verticapy.vDataFrame.between_time#

vDataFrame.between_time(ts: str, start_time: str | timedelta | None = None, end_time: str | timedelta | None = None, inplace: bool = True) → vDataFrame#

Filters the vDataFrame by only keeping the records between two input times.

Parameters#

ts: str: TS (Time Series) vDataColumn used to filter the data. The vDataColumn type must be date (date, datetime, timestamp…).
start_time: TimeInterval: Input Start Time. For example, time = ‘12:00’ will filter the data when time(‘ts’) is lesser than 12:00.
end_time: TimeInterval: Input End Time. For example, time = ‘14:00’ will filter the data when time(‘ts’) is greater than 14:00.
inplace: bool, optional: If set to True, the filtering is applied to the vDataFrame.

Returns#

vDataFrame: self

Examples#

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use a dummy time-series data:

vdf = vp.vDataFrame(
    {
        "time": [
            "1993-11-03 00:00:00",
            "1993-11-03 00:00:01",
            "1993-11-03 00:00:02",
            "1993-11-03 00:00:03",
            "1993-11-03 00:00:04",
            "1993-11-04 00:00:01",
            "1993-11-04 00:00:02",
        ],
        "val": [0., 1., 2., 4., 5., 3., 2.],
    }
)

	Abc time Varchar(19) 100%	123 val Numeric(4) 100%
1	1993-11-03 00:00:00	0.0
2	1993-11-03 00:00:01	1.0
3	1993-11-03 00:00:02	2.0
4	1993-11-03 00:00:03	4.0
5	1993-11-03 00:00:04	5.0
6	1993-11-04 00:00:01	3.0
7	1993-11-04 00:00:02	2.0

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Using between_time we can easily filter through time-series values:

vdf.between_time(ts= "time", start_time= "00:00:01", end_time = "00:00:03")

	Abc time Varchar(19) 100%	123 val Numeric(4) 100%
1	1993-11-03 00:00:01	1.0
2	1993-11-03 00:00:02	2.0
3	1993-11-03 00:00:03	4.0
4	1993-11-04 00:00:01	3.0
5	1993-11-04 00:00:02	2.0

Notice that the function ignores the dates, and outputs all the times in that range. This is because it is only using the time information from ts column and ignoring the date information.