pypdf.PDFDataset

kedro_datasets_experimental.pypdf.PDFDataset

PDFDataset(
    *,
    filepath,
    load_args=None,
    credentials=None,
    fs_args=None,
    metadata=None
)

Bases: AbstractDataset[NoReturn, list[str]]

PDFDataset loads data from PDF files using an underlying filesystem (e.g.: local, S3, GCS). It uses pypdf to read and extract text from PDF files.

This is a read-only dataset - saving is not supported.

Examples:

Using the YAML API:

my_pdf_document:
  type: pypdf.PDFDataset
  filepath: data/01_raw/document.pdf

password_protected_pdf:
  type: pypdf.PDFDataset
  filepath: data/01_raw/protected.pdf
  load_args:
    password: "pass123"  # pragma: allowlist secret

s3_pdf:
  type: pypdf.PDFDataset
  filepath: s3://your_bucket/document.pdf
  credentials: dev_s3

Using the Python API:

>>> from kedro_datasets_experimental.pypdf import PDFDataset
>>>
>>> dataset = PDFDataset(filepath="data/document.pdf")
>>> pages = dataset.load()
>>> # pages is a list of strings, one per page
>>> assert isinstance(pages, list)
>>> assert all(isinstance(page, str) for page in pages)

Parameters:

• filepath (str) –

  Filepath in POSIX format to a PDF file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec.

• load_args (dict[str, Any] | None, default: None ) –

  Pypdf options for loading PDF files (arguments passed into pypdf.PdfReader). Here you can find all available arguments: https://pypdf.readthedocs.io/en/stable/modules/PdfReader.html All defaults are preserved, except "strict", which is set to False. Common options include: - password (str): Password for encrypted PDFs - strict (bool): Whether to raise errors on malformed PDFs (default: False)

• credentials (dict[str, Any] | None, default: None ) –

  Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {"token": None}.

• fs_args (dict[str, Any] | None, default: None ) –

  Extra arguments to pass into underlying filesystem class constructor (e.g. {"project": "my-project"} for GCSFileSystem), as well as to pass to the filesystem's open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved.

• metadata (dict[str, Any] | None, default: None ) –

  Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def __init__(
    self,
    *,
    filepath: str,
    load_args: dict[str, Any] | None = None,
    credentials: dict[str, Any] | None = None,
    fs_args: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
) -> None:
    """Creates a new instance of ``PDFDataset`` pointing to a concrete PDF file
    on a specific filesystem.

    Args:
        filepath: Filepath in POSIX format to a PDF file prefixed with a protocol like `s3://`.
            If prefix is not provided, `file` protocol (local filesystem) will be used.
            The prefix should be any protocol supported by ``fsspec``.
        load_args: Pypdf options for loading PDF files (arguments passed
            into ``pypdf.PdfReader``). Here you can find all available arguments:
            https://pypdf.readthedocs.io/en/stable/modules/PdfReader.html
            All defaults are preserved, except "strict", which is set to False.
            Common options include:
            - password (str): Password for encrypted PDFs
            - strict (bool): Whether to raise errors on malformed PDFs (default: False)
        credentials: Credentials required to get access to the underlying filesystem.
            E.g. for ``GCSFileSystem`` it should look like `{"token": None}`.
        fs_args: Extra arguments to pass into underlying filesystem class constructor
            (e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
            to pass to the filesystem's `open` method through nested keys
            `open_args_load` and `open_args_save`.
            Here you can find all available arguments for `open`:
            https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
            All defaults are preserved.
        metadata: Any arbitrary metadata.
            This is ignored by Kedro, but may be consumed by users or external plugins.
    """
    _fs_args = deepcopy(fs_args) or {}
    _fs_open_args_load = _fs_args.pop("open_args_load", {})
    _credentials = deepcopy(credentials) or {}

    super().__init__()

    protocol, path = get_protocol_and_path(filepath)
    if protocol == "file":
        _fs_args.setdefault("auto_mkdir", True)

    self._protocol = protocol
    self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)
    self._filepath = PurePosixPath(path)
    self.metadata = metadata

    # Handle default load and fs arguments
    self._load_args = {**self.DEFAULT_LOAD_ARGS, **(load_args or {})}
    self._fs_open_args_load = _fs_open_args_load or {}

DEFAULT_LOAD_ARGS class-attribute instance-attribute

DEFAULT_LOAD_ARGS = {'strict': False}

_filepath instance-attribute

_filepath = PurePosixPath(path)

_fs instance-attribute

_fs = filesystem(_protocol, **_credentials, **_fs_args)

_fs_open_args_load instance-attribute

_fs_open_args_load = _fs_open_args_load or {}

_load_args instance-attribute

_load_args = {
    None: DEFAULT_LOAD_ARGS,
    None: load_args or {},
}

_protocol instance-attribute

_protocol = protocol

metadata instance-attribute

metadata = metadata

_describe

_describe()

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def _describe(self) -> dict[str, Any]:
    return {
        "filepath": self._filepath,
        "protocol": self._protocol,
        "load_args": self._load_args,
    }

_exists

_exists()

Check if the PDF file exists.

Returns:

• bool –

  True if the file exists, False otherwise.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def _exists(self) -> bool:
    """Check if the PDF file exists.

    Returns:
        bool: True if the file exists, False otherwise.
    """
    load_path = get_filepath_str(self._filepath, self._protocol)
    return self._fs.exists(load_path)

_invalidate_cache

_invalidate_cache()

Invalidate underlying filesystem caches.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def _invalidate_cache(self) -> None:
    """Invalidate underlying filesystem caches."""
    filepath = get_filepath_str(self._filepath, self._protocol)
    self._fs.invalidate_cache(filepath)

_release

_release()

Release any cached filesystem information.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def _release(self) -> None:
    """Release any cached filesystem information."""
    self._invalidate_cache()

load

load()

Loads data from a PDF file.

Returns:

• list[str] –

  A list of strings, where each string contains the text extracted from one page.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def load(self) -> list[str]:
    """Loads data from a PDF file.

    Returns:
        list[str]: A list of strings, where each string contains the text extracted from one page.
    """
    load_path = get_filepath_str(self._filepath, self._protocol)

    with self._fs.open(load_path, mode="rb", **self._fs_open_args_load) as fs_file:
        pdf_reader = pypdf.PdfReader(stream=fs_file, **self._load_args)
        pages = []
        for page in pdf_reader.pages:
            pages.append(page.extract_text())
        return pages

save

save(data)

Saving to PDFDataset is not supported.

Parameters:

• data (NoReturn) –

  Data to save.

Raises:

• DatasetError –

  Always raised as saving is not supported.

Source code in kedro_datasets_experimental/pypdf/pdf_dataset.py

def save(self, data: NoReturn) -> None:
    """Saving to PDFDataset is not supported.

    Args:
        data: Data to save.

    Raises:
        DatasetError: Always raised as saving is not supported.
    """
    raise DatasetError("Saving to PDFDataset is not supported.")