Question

是否有可能根据先前列的最大值添加新列，其中先前列是字符串文字。考虑以下数据框：

df = spark.createDataFrame(
    [
        ('1',25000,"black","black","white"),
        ('2',16000,"red","black","white"),
    ],
    ['ID','cash','colour_body','colour_head','colour_foot']
)

然后目标框架应如下所示：

df = spark.createDataFrame(
    [
        ('1',25000,"black","black","white", "black" ),
        ('2',16000,"red","black","white", "white" ),
    ],
    ['ID','cash','colour_body','colour_head','colour_foot', 'max_v']
)

如果没有最大可检测值，则应使用最后一个有效颜色。

是否存在某种反制可能性或udf？

Answer 1

在statistics.mode周围定义UDF，以使用所需的语义计算行模式：

import statistics

from pyspark.sql.functions import udf, col
from pyspark.sql.types import StringType

def mode(*x):
    try:
        return statistics.mode(x)
    except statistics.StatisticsError:
        return x[-1]

mode = udf(mode, StringType())

df.withColumn("max_v", mode(*[col(c) for c in df.columns if 'colour' in c])).show()

+---+-----+-----------+-----------+-----------+-----+
| ID| cash|colour_body|colour_head|colour_foot|max_v|
+---+-----+-----------+-----------+-----------+-----+
|  1|25000|      black|      black|      white|black|
|  2|16000|        red|      black|      white|white|
+---+-----+-----------+-----------+-----------+-----+

Answer 2

对于任意数量的列，通常使用udf solution by @cs95。

但是，在这种只有3列的特定情况下，您实际上可以仅使用pyspark.sql.functions.when（即more efficient than using a udf）来简化逻辑。

from pyspark.sql.functions import col, when

def mode_of_3_cols(body, head, foot):
    return(
        when(
            (body == head)|(body == foot), 
            body
        ).when(
            (head == foot),
            head
        ).otherwise(foot)
    )

df.withColumn(
    "max_v", 
    mode_of_3_cols(col("colour_body"), col("colour_head"), col("colour_foot"))
).show()
#+---+-----+-----------+-----------+-----------+-----+
#| ID| cash|colour_body|colour_head|colour_foot|max_v|
#+---+-----+-----------+-----------+-----------+-----+
#|  1|25000|      black|      black|      white|black|
#|  2|16000|        red|      black|      white|white|
#+---+-----+-----------+-----------+-----------+-----+

您只需要检查两列是否相等-如果是，则该值必须是模式。如果不是，则返回最后一列。

行模式作为PySpark DataFrame中的新列

2 个答案: