Reuven M. Lerner

Jupyter notebooks have long been the standard among people working with data. But over the years, we've seen that their have a number of limitations, too.

We use assignment all the time in Python. But what really happens when we use an = in our code?

Pandas has long used NumPy for its back-end storage. But things are changing, and the future of Pandas will likely be tied closely with PyArrow.

Pandas has long used NumPy for its back-end storage. But things are changing, and the future of Pandas will likely be tied closely with PyArrow.

Pandas is famously flexible and capable at analyzing numeric data. But Pandas is also flexible and capable at working with times and dates.

If you're like me, then you've long known about Python's "logging" module, but you've ignored it because it seemed too complex.

Generator functions have been a part of Python for many years already, and are a well known technique for creating iterators.

It's a rare program that doesn't include at least one "import" statement. But what actually happens when we import a module?

Generator functions have been a part of Python for many years already, and are a well known technique for creating iterators.

When you invoke a function, how does it know how many arguments to expect? How does it distinguish between local and global (and enclosing) variables?

Compiler and Interpreters Data Structures Programming Python Skills Python general

Decorators are one of Python's most powerful features. But even if you understand what they do, it's not always obvious what you can do with them.