obsplus.DataFrameExtractor

class obsplus.DataFrameExtractor(cls, required_columns=None, dtypes=None, pass_dataframe=True, column_funcs=None)

A class to extract dataframes from nested object trees.

Generally used to construct summary dataframes from nested object structures such as the obspy Catalog.

Parameters:

• cls – The top-level class the extractor acts on.

• required_columns (Optional[Sequence[str]]) – If not None, assert required columns are in dataframe, and order columns the same as required_columns, with extra columns at the end.

• dtypes – A dict of {column name: required data type}. Can also be specified when registering extractors.

• pass_dataframe – If True, return dataframes passed to DataFrameExtractor.__call__. This allows the DataFrameExtractor to be idempotent.

• column_funcs (Optional[Mapping[str, Callable[[Series], Union[Series, ndarray]]]]) – Columns that are UTCDateTime objects. Will correctly handle UTCDateTime-able objects (like date-time strings, floats, etc).

__init__(cls, required_columns=None, dtypes=None, pass_dataframe=True, column_funcs=None)

Methods

__init__(cls[, required_columns, dtypes, ...])
clear()
copy()	Return a deep copy of the fetcher.
extractor([dtypes])	Register an extractor.
fromkeys(iterable[, value])
get(k[,d])
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
register(cls)	Registers an alternate constructor.
setdefault(k[,d])
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()

Attributes

dtypes	return a dictionary of datatypes.
nslc