我正在使用n列的PySpark DataFrame。我有一组m列(m <n),我的任务是选择包含最大值的列。
例如:
输入:PySpark DataFrame包含:
col_1 = [1,2,3], col_2 = [2,1,4], col_3 = [3,2,5]
输出:
col_4 = max(col1, col_2, col_3) = [3,2,5]
在this问题中解释了大熊猫中有类似的东西。
有什么方法可以在PySpark中执行此操作,还是应该将我的PySpark df转换为Pandas df然后执行操作?
您可以减少在列表列表中使用SQL表达式:
from pyspark.sql.functions import max as max_, col, when
from functools import reduce
def row_max(*cols):
return reduce(
lambda x, y: when(x > y, x).otherwise(y),
[col(c) if isinstance(c, str) else c for c in cols]
)
df = (sc.parallelize([(1, 2, 3), (2, 1, 2), (3, 4, 5)])
.toDF(["a", "b", "c"]))
df.select(row_max("a", "b", "c").alias("max")))
Spark 1.5+还提供least
,greatest
from pyspark.sql.functions import greatest
df.select(greatest("a", "b", "c"))
如果你想保留最大名称你可以使用`结构:
from pyspark.sql.functions import struct, lit
def row_max_with_name(*cols):
cols_ = [struct(col(c).alias("value"), lit(c).alias("col")) for c in cols]
return greatest(*cols_).alias("greatest({0})".format(",".join(cols)))
maxs = df.select(row_max_with_name("a", "b", "c").alias("maxs"))
最后你可以使用上面的选择“顶部”列:
from pyspark.sql.functions import max
((_, c), ) = (maxs
.groupBy(col("maxs")["col"].alias("col"))
.count()
.agg(max(struct(col("count"), col("col"))))
.first())
df.select(c)
你也可以使用pyspark内置的least
:
from pyspark.sql.functions import least, col
df = df.withColumn('min', least(col('c1'), col('c2'), col('c3')))
这样做的另一种简单方法。让我们说下面的df
是你的数据帧
df = sc.parallelize([(10, 10, 1 ), (200, 2, 20), (3, 30, 300), (400, 40, 4)]).toDF(["c1", "c2", "c3"])
df.show()
+---+---+---+
| c1| c2| c3|
+---+---+---+
| 10| 10| 1|
|200| 2| 20|
| 3| 30|300|
|400| 40| 4|
+---+---+---+
您可以按如下方式处理上述df以获得所需的结果
from pyspark.sql.functions import lit, min
df.select( lit('c1').alias('cn1'), min(df.c1).alias('c1'),
lit('c2').alias('cn2'), min(df.c2).alias('c2'),
lit('c3').alias('cn3'), min(df.c3).alias('c3')
)\
.rdd.flatMap(lambda r: [ (r.cn1, r.c1), (r.cn2, r.c2), (r.cn3, r.c3)])\
.toDF(['Columnn', 'Min']).show()
+-------+---+
|Columnn|Min|
+-------+---+
| c1| 3|
| c2| 2|
| c3| 1|
+-------+---+
规模解决方案
df = sc.parallelize(Seq((10, 10, 1 ), (200, 2, 20), (3, 30, 300), (400, 40, 4))).toDF("c1", "c2", "c3"))
df.rdd.map(row=>List[String](row(0).toString,row(1).toString,row(2).toString)).map(x=>(x(0),x(1),x(2),x.min)).toDF("c1","c2","c3","min").show
+---+---+---+---+
| c1| c2| c3|min|
+---+---+---+---+
| 10| 10| 1| 1|
|200| 2| 20| 2|
| 3| 30|300| 3|
|400| 40| 4| 4|
+---+---+---+---+