根据列值是否在另一列中，将列添加到 PySpark DataFrame [英] Adding column to PySpark DataFrame depending on whether column value is in another column

问题描述

我有一个 PySpark DataFrame，其结构由

I have a PySpark DataFrame with structure given by

[('u1', 1, [1 ,2, 3]), ('u1', 4, [1, 2, 3])].toDF('user', 'item', 'fav_items')

根据item"是否在fav_items"中，我需要再添加一个 1 或 0 列.

I need to add a further column with 1 or 0 depending on whether 'item' is in 'fav_items' or not.

所以我想要

[('u1', 1, [1 ,2, 3], 1), ('u1', 4, [1, 2, 3], 0)]

我将如何查找第二列到第三列以确定值，然后我将如何添加它?

How would I look up for second column into third column to decide value and how would I then add it?

推荐答案

以下代码执行请求的任务.定义了一个用户定义的函数，它接收 DataFrame 的两列作为参数.因此，对于每一行，搜索一个项目是否在项目列表中.如果找到该项目，则返回 1，否则返回 0.

The following code does the requested task. An user defined function was defined that receives two columns of a DataFrame as parameters. So, for each row, search if an item is in the item list. If the item is found, a 1 is return, otherwise a 0.

# Imports
from pyspark.sql.types import IntegerType
from pyspark.sql.functions import udf
# First we create a RDD in order to create a dataFrame:
rdd = sc.parallelize([('u1', 1, [1 ,2, 3]), ('u1', 4, [1, 2, 3])])
df = rdd.toDF(['user', 'item', 'fav_items'])
# Print dataFrame
df.show()

# We make an user define function that receives two columns and do operation
function = udf(lambda item, items: 1 if item in items else 0, IntegerType())

df.select('user', 'item', 'fav_items', function(col('item'), col('fav_items')).alias('result')).show()

结果如下:

+----+----+---------+
|user|item|fav_items|
+----+----+---------+
|  u1|   1|[1, 2, 3]|
|  u1|   4|[1, 2, 3]|
+----+----+---------+

+----+----+---------+------+
|user|item|fav_items|result|
+----+----+---------+------+
|  u1|   1|[1, 2, 3]|     1|
|  u1|   4|[1, 2, 3]|     0|
+----+----+---------+------+

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