I have a table which has columns [col1, col2, col3.... col9]. I want to merge all the columns data into one column as col in python?
from pyspark.sql.functions import concat
values = [('A','B','C','D'),('E','F','G','H'),('I','J','K','L')]
df = sqlContext.createDataFrame(values,['col1','col2','col3','col4'])
df.show()
+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
| A| B| C| D|
| E| F| G| H|
| I| J| K| L|
+----+----+----+----+
req_column = ['col1','col2','col3','col4']
df = df.withColumn('concatenated_cols',concat(*req_column))
df.show()
+----+----+----+----+-----------------+
|col1|col2|col3|col4|concatenated_cols|
+----+----+----+----+-----------------+
| A| B| C| D| ABCD|
| E| F| G| H| EFGH|
| I| J| K| L| IJKL|
+----+----+----+----+-----------------+
using Spark SQL
new_df=sqlContext.sql("SELECT CONCAT(col1,col2,col3,col3) FROM df")
Using Non Spark SQL way you can use Concat function
new_df = df.withColumn('joined_column', concat(col('col1'),col('col2'),col('col3'),col('col4'))
In Spark(pySpark) for reasons, there is no edit of existing data. What you can do is create a new column. Please check the following link.
How do I add a new column to a Spark DataFrame (using PySpark)?
Using a UDF function , you can aggregate/combine all those values in a row and return you as a single value.
Few cautions, please look out for following data issues while aggregation
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