Lazy evaluation is computer programming jargon that refers to data (e.g. variables) that are not fetched/prepared until

If we don’t use list comprehension, we can use a for loop to iterate the range object and append the square to the list object. In the end, both lists have the same items. However, list comprehension is apparently a more concise way to create list objects and should be the preferred way to create a list object if we start with an iterable.

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Youtube videos on how to take good photos. I bought a fancy new camera to shoot videos, but I figured photography would be a good hobby to try since I have the camera and all. I came across a video from this guy who talked about how he banged his head against the wall for years learning photography.

In the initialization method (i.e. __init__), we set the protected attribute (_profile_data) to be None, which simply serves as a placeholder. In many cases, we don’t really need the profile data.

One of the cool Python features is the comprehension technique. Even for beginners, list comprehension is probably one of the advanced techniques that they’ve heard of. The purpose of using list comprehension is to create a list object using an iterable in a very concise way. As a matter of fact, I used the list comprehension in the previous section to create this list of squares:

Virtual environments are a way that we can create isolated workspaces for our projects such that they can have different Python versions and distinct dependency requirements.

Lazy evaluation is a programming technique that allows our program to delay the expensive operations (e.g. a web request, a computationally heavy calculation) as much as possible.

Let’s start with an example in which we build a React application and we want to dockerize it. After running the npx command and creating the Dockerfile, we have a file structure like the one in Picture 1.

However, if we do need to get the profile data, we can wrap it around a property decorator. It will check if the _profile_data is None or not, and it will only run the expensive web request when we don’t have data for the _profile_data attribute.

String interpolation is known as the f-string in Python, which is a more readable way to create formatted strings by having the relevant variable/expression next to the applicable formatting settings.

Lazy evaluation is a programming technique that allows our program to delay the expensive operations (e.g. a web request, a computationally heavy calculation) as much as possible.

Compared to the list counterpart, the generator has only a tiny fraction of the size that the list occupies (96 bytes vs. 9,000 bytes). However, both achieve the same effect — the sums of both are equal.

Docker is a platform for software developers and sysadmins to build, run, and share applications with containers. A container is a process that runs in an isolated environment, on its own filesystem; that filesystem is built using a docker image. Images include everything you need to run an application (compiled code, dependencies, libraries, etc.). Images are defined using a file called Dockerfile.

For another example, generators in Python also take advantage of the lazy evaluation technique. As you may know, generators are a special kind of iterator that render elements when they’re requested. Unlike some common iterators created from lists and dictionaries, generators don’t have all of their elements loaded in the memory, and thus they’re very memory-efficient. It’s all because they hold the state of the iteration and “lazily” render the next applicable item. The following code shows you related usage of generators:

For one example, we can have lazy attributes for our custom class instances. The following code shows you how we can use the property decorator to realize this functionality (please feel free to explore the @cached_property decorator in the functools module for a similar effect):

Normally, images based on Alpine or BusyBox are extremely small in size compared to those based on other Linux distributions, such as Ubuntu. This is because Alpine and those other images have been optimized to include the minimum and must-have packages. In the following image you can see a comparison between the sizes of Ubuntu, Alpine, Node and Node based on the alpine.