reference/generated/tensordict.from_csv

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from_csv

tensordict.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) → TensorDictBase

Reads a CSV file into a tensordict, with one entry per column.

Requires pandas or pyarrow. See TensorDictBase.from_csv.

Parameters:

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

Keyword Arguments:

• auto_batch_size (bool, optional) – if True, sets the batch size to the largest leading shape that all the entries share. Exclusive with batch_size. Defaults to False.

• batch_dims (int, optional) – with auto_batch_size, the maximum number of batch dimensions. Ignored without auto_batch_size, with a FutureWarning when it would change the result. Starting with TensorDict 0.17, the default will change to turning on auto_batch_size when batch_dims is given, as from_dict does. Defaults to None.

• device (device, str or int, optional) – the device of the result. Leaves on another device are moved to it. None leaves each leaf where it is, and the result has no device. Defaults to None.

• batch_size (Size or list of int, optional) – the batch size of the result: the leading dimensions that all its entries share. Exclusive with auto_batch_size. Defaults to None, which gives [num_rows].

• separator (str, optional) – if given, splits the column names on it to build nested entries: with separator=".", the column "obs.x" becomes the entry ("obs", "x"). Defaults to None.

• dtype (dtype, optional) – if given, casts the numeric and boolean columns to it. Defaults to None.

• **kwargs – passed to the CSV reader.

Returns:

A TensorDict.