[SPARK-5704] [SQL] [PySpark] createDataFrame from RDD with columns

Deprecate inferSchema() and applySchema(), use createDataFrame() instead, which could take an optional `schema` to create an DataFrame from an RDD. The `schema` could be StructType or list of names of columns.

Author: Davies Liu <davies@databricks.com>

Closes #4498 from davies/create and squashes the following commits:

08469c1 [Davies Liu] remove Scala/Java API for now
c80a7a9 [Davies Liu] fix hive test
d1bd8f2 [Davies Liu] cleanup applySchema
9526e97 [Davies Liu] createDataFrame from RDD with columns

1 files changed, 2 insertions, 2 deletions

diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py
index 7f5c68e3d0..47202fde75 100644
--- a/examples/src/main/python/sql.py
+++ b/examples/src/main/python/sql.py
@@ -31,7 +31,7 @@ if __name__ == "__main__":
                               Row(name="Smith", age=23),
                               Row(name="Sarah", age=18)])
     # Infer schema from the first row, create a DataFrame and print the schema
-    some_df = sqlContext.inferSchema(some_rdd)
+    some_df = sqlContext.createDataFrame(some_rdd)
     some_df.printSchema()
 
     # Another RDD is created from a list of tuples
@@ -40,7 +40,7 @@ if __name__ == "__main__":
     schema = StructType([StructField("person_name", StringType(), False),
                         StructField("person_age", IntegerType(), False)])
     # Create a DataFrame by applying the schema to the RDD and print the schema
-    another_df = sqlContext.applySchema(another_rdd, schema)
+    another_df = sqlContext.createDataFrame(another_rdd, schema)
     another_df.printSchema()
     # root
     #  |-- age: integer (nullable = true)