HyukjinKwon commented on code in PR #37560:
URL: https://github.com/apache/spark/pull/37560#discussion_r948572473
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python/pyspark/sql/tests/test_functions.py:
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@@ -959,6 +961,25 @@ def test_lit_day_time_interval(self):
actual = self.spark.range(1).select(lit(td)).first()[0]
self.assertEqual(actual, td)
+ def test_np_scalars(self):
+ from pyspark.sql.functions import array_contains, array_position
+
+ df = self.spark.createDataFrame([([1, 2, 3],), ([],)], ["data"])
+ for dtype in [np.int8, np.int16, np.int32, np.int64]:
+ self.assertEqual(df.select(lit(dtype(1))).dtypes, [("1", "int")])
+ res = df.select(array_contains(df.data,
dtype(1)).alias("b")).collect()
+ self.assertEqual([Row(b=True), Row(b=False)], res)
+ res = df.select(array_position(df.data,
dtype(1)).alias("c")).collect()
+ self.assertEqual([Row(c=1), Row(c=0)], res)
+
+ df = self.spark.createDataFrame([([1.0, 2.0, 3.0],), ([],)], ["data"])
+ for dtype in [np.float32, np.float64]:
+ self.assertEqual(df.select(lit(dtype(1))).dtypes, [("1.0",
"double")])
Review Comment:
Probably we should see how list works.
IIRC, it works as a ArrayList but we would have to convert it into an array.
We can do this separately too.
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