将多个 groupBy 函数合并为 1 [英] Combining multiple groupBy functions into 1
问题描述
使用此代码查找模态:
将 numpy 导入为 npnp.random.seed(1)df2 = sc.parallelize([(int(x), ) for x in np.random.randint(50, size=10000)]).toDF(["x"])cnts = df2.groupBy("x").count()模式 = cnts.join(cnts.agg(max("count").alias("max_")), col("count") == col("max_")).limit(1).select("x")mode.first()[0]
来自
c1
的模态 &c2
分别是 2.0 和 3.0
这是否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明确选择每一列?
看起来您使用的是内置 max
,而不是 SQL 函数.
import pyspark.sql.functions as Fcnts.agg(F.max("count").alias("max_"))
要在相同类型的多个列上查找模式,您可以将其整形为长(melt
,如中所定义)Apache Spark 中的 Pandas Melt 函数):
(melt(df, [], df.columns)# 按列和值计数.groupBy("变量", "值").数数()# 每列查找模式.groupBy("变量").agg(F.max(F.struct("count", "value")).alias("mode")).select("变量", "mode.value"))
+--------+-----+|变量|值|+--------+-----+|c5|6.0||c1|2.0||c4|5.0||c3|4.0||c2|3.0|+--------+-----+
Using this code to find modal :
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]
from Calculate the mode of a PySpark DataFrame column?
returns error :
---------------------------------------------------------------------------
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'
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()
which returns :
So modal of c1
& c2
are 2.0 and 3.0 respectively
Can this be applied to all columns c1,c2,c3,c4,c5
in dataframe instead of explicitly selecting each column as I have done ?
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_"))
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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