使用 Pyspark 获取数组中最常见的元素

Question

如何在使用 Pyspark 连接两列后获取数组中最常见的元素

df = spark.createDataFrame([
  [['a','a','b'],['a']],
  [['c','d','d'],['']],
  [['e'],['e','f']],
  [[''],['']]
]).toDF("arr_1","arr2")

df_new = df.withColumn('arr',F.concat(F.col('arr_1'),F.col('arr_2'))

预期输出：

+------------------------+
| arr  | arr_1   | arr_2 |
+------------------------+
| [a]  | [a,a,b] | [a]   |
| [d]  | [c,d,d] | []    |
| [e]  | [e]     | [e,f] |
| []   | []      | []    | 
+------------------------+

Answer 1

你可以explode the array然后通过group by count, Window我们可以获得出现次数最多的元素。

Example:

df = spark.createDataFrame([
  [['a','a','b'],['a']],
  [['c','d','d'],['']],
  [['e'],['e','f']],
  [[''],['']]
]).toDF("arr_1","arr_2")

df_new = df.withColumn('arr_concat',concat(col('arr_1'),col('arr_2')))

from pyspark.sql.functions import *
from pyspark.sql import *
df1=df_new.withColumn("mid",monotonically_increasing_id())
df2=df1.selectExpr("explode(arr_concat) as arr","mid").groupBy("mid","arr").agg(count(lit("1")).alias("cnt"))
w=Window.partitionBy("mid").orderBy(desc("cnt"))
df3=df2.withColumn("rn",row_number().over(w)).filter(col("rn") ==1).drop(*["rn","cnt"])
df3.join(df1,['mid'],'inner').drop(*['mid','arr_concat']).withColumn("arr",array(col("arr"))).show()

#+---+---------+------+
#|arr|    arr_1| arr_2|
#+---+---------+------+
#|[d]|[c, d, d]|    []|
#|[e]|      [e]|[e, f]|
#|[a]|[a, a, b]|   [a]|
#| []|       []|    []|
#+---+---------+------+

Answer 2

试一试

df1 = df.select('arr_1','arr_2',monotonically_increasing_id().alias('id'),concat('arr_1','arr_2').alias('arr'))
   
df1.select('id',explode('arr')).\
   groupBy('id','col').count().\
   select('id','col','count',rank().over(Window.partitionBy('id').orderBy(desc('count'))).alias('rank')).\
   filter(col('rank')==1).\
   join(df1,'id').\
   select(col('col').alias('arr'), 'arr_1', 'arr_2').show()

+---+---------+------+
|arr|    arr_1| arr_2|
+---+---------+------+
|  a|[a, a, b]|   [a]|
|   |       []|    []|
|  e|      [e]|[e, f]|
|  d|[c, d, d]|    []|
+---+---------+------+

使用 Pyspark 获取数组中最常见的元素

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