将大数组列拆分为多列 - Pyspark [英] Split large array columns into multiple columns - Pyspark

问题描述

我有:

+---+-------+-------+
| id|   var1|   var2|
+---+-------+-------+
|  a|[1,2,3]|[1,2,3]|
|  b|[2,3,4]|[2,3,4]|
+---+-------+-------+

我想要:

+---+-------+-------+-------+-------+-------+-------+
| id|var1[0]|var1[1]|var1[2]|var2[0]|var2[1]|var2[2]|
+---+-------+-------+-------+-------+-------+-------+
|  a|      1|      2|      3|      1|      2|      3|
|  b|      2|      3|      4|      2|      3|      4|
+---+-------+-------+-------+-------+-------+-------+

如何拆分列表提供的解决方案到 Pyspark 中的多列?

df1.select('id', df1.var1[0], df1.var1[1], ...).show()

有效，但我的一些数组很长(最多 332 个).

works, but some of my arrays are very long (max 332).

我该如何编写它才能考虑到所有长度的数组?

How can I write this so that it takes account of all length arrays?

推荐答案

无论初始列的数量和数组的大小如何，此解决方案都适用于您的问题.此外，如果一列具有不同的数组大小(例如 [1,2]、[3,4,5])，则会导致空值的最大列数填补空白.

This solution will work for your problem, no matter the number of initial columns and the size of your arrays. Moreover, if a column has different array sizes (eg [1,2], [3,4,5]), it will result in the maximum number of columns with null values filling the gap.

from pyspark.sql import functions as F

df = spark.createDataFrame(sc.parallelize([['a', [1,2,3], [1,2,3]], ['b', [2,3,4], [2,3,4]]]), ["id", "var1", "var2"])

columns = df.drop('id').columns
df_sizes = df.select(*[F.size(col).alias(col) for col in columns])
df_max = df_sizes.agg(*[F.max(col).alias(col) for col in columns])
max_dict = df_max.collect()[0].asDict()

df_result = df.select('id', *[df[col][i] for col in columns for i in range(max_dict[col])])
df_result.show()
>>>
+---+-------+-------+-------+-------+-------+-------+
| id|var1[0]|var1[1]|var1[2]|var2[0]|var2[1]|var2[2]|
+---+-------+-------+-------+-------+-------+-------+
|  a|      1|      2|      3|      1|      2|      3|
|  b|      2|      3|      4|      2|      3|      4|
+---+-------+-------+-------+-------+-------+-------+

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