我有两个数据帧,如下所示,我正在尝试使用外键搜索第二个df,然后生成一个新的数据帧。我正在考虑做一个spark.sql("""select history.value as previous_year 1 from df1, history where df1.key=history.key and history.date=add_months($currentdate,-1*12)"""
但是我需要多次这样做10 previous_year
s。并将它们重新组合在一起。如何为此创建功能?非常感谢。这里相当新鲜。
dataframe one:
+---+---+-----------+
|key|val| date |
+---+---+-----------+
| 1|100| 2018-04-16|
| 2|200| 2018-04-16|
+---+---+-----------+
dataframe two : historical data
+---+---+-----------+
|key|val| date |
+---+---+-----------+
| 1|10 | 2017-04-16|
| 1|20 | 2016-04-16|
+---+---+-----------+
我想要生成的结果是
+---+----------+-----------------+-----------------+
|key|date | previous_year_1 | previous_year_2 |
+---+----------+-----------------+-----------------+
| 1|2018-04-16| 10 | 20 |
| 2|null | null | null |
+---+----------+-----------------+-----------------+
答案 0 :(得分:1)
要解决此问题,可以应用以下方法:
1)按key
加入两个数据帧。
2)过滤掉之前日期不是参考日期前几年的所有行。
3)计算行的年份差异并将值放在专用列中。
4)围绕上一步计算的列旋转DataFrame,并汇总相应年份的值。
private def generateWhereForPreviousYears(nbYears: Int): Column =
(-1 to -nbYears by -1) // loop on each backwards year value
.map(yearsBack =>
/*
* Each year back count number is transformed in an expression
* to be included into the WHERE clause.
* This is equivalent to "history.date=add_months($currentdate,-1*12)"
* in your comment in the question.
*/
add_months($"df1.date", 12 * yearsBack) === $"df2.date"
)
/*
The previous .map call produces a sequence of Column expressions,
we need to concatenate them with "or" in order to obtain
a single Spark Column reference. .reduce() function is most
appropriate here.
*/
.reduce(_ or _) or $"df2.date".isNull // the last "or" is added to include empty lines in the result.
val nbYearsBack = 3
val result = sourceDf1.as("df1")
.join(sourceDf2.as("df2"), $"df1.key" === $"df2.key", "left")
.where(generateWhereForPreviousYears(nbYearsBack))
.withColumn("diff_years", concat(lit("previous_year_"), year($"df1.date") - year($"df2.date")))
.groupBy($"df1.key", $"df1.date")
.pivot("diff_years")
.agg(first($"df2.value"))
.drop("null") // drop the unwanted extra column with null values
输出结果为:
+---+----------+---------------+---------------+
|key|date |previous_year_1|previous_year_2|
+---+----------+---------------+---------------+
|1 |2018-04-16|10 |20 |
|2 |2018-04-16|null |null |
+---+----------+---------------+---------------+
答案 1 :(得分:1)
让我“仔细阅读”并为您提供一个“类似”的解决方案:
flush()
如果您确实需要更改列名,请随意操作数据。
甚至更好:
val df1Pivot = df1.groupBy("key").pivot("date").agg(max("val"))
val df2Pivot = df2.groupBy("key").pivot("date").agg(max("val"))
val result = df1Pivot.join(df2Pivot, Seq("key"), "left")
result.show
+---+----------+----------+----------+
|key|2018-04-16|2016-04-16|2017-04-16|
+---+----------+----------+----------+
| 1| 100| 20| 10|
| 2| 200| null| null|
+---+----------+----------+----------+