pyspark'DataFrame'对象没有属性'_get_object_id' [英] pyspark 'DataFrame' object has no attribute '_get_object_id'

问题描述

我正在尝试运行一些代码，但出现错误:

I am trying to run some code, but getting error:

"DataFrame"对象没有属性"_get_object_id"

'DataFrame' object has no attribute '_get_object_id'

代码:

items = [(1,12),(1,float('Nan')),(1,14),(1,10),(2,22),(2,20),(2,float('Nan')),(3,300),
         (3,float('Nan'))]

sc = spark.sparkContext
rdd = sc.parallelize(items)
df = rdd.toDF(["id", "col1"])

import pyspark.sql.functions as func
means = df.groupby("id").agg(func.mean("col1"))

# The error is thrown at this line
df = df.withColumn("col1", func.when((df["col1"].isNull()), means.where(func.col("id")==df["id"])).otherwise(func.col("col1"))) 

推荐答案

除非使用联接，否则无法在函数内引用第二个Spark DataFrame.IIUC，您可以执行以下操作以获得所需的结果.

You can't reference a second spark DataFrame inside a function, unless you're using a join. IIUC, you can do the following to achieve your desired result.

假设表示为以下内容:

#means.show()
#+---+---------+
#| id|avg(col1)|
#+---+---------+
#|  1|     12.0|
#|  3|    300.0|
#|  2|     21.0|
#+---+---------+

在 id 列上加入 df 和 means ，然后在 when 条件

Join df and means on the id column, then apply your when condition

from pyspark.sql.functions import when

df.join(means, on="id")\
    .withColumn(
        "col1",
        when(
            (df["col1"].isNull()), 
            means["avg(col1)"]
        ).otherwise(df["col1"])
    )\
    .select(*df.columns)\
    .show()
#+---+-----+
#| id| col1|
#+---+-----+
#|  1| 12.0|
#|  1| 12.0|
#|  1| 14.0|
#|  1| 10.0|
#|  3|300.0|
#|  3|300.0|
#|  2| 21.0|
#|  2| 22.0|
#|  2| 20.0|
#+---+-----+

但是在这种情况下，我实际上建议使用 Window 和

But in this case, I'd actually recommend using a Window with pyspark.sql.functions.mean:

from pyspark.sql import Window
from pyspark.sql.functions import col, mean

df.withColumn(
    "col1",
    when(
        col("col1").isNull(), 
        mean("col1").over(Window.partitionBy("id"))
    ).otherwise(col("col1"))
).show()
#+---+-----+
#| id| col1|
#+---+-----+
#|  1| 12.0|
#|  1| 10.0|
#|  1| 12.0|
#|  1| 14.0|
#|  3|300.0|
#|  3|300.0|
#|  2| 22.0|
#|  2| 20.0|
#|  2| 21.0|
#+---+-----+

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