PandasOnDaskDataframeVirtualPartition#

The class is the specific implementation of PandasOnDaskDataframeVirtualPartition, providing the API to perform operations on an axis (column or row) partition using Dask as the execution engine. The axis partition is a wrapper over a list of block partitions that are stored in this class.

Public API#

class modin.core.execution.dask.implementations.pandas_on_dask.partitioning.PandasOnDaskDataframeVirtualPartition(list_of_partitions, get_ip=False, full_axis=True, call_queue=None, length=None, width=None)#

The class implements the interface in PandasDataframeAxisPartition.

Parameters

  • list_of_partitions (Union[list, PandasOnDaskDataframePartition]) – List of PandasOnDaskDataframePartition and PandasOnDaskDataframeVirtualPartition objects, or a single PandasOnDaskDataframePartition.

  • get_ip (bool, default: False) – Whether to get node IP addresses of conforming partitions or not.

  • full_axis (bool, default: True) – Whether or not the virtual partition encompasses the whole axis.

  • call_queue (list, optional) – A list of tuples (callable, args, kwargs) that contains deferred calls.

  • length (distributed.Future or int, optional) – Length, or reference to length, of wrapped pandas.DataFrame.

  • width (distributed.Future or int, optional) – Width, or reference to width, of wrapped pandas.DataFrame.

add_to_apply_calls(func, *args, length=None, width=None, **kwargs)#

Add a function to the call queue.

Parameters

  • func (callable) – Function to be added to the call queue.

  • *args (iterable) – Additional positional arguments to be passed in func.

  • length (distributed.Future or int, optional) – Length, or reference to length, of wrapped pandas.DataFrame.

  • width (distributed.Future or int, optional) – Width, or reference to width, of wrapped pandas.DataFrame.

  • **kwargs (dict) – Additional keyword arguments to be passed in func.

Returns

A new PandasOnDaskDataframeVirtualPartition object.

Return type

PandasOnDaskDataframeVirtualPartition

Notes

The keyword arguments are sent as a dictionary.

apply(func, *args, num_splits=None, other_axis_partition=None, maintain_partitioning=True, **kwargs)#

Apply a function to this axis partition along full axis.

Parameters

  • func (callable) – The function to apply.

  • *args (iterable) – Additional positional arguments to be passed in func.

  • num_splits (int, default: None) – The number of times to split the result object.

  • other_axis_partition (PandasDataframeAxisPartition, default: None) – Another PandasDataframeAxisPartition object to be applied to func. This is for operations that are between two data sets.

  • maintain_partitioning (bool, default: True) – Whether to keep the partitioning in the same orientation as it was previously or not. This is important because we may be operating on an individual AxisPartition and not touching the rest. In this case, we have to return the partitioning to its previous orientation (the lengths will remain the same). This is ignored between two axis partitions.

  • **kwargs (dict) – Additional keywords arguments to be passed in func.

Returns

A list of PandasOnDaskDataframeVirtualPartition objects.

Return type

list

classmethod deploy_axis_func(axis, func, f_args, f_kwargs, num_splits, maintain_partitioning, *partitions, lengths=None, manual_partition=False)#

Deploy a function along a full axis.

Parameters

  • axis ({0, 1}) – The axis to perform the function along.

  • func (callable) – The function to perform.

  • f_args (list or tuple) – Positional arguments to pass to func.

  • f_kwargs (dict) – Keyword arguments to pass to func.

  • num_splits (int) – The number of splits to return (see split_result_of_axis_func_pandas).

  • maintain_partitioning (bool) – If True, keep the old partitioning if possible. If False, create a new partition layout.

  • *partitions (iterable) – All partitions that make up the full axis (row or column).

  • lengths (iterable, default: None) – The list of lengths to shuffle the partition into.

  • manual_partition (bool, default: False) – If True, partition the result with lengths.

Returns

A list of distributed.Future.

Return type

list

classmethod deploy_func_between_two_axis_partitions(axis, func, f_args, f_kwargs, num_splits, len_of_left, other_shape, *partitions)#

Deploy a function along a full axis between two data sets.

Parameters

  • axis ({0, 1}) – The axis to perform the function along.

  • func (callable) – The function to perform.

  • f_args (list or tuple) – Positional arguments to pass to func.

  • f_kwargs (dict) – Keyword arguments to pass to func.

  • num_splits (int) – The number of splits to return (see split_result_of_axis_func_pandas).

  • len_of_left (int) – The number of values in partitions that belong to the left data set.

  • other_shape (np.ndarray) – The shape of right frame in terms of partitions, i.e. (other_shape[i-1], other_shape[i]) will indicate slice to restore i-1 axis partition.

  • *partitions (iterable) – All partitions that make up the full axis (row or column) for both data sets.

Returns

A list of distributed.Future.

Return type

list

drain_call_queue(num_splits=None)#

Execute all operations stored in this partition’s call queue.

Parameters

num_splits (int, default: None) – The number of times to split the result object.

force_materialization(get_ip=False)#

Materialize partitions into a single partition.

Parameters

get_ip (bool, default: False) – Whether to get node ip address to a single partition or not.

Returns

An axis partition containing only a single materialized partition.

Return type

PandasOnDaskDataframeVirtualPartition

instance_type#

alias of Future

length()#

Get the length of this partition.

Returns

The length of the partition.

Return type

int

property list_of_block_partitions: list#

Get the list of block partitions that compose this partition.

Returns

A list of PandasOnDaskDataframePartition.

Return type

List

property list_of_ips#

Get the IPs holding the physical objects composing this partition.

Returns

A list of IPs as distributed.Future or str.

Return type

List

mask(row_indices, col_indices)#

Create (synchronously) a mask that extracts the indices provided.

Parameters

  • row_indices (list-like, slice or label) – The row labels for the rows to extract.

  • col_indices (list-like, slice or label) – The column labels for the columns to extract.

Returns

A new PandasOnDaskDataframeVirtualPartition object, materialized.

Return type

PandasOnDaskDataframeVirtualPartition

partition_type#

alias of PandasOnDaskDataframePartition

to_numpy()#

Convert the data in this partition to a numpy.array.

Return type

NumPy array.

to_pandas()#

Convert the data in this partition to a pandas.DataFrame.

Return type

pandas DataFrame.

wait()#

Wait completing computations on the object wrapped by the partition.

width()#

Get the width of this partition.

Returns

The width of the partition.

Return type

int

PandasOnDaskDataframeColumnPartition#

Public API#

class modin.core.execution.dask.implementations.pandas_on_dask.partitioning.PandasOnDaskDataframeColumnPartition(list_of_partitions, get_ip=False, full_axis=True, call_queue=None, length=None, width=None)#

PandasOnDaskDataframeRowPartition#

Public API#

class modin.core.execution.dask.implementations.pandas_on_dask.partitioning.PandasOnDaskDataframeRowPartition(list_of_partitions, get_ip=False, full_axis=True, call_queue=None, length=None, width=None)#

Initialize self. See help(type(self)) for accurate signature.