Github user zasdfgbnm commented on a diff in the pull request:
https://github.com/apache/spark/pull/18444#discussion_r127814477
--- Diff: python/pyspark/sql/types.py ---
@@ -938,12 +1023,17 @@ def _infer_type(obj):
return MapType(_infer_type(key), _infer_type(value), True)
else:
return MapType(NullType(), NullType(), True)
- elif isinstance(obj, (list, array)):
+ elif isinstance(obj, list):
for v in obj:
if v is not None:
return ArrayType(_infer_type(obj[0]), True)
else:
return ArrayType(NullType(), True)
+ elif isinstance(obj, array):
+ if obj.typecode in _array_type_mappings:
--- End diff --
I don't think `i in my_dict` and `i in my_dict.keys()` has much difference
in performance. A simple test
```python
from random import random,shuffle
from time import time
N = 10000000
d = {}
for i in range(N):
d[i] = random()
tests = list(range(2*N))
shuffle(tests)
time1 = time()
dummy = 0
for i in tests:
if i in d:
dummy += 1
time2 = time()
dummy = 0
for i in tests:
if i in d.keys():
dummy += 1
time3 = time()
print(time2-time1)
print(time3-time2)
```
gives
```
11.389298915863037
12.333340167999268
```
on my MacBook Pro
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