问题
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 ?
回答1:
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|
+--------+-----+
来源:https://stackoverflow.com/questions/42396932/combining-multiple-groupby-functions-into-1