dataframe

DataFrame(data)

Bases: OfflineEnvironment

A dataset environment.

This environment represents static tabular datasets.

Attributes:

Name	Type	Description
data	LazyFrame	The data to represent.

Initialize the dataset environment.

Parameters:

Name	Type	Description	Default
data	DataFrame | LazyFrame	The data to represent.	required

Source code in src/flowcean/polars/environments/dataframe.py

def __init__(self, data: pl.DataFrame | pl.LazyFrame) -> None:
    """Initialize the dataset environment.

    Args:
        data: The data to represent.
    """
    if isinstance(data, pl.DataFrame):
        self.data = data.lazy()
        self._length = len(data)
    else:
        self.data = data
    super().__init__()

from_csv(path, separator=',') classmethod

Load a dataset from a CSV file.

Parameters:

Name	Type	Description	Default
path	str | Path	Path to the CSV file.	required
separator	str	Value separator. Defaults to ",".	','

Source code in src/flowcean/polars/environments/dataframe.py

@classmethod
def from_csv(cls, path: str | Path, separator: str = ",") -> Self:
    """Load a dataset from a CSV file.

    Args:
        path: Path to the CSV file.
        separator: Value separator. Defaults to ",".
    """
    data = pl.scan_csv(path, separator=separator)
    data = data.rename(lambda column_name: column_name.strip())
    return cls(data)

from_json(path) classmethod

Load a dataset from a JSON file.

Parameters:

Name	Type	Description	Default
path	str | Path	Path to the JSON file.	required

Source code in src/flowcean/polars/environments/dataframe.py

@classmethod
def from_json(cls, path: str | Path) -> Self:
    """Load a dataset from a JSON file.

    Args:
        path: Path to the JSON file.
    """
    data = pl.read_json(path)
    return cls(data)

from_parquet(path) classmethod

Load a dataset from a Parquet file.

Parameters:

Name	Type	Description	Default
path	str | Path	Path to the Parquet file.	required

Source code in src/flowcean/polars/environments/dataframe.py

@classmethod
def from_parquet(cls, path: str | Path) -> Self:
    """Load a dataset from a Parquet file.

    Args:
        path: Path to the Parquet file.
    """
    data = pl.scan_parquet(path)
    return cls(data)

from_yaml(path) classmethod

Load a dataset from a YAML file.

Parameters:

Name	Type	Description	Default
path	str | Path	Path to the YAML file.	required

Source code in src/flowcean/polars/environments/dataframe.py

@classmethod
def from_yaml(cls, path: str | Path) -> Self:
    """Load a dataset from a YAML file.

    Args:
        path: Path to the YAML file.
    """
    data = pl.LazyFrame(YAML(typ="safe").load(path))
    return cls(data)

from_uri(uri) classmethod

Load a dataset from a URI.

Parameters:

Name	Type	Description	Default
uri	str	The URI to load the dataset from.	required

Source code in src/flowcean/polars/environments/dataframe.py

@classmethod
def from_uri(cls, uri: str) -> Self:
    """Load a dataset from a URI.

    Args:
        uri: The URI to load the dataset from.
    """
    path = _file_uri_to_path(uri)
    suffix = path.suffix

    if suffix == ".csv":
        return cls.from_csv(path)
    if suffix == ".json":
        return cls.from_json(path)
    if suffix == ".parquet":
        return cls.from_parquet(path)
    if suffix in (".yaml", ".yml"):
        return cls.from_yaml(path)

    raise UnsupportedFileTypeError(suffix)

__len__()

Return the number of samples in the dataset.

Source code in src/flowcean/polars/environments/dataframe.py

def __len__(self) -> int:
    """Return the number of samples in the dataset."""
    if self._length is None:
        # This operation is potentially very slow / costly
        self._length = cast(
            int,
            self.data.select(pl.len()).collect().item(),
        )
    return self._length

InvalidUriSchemeError(scheme)

Bases: Exception

Exception raised when an URI scheme is invalid.

Initialize the InvalidUriSchemeError.

Parameters:

Name	Type	Description	Default
scheme	str	Invalid URI scheme.	required

Source code in src/flowcean/polars/environments/dataframe.py

def __init__(self, scheme: str) -> None:
    """Initialize the InvalidUriSchemeError.

    Args:
        scheme: Invalid URI scheme.
    """
    super().__init__(
        f"only file URIs can be converted to a path, but got `{scheme}`",
    )

UnsupportedFileTypeError(suffix)

Bases: Exception

Exception raised when a file type is not supported.

Initialize the UnsupportedFileTypeError.

Parameters:

Name	Type	Description	Default
suffix	str	File type suffix.	required

Source code in src/flowcean/polars/environments/dataframe.py

def __init__(self, suffix: str) -> None:
    """Initialize the UnsupportedFileTypeError.

    Args:
        suffix: File type suffix.
    """
    super().__init__(f"file type `{suffix}` is not supported")

collect(environment, n=None, *, progress_bar=True)

Collect data from an environment.

Parameters:

Name	Type	Description	Default
environment	Iterable[LazyFrame] | Collection[LazyFrame]	The environment to collect data from.	required
n	int | None	Number of samples to collect. If None, all samples are collected.	None
progress_bar	bool | dict[str, Any]	Whether to show a progress bar. If a dictionary is provided, it will be passed to the progress bar.	True

Returns:

Type	Description
DataFrame	The collected dataset.

Source code in src/flowcean/polars/environments/dataframe.py

def collect(
    environment: Iterable[pl.LazyFrame] | Collection[pl.LazyFrame],
    n: int | None = None,
    *,
    progress_bar: bool | dict[str, Any] = True,
) -> DataFrame:
    """Collect data from an environment.

    Args:
        environment: The environment to collect data from.
        n: Number of samples to collect. If None, all samples are collected.
        progress_bar: Whether to show a progress bar. If a dictionary is
            provided, it will be passed to the progress bar.

    Returns:
        The collected dataset.
    """
    samples = islice(environment, n)

    if n is not None:
        total = n
    elif isinstance(environment, Collection):
        total = len(environment)
    else:
        total = None

    if isinstance(progress_bar, dict):
        progress_bar.setdefault("desc", "Collecting samples")
        progress_bar.setdefault("total", total)
        samples = tqdm(
            samples,
            **progress_bar,
        )
    elif progress_bar:
        samples = tqdm(samples, desc="Collecting samples", total=total)

    data = pl.concat(samples, how="vertical")
    return DataFrame(data)