# π Plato's Pizza Sales

You are working as a π Data Analyst π at Plato's New York Style Pizza Kitchen, the most π₯ lit π₯ pizza joint in Brooklyn. Sales are slumping, and while we've been collecting transactional data, your boss, Plato π, is a data boomer π§ββοΈ. Plato needs you to grab a bigger slice of the market and find a recipe for success in the data.

Well, this is sample data that Deepnote gives you to work with, as soon as you set up an account. I'm going to assume a few things about these columns and this data, and try to answer the following questions. (These questions also come with the data.)

It looks like Classic pizzas sell the most, and Veggies sell the least.

It looks like column01 has the Order ID, and the dataframe has one record per order item. Grouping by order ID and averaging the number of rows should be good enough.

There are just over 2 pizzas on average per order. This, however, does not distinguish between pizza sizes (column08), which might be interesting to look at.

So, it seems column07 has the total cost or value for each item in that order. We can sum up column07 to get the total sales for each order.

It looks like Order 144 was the largest order by value (order value of 238.45).

"column10" seems most appropriate for this. It seems to list the ingredients in each pizza, so we can check how many of them contain pineapples.

That's 48 more pizzas sold than ideal, which have pineapples on them.

Anyway, that's about it. Deepnote seems cool :)

Thanks for checking this out. Please share any feedback you might have, I'd be glad to hear from you.