abracudabra.conversion.cframe
=============================

.. py:module:: abracudabra.conversion.cframe

.. autoapi-nested-parse::

   Convert to a Pandas/cuDF series or dataframe.



Functions
---------

.. autoapisummary::

   abracudabra.conversion.cframe.to_series
   abracudabra.conversion.cframe.to_dataframe


Module Contents
---------------

.. py:function:: to_series(sequence, /, index = None, device = None, *, strict = False, **kwargs)

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

   :param sequence: The array or tensor to convert.
   :param index: The optional index for the series.
   :param device: The device to use for the series. If not provided, the array stays
                  on the same device.
   :param strict: Whether to raise an error if the sequence is not
                  a NumPy/CuPy array or Torch tensor.
   :param \*\*kwargs: Additional keyword arguments for the series.

   :returns: The converted series.

   .. rubric:: 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'>


.. py:function:: to_dataframe(data, /, index = None, device = None, *, strict = False, **kwargs)

   Convert to a Pandas/cuDF dataframe.

   :param data: The data to convert. If a mapping, the keys will be used as column names.
   :param index: The optional index for the dataframe.
   :param device: The device to use for the dataframe. If not provided,
                  the type is guessed from the data.
   :param strict: Whether to raise an error if the provided data does not consist of
                  NumPy/CuPy arrays or Torch tensors.
   :param \*\*kwargs: Additional keyword arguments for the dataframe.

   :returns: The converted dataframe.

   .. rubric:: 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'>