vastorbit.sql.functions.regexp_extract¶
- vastorbit.sql.functions.regexp_extract(expr: Annotated[str | list[str] | StringSQL | list[StringSQL], ''], pattern: Annotated[str | list[str] | StringSQL | list[StringSQL], ''], position: int = 1, occurrence: int = 1) StringSQL¶
Returns the substring that matches a regular expression within a string.
- Parameters:
expr (SQLExpression) – Expression.
pattern (SQLExpression) – The regular expression to find a substring to extract.
position (int, optional) – The number of characters from the start of the string where the function should start searching for matches.
occurrence (int, optional) – Controls which occurrence of a pattern match in the string to return.
- Returns:
SQL string.
- Return type:
StringSQL
Examples
For this example, we will use the Titanic dataset.
from vastorbit.datasets import load_titanic titanic = load_titanic()
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.
Now, let’s import the vastorbit SQL functions.
import vastorbit.sql.functions as vof
Now, let’s go ahead and apply the function.
titanic["title"] = vof.regexp_extract( titanic["name"], r'([A-Za-z])+\.', ) display(titanic[["name", "title"]])
AbcnameVarchar(164)AbctitleVarchar(164)1 McCormack, Mr. Thomas Joseph Mr. 2 McCoy, Miss. Agnes Miss. 3 McCoy, Miss. Alicia Miss. 4 McCoy, Mr. Bernard Mr. 5 McDermott, Miss. Brigdet Delia Miss. Note
It’s crucial to utilize vastorbit SQL functions in coding, as they can be updated over time with new syntax. While SQL functions typically remain stable, they may vary across platforms or versions. vastorbit effectively manages these changes, a task not achievable with pure SQL.
See also
VastFrame.eval(): Evaluates the expression.