根据pyspark数据帧中多列的笛卡尔乘积创建新列的方法如下:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, explode
from pyspark.sql.types import ArrayType, StructType, StructField, StringType
spark = SparkSession.builder.appName("Cartesian Product").getOrCreate()
data = [("A", [1, 2, 3]), ("B", [4, 5]), ("C", [6])]
df = spark.createDataFrame(data, ["col1", "col2"])
df.show()
示例数据帧如下:
+----+---------+
|col1| col2|
+----+---------+
| A|[1, 2, 3]|
| B| [4, 5]|
| C| [6]|
+----+---------+
def cartesian_product(col1, col2):
return [(c1, c2) for c1 in col1 for c2 in col2]
cartesian_product_udf = spark.udf.register("cartesian_product", cartesian_product, ArrayType(StructType([
StructField("col1", StringType(), True),
StructField("col2", StringType(), True)
])))
df.withColumn("cartesian_product", explode(cartesian_product_udf(col("col1"), col("col2")))).show(truncate=False)
输出结果如下:
+----+---------+----------------+
|col1|col2 |cartesian_product|
+----+---------+----------------+
|A |[1, 2, 3]|[A, 1] |
|A |[1, 2, 3]|[A, 2] |
|A |[1, 2, 3]|[A, 3] |
|B |[4, 5] |[B, 4] |
|B |[4, 5] |[B, 5] |
|C |[6] |[C, 6] |
+----+---------+----------------+
这样,我们根据pyspark数据帧中多列的笛卡尔乘积成功创建了新列。在这个例子中,我们使用了pyspark的函数explode
来展开数组,并使用了UDF来计算笛卡尔乘积。最后,我们使用withColumn
方法将新列添加到数据帧中,并使用show
方法显示结果。
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