reference/generated/tensordict.TensorDictBase.from_csv

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TensorDictBase.from_csv

classmethod TensorDictBase.from_csv(path, *, 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, **kwargs) → Self

Creates a TensorDict from a CSV file.

Requires either pandas or pyarrow to be installed.

Parameters:

path (str or Path) – Path to the CSV file.

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. Defaults to None.

• dtype (dtype, optional) – If provided, all numeric columns are cast to this dtype. Defaults to None.

• **kwargs – Additional keyword arguments forwarded to the CSV reader (pandas.read_csv or pyarrow.csv.read_csv).

Returns:

A TensorDict representation of the CSV data.

Examples

>>> import os
>>> import tempfile
>>> import torch
>>> from tensordict import TensorDict
>>> data = TensorDict(obs=torch.arange(3.0), reward=torch.ones(3), batch_size=[3])
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     path = os.path.join(tmpdir, "data.csv")
...     data.to_csv(path)
...     td = TensorDict.from_csv(path)
...     td32 = TensorDict.from_csv(path, dtype=torch.float32)
>>> td.batch_size, td["obs"].dtype, td32["obs"].dtype
(torch.Size([3]), torch.float64, torch.float32)

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