BIG ANNOUNCEMENT: This week, we're doing our first-ever Bamboo Weekly Community Contest! It's open to all BW subscribers (free and paid). The rules are here, but the basic idea is (a) on a public GitHub repo, solve one of the problems better than I did, or (b) come up with a new, interesting question for this data set and solve it. Put a link to your GitHub repo by Monday morning. I'll announce the winners on Tuesday.
I look forward to seeing your entries, and to learning from you!
And now, back to this week's topic:
I really like cheese, but never expected to hear about it on a financial podcast – let alone in the context of global warming and the blockchain.
Slate Money (https://slate.com/podcasts/slate-money/2026/08/business-trump-defangs-the-corporate-transparency-act) recently discussed how rising temperatures in Italy are causing cows to rest, rather than graze. Less grazing means less milk, and less milk means less cheese.
But wait, it gets worse: The heat is also putting financial pressure on cheese "banks," which lend money to cheesemakers. The collateral for these loans? The cheese itself, which is stored in special cheese vaults during the multi-year aging period. Rising temperatures mean that the banks need to spend more on insulating and cooling their vaults, cutting into their profit margins.
Credito Emiliano Bank apparently has more than half a million wheels of Parmesan in its vaults. These vaults are so full that some cheesemakers are allowed to keep the cheese at their own facilities. The cheese bank uses blockchain technology to track the cheese and ensure it isn't sold secretly or prematurely.
You can read more about this here: https://edition.cnn.com/2026/05/02/food/italy-cheese-bank-parmigiano-reggiano-intl
This week, we'll look at some of the factors affecting this cheese crisis, evaluating trends in temperature, milk production, and electricity usage.
Data and five questions
I didn't have any direct information about Italian cheese production. I thus found several data sets that, together, can help us to better understand what's going on.
First, we'll look at the max daily temperature information for the cheesemaking cities of Parma and Reggio Emilia, Italy from 1950 until today. Those are the cities mentioned in the story. When researching this issue of BW, I accidentally started to look at Reggio Calabria, in the very south of Italy. No famous cheeses are made there (so far as I know), but it'll be interesting to compare a city in Italy's south with the two northern, more famous ones.
To get this, go to https://open-meteo.com/en/docs/historical-weather-api, giving you access to historical data. You'll want:
- From January 1, 1950 until today
- Time zone is Europe/Berlin, aka Central European Time (and yes, we would normally specify it as Europe/Rome, but that isn't an option in their menu)
- You want the daily maximum temperature
- Specify three different cities:
- Parma: Latitude 44.802905123655684, longitude 10.32299074087261
- Reggio Emilia: Latitude 44.70736734971976, longitude 10.635165657656081
- Reggio Calabria: Latitude 38.11539126274953, longitude 15.666539088745575
For each set of coordinates, you can then download either a CSV file or an Excel file.
Next, we'll look at how much milk is being produced, as reported by Eurostat (https://ec.europa.eu/eurostat/). Go to their site for apro_mk_colm, the measurement for the quantity of milk being provided:
- Location: Italy
- Product obtained
- Raw cows' milk delivered to dairies
- Unit of measure: Thousands of tonnes
I downloaded an Excel file with footnotes and summary in a separate sheet, using the "download" button.
Finally, I downloaded wholesale electricity price data for the EU from Ember. I retrieved the CSV file with monthly price data from https://files.ember-energy.org/public-downloads/price/outputs/european_wholesale_electricity_price_data_monthly.csv .
Paid subscribers, both to Bamboo Weekly and to my LernerPython+data membership program (https://LernerPython.com) get all of the questions and answers, as well as downloadable data files, downloadable versions of my notebooks, one-click access to my notebooks, and invitations to monthly office hours.
Learning goals for this week include working with Excel and CSV files, dates and times, data cleaning, joins, and plotting.
Here are my five questions and tasks for this week. I'll be back tomorrow with my solutions and explanations:
- Download the daily max temperature information for all three cities from open-meteo.com, and put them into a single Pandas data frame. The index should be the dates as
datetimevalues. Create a line plot showing the annual mean for the max temperature in each city. - Calculate the mean of each city's max temperatures in each decade. Have the temperatures changed over time? Create a bar plot showing the mean of these max temps for each decade. Repeat these queries, but only between the months of April and October; does that make any difference? Do the cities' temperatures seem to have changed to a similar degree?