在 Pyspark df 中添加字典键作为列名和字典值作为该列的常量值

Question

我有一本字典 x = {'colA': 20, 'colB': 30} 和一个 pyspark df。

ID Value
1  ABC
1  BCD
1  AKB
2  CAB
2  AIK
3  KIB 

我想使用 x 创建 df1，如下所示：

ID Value colA colB
1  ABC    20.0  30.0
1  BCD    20.0  30.0
1  AKB    20.0  30.0
2  CAB    20.0  30.0
...

任何想法如何做到这一点 Pyspark。 我知道我可以像这样创建一个常量列，

df1 = df.withColumn('colA', lit(20.0))
df1 = df1.withColumn('colB', lit(30.0))

但不确定从字典中执行此操作的动态过程

Answer 1

有一些方法可以隐藏循环，但是执行是一样的。例如，您可以使用select：

from pyspark.sql.functions import lit

df2 = df.select("*", *[lit(val).alias(key) for key, val in x.items()])
df2.show()
#+---+-----+----+----+
#| ID|Value|colB|colA|
#+---+-----+----+----+
#|  1|  ABC|  30|  20|
#|  1|  BCD|  30|  20|
#|  1|  AKB|  30|  20|
#|  2|  CAB|  30|  20|
#|  2|  AIK|  30|  20|
#|  3|  KIB|  30|  20|
#+---+-----+----+----+

或者functools.reduce和withColumn：

from functools import reduce
df3 = reduce(lambda df, key: df.withColumn(key, lit(x[key])), x, df)
df3.show()
# Same as above

或者pyspark.sql.functions.struct 和select() and the "*" syntax：

from pyspark.sql.functions import struct
df4 = df.withColumn('x', struct([lit(val).alias(key) for key, val in x.items()]))\
    .select("ID", "Value", "x.*")
df4.show()
#Same as above

但是如果你看一下这些方法的执行计划，你会发现它们是完全一样的：

df2.explain()
#== Physical Plan ==
#*Project [ID#44L, Value#45, 30 AS colB#151, 20 AS colA#152]
#+- Scan ExistingRDD[ID#44L,Value#45]

df3.explain()
#== Physical Plan ==
#*Project [ID#44L, Value#45, 30 AS colB#102, 20 AS colA#107]
#+- Scan ExistingRDD[ID#44L,Value#45]

df4.explain()
#== Physical Plan ==
#*Project [ID#44L, Value#45, 30 AS colB#120, 20 AS colA#121]
#+- Scan ExistingRDD[ID#44L,Value#45]

进一步如果你比较@anil的answer中的循环方法：

df1 = df  
for key in x:
    df1 = df1.withColumn(key, lit(x[key]))
df1.explain()
#== Physical Plan ==
#*Project [ID#44L, Value#45, 30 AS colB#127, 20 AS colA#132]
#+- Scan ExistingRDD[ID#44L,Value#45]

你会发现这也是一样的。

Answer 2

如下遍历字典

df1 = df  
for key in x:
    df1 = df1.withColumn(key, lit(x[key]))