How to skip lines while reading a CSV file as a dataFrame using PySpark?

I have a CSV file that is structured this way:

Header
Blank Row
"Col1","Col2"
"1,200","1,456"
"2,000","3,450"

I have two problems in reading this file.

1. I want to Ignore the Header and Ignore the blank row
2. The commas within the value is not a separator

Here is what I tried:

df = sc.textFile("myFile.csv")\
              .map(lambda line: line.split(","))\ #Split By comma
              .filter(lambda line: len(line) == 2).collect() #This helped me ignore the first two rows

However, This did not work, because the commas within the value was being read as a separator and the len(line) was returning 4 instead of 2.

I tried an alternate approach:

data = sc.textFile("myFile.csv")
headers = data.take(2) #First two rows to be skipped

The idea was to then use filter and not read the headers. But, when I tried to print the headers, I got encoded values.

[\x00A\x00Y\x00 \x00J\x00u\x00l\x00y\x00 \x002\x000\x001\x006\x00]

What is the correct way to read a CSV file and skip the first two rows?