gwexpy.interop.pandas_

Functions

from_pandas_dataframe(cls, df, *[, ...])

Convert DataFrame to TimeSeriesDict.

from_pandas_series(cls, series, *[, unit, ...])

Create a TimeSeries from pandas.Series.

to_pandas_dataframe(tsd[, index, copy])

Convert TimeSeriesDict to 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[source]

Convert TimeSeries to pandas.Series.

Parameters:

  • ts (TimeSeries) – Input time series to convert.

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

  • name (str, optional) – Override name for the resulting series.

  • 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, channel: str | None = None, name: str | None = None) → T[source]

Create a TimeSeries from pandas.Series.

t0/dt are inferred from the index when possible; if inference fails they fall back to 0/1 with a UserWarning rather than silently. channel/name (which a plain Series cannot carry) may be supplied explicitly.

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

Convert TimeSeriesDict to 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[source]

Convert DataFrame to TimeSeriesDict.