xinrong-meng commented on code in PR #37560:
URL: https://github.com/apache/spark/pull/37560#discussion_r952882996
##########
python/pyspark/sql/tests/test_functions.py:
##########
@@ -959,9 +961,29 @@ def test_lit_day_time_interval(self):
actual = self.spark.range(1).select(lit(td)).first()[0]
self.assertEqual(actual, td)
+ @unittest.skipIf(not have_numpy, "NumPy not installed")
+ def test_np_scalar_input(self):
+ import numpy as np
+ 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")])
Review Comment:
The data mapping between NumPy dtype and PySpark dtypes when
`spark.createDataFrame` looks as below:
```
NumPy dtype: ["int8", "int16", "int32", "int64", "float32", "float64"]
PySpark dtypes: ["tinyint", "smallint", "int", "bigint", "float", "double"]
```
Unfortunately, we lost the precision here in Py4J. I may file a followup PR
to at least adjust that for `lit` function.
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