df = spark.createDataFrame([
    (1, "a", "xxx", None, "abc", "xyz","fgh"), 
    (2, "b", None, 3, "abc", "xyz","fgh"),
    (3, "c", "a23", None, None, "xyz","fgh")
], ("ID","flag", "col1", "col2", "col3", "col4", "col5"))

from pyspark.sql.types import *
num_cols = [f.name for f in df.schema.fields if not isinstance(f.dataType, StringType)]

df2 = df.select([c for c in num_cols])
df2.show()

+---+----+
| ID|col2|
+---+----+
|  1|null|
|  2|   3|
|  3|null|
+---+----+