pivot (GroupedData)

Pivoterar en kolumn med den aktuella DataFrame och utför den angivna aggregeringen.

Syntax

pivot(pivot_col, values=None)

Parameters

Parameter Type Beskrivning
pivot_col str Namnet på kolumnen som ska pivoteras.
values list, valfritt Lista över värden som ska översättas till kolumner i utdata DataFrame. Om det inte tillhandahålls beräknar Spark ivrigt de distinkta värdena i pivot_col för att fastställa det resulterande schemat. Om du anger en explicit lista undviker du den här ivriga beräkningen.

Retur

GroupedData

Exempel

from pyspark.sql import Row, functions as sf

df1 = spark.createDataFrame([
    Row(course="dotNET", year=2012, earnings=10000),
    Row(course="Java", year=2012, earnings=20000),
    Row(course="dotNET", year=2012, earnings=5000),
    Row(course="dotNET", year=2013, earnings=48000),
    Row(course="Java", year=2013, earnings=30000),
])

# Compute the sum of earnings for each year by course with each course as a separate column.
df1.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").sort("year").show()
# +----+------+-----+
# |year|dotNET| Java|
# +----+------+-----+
# |2012| 15000|20000|
# |2013| 48000|30000|
# +----+------+-----+

# Without specifying column values (less efficient).
df1.groupBy("year").pivot("course").sum("earnings").sort("year").show()
# +----+-----+------+
# |year| Java|dotNET|
# +----+-----+------+
# |2012|20000| 15000|
# |2013|30000| 48000|
# +----+-----+------+

# Using a nested column as the pivot column.
df2 = spark.createDataFrame([
    Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=10000)),
    Row(training="junior", sales=Row(course="Java", year=2012, earnings=20000)),
    Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=5000)),
    Row(training="junior", sales=Row(course="dotNET", year=2013, earnings=48000)),
    Row(training="expert", sales=Row(course="Java", year=2013, earnings=30000)),
])
df2.groupBy("sales.year").pivot("sales.course").agg(sf.sum("sales.earnings")).sort("year").show()
# +----+-----+------+
# |year| Java|dotNET|
# +----+-----+------+
# |2012|20000| 15000|
# |2013|30000| 48000|
# +----+-----+------+
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