SparkContext doesn't have, SQLContext has:

from pyspark.sql import SQLContext

sqlContext = SQLContext(sc)
sqlContext.createDataFrame(pandas_df)

SparkSession is not a replacement for a SparkContext but an equivalent of the SQLContext. Just use it use the same way as you used to use SQLContext:

spark.createDataFrame(...)

and if you ever have to access SparkContext use sparkContext attribute:

spark.sparkContext

so if you need SQLContext for backwards compatibility you can:

SQLContext(sparkContext=spark.sparkContext, sparkSession=spark)

You can try this way:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName('so')\
    .getOrCreate()

sc = spark.sparkContext

map = {'a': 3, 'b': 44}
data = sc.parallelize([(k, v) for k, v in map.items()]).toDF(['A', 'B'])

data.show()

# +---+---+
# |  A|  B|
# +---+---+
# |  a|  3|
# |  b| 44|
# +---+---+

getOrCreate is a method on SparkSession.Builder. You need to invoke it by adding the parentheses after:

spark = SparkSession.builder.master("local").appName("Search").config(conf=SparkConf()).getOrCreate()

See for more information: https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/sql/SparkSession.html

In general, the 'function' object has no attribute error is very common when you are accidentally referencing a function rather than invoking it.

Instead of

nums = sc.map([23,232,1,232,43,43])

try

nums = sc.parallelize([23,232,1,232,43,43])

That tells Spark to distribute the sequence over the cluster and create an RDD out of it. You can then call map with a lambda function over the RDD, as you do in the next line.

This should work (except in the code you have a missing ')' in the end of sc creation which I imagine is a type). You can try creating sc as follows:

conf = SparkConf().setAppName("app1").setMaster("local")
sc = SparkContext(conf=conf)

BTW sc.stop means you already have a spark context which is true if you used pyspark but not if you use spark-submit. It is better to use SparkContext.getOrCreate which works in both cases.