Pyspark中pyspark.sql.functions常用方法(4)

pyspark sql functions

forall 判断array是否满足all

df = spark.createDataFrame([(1, ["bar"]), (2, ["foo", "bar"]), (3, ["foobar", "foo"])],("key", "values")
)
df.show()
+---+-------------+
|key|       values|
+---+-------------+
|  1|        [bar]|
|  2|   [foo, bar]|
|  3|[foobar, foo]|
+---+-------------+
df.select(forall("values", lambda x: x.rlike("foo")).alias("all_foo")).show()
+-------+
|all_foo|
+-------+
|  false|
|  false|
|   true|
+-------+

filter 过滤

df = spark.createDataFrame([([1, None, 2, 3],), ([4, 5, None, 4],)], ['data'])
df.show()
+---------------+
|           data|
+---------------+
|[1, null, 2, 3]|
|[4, 5, null, 4]|
+---------------+
df.select(fs.filter(df.data,lambda x: x>1).alias('filter')).show()
+---------+
|   filter|
+---------+
|   [2, 3]|
|[4, 5, 4]|
+---------+

zip_with 数组合并

使用函数将两个给定的数组按元素合并为一个数组。如果一个数组较短，则在应用函数之前，在末尾附加null以匹配较长数组的长度。

df.select(fs.zip_with("xs", "ys", lambda x, y: x ** y).alias("powers")).show(truncate=False)
+---------------------------+
|powers                     |
+---------------------------+
|[1.0, 9.0, 625.0, 262144.0]|
+---------------------------+