pyspark转置

pdkcd3nj 于 2021-05-24 发布在 Spark

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我有以下格式的数据，其中有38个度量列，用于不同月份，如下所示。

+---------+-----------------+-----------------+------+------------------+------------------+------------------+---------+------------------+
| Cust_No | Measure1_month1 | Measure1_month2 | .... | Measure1_month72 | Measure2_month_1 | Measure2_month_2 | ….so on | Measure2_month72 |....Measure38_month1...
+---------+-----------------+-----------------+------+------------------+------------------+------------------+---------+------------------+
|       1 |              10 |              20 | ….   |              500 |               40 |               50 | …       |                  |
|       2 |              20 |              40 | ….   |              800 |               70 |              150 | …       |                  |
+---------+-----------------+-----------------+------+------------------+------------------+------------------+---------+------------------+

我想用pyspark实现以下格式。

+---------+-------+----------+----------+
| CustNum | Month | Measure1 | Measure2.......measure38 |
+---------+-------+----------+----------+
|       1 |     1 |       10 |       30 |
|       1 |     2 |       20 |       40 |
|       1 |     3 |       30 |       80 |
|       1 |     4 |       70 |       90 |
|       1 |     5 |       40 |      100 |
|       . |     . |        . |        . |
|       . |     . |        . |        . |
|       1 |    72 |      700 |       50 |
+---------+-------+----------+----------+

每一个客户号码都是如此
你能帮我做这个吗？
谢谢

apache-spark pyspark apache-spark-sql

来源：https://stackoverflow.com/questions/64144812/pyspark-transpose

1条答案

按热度按时间

zdwk9cvp1#

iiuc，你需要 wide to long 一种可以通过 stack 在Pypark
我用5个月的数据创建了一个示例数据框

df = spark.createDataFrame([(1,10,20,30,40,50,10,20,30,40,50),(2,10,20,30,40,50,10,20,30,40,50)],['cust','Measrue1_month1','Measrue1_month2','Measrue1_month3','Measrue1_month4','Measrue1_month5','Measrue2_month1','Measrue2_month2','Measrue2_month3','Measrue2_month4','Measrue2_month5'])

现在为堆栈操作生成子句。可以用更好的方法来做，但这里是最简单的例子

Measure1 = [i for i in df.columns if i.startswith('Measrue1')]
Measure2 = [i for i in df.columns if i.startswith('Measrue2')]
final = []
for i in Measure1:
    for j in Measure2:
        if(i.split('_')[1]==j.split('_')[1]):
            final.append((i,j))
rows = len(final)
values = ','.join([f"'{i.split('_')[1]}',{i},{j}" for i,j in final])

现在实际应用堆栈操作

df.select('cust',expr(f'''stack({rows},{values})''').alias('Month','Measure1','Measure2')).show()

+----+------+--------+--------+
|cust| Month|Measure1|Measure2|
+----+------+--------+--------+
|   1|month1|      10|      10|
|   1|month2|      20|      20|
|   1|month3|      30|      30|
|   1|month4|      40|      40|
|   1|month5|      50|      50|
|   2|month1|      10|      10|
|   2|month2|      20|      20|
|   2|month3|      30|      30|
|   2|month4|      40|      40|
|   2|month5|      50|      50|
+----+------+--------+--------+

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pyspark转置

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