如何将多个列名重命名为单列？

Question

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?

Answer 1

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

Answer 2

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'))

Answer 3

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

1. Null values
2. Type mismatches
3. String Encoding issues