Merge pull request #233 from nijanthanvijayakumar/issue-49-remove-exi…

…sts-forall

Issue 49 remove exists forall

quinn/__init__.py

@@ -34,8 +34,6 @@
     approx_equal,
     array_choice,
     business_days_between,
-    exists,
-    forall,
     is_false,
     is_falsy,
     is_not_in,

quinn/functions.py

@@ -16,7 +16,6 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
     from numbers import Number
 
     from pyspark.sql import Column
@@ -84,45 +83,6 @@ def remove_non_word_characters(col: Column) -> Column:
     return F.regexp_replace(col, "[^\\w\\s]+", "")
 
 
-def exists(f: Callable[[Any], bool]) -> udf:
-    """Create a user-defined function.
-
-    It takes a list expressed as a column of type ``ArrayType(AnyType)`` as an argument and returns a boolean value indicating
-    whether any element in the list is true according to the argument ``f`` of the ``exists()`` function.
-
-    :param f: Callable function - A callable function that takes an element of
-    type Any and returns a boolean value.
-    :return: A user-defined function that takes
-    a list expressed as a column of type ArrayType(AnyType) as an argument and
-    returns a boolean value indicating whether any element in the list is true
-    according to the argument ``f`` of the ``exists()`` function.
-    :rtype: UserDefinedFunction
-    """
-
-    def temp_udf(list_: list) -> bool:
-        return any(map(f, list_))
-
-    return F.udf(temp_udf, BooleanType())
-
-
-def forall(f: Callable[[Any], bool]) -> udf:
-    """The **forall** function allows for mapping a given boolean function to a list of arguments and return a single boolean value.
-
-    It does this by creating a Spark UDF which takes in a list of arguments, applying the given boolean function to
-    each element of the list and returning a single boolean value if all the elements pass through the given boolean function.
-
-    :param f: A callable function ``f`` which takes in any type and returns a boolean
-    :return: A spark UDF which accepts a list of arguments and returns True if all
-    elements pass through the given boolean function, False otherwise.
-    :rtype: UserDefinedFunction
-    """
-
-    def temp_udf(list_: list) -> bool:
-        return all(map(f, list_))
-
-    return F.udf(temp_udf, BooleanType())
-
-
 def multi_equals(value: Any) -> udf:  # noqa: ANN401
     """Create a user-defined function that checks if all the given columns have the designated value.
 

tests/test_functions.py

@@ -97,46 +97,6 @@ def test_anti_trim():
     chispa.assert_column_equality(actual_df, "words_anti_trimmed", "expected")
 
 
-def test_exists():
-    df = spark.createDataFrame(
-        [
-            ([1, 2, 3], False),
-            ([4, 5, 6], True),
-            ([10, 11, 12], True),
-        ],
-        StructType(
-            [
-                StructField("nums", ArrayType(IntegerType(), True), True),
-                StructField("expected", BooleanType(), True),
-            ]
-        ),
-    )
-    actual_df = df.withColumn(
-        "any_num_greater_than_5", quinn.exists(lambda n: n > 5)(F.col("nums"))
-    )
-    chispa.assert_column_equality(actual_df, "any_num_greater_than_5", "expected")
-
-
-def test_forall():
-    df = spark.createDataFrame(
-        [
-            ([1, 2, 3], False),
-            ([4, 5, 6], True),
-            ([10, 11, 12], True),
-        ],
-        StructType(
-            [
-                StructField("nums", ArrayType(IntegerType(), True), True),
-                StructField("expected", BooleanType(), True),
-            ]
-        ),
-    )
-    actual_df = df.withColumn(
-        "all_nums_greater_than_3", quinn.forall(lambda n: n > 3)(F.col("nums"))
-    )
-    chispa.assert_column_equality(actual_df, "all_nums_greater_than_3", "expected")
-
-
 def test_multi_equals():
     df = quinn.create_df(
         spark,