vastorbit.VastFrame.interpolate

VastFrame.interpolate(ts: str, rule: Annotated[str | timedelta, 'Time Interval'], method: dict | None = None, by: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None) → VastFrame

Computes a regular time interval VastFrame by interpolating the missing values using different techniques.

Parameters:

• ts (str) – TS (Time Series) VastColumn used to order the data. The VastColumn type must be date (date, datetime, timestamp…).

• rule (TimeInterval) – Interval used to create the time slices. The final interpolation is divided by these intervals. For example, specifying ‘5 minutes’ creates records separated by time intervals of ‘5 minutes’. Format: ‘1 second’, ‘5 minutes’, ‘1 hour’, ‘1 day’, etc.

• method (dict, optional) –

  Dictionary of interpolation methods. Must be in the following format: {“column1”: “interpolation1” …, “columnk”: “interpolationk”} Interpolation methods must be one of the following:

  • bfill/backfill:

    Interpolates with the next non-null value.

  • ffill/pad:

    Interpolates with the previous non-null value.

  • linear:

    Linear interpolation between points.

• by (SQLColumns, optional) – VastColumns used in the partition.

Returns:

object result of the interpolation.

Return type:

VastFrame

Examples

import vastorbit as vo

vdf = vo.VastFrame({
    "time": [
        "1993-11-03 00:00:00",
        "1993-11-03 00:00:01",
        "1993-11-03 00:00:02",
        "1993-11-03 00:00:04",
        "1993-11-03 00:00:05",
    ],
    "val": [0., 1., 2., 4., 5.],
})

vdf["time"].astype("timestamp")

# Linear interpolation
result = vdf.interpolate(
    ts="time",
    rule="1 second",
    method={"val": "linear"},
)