What is a product most frequently bought together with I want to find out what people also buy when they buy a "Bike Helmet". There are only 77 different products in my dataframe.

I might solve this using a for loop, and creating a counter. So whenever ProductName == 'Bike Helmet, look at the OrderID and increase the counter by 1 for all those product names. Then find the next instance of a bike helmet purchase. This approach, however, is slow.  

  OrderID           ProductName  PricePerOrder  ProfitPerOrder  Quantity  \
239    10337           Bike Helmet     805.200000      178.080000        24   
238    10337           Mehmet-Skor       5.200000        0.626667        28   
237    10337          Mehmet-Tröja       7.990000        1.540000        20   
236    10337          Oyaki Kimono      10.025000        2.558333        25   
240    10337           DSW Sandals       3.600000        0.628000        40   
40     10262          Runner Shoes      14.362500        1.946250        15   
42     10262       O-Man Underwear       0.535680       -0.030720        12   
41     10262         Slip-on Shoes       4.606667        0.050667         2   
336    10374           Onesy Dress      38.677500       10.800000        15   
337    10374  Rossi Bermuda Shorts      13.665000        2.370000        30
Two approaches, both based on discrete mathematics. The first one is a set-theoretic approach to frequent item-set mining and association rule mining. For instance A-priori algorithm works here.

the next approach is creating a graph based on orders and do graph mining on them. A-priori might be a better approach as in this case you probably need to deal with hypergraphs which makes life a bit complicated.

I suggest you first extract sets of different orders from your dataframe. As your problem is not general and you already know that there is only association to one product is important, you can prune your data much. Then simply rank other products bought together with Bike Helmet by counting number of their occurences and dividing by number of all (it gives you the probability estimate of each product is bought together with bike helmet).