reference/generated/tensordict.TensorDictBase.from_parquet

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

TensorDictBase.from_parquet

classmethod TensorDictBase.from_parquet(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, columns: list[str] | None = None, **kwargs) → Self

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

Requires pyarrow or pandas: the file is read with pyarrow.parquet.read_table when pyarrow is installed, and with pandas.read_parquet() otherwise. The columns are converted as in from_pandas(). to_parquet() writes such a file. Through pyarrow, a string column is a NonTensorStack with one value per row; through pandas, it is one value for the whole batch (see the note in from_pandas()).

Parameters:

path (str or Path) – the path of the Parquet 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, which keeps the column names as keys.

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

• columns (list of str, optional) – the columns to read. Defaults to None, which reads all the columns.

• **kwargs – passed to the Parquet reader.

Returns:

A tensordict of the class this method is called on (TensorDict for TensorDictBase).

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.parquet")
...     data.to_parquet(path)
...     td = TensorDict.from_parquet(path)
...     td_obs = TensorDict.from_parquet(path, columns=["obs"])
>>> sorted(td.keys()), sorted(td_obs.keys())
(['obs', 'reward'], ['obs'])

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