{"id":177766,"date":"2024-02-08T10:18:29","date_gmt":"2024-02-08T09:18:29","guid":{"rendered":"https:\/\/liora.io\/en\/?p=177766"},"modified":"2026-08-09T19:38:26","modified_gmt":"2026-08-09T18:38:26","slug":"variable-types-in-python-how-about-clarifying-the-dynamics","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/variable-types-in-python-how-about-clarifying-the-dynamics","title":{"rendered":"Variable types in Python: how about clarifying the dynamics?"},"content":{"rendered":"\n

Python has recently established itself as one of the leading programming languages for Data Science: easy to learn for people with a statistician’s background, well supplied with libraries, capable of anything, it’s a faithful companion for the Data Scientist.<\/strong><\/p>\n\n\n

However, it’s possible to do Data Science at a fairly high level without seeing all its particularities: how many Data Scientists use decorators? Is Python multi-threaded or multi-core?<\/p>\n\n\n

So the other day, trying to answer the question of why the following code displays two different results,<\/p>\n\n\n

\"java\"<\/figure>\n\n\n

I came across this.<\/a><\/p>\n\n\n

\"java\"<\/figure>\n\n\n

For those who, like me, have already worked with Java, this may evoke memories (or even wounds), but for an inexperienced Python user, it may come as a surprise.<\/p>\n\n\n

\n\n

Related articles<\/p>\n\n\n