Reading csv file with DROPMALFORMED showing incorrect record count

The spark sql csv reader is dropping malformed records as expected, but the record count is showing as incorrect.

Consider this file (fruit.csv)

apple,red,1,3
banana,yellow,2,4.56
orange,orange,3,5

Defining schema as follows:

schema = "Fruit string,color string,price int,quantity int"

Notice that the "quantity" field is defined as integer type, but the 2nd row in the file contains a floating point value, hence it is a corrupt record.

>>> df = spark.read.csv(path="fruit.csv",mode="DROPMALFORMED",schema=schema)
>>> df.show()
+------+------+-----+--------+
| Fruit| color|price|quantity|
+------+------+-----+--------+
| apple|   red|    1|       3|
|orange|orange|    3|       5|
+------+------+-----+--------+

>>> df.count()
3

Malformed record is getting dropped as expected, but incorrect record count is getting displayed.

Here the df.count() should give value as 2