I have a DataFrame like this;

```
df = spark.createDataFrame([
  [["Apple"],['iPhone EE','iPhone 11', 'iPhone 11 Pro']],   
  [["Acer"],['Iconia Talk S','liquid Z6 Plus']],   
  [["Casio"],['Casio G\'zOne Brigade']],
  [["Alcatel"[,[]],
  [["HTC", "Honor"].["Play 4", "Play 7"]]
]).toDF("brand","type")

```

And a csv like this;

```
Apple;iPhone EE
Apple;iPhone 11 Pro
Apple;iPhone XS
Acer;liquid Z6 Plus
Acer;Acer Predator 8
Casio;Casio G'zOne Ravine
Alcatel;3L
HTC;Play 4
Honor;Play 7

```

I need to create a new boolean column `match`.  

If the combination of `brand` **and** `type` matches one of the rows from the CSV it's `True` otherwise `False`.

Expected output:

```
    Brand      | Type                                  | Match
    -------------------------------------------------------------
    Apple      | [iPhone EE, iPhone 11, iPhone 11 Pro] | True
    Acer       | [Iconia Talk S, liquid Z6 Plus]       | True
    Casio      | [Casio G\'zOne Brigade]               | False
    Alcatel    | []                                    | False
    HTC, Honor | [Play 4, Play 7]                      | True

```

**Update  

`brand` is also of type `array<string>`**

The csv file is just a start. it can be converted to a Dataframe or Dictionary (or whatever fits best).

How can I best accomplish this?