PySpark DataFrame:根据另一列中的最小/最大条件更改单元格值

Question

我有以下pySpark数据帧:

+------------------+------------------+--------------------+--------------+-------+
|              col1|              col2|                col3|             X|      Y|
+------------------+------------------+--------------------+--------------+-------+
|2.1729247374294496| 3.558069532647046|   6.607603368496324|             1|   null|
|0.2654841575294071|1.2633077949463256|0.023578679968183733|             0|   null|
|0.4253301781296708|3.4566490739823483| 0.11711202266039554|             3|   null|
| 2.608497168338446| 3.529397129549324|   0.373034222141551|             2|   null|
+------------------+------------------+--------------------+--------------+-------+

这是一个相当简单的操作,我可以很容易地用熊猫做.但是,我需要只使用pySpark.

我想做以下(我会写一些伪代码):

在col3 == max(col3)的行中,将Y从null更改为'K'

在剩下的行中,在col1 == max(col1)的行中,将Y从null更改为'Z'

在其余行中,在col1 == min(col1)的行中,将Y从null更改为"U"

在剩下的行中:将Y从null更改为"I".

因此,预期的输出是:

+------------------+------------------+--------------------+--------------+-------+
|              col1|              col2|                col3|             X|      Y|
+------------------+------------------+--------------------+--------------+-------+
|2.1729247374294496| 3.558069532647046|   6.607603368496324|             1|      K|
|0.2654841575294071|1.2633077949463256|0.023578679968183733|             0|      U|
|0.4253301781296708|3.4566490739823483| 0.11711202266039554|             3|      I|
| 2.608497168338446| 3.529397129549324|   0.373034222141551|             2|      Z|
+------------------+------------------+--------------------+--------------+-------+

完成后,我需要使用此表作为另一个表的查找:

+--------------------+--------+-----+------------------+--------------+------------+
|                  x1|      x2|   x3|                x4|             X|           d|
+--------------------+--------+-----+------------------+--------------+------------+
|0057f68a-6330-42a...|    2876|   30| 5.989999771118164|             0|    20171219|
|05cc0191-4ee4-412...|  108381|   34|24.979999542236328|             3|    20171219|
|06f353af-e9d3-4d0...|  118798|   34|               0.0|             3|    20171219|
|0c69b607-112b-4f3...|   20993|   34|               0.0|             0|    20171219|
|0d1b52ba-1502-4ff...|   23817|   34|               0.0|             0|    20171219|

我想使用第一个表作为查找在第二个表中创建一个新列.新列的值应该在第Y列在第一个表中的第二个表使用X列作为键(因此,我们查找在第一个表中对应于列X值列Y值进行查找,这些值在来自列X第二表).

UPD:我需要一个满足两个条件的稳健解决方案,例如:

+------------------+------------------+--------------------+--------------+-------+
|              col1|              col2|                col3|             X|      Y|
+------------------+------------------+--------------------+--------------+-------+
| 2.608497168338446| 3.558069532647046|   6.607603368496324|             1|   null|
|0.2654841575294071|1.2633077949463256|0.023578679968183733|             0|   null|
|0.4253301781296708|3.4566490739823483| 0.11711202266039554|             3|   null|
|2.1729247374294496| 3.529397129549324|   0.373034222141551|             2|   null|
+------------------+------------------+--------------------+--------------+-------+

在这种情况下,行0满足max('col3')和max('col1')条件.

所以需要发生的是:

第0行变为'K'

第3行变为'Z'(因为剩下的行(0已经'K'第3行满足max('col1')条件

第1行变为'U'

第2行变为"我"

我不能在表1中有多行,其中包含'I'.

Answer 1

计算聚合:

from pyspark.sql import functions as F

df = spark.createDataFrame([
    (2.1729247374294496,  3.558069532647046,    6.607603368496324, 1),
    (0.2654841575294071, 1.2633077949463256, 0.023578679968183733, 0),
    (0.4253301781296708, 3.4566490739823483,  0.11711202266039554, 3),
    (2.608497168338446,  3.529397129549324,    0.373034222141551, 2)
], ("col1", "col2", "col3", "x"))

min1, max1, max3 = df.select(F.min("col1"), F.max("col1"), F.max("col3")).first()

添加列when:

y = (F.when(F.col("col3") == max3, "K")  # In row where col3 == max(col3), change Y from null to 'K'
    .when(F.col("col1") == max1, "Z")    # In the remaining rows, in the row where col1 == max(col1), change Y from null to 'Z'
    .when(F.col("col1") == min1, "U")    # In the remaining rows, in the row where col1 == min(col1), change Y from null to 'U'
    .otherwise("I"))                     # In the remaining row: change Y from null to 'I'

df_with_y = df.withColumn("y", y)


df_with_y.show()
# +------------------+------------------+--------------------+---+---+
# |              col1|              col2|                col3|  x|  y|
# +------------------+------------------+--------------------+---+---+
# |2.1729247374294496| 3.558069532647046|   6.607603368496324|  1|  K|
# |0.2654841575294071|1.2633077949463256|0.023578679968183733|  0|  U|
# |0.4253301781296708|3.4566490739823483| 0.11711202266039554|  3|  I|
# | 2.608497168338446| 3.529397129549324|   0.373034222141551|  2|  Z|
# +------------------+------------------+--------------------+---+---+

应使用第二个表中的X列作为键,在第一个表的第Y列中查找新列的值

df_with_y.select("x", "Y").join(df2, ["x"])

如果y已经存在,并且您要保留非空值:

df_ = spark.createDataFrame([
    (2.1729247374294496,  3.558069532647046,    6.607603368496324, 1, "G"),
    (0.2654841575294071, 1.2633077949463256, 0.023578679968183733, 0, None),
    (0.4253301781296708, 3.4566490739823483,  0.11711202266039554, 3, None),
    (2.608497168338446,  3.529397129549324,    0.373034222141551, 2, None)
], ("col1", "col2", "col3", "x", "y"))

min1_, max1_, max3_ = df.filter(F.col("y").isNull()).select(F.min("col1"), F.max("col1"), F.max("col3")).first()

y_ = (F.when(F.col("col3") == max3_, "K") 
    .when(F.col("col1") == max1_, "Z")
    .when(F.col("col1") == min1_, "U") 
    .otherwise("I"))

df_.withColumn("y", F.coalesce(F.col("y"), y_)).show()


# +------------------+------------------+--------------------+---+---+
# |              col1|              col2|                col3|  x|  y|
# +------------------+------------------+--------------------+---+---+
# |2.1729247374294496| 3.558069532647046|   6.607603368496324|  1|  G|
# |0.2654841575294071|1.2633077949463256|0.023578679968183733|  0|  U|
# |0.4253301781296708|3.4566490739823483| 0.11711202266039554|  3|  I|
# | 2.608497168338446| 3.529397129549324|   0.373034222141551|  2|  K|
# +------------------+------------------+--------------------+---+---+

如果遇到数值精度问题,可以尝试:

threshold = 0.0000001 # Choose appropriate 

y_t = (F.when(F.abs(F.col("col3") - max3) < threshold, "K")  # In row where col3 == max(col3), change Y from null to 'K'
    .when(F.abs(F.col("col1") - max1) < threshold, "Z")    # In the remaining rows, in the row where col1 == max(col1), change Y from null to 'Z'
    .when(F.abs(F.col("col1") - min1) < threshold, "U")    # In the remaining rows, in the row where col1 == min(col1), change Y from null to 'U'
    .otherwise("I"))                     # In the remaining row: change Y from null to 'I'

df.withColumn("y", y_t).show()
# +------------------+------------------+--------------------+---+---+
# |              col1|              col2|                col3|  x|  y|
# +------------------+------------------+--------------------+---+---+
# |2.1729247374294496| 3.558069532647046|   6.607603368496324|  1|  K|
# |0.2654841575294071|1.2633077949463256|0.023578679968183733|  0|  U|
# |0.4253301781296708|3.4566490739823483| 0.11711202266039554|  3|  I|
# | 2.608497168338446| 3.529397129549324|   0.373034222141551|  2|  Z|
# +------------------+------------------+--------------------+---+---+