Profiler: add StringColumnProfile

docs/profiles.md

@@ -22,4 +22,5 @@ Here are the current supported functionalities of Profiles.
 |  | property: profiles | Done |
 |  | property: numRecords | Done |
 | StandardColumnProfile | StandardColumnProfile(spark_session, column, java_column_profile) | Done |
+| StringColumnProfile | StringColumnProfile(spark_session, column, java_column_profile) | Done |
 | NumericColumnProfile | NumericColumnProfile(spark_session, column, java_column_profile) | Done |

pydeequ/profiles.py

@@ -2,6 +2,7 @@
 """ Profiles file for all the Profiles classes in Deequ"""
 import json
 from collections import namedtuple
+from typing import Optional
 
 from pyspark.sql import DataFrame, SparkSession
 from pydeequ.analyzers import KLLParameters
@@ -241,9 +242,8 @@ def __init__(self, spark_session: SparkSession):
         self._numRecords = 0
         self.columnProfileClasses = {
             "StandardColumnProfile": StandardColumnProfile,
-            "StringColumnProfile": StandardColumnProfile,
+            "StringColumnProfile": StringColumnProfile,
             "NumericColumnProfile": NumericColumnProfile,
-
         }
 
     def _columnProfilesFromColumnRunBuilderRun(self, run):
@@ -528,3 +528,40 @@ def approxPercentiles(self):
         """
         return self._approxPercentiles
 
+
+class StringColumnProfile(ColumnProfile):
+    """
+    String Column Profile class
+
+    :param SparkSession spark_session: sparkSession
+    :param str column: the designated column of which the profile is run on
+    :param JavaObject java_column_profile: The profile mapped as a Java map
+    """
+
+    def __init__(
+        self, spark_session: SparkSession, column: str, java_column_profile
+    ) -> None:
+        super().__init__(spark_session, column, java_column_profile)
+        self._minLength = get_or_else_none(java_column_profile.minLength())
+        self._maxLength = get_or_else_none(java_column_profile.maxLength())
+        self.all = {
+            "completeness": self.completeness,
+            "approximateNumDistinctValues": self.approximateNumDistinctValues,
+            "dataType": self.dataType,
+            "isDataTypeInferred": self.isDataTypeInferred,
+            "typeCounts": self.typeCounts,
+            "histogram": self.histogram,
+            "minLength": self._minLength,
+            "maxLength": self._maxLength,
+        }
+
+    @property
+    def minLength(self) -> Optional[int]:
+        return self._minLength
+
+    @property
+    def maxLength(self) -> Optional[int]:
+        return self._maxLength
+
+    def __str__(self) -> str:
+        return f"StringProfiles for column: {self.column}: {json.dumps(self.all, indent=4)}"

tests/test_profiles.py

@@ -1,28 +1,43 @@
 # -*- coding: utf-8 -*-
 import unittest
 from pyspark.sql import Row
-from pydeequ.analyzers import KLLParameters
-from pydeequ.profiles import ColumnProfilerRunBuilder, ColumnProfilerRunner
+from pydeequ.profiles import (
+    ColumnProfilerRunBuilder,
+    ColumnProfilerRunner,
+    DistributionValue,
+    StringColumnProfile,
+)
 from pydeequ.analyzers import KLLParameters, DataTypeInstances
 from tests.conftest import setup_pyspark
 
+
 class TestProfiles(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls.spark = setup_pyspark().appName("test-profiles-local").getOrCreate()
         cls.sc = cls.spark.sparkContext
-        cls.df = cls.sc.parallelize([Row(a="foo", b=1, c=5), Row(a="bar", b=2, c=6), Row(a="baz", b=3, c=None)]).toDF()
+        cls.df = cls.sc.parallelize(
+            [Row(a="foo", b=1, c=5), Row(a="bar", b=2, c=6), Row(a="baz", b=3, c=None)]
+        ).toDF()
 
     @classmethod
     def tearDownClass(cls):
         cls.spark.sparkContext._gateway.shutdown_callback_server()
         cls.spark.stop()
 
     def test_setPredefinedTypes(self):
-        result = ColumnProfilerRunner(self.spark) \
-            .onData(self.df) \
-            .setPredefinedTypes({'a': DataTypeInstances.Unknown, 'b': DataTypeInstances.String, 'c': DataTypeInstances.Fractional}) \
+        result = (
+            ColumnProfilerRunner(self.spark)
+            .onData(self.df)
+            .setPredefinedTypes(
+                {
+                    "a": DataTypeInstances.Unknown,
+                    "b": DataTypeInstances.String,
+                    "c": DataTypeInstances.Fractional,
+                }
+            )
             .run()
+        )
         print(result)
         for col, profile in result.profiles.items():
             print("Profiles:", profile)
@@ -76,6 +91,46 @@ def test_profile_numRecords(self):
         result = ColumnProfilerRunner(self.spark).onData(self.df).run()
         self.assertEqual(result.numRecords, 3)
 
+    def test_StringColumnProfile(self):
+        df = self.sc.parallelize(
+            [
+                Row(a="ant", b="dragonfly"),
+                Row(a="bee", b="earwig"),
+                Row(a="bee", b=None),
+                Row(a="cricket", b=None),
+            ]
+        ).toDF()
+        result = ColumnProfilerRunner(self.spark).onData(df).run()
+        column_profile = result.profiles["a"]
+        self.assertIsInstance(column_profile, StringColumnProfile)
+        self.assertEqual(column_profile.minLength, 3)
+        self.assertEqual(column_profile.maxLength, 7)
+        self.assertEqual(str(column_profile)[0:29], "StringProfiles for column: a:")
+        self.assertIn('"minLength": 3', str(column_profile))
+
+        self.assertEqual(column_profile.completeness, 1)
+        self.assertEqual(column_profile.approximateNumDistinctValues, 3)
+        self.assertEqual(column_profile.typeCounts["String"], 4)
+        self.assertEqual(column_profile.isDataTypeInferred, False)
+        actual_histogram = sorted(column_profile.histogram, key=lambda x: x.value)
+        self.assertEqual(len(actual_histogram), 3)
+        expected_histogram = [
+            DistributionValue("ant", 1, 0.25),
+            DistributionValue("bee", 2, 0.5),
+            DistributionValue("cricket", 1, 0.25),
+        ]
+        for actual, expected in zip(actual_histogram, expected_histogram):
+            self.assertEqual(actual.value, expected.value)
+            self.assertEqual(actual.count, expected.count)
+            self.assertAlmostEqual(actual.ratio, expected.ratio)
+
+        column_profile = result.profiles["b"]
+        self.assertEqual(column_profile.completeness, 0.5)
+        self.assertEqual(column_profile.approximateNumDistinctValues, 2)
+        self.assertEqual(column_profile.typeCounts["String"], 2)
+        self.assertEqual(column_profile.minLength, 0)
+        self.assertEqual(column_profile.maxLength, 9)
+
 
 if __name__ == "__main__":
     unittest.main()