在pyspark数据帧中处理字符串到数组的转换 [英] Handle string to array conversion in pyspark dataframe

问题描述

我有一个文件(csv)，当在spark数据框中读取该文件时，其打印模式具有以下值

I have a file(csv) which when read in spark dataframe has the below values for print schema

-- list_values: string (nullable = true)

list_values列中的值类似于:

the values in the column list_values are something like:

[[[167, 109, 80, ...]]]

是否可以将其转换为数组类型而不是字符串?

Is it possible to convert this to array type instead of string?

我尝试将其拆分，并使用在线提供的代码来解决类似问题:

I tried splitting it and using code available online for similar problems:

df_1 = df.select('list_values', split(col("list_values"), ",\s*").alias("list_values"))

但是如果我运行上面的代码，得到的数组将跳过原始数组中的很多值，即

but if I run the above code the array which I get skips a lot of values in the original array i.e.

以上代码的输出为:

[, 109, 80, 69, 5...

与原始数组不同(即-缺少167)

which is different from original array i.e. (-- 167 is missing)

[[[167, 109, 80, ...]]] 

由于我是火花的新手，所以我对它的完成方法并不了解(对于python，我可以完成ast.literal_eval，但是spark没有为此做准备.

Since I am new to spark I don't have much knowledge how it is done (For python I could have done ast.literal_eval but spark has no provision for this.

所以我将再次重复这个问题:

So I'll repeat the question again :

如何将存储为字符串的数组转换/广播为array，即

How can I convert/cast an array stored as string to array i.e.

'[]' to [] conversion

推荐答案

假设您的DataFrame是以下内容:

Suppose your DataFrame was the following:

df.show()
#+----+------------------+
#|col1|              col2|
#+----+------------------+
#|   a|[[[167, 109, 80]]]|
#+----+------------------+

df.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)

您可以使用 pyspark.sql.functions.regexp_replace 删除前和后方括号.完成后，您可以在", "上split生成的字符串:

You could use pyspark.sql.functions.regexp_replace to remove the leading and trailing square brackets. Once that's done, you can split the resulting string on ", ":

from pyspark.sql.functions import split, regexp_replace

df2 = df.withColumn(
    "col3",
    split(regexp_replace("col2", r"(^\[\[\[)|(\]\]\]$)", ""), ", ")
)
df2.show()

#+----+------------------+--------------+
#|col1|              col2|          col3|
#+----+------------------+--------------+
#|   a|[[[167, 109, 80]]]|[167, 109, 80]|
#+----+------------------+--------------+

df2.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)
# |-- col3: array (nullable = true)
# |    |-- element: string (containsNull = true)

如果您希望该列为整数数组，则可以使用cast:

If you wanted the column as an array of integers, you could use cast:

from pyspark.sql.functions import col
df2 = df2.withColumn("col3", col("col3").cast("array<int>"))
df2.printSchema()
#root
# |-- col1: string (nullable = true)
# |-- col2: string (nullable = true)
# |-- col3: array (nullable = true)
# |    |-- element: integer (containsNull = true)

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