Pyspark数据框上的数据透视字符串列

Question

我有一个像这样的简单数据框：

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|
+---+----+----+------+----+

我需要按日期进行调整：

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|
+---+----+------+------+------+

一切正常。但是现在我需要对其进行透视，并获得一个非数字列：

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

当然，我会得到一个例外：

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

我想产生一些东西

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

有可能pivot吗？

Answer 1

假设(id |type | date)组合是唯一的，并且您的唯一目标是枢纽而不是合计，则可以使用first（或任何其他不限于数值的函数）：

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，则必须预先汇总数据。例如，对于最常见的ship值：

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|
## +---+----+--------+--------+--------+