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vastorbit.sql.functions.edit_distance

vastorbit.sql.functions.edit_distance(expr1: Annotated[str | list[str] | StringSQL | list[StringSQL], ''], expr2: Annotated[str | list[str] | StringSQL | list[StringSQL], '']) StringSQL

Calculates and returns the Levenshtein distance between two strings.

Parameters:
  • expr1 (SQLExpression) – Expression.

  • expr2 (SQLExpression) – Expression.

Returns:

SQL string.

Return type:

StringSQL

Examples

First, let’s import the VastFrame in order to create a dummy dataset.

from vastorbit import VastFrame

Now, let’s import the vastorbit SQL functions.

import vastorbit.sql.functions as vof

We can now build a dummy dataset.

df = VastFrame({"x": ["hello", "apple", "heroes", "allo"]})

Now, let’s go ahead and apply the function.

df["edit_distance_x"] = vof.edit_distance(df["x"], 'heyllow')
display(df)
Abc
x
Varchar(6)
100%
123
edit_distance_x
Bigint
100%
1apple6
2heroes5
3hello2
4allo4

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.