This question already has an answer here:
The case is really simple, I need to convert a python list into data frame with following code
from pyspark.sql.types import StructType
from pyspark.sql.types import StructField
from pyspark.sql.types import StringType, IntegerType
schema = StructType([StructField("value", IntegerType(), True)])
my_list = [1, 2, 3, 4]
rdd = sc.parallelize(my_list)
df = sqlContext.createDataFrame(rdd, schema)
df.show()
it failed with following error:
raise TypeError("StructType can not accept object %r in type %s" % (obj, type(obj)))
TypeError: StructType can not accept object 1 in type <class 'int'>
This solution is also an approach that uses less code, avoids serialization to RDD and is likely easier to understand:
from pyspark.sql.types import IntegerType
# notice the variable name (more below)
mylist = [1, 2, 3, 4]
# notice the parens after the type name
spark.createDataFrame(mylist, IntegerType()).show()
NOTE: About naming your variable list: the term list is a Python builtin function and as such, it is strongly recommended that we avoid using builtin names as the name/label for our variables because we end up overwriting things like the list() function. When prototyping something fast and dirty, a number of folks use something like: mylist.
user15051990
Please see the below code:
from pyspark.sql import Row
li=[1,2,3,4]
rdd1 = sc.parallelize(li)
row_rdd = rdd1.map(lambda x: Row(x))
df=sqlContext.createDataFrame(row_rdd,['numbers']).show()
df
+-------+
|numbers|
+-------+
| 1|
| 2|
| 3|
| 4|
+-------+
来源：https://stackoverflow.com/questions/48448473/pyspark-convert-a-standard-list-to-data-frame