fix: add handling for spark correlations with no numeric fields

Assembling a vector column in Spark with no numeric columns results in features with
a NULL size, NULL indices, and an empty list of values.
This causes an exception to be raised when computing correlations.

The solution here is to avoid computing the correlation matrix when there are no
interval columns (numeric).

This change addresses issue #1722.

Author: MCBoarder289

src/ydata_profiling/model/spark/correlations_spark.py

@@ -49,6 +49,10 @@ def _compute_corr_natively(df: DataFrame, summary: dict, corr_type: str) -> Arra
     interval_columns = [
         column for column, type_name in variables.items() if type_name == "Numeric"
     ]
+
+    if not interval_columns:
+        return [], interval_columns
+
     df = df.select(*interval_columns)
 
     # convert to vector column first

tests/backends/spark_backend/test_issue1722.py

@@ -0,0 +1,72 @@
+"""
+Test for issue 1722:
+https://github.com/ydataai/ydata-profiling/issues/1722
+"""
+
+from ydata_profiling import ProfileReport
+from datetime import date, datetime
+from pyspark.sql import types as T, SparkSession
+
+def make_non_numeric_df(spark: SparkSession):
+    # Intentionally not including any numeric types
+    schema = T.StructType(
+        [
+            T.StructField("id", T.StringType(), False),
+            T.StructField("d", T.DateType(), False),
+            T.StructField("ts", T.TimestampType(), False),
+            T.StructField("arr", T.ArrayType(T.IntegerType()), False),
+            T.StructField("mp", T.MapType(T.StringType(), T.IntegerType()), False),
+            T.StructField(
+                "struct",
+                T.StructType(
+                    [
+                        T.StructField("a", T.IntegerType(), False),
+                        T.StructField("b", T.StringType(), False),
+                    ]
+                ),
+                False,
+            ),
+        ]
+    )
+
+    data = [
+        ("r1", date(2020, 1, 1), datetime(2020, 1, 1, 12, 0), [1, 2], {"x": 1, "y": 2}, (10, "aa")),
+        ("r2", date(2021, 6, 15), datetime(2021, 6, 15, 8, 30), [3], {"z": 3}, (20, "bb")),
+        ("r3", date(2022, 12, 31), datetime(2022, 12, 31, 23, 59), [], {}, (30, "cc")),
+    ]
+
+    return spark.createDataFrame(data, schema=schema)
+
+
+def test_issue1722(test_output_dir, spark_session):
+    from pyspark.sql import functions as F
+
+    spark = spark_session
+
+    non_numeric_df = make_non_numeric_df(spark)
+
+    # type casting 1
+    df_casted = non_numeric_df.select(
+        [
+            (
+                F.col(field.name).cast("string").alias(field.name)
+                if isinstance(field.dataType, (T.DateType, T.TimestampType))
+                else F.col(field.name)
+            )
+            for field in non_numeric_df.schema
+        ]
+    )
+    # type casting 2
+    complex_columns = [
+        field.name
+        for field in non_numeric_df.schema.fields
+        if isinstance(field.dataType, (T.ArrayType, T.MapType, T.StructType))
+    ]
+    for col_name in complex_columns:
+        df_casted = df_casted.withColumn(col_name, F.to_json(F.col(col_name)))
+
+    profile = ProfileReport(df_casted, title="non_numeric_1722", explorative=True)
+    output_file = test_output_dir / "non_numeric_1722.html"
+    profile.to_file(output_file)
+
+    assert output_file.exists()