.get as function.

Returns:

pandas.Series` or `pandas.DataFrame

pandas.Series or pandas.DataFrame depending on type of object

to_string[[datasets.table.ConcatenationTable.to_string]]

Source

field[[datasets.table.ConcatenationTable.field]]

Source

Select a schema field by its column name or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the field to retrieve.

Returns:

pyarrow.Field

column[[datasets.table.ConcatenationTable.column]]

Source

Select a column by its column name, or numeric index.

Parameters:

i (Union[int, str]) : The index or name of the column to retrieve.

Returns:

pyarrow.ChunkedArray

itercolumns[[datasets.table.ConcatenationTable.itercolumns]]

Source

Iterator over all columns in their numerical order.

schema[[datasets.table.ConcatenationTable.schema]]

Source

Schema of the table and its columns.

Returns:

pyarrow.Schema

columns[[datasets.table.ConcatenationTable.columns]]

Source

List of all columns in numerical order.

Returns:

List[pa.ChunkedArray]

num_columns[[datasets.table.ConcatenationTable.num_columns]]

Source

Number of columns in this table.

Returns:

int

num_rows[[datasets.table.ConcatenationTable.num_rows]]

Source

Number of rows in this table.

Due to the definition of a table, all columns have the same number of rows.

Returns:

int

shape[[datasets.table.ConcatenationTable.shape]]

Source

Dimensions of the table: (#rows, #columns).

Returns:

(int, int)

Number of rows and number of columns.

nbytes[[datasets.table.ConcatenationTable.nbytes]]

Source

Total number of bytes consumed by the elements of the table.

column_names[[datasets.table.ConcatenationTable.column_names]]

Source

Names of the table's columns.

slice[[datasets.table.ConcatenationTable.slice]]

Source

Compute zero-copy slice of this Table.

Parameters:

offset (int, defaults to 0) : Offset from start of table to slice.

length (int, defaults to None) : Length of slice (default is until end of table starting from offset).

Returns:

datasets.table.Table

filter[[datasets.table.ConcatenationTable.filter]]

Source

Select records from a Table. See pyarrow.compute.filter for full usage.

flatten[[datasets.table.ConcatenationTable.flatten]]

Source

Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

combine_chunks[[datasets.table.ConcatenationTable.combine_chunks]]

Source

Make a new table by combining the chunks this table has.

All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk.

Parameters:

memory_pool (MemoryPool, defaults to None) : For memory allocations, if required, otherwise use default pool.

Returns:

datasets.table.Table

cast[[datasets.table.ConcatenationTable.cast]]

Source

Cast table values to another schema.

Parameters:

target_schema (Schema) : Schema to cast to, the names and order of fields must match.

safe (bool, defaults to True) : Check for overflows or other unsafe conversions.

Returns:

datasets.table.Table

replace_schema_metadata[[datasets.table.ConcatenationTable.replace_schema_metadata]]

Source

EXPERIMENTAL: Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata).

Parameters:

metadata (dict, defaults to None) --

Returns:

datasets.table.Table

shallow_copy

add_column[[datasets.table.ConcatenationTable.add_column]]

Source

Add column to Table at position.

A new table is returned with the column added, the original table object is left unchanged.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

append_column[[datasets.table.ConcatenationTable.append_column]]

Source

Append column at end of columns.

Parameters:

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column added.

remove_column[[datasets.table.ConcatenationTable.remove_column]]

Source

Create new Table with the indicated column removed.

Parameters:

i (int) : Index of column to remove.

Returns:

datasets.table.Table

New table without the column.

set_column[[datasets.table.ConcatenationTable.set_column]]

Source

Replace column in Table at position.

Parameters:

i (int) : Index to place the column at.

field_ (Union[str, pyarrow.Field]) : If a string is passed then the type is deduced from the column data.

column (Union[pyarrow.Array, List[pyarrow.Array]]) : Column data.

Returns:

datasets.table.Table

New table with the passed column set.

rename_columns[[datasets.table.ConcatenationTable.rename_columns]]

Source

Create new table with columns renamed to provided names.

select[[datasets.table.ConcatenationTable.select]]

Source

Select columns of the table.

Returns a new table with the specified columns, and metadata preserved.

Parameters:

columns (Union[List[str], List[int]]) : The column names or integer indices to select.

Returns:

[datasets.table.Table](/docs/datasets/pr_8113/en/package_reference/table_classes#datasets.table.Table)

New table with the specified columns, and metadata preserved.

drop[[datasets.table.ConcatenationTable.drop]]

Source

Drop one or more columns and return a new table.

Parameters:

columns (List[str]) : List of field names referencing existing columns.

Returns:

datasets.table.Table

New table without the columns.

from_blocks[[datasets.table.ConcatenationTable.from_blocks]]

Source

from_tables[[datasets.table.ConcatenationTable.from_tables]]

Source

Create ConcatenationTable from list of tables.

Parameters:

tables (list of Table or list of pyarrow.Table) : List of tables.

axis ({0, 1}, defaults to 0, meaning over rows) : Axis to concatenate over, where 0 means over rows (vertically) and 1 means over columns (horizontally).

Utils[[datasets.table.concat_tables]]

datasets.table.concat_tables[[datasets.table.concat_tables]]

Source

Concatenate tables.

Parameters:

tables (list of Table) : List of tables to be concatenated.

axis ({0, 1}, defaults to 0, meaning over rows) : Axis to concatenate over, where 0 means over rows (vertically) and 1 means over columns (horizontally).

Returns:

datasets.table.Table

If the number of input tables is > 1, then the returned table is a datasets.table.ConcatenationTable. Otherwise if there's only one table, it is returned as is.

datasets.table.list_table_cache_files[[datasets.table.list_table_cache_files]]

Source

Get the cache files that are loaded by the table. Cache file are used when parts of the table come from the disk via memory mapping.

Returns:

List[str]

A list of paths to the cache files loaded by the table.