When I have started coding on transposing Dataframes, I found below different methods. I am sharing all those info here. Creation of a test Input Dataframe to be Transposed ds = {'one':[0.3, 1.2, 1.3, 1.5, 1.4, 1.0],'two':[0.6, 1.2, 1.7, 1.5, 1.4, 2.0]} df = sc.parallelize([ (k,) + tuple(v[0:]) for k,v in ds.items()]).toDF() df.show() method 1 (This method involves conversion of spark object to a python object (rdd to list of tuples of entire data)): inp_list=df.rdd.map(tuple).collect() # Creating a list of tuples of rows # Unpacking the list and zipping all tuples together except the header #list(zip(*inp_list))[1:] is having the transposed tuples and list(zip(*inp_list))[0] is having the header df_transpose=spark.createDataFrame(list(zip(*inp_list))[1:],list(zip(*inp_list))[0]) df_transpose.show() method 2 (In this method only header data we are converting into python list. Rest of the transformations are carried out as