将多个groupBy函数合并为1 [英] Combining multiple groupBy functions into 1

问题描述

使用此代码查找模式:

import numpy as np
np.random.seed(1)

df2 = sc.parallelize([
    (int(x), ) for x in np.random.randint(50, size=10000)
]).toDF(["x"])

cnts = df2.groupBy("x").count()
mode = cnts.join(
    cnts.agg(max("count").alias("max_")), col("count") == col("max_")
).limit(1).select("x")
mode.first()[0]

来自计算PySpark DataFrame列的模式?

返回错误:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-53-2a9274e248ac> in <module>()
      8 cnts = df.groupBy("x").count()
      9 mode = cnts.join(
---> 10     cnts.agg(max("count").alias("max_")), col("count") == col("max_")
     11 ).limit(1).select("x")
     12 mode.first()[0]

AttributeError: 'str' object has no attribute 'alias'

我正在尝试使用此自定义方法:

Instead of this solution I'm attempting this custom one:

df.show()

cnts = df.groupBy("c1").count()
print cnts.rdd.map(tuple).sortBy(lambda a: a[1], ascending=False).first()

cnts = df.groupBy("c2").count()
print cnts.rdd.map(tuple).sortBy(lambda a: a[1] , ascending=False).first()

返回:

c1&的模态c2分别是2.0和3.0

So modal of c1 & c2 are 2.0 and 3.0 respectively

这可以应用于数据框中的所有列c1,c2,c3,c4,c5，而不是像我所做的那样显式选择每个列吗?

Can this be applied to all columns c1,c2,c3,c4,c5 in dataframe instead of explicitly selecting each column as I have done ?

推荐答案

似乎您正在使用内置的max，而不是SQL函数.

It looks like you're using built-in max, not a SQL function.

import pyspark.sql.functions as F

cnts.agg(F.max("count").alias("max_"))

要在同一类型的多列上查找模式，可以将其整形为long(如 Apache中的熊猫融化函数所定义的melt Spark ):

To find mode over multiple columns of the same type you can reshape to long (melt as defined in Pandas Melt function in Apache Spark):

(melt(df, [], df.columns)
    # Count by column and value
    .groupBy("variable", "value")
    .count()
    # Find mode per column
    .groupBy("variable")
    .agg(F.max(F.struct("count", "value")).alias("mode"))
    .select("variable", "mode.value"))

+--------+-----+
|variable|value|
+--------+-----+
|      c5|  6.0|
|      c1|  2.0|
|      c4|  5.0|
|      c3|  4.0|
|      c2|  3.0|
+--------+-----+

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