通过在白色 space 上拆分值来创建新的 pyspark 数据框列

Question

我有一个 pyspark 数据框，就像下面的输入数据一样。我想将 productname 列中的值拆分为白色 space。然后我想用前 3 个值创建新列。我在下面有示例输入和输出数据。有人可以建议如何使用 pyspark 执行此操作吗？

输入数据：

+------+-------------------+
|id    |productname        |
+------+-------------------+
|235832|EXTREME BERRY Sweet|             
|419736|BLUE CHASER SAUCE  |             
|124513|LAAVA C2L5         |
+------+-------------------+

输出：

+------+-------------------+-------------+-------------+-------------+
|id    |productname        |product1     |product2     |product3     |
+------+-------------------+-------------+-------------+-------------+
|235832|EXTREME BERRY Sweet|EXTREME      |BERRY        |Sweet        |
|419736|BLUE CHASER SAUCE  |BLUE         |CHASER       |SAUCE        |
|124513|LAAVA C2L5         |LAAVA        |C2L5         |             |
+------+-------------------+-------------+-------------+-------------+

Answer 1

Split productname 列然后使用 element_at（或）[=20= 创建新列].getItem() 关于索引值。

df.withColumn("tmp",split(col("productname"),"\s+")).\
withColumn("product1",element_at(col("tmp"),1)).\
withColumn("product2",element_at(col("tmp"),2)).\
withColumn("product3",coalesce(element_at(col("tmp"),3),lit(""))).drop("tmp").show()

#or

df.withColumn("tmp",split(col("productname"),"\s+")).\
withColumn("product1",col("tmp").getItem(0)).\
withColumn("product2",col("tmp").getItem(1)).\
withColumn("product3",coalesce(col("tmp").getItem(2),lit(""))).drop("tmp").show()
#+------+-------------------+--------+--------+--------+
#|    id|        productname|product1|product2|product3|
#+------+-------------------+--------+--------+--------+
#|235832|EXTREME BERRY Sweet| EXTREME|   BERRY|   Sweet|
#|     4|  BLUE CHASER SAUCE|    BLUE|  CHASER|   SAUCE|
#|     1|         LAAVA C2L5|   LAAVA|    C2L5|        |
#+------+-------------------+--------+--------+--------+

To do more dynamic way:

df.show()
#+------+-------------------+
#|    id|        productname|
#+------+-------------------+
#|235832|EXTREME BERRY Sweet|
#|     4|  BLUE CHASER SAUCE|
#|     1|         LAAVA C2L5|
#+------+-------------------+
#caluculate array max size and store into variable
arr=int(df.select(size(split(col("productname"),"\s+")).alias("size")).orderBy(desc("size")).collect()[0][0])

#loop through arr variable and add the columns replace null with ""
(df.withColumn('temp', split('productname', '\s+')).select("*",*(coalesce(col('temp').getItem(i),lit("")).alias('product{}'.format(i+1)) for i in range(arr))).drop("temp").show())

#+------+-------------------+--------+--------+--------+
#|    id|        productname|product1|product2|product3|
#+------+-------------------+--------+--------+--------+
#|235832|EXTREME BERRY Sweet| EXTREME|   BERRY|   Sweet|
#|     4|  BLUE CHASER SAUCE|    BLUE|  CHASER|   SAUCE|
#|     1|         LAAVA C2L5|   LAAVA|    C2L5|        |
#+------+-------------------+--------+--------+--------+

Answer 2

您可以将 split、element_at 和 when/otherwise 子句与 array_union 一起使用来放置空字符串。

from pyspark.sql import functions as F
from pyspark.sql.functions import when
df.withColumn("array", F.split("productname","\ "))\
  .withColumn("array", F.when(F.size("array")==2, F.array_union(F.col("array"),F.array(F.lit(""))))\
                        .when(F.size("array")==1, F.array_union(F.col("array"),F.array(F.lit(" "),F.lit(""))))\
                        .otherwise(F.col("array")))\
  .withColumn("product1", F.element_at("array",1))\
  .withColumn("product2", F.element_at("array",2))\
  .withColumn("product3", F.element_at("array",3)).drop("array")\
  .show(truncate=False)

+------+-------------------+--------+--------+--------+
|id    |productname        |product1|product2|product3|
+------+-------------------+--------+--------+--------+
|235832|EXTREME BERRY Sweet|EXTREME |BERRY   |Sweet   |
|419736|BLUE CHASER SAUCE  |BLUE    |CHASER  |SAUCE   |
|124513|LAAVA C2L5         |LAAVA   |C2L5    |        |
|123455|LAVA               |LAVA    |        |        |
+------+-------------------+--------+--------+--------+

通过在白色 space 上拆分值来创建新的 pyspark 数据框列

create new pyspark dataframe columns by splitting values on white space

python

pyspark

pyspark-sql

pyspark-dataframes