PySpark 一次替换多个列中的值

Question

我想用另一个值替换数据框列中的一个值，我必须对许多列（比如 30/100 列）执行此操作

我已经完成了 and 。

from pyspark.sql.functions import when, lit, col

df = sc.parallelize([(1, "foo", "val"), (2, "bar", "baz"), (3, "baz", "buz")]).toDF(["x", "y", "z"])
df.show()

# I can replace "baz" with Null separaely in column y and z
def replace(column, value):
    return when(column != value, column).otherwise(lit(None))

df = df.withColumn("y", replace(col("y"), "baz"))\
    .withColumn("z", replace(col("z"), "baz"))
df.show()

我可以在 y 和 z 列中分别用 Null 替换 "baz"。但我想对所有列都这样做 - 类似于下面的列表理解方式

[replace(df[col], "baz") for col in df.columns]

Answer 1

使用 reduce() 函数：

from functools import reduce

reduce(lambda d, c: d.withColumn(c, replace(col(c), "baz")), [df, 'y', 'z']).show()
#+---+----+----+
#|  x|   y|   z|
#+---+----+----+
#|  1| foo| val|
#|  2| bar|null|
#|  3|null| buz|
#+---+----+----+

Answer 2

您可以使用 select 和列表理解：

df = df.select([replace(f.col(column), 'baz').alias(column) if column!='x' else f.col(column)
                for column in df.columns])
df.show()

Answer 3

由于大约有 30/100 列，所以让我们在 DataFrame 中再添加几列以更好地概括它。

# Loading the requisite packages
from pyspark.sql.functions import col, when
df = sc.parallelize([(1,"foo","val","baz","gun","can","baz","buz","oof"), 
                     (2,"bar","baz","baz","baz","got","pet","stu","got"), 
                     (3,"baz","buz","pun","iam","you","omg","sic","baz")]).toDF(["x","y","z","a","b","c","d","e","f"])
df.show()
+---+---+---+---+---+---+---+---+---+ 
|  x|  y|  z|  a|  b|  c|  d|  e|  f| 
+---+---+---+---+---+---+---+---+---+ 
|  1|foo|val|baz|gun|can|baz|buz|oof| 
|  2|bar|baz|baz|baz|got|pet|stu|got| 
|  3|baz|buz|pun|iam|you|omg|sic|baz| 
+---+---+---+---+---+---+---+---+---+

假设我们想要 replace baz 和 Null 在所有列中，除了列 x 和 a。使用 list comprehensions 选择那些必须完成 replacement 的列。

# This contains the list of columns where we apply replace() function
all_column_names = df.columns
print(all_column_names)
    ['x', 'y', 'z', 'a', 'b', 'c', 'd', 'e', 'f']
columns_to_remove = ['x','a']
columns_for_replacement = [i for i in all_column_names if i not in columns_to_remove]
print(columns_for_replacement)
    ['y', 'z', 'b', 'c', 'd', 'e', 'f']

最后，使用when()进行替换，这实际上是if子句的假名。

# Doing the replacement on all the requisite columns
for i in columns_for_replacement:
    df = df.withColumn(i,when((col(i)=='baz'),None).otherwise(col(i)))
df.show()
+---+----+----+---+----+---+----+---+----+ 
|  x|   y|   z|  a|   b|  c|   d|  e|   f| 
+---+----+----+---+----+---+----+---+----+ 
|  1| foo| val|baz| gun|can|null|buz| oof| 
|  2| bar|null|baz|null|got| pet|stu| got| 
|  3|null| buz|pun| iam|you| omg|sic|null| 
+---+----+----+---+----+---+----+---+----+

如果可以用普通的 if-else 子句完成替换，则无需创建 UDF 和定义函数来进行替换。 UDFs 通常是一项代价高昂的操作，应尽可能避免。

PySpark 一次替换多个列中的值

PySpark replace value in several column at once

python

dataframe

pyspark

list-comprehension

replaceall