Repository: huggingface/datasets
File: src/datasets/arrow_dataset.py (Python)

Code before:
                key=slice(0, len(self)),
                indices=self._indices if self._indices is not None else None,
            ).to_pandas(types_mapper=pandas_types_mapper)
        else:
            batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            return (
                query_table(
                    table=self._data,
                    key=slice(offset, offset + batch_size),
                    indices=self._indices if self._indices is not None else None,
                ).to_pandas(types_mapper=pandas_types_mapper)
                for offset in range(0, len(self), batch_size)
            )

    def to_parquet(
        self,
        path_or_buf: Union[PathLike, BinaryIO],
        batch_size: Optional[int] = None,
        storage_options: Optional[dict] = None,
        **parquet_writer_kwargs,
    ) -> int:
        """Exports the dataset to parquet

        Args:
            path_or_buf (`PathLike` or `FileOrBuffer`):
                Either a path to a file (e.g. `file.parquet`), a remote URI (e.g. `s3://my-bucket/file.parquet`),
                or a BinaryIO, where the dataset will be saved to in the specified format.
            batch_size (`int`, *optional*):
                Size of the batch to load in memory and write at once.
                Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
            storage_options (`dict`, *optional*):
                Key/value pairs to be passed on to the file-system backend, if any.

                <Added version="2.15.0"/>
            **parquet_writer_kwargs (additional keyword arguments):
                Parameters to pass to PyArrow's `pyarrow.parquet.ParquetWriter`.

        Returns:
            `int`: The number of characters or bytes written.

        Example:

        ```py
        >>> ds.to_parquet("path/to/dataset/directory")
        ```
        """
        # Dynamic import to avoid circular dependency
        from .io.parquet import ParquetDatasetWriter

        return ParquetDatasetWriter(
            self, path_or_buf, batch_size=batch_size, storage_options=storage_options, **parquet_writer_kwargs

Review comment (suggestion):
```suggestion
                Either a path to a file (e.g. `file.parquet`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.parquet`),
```

Code after:
        Args:
            batched (`bool`):
                Set to `True` to return a generator that yields the dataset as batches
                of `batch_size` rows. Defaults to `False` (returns the whole datasets once).
            batch_size (`int`, *optional*):
                The size (number of rows) of the batches if `batched` is `True`.
                Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.

        Returns:
            `pandas.DataFrame` or `Iterator[pandas.DataFrame]`

        Example:

        ```py
        >>> ds.to_pandas()
        ```
        """
        if not batched:
            return query_table(
                table=self._data,
                key=slice(0, len(self)),
                indices=self._indices,
            ).to_pandas(types_mapper=pandas_types_mapper)
        else:
            batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            return (
                query_table(
                    table=self._data,
                    key=slice(offset, offset + batch_size),
                    indices=self._indices,
                ).to_pandas(types_mapper=pandas_types_mapper)
                for offset in range(0, len(self), batch_size)
            )

    def to_parquet(
        self,
        path_or_buf: Union[PathLike, BinaryIO],
        batch_size: Optional[int] = None,
        storage_options: Optional[dict] = None,
        **parquet_writer_kwargs,
    ) -> int:
        """Exports the dataset to parquet

        Args:
            path_or_buf (`PathLike` or `FileOrBuffer`):
                Either a path to a file (e.g. `file.parquet`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.parquet`),
                or a BinaryIO, where the dataset will be saved to in the specified format.
            batch_size (`int`, *optional*):
                Size of the batch to load in memory and write at once.
                Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
            storage_options (`dict`, *optional*):