diff --git a/python/pyspark/sql/session.py b/python/pyspark/sql/session.py
index 9eb75757bbafa..a1da8fcdd4152 100644
--- a/python/pyspark/sql/session.py
+++ b/python/pyspark/sql/session.py
@@ -421,8 +421,8 @@ def _createFromPandasWithArrow(self, pdf, schema):
         to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
         data types will be used to coerce the data in Pandas to Arrow conversion.
         """
-        from pyspark.serializers import ArrowSerializer
-        from pyspark.sql.types import from_arrow_schema, to_arrow_type, _cast_pandas_series_type
+        from pyspark.serializers import ArrowSerializer, _create_batch
+        from pyspark.sql.types import from_arrow_schema, to_arrow_type
         import pyarrow as pa
 
         # Slice the DataFrame into batches
@@ -446,33 +446,10 @@ def _createFromPandasWithArrow(self, pdf, schema):
             else:
                 schema = schema_from_arrow
         else:
-            batches = []
-            for i, pdf_slice in enumerate(pdf_slices):
-
-                # convert to series to pyarrow.Arrays to use mask when creating Arrow batches
-                arrs = []
-                names = []
-                for c, (_, series) in enumerate(pdf_slice.iteritems()):
-                    field = schema[c]
-                    names.append(field.name)
-                    t = to_arrow_type(field.dataType)
-                    try:
-                        # NOTE: casting is not necessary with Arrow >= 0.7
-                        arrs.append(pa.Array.from_pandas(_cast_pandas_series_type(series, t),
-                                                         mask=series.isnull(), type=t))
-                    except ValueError as e:
-                        warnings.warn("Arrow will not be used in createDataFrame: %s" % str(e))
-                        return None
-                batches.append(pa.RecordBatch.from_arrays(arrs, names))
-
-                # Verify schema of first batch, return None if not equal and fallback without Arrow
-                if i == 0:
-                    schema_from_arrow = from_arrow_schema(batches[i].schema)
-                    if schema != schema_from_arrow:
-                        warnings.warn("Arrow will not be used in createDataFrame.\n" +
-                                      "Supplied schema: %s\n!=\nArrow schema: %s"
-                                      % (str(schema), str(schema_from_arrow)))
-                        return None
+            arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
+            batches = [_create_batch([(c, t)
+                                      for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)])
+                       for pdf_slice in pdf_slices]
 
         # Create the Spark DataFrame directly from the Arrow data and schema
         jrdd = self._sc._serialize_to_jvm(batches, len(batches), ArrowSerializer())
@@ -580,10 +557,11 @@ def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=Tr
         if has_pandas and isinstance(data, pandas.DataFrame):
             if self.conf.get("spark.sql.execution.arrow.enabled", "false").lower() == "true" \
                     and len(data) > 0:
-                df = self._createFromPandasWithArrow(data, schema)
-                # Fallback to create DataFrame without arrow if return None
-                if df is not None:
-                    return df
+                try:
+                    return self._createFromPandasWithArrow(data, schema)
+                except Exception as e:
+                    warnings.warn("Arrow will not be used in createDataFrame: %s" % str(e))
+                    # Fallback to create DataFrame without arrow if raise some exception
             if schema is None:
                 schema = [str(x) for x in data.columns]
             data = [r.tolist() for r in data.to_records(index=False)]