Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/11492#discussion_r54870946
--- Diff: python/pyspark/sql/types.py ---
@@ -1137,7 +1148,28 @@ def _verify_type(obj, dataType):
if type(obj) not in _acceptable_types[_type]:
raise TypeError("%s can not accept object %r in type %s" %
(dataType, obj, type(obj)))
- if isinstance(dataType, ArrayType):
+ if isinstance(dataType, ByteType):
+ if obj < -128 or obj > 127:
+ raise ValueError("object of ByteType out of range, got: %s" %
obj)
+
+ elif isinstance(dataType, ShortType):
+ if obj < -32768 or obj > 32767:
+ raise ValueError("object of ShortType out of range, got: %s" %
obj)
+
+ elif isinstance(dataType, IntegerType):
+ if obj < -2147483648 or obj > 2147483647:
+ raise ValueError("object of IntegerType out of range, got: %s"
% obj)
+
+ elif isinstance(dataType, FloatType):
+ from math import isinf
+ from struct import pack, unpack
+
+ if not isinf(obj):
+ f = unpack("f", pack("f", obj))[0]
--- End diff --
Here I try to convert the `obj` to binary string as a `float32`, and then
convert it back. If it becomes an `inf`, then it means the `obj` can not fit in
`float32`.
Is there a better way to check if a python float can fit in `float32`?
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