Table Classes
Last updated
Last updated
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 )
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.
validate
( *args**kwargs )
Parameters
full (bool
, defaults to False
) — If True
, run expensive checks, otherwise cheap checks only.
Raises
pa.lib.ArrowInvalid
pa.lib.ArrowInvalid
— if validation fails
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)
).
equals
( *args**kwargs ) → bool
Parameters
check_metadata bool
, defaults to False
) — Whether schema metadata equality should be checked as well.
Returns
bool
Check if contents of two tables are equal.
to_batches
( *args**kwargs )
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.
Convert Table to list of (contiguous) RecordBatch
objects.
to_pydict
( *args**kwargs ) → dict
Returns
dict
Convert the Table to a dict
or OrderedDict
.
to_pandas
( *args**kwargs ) → pandas.Series
or pandas.DataFrame
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
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.
to_string
( *args**kwargs )
field
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the field to retrieve.
Select a schema field by its column name or numeric index.
column
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the column to retrieve.
Select a column by its column name, or numeric index.
itercolumns
( *args**kwargs )
Iterator over all columns in their numerical order.
schema
( )
Schema of the table and its columns.
columns
( )
List of all columns in numerical order.
num_columns
( )
Number of columns in this table.
num_rows
( )
Number of rows in this table.
Due to the definition of a table, all columns have the same number of rows.
shape
( ) → (int, int)
Returns
(int, int)
Number of rows and number of columns.
Dimensions of the table: (#rows, #columns).
nbytes
( )
Total number of bytes consumed by the elements of the table.
( table: Table )
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.
validate
( *args**kwargs )
Parameters
full (bool
, defaults to False
) — If True
, run expensive checks, otherwise cheap checks only.
Raises
pa.lib.ArrowInvalid
pa.lib.ArrowInvalid
— if validation fails
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)
).
equals
( *args**kwargs ) → bool
Parameters
check_metadata bool
, defaults to False
) — Whether schema metadata equality should be checked as well.
Returns
bool
Check if contents of two tables are equal.
to_batches
( *args**kwargs )
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.
Convert Table to list of (contiguous) RecordBatch
objects.
to_pydict
( *args**kwargs ) → dict
Returns
dict
Convert the Table to a dict
or OrderedDict
.
to_pandas
( *args**kwargs ) → pandas.Series
or pandas.DataFrame
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
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.
to_string
( *args**kwargs )
field
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the field to retrieve.
Select a schema field by its column name or numeric index.
column
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the column to retrieve.
Select a column by its column name, or numeric index.
itercolumns
( *args**kwargs )
Iterator over all columns in their numerical order.
schema
( )
Schema of the table and its columns.
columns
( )
List of all columns in numerical order.
num_columns
( )
Number of columns in this table.
num_rows
( )
Number of rows in this table.
Due to the definition of a table, all columns have the same number of rows.
shape
( ) → (int, int)
Returns
(int, int)
Number of rows and number of columns.
Dimensions of the table: (#rows, #columns).
nbytes
( )
Total number of bytes consumed by the elements of the table.
column_names
( )
Names of the table’s columns.
slice
( offset = 0length = None )
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).
Compute zero-copy slice of this Table.
filter
( *args**kwargs )
Select records from a Table. See pyarrow.compute.filter
for full usage.
flatten
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.
combine_chunks
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
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.
cast
( *args**kwargs )
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.
Cast table values to another schema.
replace_schema_metadata
( *args**kwargs ) → datasets.table.Table
Parameters
metadata (dict
, defaults to None
) —
Returns
datasets.table.Table
shallow_copy
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).
add_column
( *args**kwargs ) → datasets.table.Table
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.
Add column to Table at position.
A new table is returned with the column added, the original table object is left unchanged.
append_column
( *args**kwargs ) → datasets.table.Table
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.
Append column at end of columns.
remove_column
( *args**kwargs ) → datasets.table.Table
Parameters
i (int
) — Index of column to remove.
Returns
datasets.table.Table
New table without the column.
Create new Table with the indicated column removed.
set_column
( *args**kwargs ) → datasets.table.Table
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.
Replace column in Table at position.
rename_columns
( *args**kwargs )
Create new table with columns renamed to provided names.
select
Parameters
columns (Union[List[str], List[int]]
) — The column names or integer indices to select.
Returns
New table with the specified columns, and metadata preserved.
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved.
drop
( *args**kwargs ) → datasets.table.Table
Parameters
columns (List[str]
) — List of field names referencing existing columns.
Returns
datasets.table.Table
New table without the columns.
Raises
KeyError
KeyError
— : if any of the passed columns name are not existing.
Drop one or more columns and return a new table.
from_file
( filename: str )
from_buffer
( buffer: Buffer )
from_pandas
( *args**kwargs ) → datasets.table.Table
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
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:
Copied
from_arrays
( *args**kwargs )
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).
Construct a Table from Arrow arrays.
from_pydict
( *args**kwargs )
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).
Construct a Table from Arrow arrays or columns.
from_batches
( *args**kwargs ) → datasets.table.Table
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
Construct a Table from a sequence or iterator of Arrow RecordBatches
.
( table: Tablepath: strreplays: typing.Union[typing.List[typing.Tuple[str, tuple, dict]], NoneType] = None )
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.
validate
( *args**kwargs )
Parameters
full (bool
, defaults to False
) — If True
, run expensive checks, otherwise cheap checks only.
Raises
pa.lib.ArrowInvalid
pa.lib.ArrowInvalid
— if validation fails
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)
).
equals
( *args**kwargs ) → bool
Parameters
check_metadata bool
, defaults to False
) — Whether schema metadata equality should be checked as well.
Returns
bool
Check if contents of two tables are equal.
to_batches
( *args**kwargs )
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.
Convert Table to list of (contiguous) RecordBatch
objects.
to_pydict
( *args**kwargs ) → dict
Returns
dict
Convert the Table to a dict
or OrderedDict
.
to_pandas
( *args**kwargs ) → pandas.Series
or pandas.DataFrame
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
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.
to_string
( *args**kwargs )
field
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the field to retrieve.
Select a schema field by its column name or numeric index.
column
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the column to retrieve.
Select a column by its column name, or numeric index.
itercolumns
( *args**kwargs )
Iterator over all columns in their numerical order.
schema
( )
Schema of the table and its columns.
columns
( )
List of all columns in numerical order.
num_columns
( )
Number of columns in this table.
num_rows
( )
Number of rows in this table.
Due to the definition of a table, all columns have the same number of rows.
shape
( ) → (int, int)
Returns
(int, int)
Number of rows and number of columns.
Dimensions of the table: (#rows, #columns).
nbytes
( )
Total number of bytes consumed by the elements of the table.
column_names
( )
Names of the table’s columns.
slice
( offset = 0length = None )
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).
Compute zero-copy slice of this Table.
filter
( *args**kwargs )
Select records from a Table. See pyarrow.compute.filter
for full usage.
flatten
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.
combine_chunks
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
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.
cast
( *args**kwargs )
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.
Cast table values to another schema
replace_schema_metadata
( *args**kwargs ) → datasets.table.Table
Parameters
metadata (dict
, defaults to None
) —
Returns
datasets.table.Table
shallow_copy
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.
add_column
( *args**kwargs ) → datasets.table.Table
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.
Add column to Table at position.
A new table is returned with the column added, the original table object is left unchanged.
append_column
( *args**kwargs ) → datasets.table.Table
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.
Append column at end of columns.
remove_column
( *args**kwargs ) → datasets.table.Table
Parameters
i (int
) — Index of column to remove.
Returns
datasets.table.Table
New table without the column.
Create new Table with the indicated column removed.
set_column
( *args**kwargs ) → datasets.table.Table
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.
Replace column in Table at position.
rename_columns
( *args**kwargs )
Create new table with columns renamed to provided names.
select
Parameters
columns (Union[List[str], List[int]]
) — The column names or integer indices to select.
Returns
New table with the specified columns, and metadata preserved.
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved.
drop
( *args**kwargs ) → datasets.table.Table
Parameters
columns (List[str]
) — List of field names referencing existing columns.
Returns
datasets.table.Table
New table without the columns.
Raises
KeyError
KeyError
— : if any of the passed columns name are not existing.
Drop one or more columns and return a new table.
from_file
( filename: strreplays = None )
( table: Tableblocks: typing.List[typing.List[datasets.table.TableBlock]] )
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.
validate
( *args**kwargs )
Parameters
full (bool
, defaults to False
) — If True
, run expensive checks, otherwise cheap checks only.
Raises
pa.lib.ArrowInvalid
pa.lib.ArrowInvalid
— if validation fails
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)
).
equals
( *args**kwargs ) → bool
Parameters
check_metadata bool
, defaults to False
) — Whether schema metadata equality should be checked as well.
Returns
bool
Check if contents of two tables are equal.
to_batches
( *args**kwargs )
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.
Convert Table to list of (contiguous) RecordBatch
objects.
to_pydict
( *args**kwargs ) → dict
Returns
dict
Convert the Table to a dict
or OrderedDict
.
to_pandas
( *args**kwargs ) → pandas.Series
or pandas.DataFrame
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
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.
to_string
( *args**kwargs )
field
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the field to retrieve.
Select a schema field by its column name or numeric index.
column
( *args**kwargs )
Parameters
i (Union[int, str]
) — The index or name of the column to retrieve.
Select a column by its column name, or numeric index.
itercolumns
( *args**kwargs )
Iterator over all columns in their numerical order.
schema
( )
Schema of the table and its columns.
columns
( )
List of all columns in numerical order.
num_columns
( )
Number of columns in this table.
num_rows
( )
Number of rows in this table.
Due to the definition of a table, all columns have the same number of rows.
shape
( ) → (int, int)
Returns
(int, int)
Number of rows and number of columns.
Dimensions of the table: (#rows, #columns).
nbytes
( )
Total number of bytes consumed by the elements of the table.
column_names
( )
Names of the table’s columns.
slice
( offset = 0length = None )
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).
Compute zero-copy slice of this Table.
filter
( mask*args**kwargs )
Select records from a Table. See pyarrow.compute.filter
for full usage.
flatten
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.
combine_chunks
( *args**kwargs )
Parameters
memory_pool (MemoryPool
, defaults to None
) — For memory allocations, if required, otherwise use default pool.
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.
cast
( target_schema*args**kwargs )
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.
Cast table values to another schema.
replace_schema_metadata
( *args**kwargs ) → datasets.table.Table
Parameters
metadata (dict
, defaults to None
) —
Returns
datasets.table.Table
shallow_copy
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).
add_column
( *args**kwargs ) → datasets.table.Table
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.
Add column to Table at position.
A new table is returned with the column added, the original table object is left unchanged.
append_column
( *args**kwargs ) → datasets.table.Table
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.
Append column at end of columns.
remove_column
( i*args**kwargs ) → datasets.table.Table
Parameters
i (int
) — Index of column to remove.
Returns
datasets.table.Table
New table without the column.
Create new Table with the indicated column removed.
set_column
( *args**kwargs ) → datasets.table.Table
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.
Replace column in Table at position.
rename_columns
( names*args**kwargs )
Create new table with columns renamed to provided names.
select
Parameters
columns (Union[List[str], List[int]]
) — The column names or integer indices to select.
Returns
New table with the specified columns, and metadata preserved.
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved.
drop
( columns*args**kwargs ) → datasets.table.Table
Parameters
columns (List[str]
) — List of field names referencing existing columns.
Returns
datasets.table.Table
New table without the columns.
Raises
KeyError
KeyError
— : if any of the passed columns name are not existing.
Drop one or more columns and return a new table.
from_blocks
( blocks: TableBlockContainer )
from_tables
( tables: typing.List[typing.Union[pyarrow.lib.Table, datasets.table.Table]]axis: int = 0 )
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).
Added in 1.6.0
Create ConcatenationTable
from list of tables.
datasets.table.concat_tables
( tables: typing.List[datasets.table.Table]axis: int = 0 ) → datasets.table.Table
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).
Added in 1.6.0
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.
Concatenate tables.
datasets.table.list_table_cache_files
( table: Table ) → List[str]
Returns
List[str]
A list of paths to the cache files loaded by the table.
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.
other () — Table to compare against.
other () — Table to compare against.
( *args**kwargs ) →
other () — Table to compare against.
( *args**kwargs ) →
other () — Table to compare against.
( columns*args**kwargs ) →