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How to use pivot to generate a single-row matrix?
1
I need to pivot the following two-column dataframe to one-row one (long to wide).
+--------+-----+
| udate| cc|
+--------+-----+
|20090622| 458|
|20090624|31068|
|20090626| 151|
|20090629| 148|
|20090914| 453|
+--------+-----+
I need it in this format:
+--------+------------+----------+----------+
| udate| 20090622 | 20090624 | 20090626 |
+--------+------------+----------+----------+
| cc | 458| 31068 | 151 |etc
I ran this:
result_df.groupBy($"udate").pivot("udate").agg(max($"cc")).show()
but ended up with a matrix of all rows transposed to all columns:
+--------+--------+--------+--------+--------+--------+---
| udate|20090622|20090624|20090626|20090629|20090703|200
+--------+--------+--------+--------+--------+--------+---
|20090622| 458| null| null| null| null|
|20090624| null| 31068| null| null| null|
|20090626| null| null| 151| null| null|
|20090629| null| null| null| 148| null|
|20090703| null| null| null| null| 362|
|20090704| null| null| null| null| null|
|20090715| null| null| null| null| null|
|20090718| null| null| null| null| null|
|20090721| null| null| null| null| null|
|20090722| null| null| null| null| null|
I expected that pivoting a one-column dataset should result in a one-row pivoted dataset.
How can I modify the pivot command so that the result set is pivoted to one row?
apache-spark apache-spark-sql
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
add a comment |
1
I need to pivot the following two-column dataframe to one-row one (long to wide).
+--------+-----+
| udate| cc|
+--------+-----+
|20090622| 458|
|20090624|31068|
|20090626| 151|
|20090629| 148|
|20090914| 453|
+--------+-----+
I need it in this format:
+--------+------------+----------+----------+
| udate| 20090622 | 20090624 | 20090626 |
+--------+------------+----------+----------+
| cc | 458| 31068 | 151 |etc
I ran this:
result_df.groupBy($"udate").pivot("udate").agg(max($"cc")).show()
but ended up with a matrix of all rows transposed to all columns:
+--------+--------+--------+--------+--------+--------+---
| udate|20090622|20090624|20090626|20090629|20090703|200
+--------+--------+--------+--------+--------+--------+---
|20090622| 458| null| null| null| null|
|20090624| null| 31068| null| null| null|
|20090626| null| null| 151| null| null|
|20090629| null| null| null| 148| null|
|20090703| null| null| null| null| 362|
|20090704| null| null| null| null| null|
|20090715| null| null| null| null| null|
|20090718| null| null| null| null| null|
|20090721| null| null| null| null| null|
|20090722| null| null| null| null| null|
I expected that pivoting a one-column dataset should result in a one-row pivoted dataset.
How can I modify the pivot command so that the result set is pivoted to one row?
apache-spark apache-spark-sql
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
add a comment |
1
1
1
1
I need to pivot the following two-column dataframe to one-row one (long to wide).
+--------+-----+
| udate| cc|
+--------+-----+
|20090622| 458|
|20090624|31068|
|20090626| 151|
|20090629| 148|
|20090914| 453|
+--------+-----+
I need it in this format:
+--------+------------+----------+----------+
| udate| 20090622 | 20090624 | 20090626 |
+--------+------------+----------+----------+
| cc | 458| 31068 | 151 |etc
I ran this:
result_df.groupBy($"udate").pivot("udate").agg(max($"cc")).show()
but ended up with a matrix of all rows transposed to all columns:
+--------+--------+--------+--------+--------+--------+---
| udate|20090622|20090624|20090626|20090629|20090703|200
+--------+--------+--------+--------+--------+--------+---
|20090622| 458| null| null| null| null|
|20090624| null| 31068| null| null| null|
|20090626| null| null| 151| null| null|
|20090629| null| null| null| 148| null|
|20090703| null| null| null| null| 362|
|20090704| null| null| null| null| null|
|20090715| null| null| null| null| null|
|20090718| null| null| null| null| null|
|20090721| null| null| null| null| null|
|20090722| null| null| null| null| null|
I expected that pivoting a one-column dataset should result in a one-row pivoted dataset.
How can I modify the pivot command so that the result set is pivoted to one row?
apache-spark apache-spark-sql
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
I need to pivot the following two-column dataframe to one-row one (long to wide).
+--------+-----+
| udate| cc|
+--------+-----+
|20090622| 458|
|20090624|31068|
|20090626| 151|
|20090629| 148|
|20090914| 453|
+--------+-----+
I need it in this format:
+--------+------------+----------+----------+
| udate| 20090622 | 20090624 | 20090626 |
+--------+------------+----------+----------+
| cc | 458| 31068 | 151 |etc
I ran this:
result_df.groupBy($"udate").pivot("udate").agg(max($"cc")).show()
but ended up with a matrix of all rows transposed to all columns:
+--------+--------+--------+--------+--------+--------+---
| udate|20090622|20090624|20090626|20090629|20090703|200
+--------+--------+--------+--------+--------+--------+---
|20090622| 458| null| null| null| null|
|20090624| null| 31068| null| null| null|
|20090626| null| null| 151| null| null|
|20090629| null| null| null| 148| null|
|20090703| null| null| null| null| 362|
|20090704| null| null| null| null| null|
|20090715| null| null| null| null| null|
|20090718| null| null| null| null| null|
|20090721| null| null| null| null| null|
|20090722| null| null| null| null| null|
I expected that pivoting a one-column dataset should result in a one-row pivoted dataset.
How can I modify the pivot command so that the result set is pivoted to one row?
apache-spark apache-spark-sql
apache-spark apache-spark-sql
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
edited Nov 6 '18 at 22:16
Jacek Laskowski
44.7k18132268
44.7k18132268
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
asked Nov 2 '16 at 0:56
MarkTeehanMarkTeehan
113212
113212
add a comment |
add a comment |
1 Answer
1
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2
tl;dr In Spark 2.4.0 it simply boils down to using groupBy alone.
val solution = d.groupBy().pivot("udate").agg(first("cc"))
scala> solution.show
+--------+--------+--------+--------+--------+
|20090622|20090624|20090626|20090629|20090914|
+--------+--------+--------+--------+--------+
| 458| 31068| 151| 148| 453|
+--------+--------+--------+--------+--------+
If you really need the first column with the names just use withColumn and you're done.
val betterSolution = solution.select(lit("cc") as "udate", $"*")
scala> betterSolution.show
+-----+--------+--------+--------+--------+--------+
|udate|20090622|20090624|20090626|20090629|20090914|
+-----+--------+--------+--------+--------+--------+
| cc| 458| 31068| 151| 148| 453|
+-----+--------+--------+--------+--------+--------+
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
tl;dr In Spark 2.4.0 it simply boils down to using groupBy alone.
val solution = d.groupBy().pivot("udate").agg(first("cc"))
scala> solution.show
+--------+--------+--------+--------+--------+
|20090622|20090624|20090626|20090629|20090914|
+--------+--------+--------+--------+--------+
| 458| 31068| 151| 148| 453|
+--------+--------+--------+--------+--------+
If you really need the first column with the names just use withColumn and you're done.
val betterSolution = solution.select(lit("cc") as "udate", $"*")
scala> betterSolution.show
+-----+--------+--------+--------+--------+--------+
|udate|20090622|20090624|20090626|20090629|20090914|
+-----+--------+--------+--------+--------+--------+
| cc| 458| 31068| 151| 148| 453|
+-----+--------+--------+--------+--------+--------+
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
add a comment |
2
tl;dr In Spark 2.4.0 it simply boils down to using groupBy alone.
val solution = d.groupBy().pivot("udate").agg(first("cc"))
scala> solution.show
+--------+--------+--------+--------+--------+
|20090622|20090624|20090626|20090629|20090914|
+--------+--------+--------+--------+--------+
| 458| 31068| 151| 148| 453|
+--------+--------+--------+--------+--------+
If you really need the first column with the names just use withColumn and you're done.
val betterSolution = solution.select(lit("cc") as "udate", $"*")
scala> betterSolution.show
+-----+--------+--------+--------+--------+--------+
|udate|20090622|20090624|20090626|20090629|20090914|
+-----+--------+--------+--------+--------+--------+
| cc| 458| 31068| 151| 148| 453|
+-----+--------+--------+--------+--------+--------+
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
add a comment |
2
2
2
tl;dr In Spark 2.4.0 it simply boils down to using groupBy alone.
val solution = d.groupBy().pivot("udate").agg(first("cc"))
scala> solution.show
+--------+--------+--------+--------+--------+
|20090622|20090624|20090626|20090629|20090914|
+--------+--------+--------+--------+--------+
| 458| 31068| 151| 148| 453|
+--------+--------+--------+--------+--------+
If you really need the first column with the names just use withColumn and you're done.
val betterSolution = solution.select(lit("cc") as "udate", $"*")
scala> betterSolution.show
+-----+--------+--------+--------+--------+--------+
|udate|20090622|20090624|20090626|20090629|20090914|
+-----+--------+--------+--------+--------+--------+
| cc| 458| 31068| 151| 148| 453|
+-----+--------+--------+--------+--------+--------+
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
tl;dr In Spark 2.4.0 it simply boils down to using groupBy alone.
val solution = d.groupBy().pivot("udate").agg(first("cc"))
scala> solution.show
+--------+--------+--------+--------+--------+
|20090622|20090624|20090626|20090629|20090914|
+--------+--------+--------+--------+--------+
| 458| 31068| 151| 148| 453|
+--------+--------+--------+--------+--------+
If you really need the first column with the names just use withColumn and you're done.
val betterSolution = solution.select(lit("cc") as "udate", $"*")
scala> betterSolution.show
+-----+--------+--------+--------+--------+--------+
|udate|20090622|20090624|20090626|20090629|20090914|
+-----+--------+--------+--------+--------+--------+
| cc| 458| 31068| 151| 148| 453|
+-----+--------+--------+--------+--------+--------+
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
answered Nov 6 '18 at 22:13
Jacek LaskowskiJacek Laskowski
44.7k18132268
44.7k18132268
add a comment |
add a comment |
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