I have a data-frame with 3 columns and every entry is a dense vector of same length. How can I melt the Vector entries?
Current data-frame:
column1 | column2 |
[1.0,2.0,3.0]|[10.0,4.0,3.0]
[5.0,4.0,3.0]|[11.0,26.0,3.0]
[9.0,8.0,7.0]|[13.0,7.0,3.0]
Expected:
column1|column2
1.0 . 10.0
2.0 . 4.0
3.0 . 3.0
5.0 . 11.0
4.0 . 26.0
3.0 . 3.0
9.0 . 13.0
...
Step 1: Let's create the initial DataFrame:
myValues = [([1.0,2.0,3.0],[10.0,4.0,3.0]),([5.0,4.0,3.0],[11.0,26.0,3.0]),([9.0,8.0,7.0],[13.0,7.0,3.0])]
df = sqlContext.createDataFrame(myValues,['column1','column2'])
df.show()
+---------------+-----------------+
| column1| column2|
+---------------+-----------------+
|[1.0, 2.0, 3.0]| [10.0, 4.0, 3.0]|
|[5.0, 4.0, 3.0]|[11.0, 26.0, 3.0]|
|[9.0, 8.0, 7.0]| [13.0, 7.0, 3.0]|
+---------------+-----------------+
Step 2: Now, explode both the columns, but after we zip the arrays. Here we know before hand that the length of list/array is 3.
from pyspark.sql.functions import array, struct
tmp = explode(array(*[
struct(col("column1").getItem(i).alias("column1"), col("column2").getItem(i).alias("column2"))
for i in range(3)
]))
df=(df.withColumn("tmp", tmp).select(col("tmp").getItem("column1").alias('column1'), col("tmp").getItem("column2").alias('column2')))
df.show()
+-------+-------+
|column1|column2|
+-------+-------+
| 1.0| 10.0|
| 2.0| 4.0|
| 3.0| 3.0|
| 5.0| 11.0|
| 4.0| 26.0|
| 3.0| 3.0|
| 9.0| 13.0|
| 8.0| 7.0|
| 7.0| 3.0|
+-------+-------+
