gwexpy.interop.pandas_

Functions

from_pandas_dataframe(cls, df, *[, ...])	DataFrame -> TimeSeriesDict
from_pandas_series(cls, series, *[, unit, ...])	Create TimeSeries from pandas.Series.
to_pandas_dataframe(tsd[, index, copy])	TimeSeriesDict -> DataFrame
to_pandas_series(ts[, index, name, copy])	Convert TimeSeries to pandas.Series.

gwexpy.interop.pandas_.to_pandas_series(ts: TimeSeries, index: Literal['datetime', 'seconds', 'gps'] = 'datetime', name: str | None = None, copy: bool = False) → pd.Series

Convert TimeSeries to pandas.Series.

Parameters:

• index (str, default "datetime") – “datetime” (UTC aware), “seconds” (unix), or “gps”.

• copy (bool) – Whether to copy data.

Return type:

pandas.Series

gwexpy.interop.pandas_.from_pandas_series(cls: Type[T], series: pd.Series, *, unit: str | None = None, t0: float | None = None, dt: float | None = None) → T

Create TimeSeries from pandas.Series.

gwexpy.interop.pandas_.to_pandas_dataframe(tsd: TimeSeriesDict, index: Literal['datetime', 'seconds', 'gps'] = 'datetime', copy: bool = False) → pd.DataFrame

TimeSeriesDict -> DataFrame

gwexpy.interop.pandas_.from_pandas_dataframe(cls: Type[TimeSeriesDict], df: pd.DataFrame, *, unit_map: Dict[str, str] | None = None, t0: float | None = None, dt: float | None = None) → TimeSeriesDict

DataFrame -> TimeSeriesDict