pivot (GroupedData)

Geçerli DataFrame sütunu özetler ve belirtilen toplamayı gerçekleştirir.

Sözdizimi

pivot(pivot_col, values=None)

Parametreler

Parametre	Türü	Açıklama
pivot_col	str	Özetlenecek sütunun adı.
values	liste, isteğe bağlı	Çıktıdaki DataFramesütunlara çevrilecek değerlerin listesi. Sağlanmadıysa Spark, elde edilen şemayı belirlemek için içindeki pivot_col ayrı değerleri hevesle hesaplar. Açık bir liste sağlamak bu istekli hesaplamayı önler.

İadeler

GroupedData

Örnekler

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|
# +----+-----+------+