verticapy.vDataFrame.between#
- vDataFrame.between(column: str, start: bool | float | str | timedelta | datetime | None = None, end: bool | float | str | timedelta | datetime | None = None, inplace: bool = True) vDataFrame #
Filters the vDataFrame by only keeping the records between two input elements.
Parameters#
- column: str
TS (Time Series) vDataColumn used to filter the data. The vDataColumn type must be date (date, datetime, timestamp…)
- start: PythonScalar, optional
Input Python Scalar used to filter.
- end: PythonScalar, optional
Input Python Scalar used to filter.
- 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 fromverticapy
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": [ "1993-11-01", "1993-11-02", "1993-11-03", "1993-11-04", "1993-11-05", ], "val": [0., 1., 2., 4.,5.], } )
AbctimeVarchar(10)100%123valNumeric(4)100%1 1993-11-01 0.0 2 1993-11-02 1.0 3 1993-11-03 2.0 4 1993-11-04 4.0 5 1993-11-05 5.0 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
we can easily filter through time-series values:vdf.between(column= "time", start= "1993-11-02", end = "1993-11-04")
AbctimeVarchar(10)100%123valNumeric(4)100%1 1993-11-02 1.0 2 1993-11-03 2.0 3 1993-11-04 4.0