Pyspark:将平均值作为新列添加到DataFrame [英] Pyspark: Add the average as a new column to DataFrame

问题描述

我正在计算数据帧中一列的平均值，但是它导致所有值均为零.有人可以帮助我为什么会这样吗?以下是列转换前后的代码和表.

I am computing mean of a column in data-frame but it resulted in all the values zeros. Can someone help me in why this is happening? Following is the code and table before and after the transformation of a column.

result.select("dis_price_released").show(10)
 +------------------+
 |dis_price_released|
 +------------------+
 |               0.0|
 |               4.0|
 |               4.0|
 |               4.0|
 |               1.0|
 |               4.0|
 |               4.0|
 |               0.0|
 |               4.0|
 |               0.0|
 +------------------+

计算均值并添加均值列后

w = Window().partitionBy("dis_price_released").rowsBetween(-sys.maxsize, sys.maxsize)
df2 = result.withColumn("mean", avg("dis_price_released").over(w))
df2.select("dis_price_released", "mean").show(10)

+------------------+----+
|dis_price_released|mean|
+------------------+----+
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
|               0.0| 0.0|
+------------------+----+

推荐答案

您可以先为整列计算 avg ，然后使用 lit()进行添加作为 DataFrame 的变量，不需要窗口函数:

You can compute the avg first for the whole column, then use lit() to add it as a variable to your DataFrame, there is no need for window functions:

from pyspark.sql.functions import lit

mean = df.groupBy().avg("dis_price_released").take(1)[0][0]
df.withColumn("test", lit(mean)).show()
 +------------------+----+
|dis_price_released|test|
+------------------+----+
|               0.0| 2.5|
|               4.0| 2.5|
|               4.0| 2.5|
|               4.0| 2.5|
|               1.0| 2.5|
|               4.0| 2.5|
|               4.0| 2.5|
|               0.0| 2.5|
|               4.0| 2.5|
|               0.0| 2.5|
+------------------+----+

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