How to read csv without header and name them with names while reading in pyspark?

You can import the csv file into a dataframe with a predefined schema. The way you define a schema is by using the StructType and StructField objects. Assuming your data is all IntegerType data:

from pyspark.sql.types import StructType, StructField, IntegerType

schema = StructType([
    StructField("member_srl", IntegerType(), True),
    StructField("click_day", IntegerType(), True),
    StructField("productid", IntegerType(), True)])

df = spark.read.csv("user_click_seq.csv",header=False,schema=schema)

should work.

For those who would like to do this in scala and may not want to add types:

val df = spark.read.format("csv")
                   .option("header","false")
                   .load("hdfs_filepath")
                   .toDF("var0","var1","var2","var3")

You can read the data with header=False and then pass the column names with toDF as bellow:

data = spark.read.csv('data.csv', header=False)
data = data.toDF('name1', 'name2', 'name3')