diff --git a/pandera/backends/pyspark/column.py b/pandera/backends/pyspark/column.py
index 7c0ac168e..52c3081c1 100644
--- a/pandera/backends/pyspark/column.py
+++ b/pandera/backends/pyspark/column.py
@@ -125,10 +125,18 @@ def coerce_dtype(
 
     @validate_scope(scope=ValidationScope.SCHEMA)
     def check_nullable(self, check_obj: DataFrame, schema):
-        isna = (
-            check_obj.filter(col(schema.name).isNull()).limit(1).count() == 0
-        )
-        passed = schema.nullable or isna
+        passed = True
+
+        # Use schema level information to optimize execution of the `nullable` check:
+        # ignore this check if Pandera Field's `nullable` property is True
+        # (check not necessary) or if df column's `nullable` property is False
+        # (PySpark's nullable ensures the presence of values when creating the df)
+        if (not schema.nullable) and (check_obj.schema[schema.name].nullable):
+            passed = (
+                check_obj.filter(col(schema.name).isNull()).limit(1).count()
+                == 0
+            )
+
         return CoreCheckResult(
             check="not_nullable",
             reason_code=SchemaErrorReason.SERIES_CONTAINS_NULLS,
diff --git a/tests/pyspark/test_pyspark_container.py b/tests/pyspark/test_pyspark_container.py
index 6d9f65d89..87243f8d4 100644
--- a/tests/pyspark/test_pyspark_container.py
+++ b/tests/pyspark/test_pyspark_container.py
@@ -1,5 +1,6 @@
 """Unit tests for pyspark container."""
 
+from contextlib import nullcontext as does_not_raise
 from pyspark.sql import DataFrame, SparkSession
 import pyspark.sql.types as T
 import pytest
@@ -142,7 +143,7 @@ def test_pyspark_sample():
         ("Butter", 15),
         ("Ice Cream", 10),
         ("Cola", 12),
-        ("Choclate", 7),
+        ("Chocolate", 7),
     ]
 
     spark_schema = T.StructType(
@@ -185,3 +186,48 @@ def test_pyspark_regex_column():
     df_out = schema.validate(df2)
 
     assert not df_out.pandera.errors
+
+
+def test_pyspark_nullable():
+    """
+    Test the nullable functionality of pyspark
+    """
+
+    data = [
+        ("Bread", 9),
+        ("Butter", 15),
+        ("Ice Cream", None),
+        ("Cola", 12),
+        ("Chocolate", None),
+    ]
+    spark_schema = T.StructType(
+        [
+            T.StructField("product", T.StringType(), False),
+            T.StructField("price", T.IntegerType(), True),
+        ],
+    )
+    df = spark.createDataFrame(data=data, schema=spark_schema)
+
+    # Check for `nullable=False`
+    schema_nullable_false = DataFrameSchema(
+        columns={
+            "product": Column("str"),
+            "price": Column("int", nullable=False),
+        },
+    )
+    with does_not_raise():
+        df_out = schema_nullable_false.validate(df)
+    assert isinstance(df_out, DataFrame)
+    assert "SERIES_CONTAINS_NULLS" in str(dict(df_out.pandera.errors))
+
+    # Check for `nullable=True`
+    schema_nullable_true = DataFrameSchema(
+        columns={
+            "product": Column("str"),
+            "price": Column("int", nullable=True),
+        },
+    )
+    with does_not_raise():
+        df_out = schema_nullable_true.validate(df)
+    assert isinstance(df_out, DataFrame)
+    assert df_out.pandera.errors == {}