Github user gberger commented on the issue:
https://github.com/apache/spark/pull/19792
@HyukjinKwon done, with test added.
```
>>> spark.createDataFrame(spark.sparkContext.parallelize([[None, 1], ["a",
None], [1, 1]]), schema=["a", "b"], samplingRatio=0.99)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/gberger/Projects/spark/python/pyspark/sql/session.py", line
644, in createDataFrame
rdd, schema = self._createFromRDD(data.map(prepare), schema,
samplingRatio)
File "/Users/gberger/Projects/spark/python/pyspark/sql/session.py", line
383, in _createFromRDD
struct = self._inferSchema(rdd, samplingRatio, names=schema)
File "/Users/gberger/Projects/spark/python/pyspark/sql/session.py", line
375, in _inferSchema
schema = rdd.map(lambda row: _infer_schema(row,
names)).reduce(_merge_type)
File "/Users/gberger/Projects/spark/python/pyspark/rdd.py", line 852, in
reduce
return reduce(f, vals)
File "/Users/gberger/Projects/spark/python/pyspark/sql/types.py", line
1133, in _merge_type
for f in a.fields]
File "/Users/gberger/Projects/spark/python/pyspark/sql/types.py", line
1126, in _merge_type
raise TypeError(new_msg("Can not merge type %s and %s" % (type(a),
type(b))))
TypeError: field a: Can not merge type <class
'pyspark.sql.types.StringType'> and <class 'pyspark.sql.types.LongType'>
```
Also, with this last change, I could simplify the code in `_createFromRDD`.
Since I pass the field names down to `_inferSchema` (and to `_infer_schema`
from there), the inferred schema already comes with field names, so no need to
set them again in `_createFromRDD`. Tests for this still pass. Let me know if
you can think of any edge case not covered by tests that would break.
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