According to the docs of

spark.read.csv(...)

the

path

argument can be an RDD of strings:

path : str or list
     string, or list of strings, for input path(s), or RDD of Strings storing CSV rows.

With that, you may use

spark.sparkContext.textFile(...)

in combination with

zipWithIndex(...)

to perform the necessary row filtering. Putting things together this may look as follows:

n_skip_rows = ?
row_rdd = spark.sparkContext
    .textFile(your_csv_file) \
    .zipWithIndex() \
    .filter(lambda row: row[1] >= n_skip_rows) \
    .map(lambda row: row[0])
df = spark_session.read.csv(row_rdd, ...)

Hope that helps.