Improve DataFrame documentation and code samples

AND and OR are not straightforward when using the new DataFrame API.

the current convention - accepted by Pandas users - is to use the bitwise & and | instead of AND and OR. when using these, however, you need to wrap each expression in parenthesis to prevent the bitwise operator from dominating.

also, working with StructTypes is a bit confusing. the following link: https://spark.apache.org/docs/latest/sql-programming-guide.html#programmatically-specifying-the-schema (Python tab) implies that you can work with tuples directly when creating a DataFrame.

however, the following code errors out unless we explicitly use Row's:

from pyspark.sql import Row
from pyspark.sql.types import *

# The schema is encoded in a string.
schemaString = "a"

fields = [StructField(field_name, MapType(StringType(),IntegerType())) for field_name in schemaString.split()]
schema = StructType(fields)

df = sqlContext.createDataFrame([Row(a={'b': 1})], schema)