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[SPARK-32098][PYTHON] Use iloc for positional slicing instead of direct slicing in createDataFrame with Arrow #28928

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[SPARK-32098][PYTHON] Use iloc for positional slicing instead of direct slicing in createDataFrame with Arrow #28928

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2 changes: 1 addition & 1 deletion python/pyspark/sql/pandas/conversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -413,7 +413,7 @@ def _create_from_pandas_with_arrow(self, pdf, schema, timezone):

# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
pdf_slices = (pdf.iloc[start:start + step] for start in xrange(0, len(pdf), step))
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@gatorsmile gatorsmile Jun 26, 2020

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Thank you for fixing this!

While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc and .iloc.

Is it the only place?

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@HyukjinKwon HyukjinKwon Jun 26, 2020

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As far as I can tell, yes.


# Create list of Arrow (columns, type) for serializer dump_stream
arrow_data = [[(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]
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6 changes: 6 additions & 0 deletions python/pyspark/sql/tests/test_arrow.py
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Expand Up @@ -442,6 +442,12 @@ def test_createDateFrame_with_category_type(self):
self.assertIsInstance(arrow_first_category_element, str)
self.assertIsInstance(spark_first_category_element, str)

def test_createDataFrame_with_float_index(self):
# SPARK-32098: float index should not produce duplicated or truncated Spark DataFrame
self.assertEqual(
self.spark.createDataFrame(
pd.DataFrame({'a': [1, 2, 3]}, index=[2., 3., 4.])).distinct().count(), 3)


@unittest.skipIf(
not have_pandas or not have_pyarrow,
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