Create single row dataframe from list of list PySpark

Question

I have a data like this data = [[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]] I want to create a PySpark dataframe

I already use

dataframe = SQLCo         


        

Answer 1

I find it's useful to think of the argument to createDataFrame() as a list of tuples where each entry in the list corresponds to a row in the DataFrame and each element of the tuple corresponds to a column.

You can get your desired output by making each element in the list a tuple:

data = [([1.1, 1.2],), ([1.3, 1.4],), ([1.5, 1.6],)]
dataframe = sqlCtx.createDataFrame(data, ['features'])
dataframe.show()
#+----------+
#|  features|
#+----------+
#|[1.1, 1.2]|
#|[1.3, 1.4]|
#|[1.5, 1.6]|
#+----------+

Or if changing the source is cumbersome, you can equivalently do:

data = [[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]]
dataframe = sqlCtx.createDataFrame(map(lambda x: (x, ), data), ['features'])
dataframe.show()
#+----------+
#|  features|
#+----------+
#|[1.1, 1.2]|
#|[1.3, 1.4]|
#|[1.5, 1.6]|
#+----------+

Answer 2

You need a map function to convert the tuples to array and use it in createDataFrame

dataframe = sqlContext.createDataFrame(sc.parallelize(data).map(lambda x: [x]), ['features'])

You should get as you desire

+----------+
|  features|
+----------+
|[1.1, 1.2]|
|[1.3, 1.4]|
|[1.5, 1.6]|
+----------+

Answer 3

You should use the Vector Assembler function, from your code I guess you are doing this to train a machine learning model, and vector assembler works the best for that case. You can also add the assembler in the pipeline.

assemble_feature=VectorAssembler(inputCol=data.columns,outputCol='features')
pipeline=Pipeline(stages=[assemble_feature])
pipeline.fit(data).transform(data)