vastorbit.VastFrame.isin¶

VastFrame.isin(val: dict) → VastFrame¶

Checks whether specific records are in the VastFrame and returns the new VastFrame of the search.

Parameters:: val (dict) – Dictionary of the different records. Each key of the dictionary must represent a VastColumn. For example, to check if Badr Ouali and Fouad Teban are in the VastFrame. You can write the following dict: {"name": ["Teban", "Ouali"], "surname": ["Fouad", "Badr"]}
Returns:: The VastFrame of the search.
Return type:: VastFrame

Examples

We import vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use a dummy dataset:

vdf = vo.VastFrame(
    {
        "val": [3, 4, 5, 10, 12, 23],
        "cat": ['A', 'B', 'A', 'C', 'A', 'C'],
    }
)

	123 val Integer	Abc cat Varchar(1)
1	10	C
2	23	C
3	3	A
4	4	B
5	12	A
6	5	A

Rows: 1-6 | Columns: 2

Note

vastorbit 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 vastorbit environment.

Using isin we can easily filter through to get the desired results:

vdf.isin({"cat": ['A'], "val": [12]})

	123 val Integer	Abc cat Varchar(1)
1	12	A

Rows: 1-1 | Columns: 2