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verticapy.sql.functions.soundex_matches#

verticapy.sql.functions.soundex_matches(expr1: str | list[str] | StringSQL | list[StringSQL], expr2: str | list[str] | StringSQL | list[StringSQL]) StringSQL#

Generates and compares Soundex encodings of two strings, and returns a count of the matching characters (ranging from 0 for no match to 4 for an exact match).

Parameters#

expr1: SQLExpression

Expression.

expr2: SQLExpression

Expression.

Returns#

StringSQL

SQL string.

Examples#

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

from verticapy import vDataFrame

Now, let’s import the VerticaPy SQL functions.

import verticapy.sql.functions as vpf

We can now build a dummy dataset.

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

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

df["soundex_matches_x"] = vpf.soundex_matches(df["x"], 'heyllow')
display(df)
Abc
x
Varchar(6)
100%
123
soundex_matches_x
Integer
100%
1hello4
2apple1
3heroes2
4allo3

Note

It’s crucial to utilize VerticaPy 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. VerticaPy effectively manages these changes, a task not achievable with pure SQL.

See also

vDataFrame.eval() : Evaluates the expression.