Column#

class pylibcudf.column.Column(obj=None, *args, **kwargs)#

A container of nullable device data as a column of elements.

This class is an implementation of Arrow columnar data specification for data stored on GPUs. It relies on Python memoryview-like semantics to maintain shared ownership of the data it is constructed with, so any input data may also be co-owned by other data structures. The Column is designed to be operated on using algorithms backed by libcudf.

Parameters:

data_typeDataType: The type of data in the column.
sizesize_type: The number of rows in the column.
datagpumemoryview: The data the column will refer to.
maskgpumemoryview: The null mask for the column.
null_countint: The number of null rows in the column.
offsetint: The offset into the data buffer where the column’s data begins.
childrenlist: The children of this column if it is a compound column type.

Methods

`all_null_like`(Column like, size_type size)	Create an all null column from a template.
`child`(self, size_type index)	Get a child column of this column.
`children`(self)	The children of the column.
`copy`(self)	Create a copy of the column.
`data`(self)	The data buffer of the column.
`device_buffer_size`(self)	The total size of the device buffers used by the Column.
`from_array`(cls, obj)	Create a Column from any object which supports the NumPy or CUDA array interface.
`from_array_interface`(cls, obj)	Create a Column from an object implementing the NumPy Array Interface.
`from_cuda_array_interface`(cls, obj)	Create a Column from an object implementing the CUDA Array Interface.
`from_iterable_of_py`(obj[, dtype])	Create a Column from a Python iterable of scalar values or nested iterables.
`from_rmm_buffer`(DeviceBuffer buff, ...)	Create a Column from an RMM DeviceBuffer.
`from_scalar`(Scalar slr, size_type size)	Create a Column from a Scalar.
`list_view`(self)	Accessor for methods of a Column that are specific to lists.
`null_count`(self)	The number of null elements in the column.
`null_mask`(self)	The null mask of the column.
`num_children`(self)	The number of children of this column.
`offset`(self)	The offset of the column.
`size`(self)	The number of elements in the column.
`to_scalar`(self)	Return the first value of 1-element column as a Scalar.
`type`(self)	The type of data in the column.
`with_mask`(self, gpumemoryview mask, ...)	Augment this column with a new null mask.

static all_null_like(Column like, size_type size)#

Create an all null column from a template.

Parameters:

likeColumn: Column whose type we should mimic
sizeint: Number of rows in the resulting column.

Returns:

Column: An all-null column of size rows and type matching like.

child(self, size_type index) → Column#

Get a child column of this column.

Parameters:

indexsize_type: The index of the child column to get.

Returns:

Column: The child column.

children(self) → list#: The children of the column.

copy(self) → Column#: Create a copy of the column.

data(self) → gpumemoryview#: The data buffer of the column.

device_buffer_size(self) → uint64_t#

The total size of the device buffers used by the Column.

Returns:

Number of bytes.

Notes

Since Columns rely on Python memoryview-like semantics to maintain shared ownership of the data, the device buffers underlying this column might be shared between other data structures including other columns.

classmethod from_array(cls, obj)#

Create a Column from any object which supports the NumPy or CUDA array interface.

Parameters:

objobject: The input array to be converted into a pylibcudf.Column.

Returns:

Column

Raises:

TypeError: If the input does not implement a supported array interface.

Notes

Only C-contiguous host and device ndarrays are supported. For device arrays, the data is not copied.

Examples

>>> import pylibcudf as plc
>>> import cupy as cp
>>> cp_arr = cp.array([[1,2],[3,4]])
>>> col = plc.Column.from_array(cp_arr)

classmethod from_array_interface(cls, obj)#

Create a Column from an object implementing the NumPy Array Interface.

If the object provides a raw memory pointer via the “data” field, we use that pointer directly and avoid copying. Otherwise, a ValueError is raised.

Parameters:

objAny: Must implement the __array_interface__ protocol.

Returns:

Column: A Column containing the data from the array interface.

Raises:

TypeError: If the object does not implement __array_interface__.
ValueError: If the array is not 1D or 2D, or is not C-contiguous. If the number of rows exceeds size_type limit. If the ‘data’ field is invalid.
NotImplementedError: If the object has a mask.

classmethod from_cuda_array_interface(cls, obj)#

Create a Column from an object implementing the CUDA Array Interface.

Parameters:

objAny: Must implement the __cuda_array_interface__ protocol.

Returns:

Column: A Column containing the data from the CUDA array interface.

Raises:

TypeError: If the object does not support __cuda_array_interface__.
ValueError: If the object is not 1D or 2D, or is not C-contiguous. If the number of rows exceeds size_type limit.
NotImplementedError: If the object has a mask.

static from_iterable_of_py(obj: Iterable, dtype: DataType | None = None) → Column#

Create a Column from a Python iterable of scalar values or nested iterables.

Parameters:

objIterable: An iterable of scalar values (e.g., int, float, bool) or a nested iterable.
dtypeDataType | None: The data type of the elements. If not specified, the type is inferred.

Returns:

Column: A Column containing the data from the input iterable.

Raises:

TypeError: If the input contains unsupported scalar types.
ValueError: If the iterable is empty and dtype is not provided.

Notes

Only scalar types int, float, and bool are supported.
Nested iterables must be materialized as lists.

static from_rmm_buffer(DeviceBuffer buff, DataType dtype, size_type size, list children)#

Create a Column from an RMM DeviceBuffer.

Parameters:

buffDeviceBuffer: The data rmm.DeviceBuffer.
sizesize_type: The number of rows in the column.
dtypeDataType: The type of the data in the buffer.
childrenlist: List of child columns.

Notes

To provide a mask and null count, use Column.with_mask after this method.

static from_scalar(Scalar slr, size_type size)#

Create a Column from a Scalar.

Parameters:

slrScalar: The scalar to create a column from.
sizesize_type: The number of elements in the column.

Returns:

Column: A Column containing the scalar repeated size times.

list_view(self) → ListColumnView#: Accessor for methods of a Column that are specific to lists.

null_count(self) → size_type#: The number of null elements in the column.

null_mask(self) → gpumemoryview#: The null mask of the column.

num_children(self) → size_type#: The number of children of this column.

offset(self) → size_type#: The offset of the column.

size(self) → size_type#: The number of elements in the column.

to_scalar(self) → Scalar#

Return the first value of 1-element column as a Scalar.

Returns:

Scalar: A Scalar representing the only value in the column, including nulls.

Raises:

ValueError: If the column has more than one row.

type(self) → DataType#: The type of data in the column.

with_mask(self, gpumemoryview mask, size_type null_count) → Column#

Augment this column with a new null mask.

Parameters:

maskgpumemoryview: New mask (or None to unset the mask)
null_countint: New null count. If this is incorrect, bad things happen.

Returns:

New Column object sharing data with self (except for the mask which is new).

class pylibcudf.column.ListColumnView(Column col)#

Accessor for methods of a Column that are specific to lists.

Methods

`child`(self)	The data column of the underlying list column.
`offsets`(self)	The offsets column of the underlying list column.

child(self)#: The data column of the underlying list column.

offsets(self)#: The offsets column of the underlying list column.

pylibcudf.column.is_c_contiguous(shape: Sequence[int], strides: None | Sequence[int], int itemsize: int) → bool#

Determine if shape and strides are C-contiguous

Parameters:

shapeSequence[int]: Number of elements in each dimension.
stridesNone | Sequence[int]: The stride of each dimension in bytes. If None, the memory layout is C-contiguous.
itemsizeint: Size of an element in bytes.

Returns:

bool: The boolean answer.

Column#

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