BW #94: Strategic Wine Reserve

I recently read a delightful article in FT Alphaville (https://www.ft.com/content/6b8462a4-b7b9-4bc9-af60-4c24e4491791) about the UK's Government Hospitality Wine Cellar (GWC), which the article playfully calls the country's "strategic wine reserve." The GWC has existed since the end of World War I, giving the government a large collection of wine that it can use at government-sponsored events. It seems that they're quite good at buying wine that appreciates over time, such that they are now self funding, selling old, valuable wine to pay for new, less-expensive wine that will appreciate in price and/or taste. The FT article describes their many attempts to extract information about the GWC from the government, followed by an analysis of what they found.

The article is from January, so it doesn't exactly count as current events. But come on, how long do you get to review data about a government's wine cellar? And the notion of a "strategic wine reserve" for the UK ranks up there with Canada's strategic maple syrup reserve, Switzerland's strategic coffee reserve, and China's strategic pork reserve.

The FT article indicated that after a great deal of effort, the reporters managed to get their hands on data having to do with the UK's wine reserve. I looked through the article for where I could download this data, but sadly didn't find any link.

So I did the next-best thing: I e-mailed the reporters, Robin Wigglesworth (https://www.ft.com/robin-wigglesworth) and Louis Ashworth (https://www.ft.com/louis-ashworth), and asked if they would be able to share it with me. To my great delight, they responded immediately, providing me with their spreadsheet – minus the data that they weren't allowed to share.

I'm thus grateful to them not only for writing this fun article, but also for their generosity in sharing this data.

Data and six questions

I have six tasks and questions for you to answer this week. The learning goals include grouping, sorting data, and plotting with Pandas.

You won't have to work hard to find this week's data; I've enclosed it right here:

Here are my questions; I'll be back tomorrow with detailed solutions, including the Jupyter notebook I used to answer these questions:

• Read the Excel data into a Pandas data frame. How many bottles of wine, according to this data set, does the UK have on hand? How many liters of wine does that work out to?
• What is the mix of wine colors? Of countries of origin? Looking at the country with the greatest number of wines in the collection, what is the percentage breakdown of regions within that country?