Pyspark Dataframe 上的 Pivot String 列 [英] Pivot String column on Pyspark Dataframe

问题描述

我有一个像这样的简单数据框:

I have a simple dataframe like this:

rdd = sc.parallelize(
    [
        (0, "A", 223,"201603", "PORT"), 
        (0, "A", 22,"201602", "PORT"), 
        (0, "A", 422,"201601", "DOCK"), 
        (1,"B", 3213,"201602", "DOCK"), 
        (1,"B", 3213,"201601", "PORT"), 
        (2,"C", 2321,"201601", "DOCK")
    ]
)
df_data = sqlContext.createDataFrame(rdd, ["id","type", "cost", "date", "ship"])

df_data.show()
 +---+----+----+------+----+
| id|type|cost|  date|ship|
+---+----+----+------+----+
|  0|   A| 223|201603|PORT|
|  0|   A|  22|201602|PORT|
|  0|   A| 422|201601|DOCK|
|  1|   B|3213|201602|DOCK|
|  1|   B|3213|201601|PORT|
|  2|   C|2321|201601|DOCK|
+---+----+----+------+----+

我需要按日期旋转它:

df_data.groupby(df_data.id, df_data.type).pivot("date").avg("cost").show()

+---+----+------+------+------+
| id|type|201601|201602|201603|
+---+----+------+------+------+
|  2|   C|2321.0|  null|  null|
|  0|   A| 422.0|  22.0| 223.0|
|  1|   B|3213.0|3213.0|  null|
+---+----+------+------+------+

一切都按预期进行.但现在我需要旋转它并获得一个非数字列:

Everything works as expected. But now I need to pivot it and get a non-numeric column:

df_data.groupby(df_data.id, df_data.type).pivot("date").avg("ship").show()

当然我会得到一个例外:

and of course I would get an exception:

AnalysisException: u'"ship" is not a numeric column. Aggregation function can only be applied on a numeric column.;'

我想在

+---+----+------+------+------+
| id|type|201601|201602|201603|
+---+----+------+------+------+
|  2|   C|DOCK  |  null|  null|
|  0|   A| DOCK |  PORT| DOCK|
|  1|   B|DOCK  |PORT  |  null|
+---+----+------+------+------+

使用 pivot 可以吗?

推荐答案

假设 (id |type | date) 组合是唯一的，并且您唯一的目标是透视而不是聚合，您可以使用 first(或任何其他不限于数值的函数):

Assuming that (id |type | date) combinations are unique and your only goal is pivoting and not aggregation you can use first (or any other function not restricted to numeric values):

from pyspark.sql.functions import first

(df_data
    .groupby(df_data.id, df_data.type)
    .pivot("date")
    .agg(first("ship"))
    .show())

## +---+----+------+------+------+
## | id|type|201601|201602|201603|
## +---+----+------+------+------+
## |  2|   C|  DOCK|  null|  null|
## |  0|   A|  DOCK|  PORT|  PORT|
## |  1|   B|  PORT|  DOCK|  null|
## +---+----+------+------+------+

如果这些假设不正确，您就必须预先汇总您的数据.例如对于最常见的 ship 值:

If these assumptions is not correct you'll have to pre-aggregate your data. For example for the most common ship value:

from pyspark.sql.functions import max, struct

(df_data
    .groupby("id", "type", "date", "ship")
    .count()
    .groupby("id", "type")
    .pivot("date")
    .agg(max(struct("count", "ship")))
    .show())

## +---+----+--------+--------+--------+
## | id|type|  201601|  201602|  201603|
## +---+----+--------+--------+--------+
## |  2|   C|[1,DOCK]|    null|    null|
## |  0|   A|[1,DOCK]|[1,PORT]|[1,PORT]|
## |  1|   B|[1,PORT]|[1,DOCK]|    null|
## +---+----+--------+--------+--------+

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