Attention

June 2024 Status Update: Removing DataPipes and DataLoader V2

We are re-focusing the torchdata repo to be an iterative enhancement of torch.utils.data.DataLoader. We do not plan on continuing development or maintaining the [DataPipes] and [DataLoaderV2] solutions, and they will be removed from the torchdata repo. We’ll also be revisiting the DataPipes references in pytorch/pytorch. In release torchdata==0.8.0 (July 2024) they will be marked as deprecated, and in 0.9.0 (Oct 2024) they will be deleted. Existing users are advised to pin to torchdata==0.8.0 or an older version until they are able to migrate away. Subsequent releases will not include DataPipes or DataLoaderV2. Please reach out if you suggestions or comments (please use this issue for feedback)

CSVParser

class torchdata.datapipes.iter.CSVParser(source_datapipe: IterDataPipe[Tuple[str, IO]], *, skip_lines: int = 0, decode: bool = True, encoding: str = 'utf-8', errors: str = 'ignore', return_path: bool = False, as_tuple: bool = False, **fmtparams)

Accepts a DataPipe consists of tuples of file name and CSV data stream, reads and returns the contents within the CSV files one row at a time (functional name: parse_csv). Each output is a List by default, but it depends on fmtparams.

Parameters:

• source_datapipe – source DataPipe with tuples of file name and CSV data stream

• skip_lines – number of lines to skip at the beginning of each file

• strip_newline – if True, the new line character will be stripped

• decode – if True, this will decode the contents of the file based on the specified encoding

• encoding – the character encoding of the files (default=’utf-8’)

• errors – the error handling scheme used while decoding

• return_path – if True, each line will return a tuple of path and contents, rather than just the contents

• as_tuple – if True, each line will return a tuple instead of a list

Example

>>> from torchdata.datapipes.iter import IterableWrapper, FileOpener
>>> import os
>>> def get_name(path_and_stream):
>>>     return os.path.basename(path_and_stream[0]), path_and_stream[1]
>>> datapipe1 = IterableWrapper(["1.csv", "empty.csv", "empty2.csv"])
>>> datapipe2 = FileOpener(datapipe1, mode="b")
>>> datapipe3 = datapipe2.map(get_name)
>>> csv_parser_dp = datapipe3.parse_csv()
>>> list(csv_parser_dp)
[['key', 'item'], ['a', '1'], ['b', '2'], []]