[GitHub] [spark] itholic commented on a diff in pull request #37966: [SPARK-40462][PYTHON] Support np.ndarray for `functions.lit`.

[GitBox <gi...@apache.org>]

itholic commented on code in PR #37966:
URL: https://github.com/apache/spark/pull/37966#discussion_r978243957


##########
python/pyspark/sql/functions.py:
##########
@@ -164,13 +166,24 @@ def lit(col: Any) -> Column:
     +--------------+
     |     [1, 2, 3]|
     +--------------+
+
+    Create a literal from numpy.ndarray.
+
+    >>> spark.range(1).select(lit(np.ndarray([1, 2]))).show(truncate=False)
+    +------------------------------------------------------+
+    |array(array(2.058335917824E-312, 2.334195370625E-312))|
+    +------------------------------------------------------+
+    |[[2.058335917824E-312, 2.334195370625E-312]]          |

Review Comment:
   Seems like the type in this example is correct since `np.ndarray([1, 2])` returns `np.float64` as below:
   
   e.g.
   ```python
   >>> np.ndarray([1, 2])
   array([[2.05833592e-312, 2.33419537e-312]])
   >>> np.ndarray([1, 2]).dtype
   dtype('float64')
   >>> spark.range(1).select(lit(np.ndarray([1, 2]))).dtypes
   [('array(array(6.94998724E-310, 6.95001394E-310))', 'array<array<double>>')]
   ```
   
   However, they do not respect NumPy types in  other cases as below:
   
   ```python
   >>> spark.range(1).select(lit(np.array([[np.int8(1), np.int8(2)]]))).dtypes
   [('array(array(1, 2))', 'array<array<int>>')]
   ```
   
   This should return `smallint` rather than `int` as below:
   
   ```python
   >>> spark.range(1).select(lit(np.array([[np.int8(1), np.int8(2)]]))).dtypes
   [('ARRAY(ARRAY(1S, 2S))', 'array<array<smallint>>')]
   ```
   
   So, I think the PR title should be addressed such as "Support np.ndarray for `functions.lit` for multi dimensions"
   
   Now we only respect the NumPy type only for the single dimension.
   
   Let me address it.



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