gatorsmile commented on a change in pull request #28928:
URL: https://github.com/apache/spark/pull/28928#discussion_r445976618
##########
File path: python/pyspark/sql/pandas/conversion.py
##########
@@ -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))
Review comment:
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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