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Project 6: Wine Study

Corrections and Clarifications

  • Oct 14: give better error when question cell has no output


This project is a wine connoisseurs' delight! Data Science can help us understand people's drinking habits around the world. For example, take a look at Mona Chalabi's analysis here: Where Do People Drink The Most Beer, Wine And Spirits?

For our part, we will be exploring a modified subset (the first 1501 rows) of the Kaggle wine reviews dataset; you will be using various string manipulation functions that come with Python as well as rolling some of your own to solve the problems posed. Happy coding, and remember the Ballmer Peak is nothing but a myth!


Be sure to do lab 6 before starting this project; otherwise you probably won't get far.

Begin by downloading wine.csv and Create a main.ipynb file to start answering the following questions, and remember to run often. There is no this week, though we'll suggest some code in the lab that you can use to access the data. Use the #qN format as you have previously.

Q1: which country names are listed in the country column of the dataset?

Your output should be in the form of a Python list containing the country names. The tests don't care about the order, but there should should be no duplicate entries in the lists. Also, some country names are missing in the dataset (real-life data is often messy, unfortunately!). Missing values are represented as None, but you should make sure None does not appear in your answer list.

Now is a good time to run the tests with python If you did Q1 correctly, it should look like this:

  Test 1: PASS
  Test 2: not found
  Test 3: not found
  Test 4: not found
  Test 5: not found
  Test 6: not found
  Test 7: not found
  Test 8: not found
  Test 9: not found
  Test 10: not found
  Test 11: not found
  Test 12: not found
  Test 13: not found
  Test 14: not found
  Test 15: not found
  Test 16: not found
  Test 17: not found
  Test 18: not found
  Test 19: not found
  Test 20: not found


Q2: what is the average points (rating) of wine?

Be careful! There may be missing rating points information for some rows, so it's best to skip those.

Q3: List all wineries which produce wine in South Africa?

Answer in the form of a list containing no duplicates (for this and future questions).

Q4: which wine varieties contain the phrase "caramelized" in the description?

This shoud match anything containing "caramelized (in any case), regardless of spacing.

Q5: which wine varieties contain the phrase "lemon-lime soda" in the description?

Q6: which wine varieties contain the phrase "cherry-berry" in the description?

Q7: which wine varieties are anagrams of the phrase "antibus governance"?

If you liked Professor Langdon's adventures in Da Vinci Code, you'll have fun with this one. :)

An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, using all the original letters exactly once. (Read more here: For our purposes, we'll ignore case and spaces when considering whether two words are anagrams of each other.

Hint: although you'll need to loop over all the names to check for anagrams, checking whether a single word is an anagram of another word does not require writing a loop. So if you're writing something complicated, review the string methods and sequence operations to see if there is a short, clever solution.

Consider writing a function to solve Q7 and Q8 with the same code.

Q8: which wine varieties are anagrams of the phrase "Banned Petrol Mill".

Q9: which winery produces the highest-priced wine in "US"?

Your answer should be in the form of a Python list. If there is a single best, that list should contain that single best variety. If multiple varieties tie for best, the list should contain all that tie.

Consider writing a function to solve Q9 and Q10 with the same code.

Q10: what is the highest-rated wine variety made in "France"?

Q11: what is the average points-per-dollar (PPD) ratio of the "Ponzi" winery?

In this question, we're trying to find the best value using the points (the rating) and price (cost in dollars) columns.

Be careful! You need to compute the ratio for each wine of the given winery, then take the average of those ratios. Simply dividing the sum of all points by the sum of all prices will calculates the wrong answer.

Q12: what is the average PPD of the "Blue Farm" winery?

Q13: which winery in New Zealand has the lowest average PPD?

Consider writing a function to answer this and Q14 and Q15 with the same code. Make sure to return a string if there is only one winery having the lowest PPD, otherwise return a list of wineries. Hint: Suppose 'winery_list' is the list of all wineries having the lowest average PPD. Now, check if the len(winery_list) is 1, then return winery_list[0], otherwise return winery_list.

Q14: which winery in Australia has the lowest average PPD?

Careful, two wineries tie in this one! Answer with a list containing the names of the two wineries.

Q15: which winery in Canada has the lowest average PPD?

Q16: which wine varieties are produced by the "Global Wines" winery?

Produce a Python list with no duplicates.

Q17: which wine varieties are produced by the "Quinta Nova de Nossa Senhora do Carmo" winery?

Q18: what percentage of the varieties produced by "Global Wines" are also produced by "Quinta Nova de Nossa Senhora do Carmo"?

Quinta Nova wants to better understand their competition, so they hired a savvy data scientist (you!) to keep an eye on the competition.

Suppose you are throwing a birthday party for your friends and family guests. You decided to buy one bottle of the costliest wine in the US for family guests and one of the cheapest wine for your friends (but you promise your friends to treat with the costliest wine when you get a job offer).

Q19: what is the cost of cheapest wine that you could find in US?

Ignore the cells, which does not mention the price value.

Q20: What is the total cost of buying the two bottles?


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