如何计算每个键在 PySpark 数据框中的百分位数？

Question

我有一个 PySpark 数据框由三列 x、y、z 组成。

X 在此数据框中可能有多行。如何分别计算 x 中每个键的百分位数？

 +------+---------+------+
|  Name|     Role|Salary|
+------+---------+------+
|   bob|Developer|125000|
|  mark|Developer|108000|
|  carl|   Tester| 70000|
|  carl|Developer|185000|
|  carl|   Tester| 65000|
| roman|   Tester| 82000|
| simon|Developer| 98000|
|  eric|Developer|144000|
|carlos|   Tester| 75000|
| henry|Developer|110000|
+------+---------+------+

需要的输出：

 +------+---------+------+---------+
|  Name|     Role|Salary|      50%|
+------+---------+------+---------+
|   bob|Developer|125000|117500.0 |
|  mark|Developer|108000|117500.0 |
|  carl|   Tester| 70000|72500.0  |
|  carl|Developer|185000|117500.0 |
|  carl|   Tester| 65000|72500.0  |
| roman|   Tester| 82000|72500.0  |
| simon|Developer| 98000|117500.0 |
|  eric|Developer|144000|117500.0 |
|carlos|   Tester| 75000|72500.0  |
| henry|Developer|110000|117500.0 |
+------+---------+------+---------+

原文由 bib 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

尝试 groupby + F.expr ：

 import pyspark.sql.functions as F

df1 = df.groupby('Role').agg(F.expr('percentile(Salary, array(0.25))')[0].alias('%25'),
                             F.expr('percentile(Salary, array(0.50))')[0].alias('%50'),
                             F.expr('percentile(Salary, array(0.75))')[0].alias('%75'))
df1.show()

输出：

 +---------+--------+--------+--------+
|     Role|     %25|     %50|     %75|
+---------+--------+--------+--------+
|   Tester| 68750.0| 72500.0| 76750.0|
|Developer|108500.0|117500.0|139250.0|
+---------+--------+--------+--------+

现在你可以加入 df1 与原始数据框：

 df.join(df1, on='Role', how='left').show()

输出：

 +---------+------+------+--------+--------+--------+
|     Role|  Name|Salary|     %25|     %50|     %75|
+---------+------+------+--------+--------+--------+
|   Tester|  carl| 70000| 68750.0| 72500.0| 76750.0|
|   Tester|  carl| 65000| 68750.0| 72500.0| 76750.0|
|   Tester| roman| 82000| 68750.0| 72500.0| 76750.0|
|   Tester|carlos| 75000| 68750.0| 72500.0| 76750.0|
|Developer|   bob|125000|108500.0|117500.0|139250.0|
|Developer|  mark|108000|108500.0|117500.0|139250.0|
|Developer|  carl|185000|108500.0|117500.0|139250.0|
|Developer| simon| 98000|108500.0|117500.0|139250.0|
|Developer|  eric|144000|108500.0|117500.0|139250.0|
|Developer| henry|110000|108500.0|117500.0|139250.0|
+---------+------+------+--------+--------+--------+

原文由 Ala Tarighati 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

array 并不是真正必要的：

 F.expr('percentile(Salary, 0.5)')

它与窗口函数一起完成以下工作：

 df = df.withColumn('50%', F.expr('percentile(Salary, 0.5)').over(W.partitionBy('Role')))

df.show()
#  +------+---------+------+--------+
#  |  Name|     Role|Salary|     50%|
#  +------+---------+------+--------+
#  |   bob|Developer|125000|117500.0|
#  |  mark|Developer|108000|117500.0|
#  |  carl|Developer|185000|117500.0|
#  | simon|Developer| 98000|117500.0|
#  |  eric|Developer|144000|117500.0|
#  | henry|Developer|110000|117500.0|
#  |  carl|   Tester| 70000| 72500.0|
#  |  carl|   Tester| 65000| 72500.0|
#  | roman|   Tester| 82000| 72500.0|
#  |carlos|   Tester| 75000| 72500.0|
#  +------+---------+------+--------+

原文由 ZygD 发布，翻译遵循 CC BY-SA 4.0 许可协议