Python Project: Analyze My Uber Eats Data Using Pandas

I had fun analyzing my uber eats data for the past year using Jupyter Notebook in Anaconda.  I have included my python code and results below. 

I had to clean the data because the data is organized by item ordered and thus total order number contains duplicates. I removed duplicate rows using duplicate Order IDs. I removed all the zero orders and zero items because canceled items show up in the data. 

Interesting Points:
1. My most ordered item was Hash Browns from McDonald's: 40 times. 
2. Second most ordered item was Sausage McMuffins: 36 times. 
3. I spent $4372 from April 2022 to May 2023. 
4. The most expensive item I purchased was the eight piece family meal from Popeye's: $56.69.
5. The most expensive order was a large multiple item breakfast from a local diner: $93.68.
6. Total Orders: 127.
7. Average order price: $34.