解压缩pyspark数据框中的元组列表 [英] unzip list of tuples in pyspark dataframe

问题描述

我要在pyspark数据框的列中解压缩元组列表

I want unzip list of tuples in a column of a pyspark dataframe

假设一列为[(blue, 0.5), (red, 0.1), (green, 0.7)]，我想分为两列，第一列为[blue, red, green]，第二列为[0.5, 0.1, 0.7]

Let's say a column as [(blue, 0.5), (red, 0.1), (green, 0.7)], I want to split into two columns, with first column as [blue, red, green] and second column as [0.5, 0.1, 0.7]

+-----+-------------------------------------------+
|Topic|  Tokens                                   |
+-----+-------------------------------------------+
|    1|  ('blue', 0.5),('red', 0.1),('green', 0.7)|
|    2|  ('red', 0.9),('cyan', 0.5),('white', 0.4)|
+-----+-------------------------------------------+

可以用以下代码创建:

df = sqlCtx.createDataFrame(
    [
        (1, ('blue', 0.5),('red', 0.1),('green', 0.7)),
        (2, ('red', 0.9),('cyan', 0.5),('white', 0.4))
    ],
    ('Topic', 'Tokens')
)

而且，输出应类似于:

+-----+--------------------------+-----------------+
|Topic|  Tokens                  | Weights         |
+-----+--------------------------+-----------------+
|    1|  ['blue', 'red', 'green']| [0.5, 0.1, 0.7] |
|    2|  ['red', 'cyan', 'white']| [0.9, 0.5, 0.4] |
+-----+--------------------------------------------+

推荐答案

如果DataFrame的架构如下所示:

 root
  |-- Topic: long (nullable = true)
  |-- Tokens: array (nullable = true)
  |    |-- element: struct (containsNull = true)
  |    |    |-- _1: string (nullable = true)
  |    |    |-- _2: double (nullable = true)

然后您可以选择:

from pyspark.sql.functions import col

df.select(
    col("Topic"),
    col("Tokens._1").alias("Tokens"), col("Tokens._2").alias("weights")
).show()
# +-----+------------------+---------------+       
# |Topic|            Tokens|        weights|
# +-----+------------------+---------------+
# |    1|[blue, red, green]|[0.5, 0.1, 0.7]|
# |    2|[red, cyan, white]|[0.9, 0.5, 0.4]|
# +-----+------------------+---------------+

并概括为:

cols = [
    col("Tokens.{}".format(n)) for n in 
    df.schema["Tokens"].dataType.elementType.names]

df.select("Topic", *cols)

参考查询具有复杂类型的Spark SQL DataFrame

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