Convert a standard python key value dictionary list to pyspark data frame

Question

Consider i have a list of python dictionary key value pairs , where key correspond to column name of a table, so for below list how to convert it into a pyspark dataframe with two cols arg1 arg2?

 [{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]

How can i use the following construct to do it?

df = sc.parallelize([
    ...
]).toDF

Where to place arg1 arg2 in the above code (...)

Answer 1

Old way:

sc.parallelize([{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]).toDF()

New way:

from pyspark.sql import Row
from collections import OrderedDict

def convert_to_row(d: dict) -> Row:
    return Row(**OrderedDict(sorted(d.items())))

sc.parallelize([{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]) \
    .map(convert_to_row) \ 
    .toDF()