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From "Guilherme Berger (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-22566) Better error message for `_merge_type` in Pandas to Spark DF conversion
Date Mon, 20 Nov 2017 19:07:00 GMT
Guilherme Berger created SPARK-22566:
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             Summary: Better error message for `_merge_type` in Pandas to Spark DF conversion
                 Key: SPARK-22566
                 URL: https://issues.apache.org/jira/browse/SPARK-22566
             Project: Spark
          Issue Type: Improvement
          Components: PySpark
    Affects Versions: 2.2.0
            Reporter: Guilherme Berger
            Priority: Minor


When creating a Spark DF from a Pandas DF without specifying a schema, schema inference is
used. This inference can fail when a column contains values of two different types; this is
ok. The problem is the error message does not tell us in which column this happened.

When this happens, it is painful to debug since the error message is too vague.

I plan on submitting a PR which fixes this, providing a better error message for such cases,
containing the column name (and possibly the problematic values too).

>>> spark_session.createDataFrame(pandas_df)
File "redacted/pyspark/sql/session.py", line 541, in createDataFrame
      rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "redacted/pyspark/sql/session.py", line 401, in _createFromLocal
      struct = self._inferSchemaFromList(data)
File "redacted/pyspark/sql/session.py", line 333, in _inferSchemaFromList
      schema = reduce(_merge_type, map(_infer_schema, data))
File "redacted/pyspark/sql/types.py", line 1124, in _merge_type
      for f in a.fields]
File "redacted/pyspark/sql/types.py", line 1118, in _merge_type
      raise TypeError("Can not merge type %s and %s" % (type(a), type(b)))
TypeError: Can not merge type <class 'pyspark.sql.types.LongType'> and <class 'pyspark.sql.types.StringType'>
                                                                           




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