See,

There are two ways to convert an RDD to DF in Spark.

toDF() and createDataFrame(rdd, schema)

I will show you how you can do that dynamically.

toDF()

The toDF() command gives you the way to convert an RDD[Row] to a Dataframe. The point is, the object Row() can receive a **kwargs argument. So, there is an easy way to do that.

from pyspark.sql.types import Row#here you are going to create a functiondef f(x):    d = {}    for i in range(len(x)):        d[str(i)] = x[i]    return d#Now populate thatdf = rdd.map(lambda x: Row(**f(x))).toDF()

This way you are going to be able to create a dataframe dynamically.

createDataFrame(rdd, schema)

Other way to do that is creating a dynamic schema. How?

This way:

from pyspark.sql.types import StructTypefrom pyspark.sql.types import StructFieldfrom pyspark.sql.types import StringTypeschema = StructType([StructField(str(i), StringType(), True) for i in range(32)])df = sqlContext.createDataFrame(rdd, schema)

This second way is cleaner to do that...

So this is how you can create dataframes dynamically.

I liked Arun's answer better but there is a tiny problem and I could not comment or edit the answer. sparkContext does not have createDeataFrame, sqlContext does (as Thiago mentioned). So:

from pyspark.sql import SQLContext# assuming the spark environemnt is set and sc is spark.sparkContext sqlContext = SQLContext(sc)schemaPeople = sqlContext.createDataFrame(RDDName)schemaPeople.createOrReplaceTempView("RDDName")

Try if that works

sc = spark.sparkContext# Infer the schema, and register the DataFrame as a table.schemaPeople = spark.createDataFrame(RddName)schemaPeople.createOrReplaceTempView("RddName")