In this example, we will learn how to specify the schema programmatically:

import pyspark.sql.types as typ

sch = typ.StructType([
      typ.StructField('Id', typ.LongType(), False)
    , typ.StructField('Model', typ.StringType(), True)
    , typ.StructField('Year', typ.IntegerType(), True)
    , typ.StructField('ScreenSize', typ.StringType(), True)
    , typ.StructField('RAM', typ.StringType(), True)
    , typ.StructField('HDD', typ.StringType(), True)
    , typ.StructField('W', typ.DoubleType(), True)
    , typ.StructField('D', typ.DoubleType(), True)
    , typ.StructField('H', typ.DoubleType(), True)
    , typ.StructField('Weight', typ.DoubleType(), True)
])

sample_data_rdd = sc.textFile('../Data/DataFrames_sample.csv')

header = sample_data_rdd.first()

sample_data_rdd = (
    sample_data_rdd
    .filter(lambda row: row != header)
    .map(lambda row: row.split(','))
    .map(lambda row: (
                int(row[0])
                , row[1]
                , int(row[2])
                , row[3]
                , row[4]
                , row[5]
                , float(row[6])
                , float(row[7])
                , float(row[8])
                , float(row[9])
        )
    )
)

sample_data_schema = spark.createDataFrame(sample_data_rdd, schema=sch)
sample_data_schema.show()