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 […]

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. ChainMap groups multiple dictionaries and mappings in a single, updatable view […]

When you’re loading many strings into Pandas, you’re going to use a lot of memory. If you have only a limited number of strings, you can save memory with categoricals, but that’s only helpful in a limited number of situations. With Pandas 1.3, there’s a new option that can save memory on large number of strings as well, simply by changing to a new column type. Let’s see how. Pandas’ different string dtypes Every pandas.Series, and every column in a […]

In this section, you’ll look at three popular frameworks for building REST APIs in Python. Each framework has pros and cons, so you’ll have to evaluate which works best for your needs. To this end, in the next sections, you’ll look at a REST API in each framework. All the examples will be for a similar API that manages a collection of countries. The fields name, capital, and area store data about a specific country somewhere in the world. Most […]