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online orders (olo) +vizualizations =oloviz

In this project I have analyzed all the food orders I have placed in the last 9 months usingusingfoodpanda 🐼,Domino's 🍕,UberEats 🚗 orDeliveroo 😋

I have written a blog post for the detailed analysis which can be foundhere.

You can also take a look at theTableau Workbook for this project that contains all the visualizations.

• get_foodpanda_olo.py - Script for scraping foodpanda orders
• get_deliveroo_olo.py - Script for scraping deliveroo orders
• merge_all_olo.py - Script for combining orders from foodpanda, deliveroo, ubereats and domino's

• requests
• beautifulsoup
• pandas

• Go to the URL of order history for website you are scraping data.

Eg.https://www.foodpanda.sg/orders

• Then, open the Network's Tab in Developer Console

• Copy the request for the web page as CURL command

• Use thislittle tool to get the python code

• Use the cookies obtained from above in your code

For storing your cookies -

Inmain, there is a list of cookies -

cookies= ['cookies for user 1','cookies for user 2']

If there are more than one users (as in my case), store cookies of all those users in a python list (separated by,) as shown above. If there is only one user just save your cookie in that list.

For this script, update the below code snippet in the script with your own cookies.

cookies= {'cookie':'your cookies here',    }

• I did not scrape orders from UberEats and Domino's and rather wrote them in manually, so you won't find script for scraping those websites.

• I save csv files for orders of all the services that I have used and finally use themerge_all_olo.py to create a consolidated file of ordersfinal_orders.csv that I later import into Tableau for creating vizualizations.

• Accommodate for multiple users inget_deliveroo_olo.py - same as foodpanda script, mantain a list of cookies.
• Use selenium to scrape domino's orders.
• Cleanup and makeItems Ordered more structured so that it easy to anayze that data field.
• Use a food database API to gather nutrition value of each meal and items.

MIT ©Chaitanya Gupta