Plurrrr

If you’re using Python’s NumPy library, it’s usually because you’re processing large arrays that use plenty of memory. To reduce your memory usage, chances are you want to minimize unnecessary copying,

NumPy has a built-in feature that does this transparently, in many common cases: memory views. However, this feature can also cause higher memory usage by preventing arrays from being garbage collected. And in some cases it can cause bugs, with data being mutated in unexpected ways.

To avoid these problems, let’s learn how views work and the implications for your code.

In this post, we’re going to dive into the Python datetime module and explore how we can effectively implement date and time data into our Python code.

Regardless of what area of Python programming you’re pursuing, this is a must-know module with an endless amount of uses and tons of professional career applications.

Sometimes when you’re working with several different dictionaries, you need to group and manage them as a single one. In other situations, you can have multiple dictionaries representing different scopes or contexts and need to handle them as a single dictionary that allows you to access the underlying data following a given order or priority. In those cases, you can take advantage of Python’s ChainMap from the collections module.