import dascore as dc
df = dc.scan_to_df("examples://terra15_das_1_trimmed.hdf5")scan_to_df
scan_to_df(
path: str | pathlib._local.Path | upath.core.UPath | dascore.core.patch.Patch | dascore.core.spool.Spool | dascore.utils.io.IOResourceManager | collections.abc.Iterable[str, Path, UPath, Patch, Spool, IOResourceManager, collections.abc.Iterable[str | pathlib._local.Path | upath.core.UPath | dascore.core.patch.Patch | dascore.utils.io.IOResourceManager[str, Path, UPath, Patch, IOResourceManager]], DataFrame] ,
file_format: str | None[str, None] = None,
file_version: str | None[str, None] = None,
ext: str | None[str, None] = None,
timestamp: float | None[float, None] = None,
progress: Literal[‘standard’, ‘basic’, None, False] = standard,
exclude = (‘history’,),
)-> ‘pd.DataFrame’
Scan a path, return a dataframe of contents.
The columns of the dataframe depend on the attributes and coordinates found in the data files.
Parameters
| Parameter | Description |
|---|---|
| path | The path to the to file to scan |
| file_format | Format of the file. If not provided DASCore will try to determine it. |
| file_version | The version string of the file. |
| ext | The extensions to map. |
| timestamp | Time stamp indicating the minimum mtime. |
| progress |
The type of progress bar to use. None disables progress bar and “basic” is best for low latency scenarios. |
| exclude | A sequence of column names to exclude in the final dataframe. |