Last week, about $6 billion in bitcoin was stolen from Binance, one of the world's largest cryptocurrency exchanges. This might well be the largest-ever heist of crypto. Experts are pointing to North Korea as the culprit. I learned about it from several sources, including the Slate Money podcast (https://slate.com/podcasts/slate-money/2026/10/artificial-intelligence-anthropic-wall-street-stock-market-ipo-bitcoin-heist-binance) and the New York Times (https://www.nytimes.com/2026/10/30/business/binance-hack-crypto-price-what.html?unlocked_article_code=1.N1E.CRBJ.CCbv-ldQ_zXw&smid=url-share).
This week, we'll thus look at previous crypto heists. We'll see who was hacked, how much the heists were worth, how much money was returned, and whether such crimes are increasing over time. We'll also explore some of the new functionality that was included in Pandas 3.1, which was released on October 22nd (https://pandas.pydata.org/docs/dev/whatsnew/v3.1.0.html).
Data and five questions
This week's data comes from DeFiLlama (https://defillama.com/), a crypto analytics site. They provide a free API at https://api.llama.fi/hacks, which returns JSON data about famous (and known) crypto heists. This will be our data set for this week.
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: Retrieving from APIs, working with JSON data, dates and times, grouping, plotting with Plotly, and new Pandas 3.1 functionality.
Here are my five questions and tasks for this week. I'll be back tomorrow with my solutions and explanations.
- Retrieve the data from DeFiLllama's API, and turn it into a Pandas data frame in which
dateis the index. Create a line plot showing, for each year, how many hacks were reported. Do we see a trend? - Create a line plot showing, for each year, the mean and median amounts of money (in USD) involved with each hack. Are the thieves getting more from each heist over time? Or are they just robbing more often?