abracudabra.conversion.cframe

abracudabra.conversion.cframe#

Convert to a Pandas/cuDF series or dataframe.

Functions#

to_series(sequence, /[, index, device, strict])

Convert an array or tensor to a Pandas/cuDF series.

to_dataframe(data, /[, index, device, strict])

Convert to a Pandas/cuDF dataframe.

Module Contents#

abracudabra.conversion.cframe.to_series(sequence, /, index=None, device=None, *, strict=False, **kwargs)[source]#

Convert an array or tensor to a Pandas/cuDF series.

Parameters:

  • sequence (object) – The array or tensor to convert.

  • index (abracudabra._annotations.Array | torch.Tensor | None) – The optional index for the series.

  • device (str | abracudabra.device.base.Device | None) – The device to use for the series. If not provided, the array stays on the same device.

  • strict (bool) – Whether to raise an error if the sequence is not a NumPy/CuPy array or Torch tensor.

  • **kwargs (Any) – Additional keyword arguments for the series.

Returns:

The converted series.

Return type:

abracudabra._annotations.Series

Examples

Convert a list to a CuPy series

>>> series = to_series([10, 20, 30], device="cuda")
>>> print(type(series))
<class 'cudf.core.series.Series'>

Convert a CuPy array to a cuDF series

>>> import cupy as cp
>>> cupy_array = cp.array([40, 50, 60])
>>> series = to_series(cupy_array)
>>> print(type(series))
<class 'cudf.core.series.Series'>
abracudabra.conversion.cframe.to_dataframe(data, /, index=None, device=None, *, strict=False, **kwargs)[source]#

Convert to a Pandas/cuDF dataframe.

Parameters:

  • data (collections.abc.Mapping[str, abracudabra._annotations.Array | torch.Tensor] | torch.Tensor | abracudabra._annotations.Array) – The data to convert. If a mapping, the keys will be used as column names.

  • index (abracudabra._annotations.Array | torch.Tensor | None) – The optional index for the dataframe.

  • device (str | abracudabra.device.base.Device | None) – The device to use for the dataframe. If not provided, the type is guessed from the data.

  • strict (bool) – Whether to raise an error if the provided data does not consist of NumPy/CuPy arrays or Torch tensors.

  • **kwargs (Any) – Additional keyword arguments for the dataframe.

Returns:

The converted dataframe.

Return type:

abracudabra._annotations.DataFrame

Examples

Build a dataframe from mixed data types

>>> import cupy as cp
>>> import numpy as np
>>> import torch

>>> numpy_array = np.full((5,), 1, dtype=np.float32)
>>> cupy_array = cp.full((5,), 2, dtype=cp.int8)
>>> torch_tensor = torch.full((5,), 3, dtype=torch.float32, device="cuda:0")
>>> dataframe = to_dataframe(
...     {"numpy": numpy_array, "cupy": cupy_array, "torch": torch_tensor},
...     device="cuda:0",
... )
>>> print(dataframe)
numpy  cupy  torch
0    1.0     2    3.0
1    1.0     2    3.0
2    1.0     2    3.0
3    1.0     2    3.0
4    1.0     2    3.0
>>> print(type(dataframe))
<class 'cudf.core.dataframe.DataFrame'>
Contents