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[SPARK-32300][PYTHON][2.4] toPandas should work from a Spark DataFrame with no partitions #29098

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[SPARK-32300][PYTHON][2.4] toPandas should work from a Spark DataFrame with no partitions #29098

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6 changes: 6 additions & 0 deletions python/pyspark/sql/tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4579,6 +4579,12 @@ def test_createDataFrame_with_float_index(self):
self.spark.createDataFrame(
pd.DataFrame({'a': [1, 2, 3]}, index=[2., 3., 4.])).distinct().count(), 3)

def test_no_partition_toPandas(self):
# SPARK-32300: toPandas should work from a Spark DataFrame with no partitions
pdf = self.spark.sparkContext.emptyRDD().toDF("col1 int").toPandas()
self.assertEqual(len(pdf), 0)
self.assertEqual(list(pdf.columns), ["col1"])


@unittest.skipIf(
not _have_pandas or not _have_pyarrow,
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2 changes: 1 addition & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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Expand Up @@ -3290,7 +3290,7 @@ class Dataset[T] private[sql](
val numPartitions = arrowBatchRdd.partitions.length

// Store collection results for worst case of 1 to N-1 partitions
val results = new Array[Array[Array[Byte]]](numPartitions - 1)
val results = new Array[Array[Array[Byte]]](Math.max(0, numPartitions - 1))
var lastIndex = -1 // index of last partition written

// Handler to eagerly write partitions to Python in order
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