fix: Add handling for spark correlations with no numeric fields

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/issues/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()