TensorDictBase.from_pandas#

classmethod TensorDictBase.from_pandas(dataframe, *, auto_batch_size: bool = False, batch_dims: int | None = None, device: device | None = None, batch_size: Size | None = None, separator: str | None = None, dtype: dtype | None = None) → Self#

Converts a pandas DataFrame to a TensorDict.

Numeric columns become tensors, string/object columns become NonTensorData.

Parameters:

dataframe (pd.DataFrame) – The pandas DataFrame to convert.

Keyword Arguments:

auto_batch_size (bool, optional) – If True, the batch size will be computed automatically. Defaults to False.
batch_dims (int, optional) – If auto_batch_size is True, defines how many dimensions the output tensordict should have. Defaults to None.
device (device, optional) – The device for tensor data. Defaults to None.
batch_size (Size, optional) – The batch size. Defaults to [num_rows].
separator (str, optional) – If provided, column names are split on this separator to create nested TensorDicts. For example, with separator=".", a column "obs.x" becomes td["obs", "x"]. Defaults to None.
dtype (dtype, optional) – If provided, all numeric columns are cast to this dtype. Defaults to None.

Returns:

A TensorDict representation of the DataFrame.

Examples

>>> from tensordict import TensorDict
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1, 2, 3], "b": [4.0, 5.0, 6.0]})
>>> td = TensorDict.from_pandas(df)
>>> print(td)
TensorDict(
    fields={
        a: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.int64, is_shared=False),
        b: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float64, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.

TensorDictBase.from_pandas#

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