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[SPARK-16992][PYSPARK] use map comprehension in doc #14863

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[SPARK-16992][PYSPARK] use map comprehension in doc #14863

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2 changes: 1 addition & 1 deletion examples/src/main/python/ml/quantile_discretizer_example.py
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Expand Up @@ -29,7 +29,7 @@
.getOrCreate()

# $example on$
data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2)]
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@gsemet gsemet Sep 1, 2016

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these extra comma are useless

df = spark.createDataFrame(data, ["id", "hour"])
# $example off$

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4 changes: 2 additions & 2 deletions examples/src/main/python/ml/vector_slicer_example.py
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Expand Up @@ -32,8 +32,8 @@

# $example on$
df = spark.createDataFrame([
Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3}),),
Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]),)])
Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3})),
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@gsemet gsemet Sep 1, 2016

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same here, they don't do anything since Row() is a function call

Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]))])

slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])

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2 changes: 1 addition & 1 deletion examples/src/main/python/sql/hive.py
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Expand Up @@ -79,7 +79,7 @@

# You can also use DataFrames to create temporary views within a SparkSession.
Record = Row("key", "value")
recordsDF = spark.createDataFrame(map(lambda i: Record(i, "val_" + str(i)), range(1, 101)))
recordsDF = spark.createDataFrame([Record(i, "val_" + str(i)) for i in range(1, 101)])
recordsDF.createOrReplaceTempView("records")

# Queries can then join DataFrame data with data stored in Hive.
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