问题
Suppose I have a list of columns, for example:
col_list = ['col1','col2']
df = spark.read.json(path_to_file)
print(df.columns)
# ['col1','col2','col3']
I need to create a new column by concatenating col1 and col2. I don't want to hard code the column names while concatenating but need to pick it from the list.
How can I do this?
回答1:
You can use pyspark.sql.functions.concat() to concatenate as many columns as you specify in your list. Keep on passing them as arguments.
from pyspark.sql.functions import concat
# Creating an example DataFrame
values = [('A1',11,'A3','A4'),('B1',22,'B3','B4'),('C1',33,'C3','C4')]
df = sqlContext.createDataFrame(values,['col1','col2','col3','col4'])
df.show()
+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
| A1| 11| A3| A4|
| B1| 22| B3| B4|
| C1| 33| C3| C4|
+----+----+----+----+
In the concat() function, you pass all the columns you need to concatenate - like concat('col1','col2'). If you have a list, you can un-list it using *. So (*['col1','col2']) returns ('col1','col2')
col_list = ['col1','col2']
df = df.withColumn('concatenated_cols',concat(*col_list))
df.show()
+----+----+----+----+-----------------+
|col1|col2|col3|col4|concatenated_cols|
+----+----+----+----+-----------------+
| A1| 11| A3| A4| A111|
| B1| 22| B3| B4| B122|
| C1| 33| C3| C4| C133|
+----+----+----+----+-----------------+
来源：https://stackoverflow.com/questions/54921359/concat-multiple-columns-of-a-dataframe-using-pyspark