Refactoring existing code effectively is an art. If you have ever inherited someone else’s code or even looked back at y

APIs are incredibly powerful and exciting tools that make up a large part of modern technology. Knowing what they are and how to use them is a vital skill that every developer should have in their arsenal.,In the example below, we have some data about a person on a list. This data includes basic information like the person’s name, age, and profession. Let’s assume for the sake of this example that this is part of a response we received back from a web request and that we’d have to parse multiple responses just like this. If we wanted to get the person’s name, we could simply access it by the index value:,You might be thinking, Elle, this is quite a lot of information I need to gather before I access an API. Where am I supposed to find it all? Well, I would advise you to get familiar with the API’s documentation. In the documentation of any API, the developers should detail how to get an API key, what the rate limit is (if there is one), and information on the API’s endpoints.,Because, at the end of the day — especially given the current state of the world — “the most important thing for most individuals is not the workout itself, it’s how much they move,” says Gibala. “Keep the big picture in mind: However you can accumulate your ideally 150 minutes a week, it all counts.”,Put plainly, an API (or ‘Application Programming Interface’) is an intermediary software that allows for communication between two applications. Think of them like a waiter; you sit down at a restaurant and order some food from the menu (you are an application), you convey your order request to the waiter (the API), who then communicates your request for food (data) to the kitchen (the second application). When your food (data) is ready, the waiter (the API) brings it straight to your table. That’s exactly what an API does — manages requests for data and responses between two applications.,In this article, we’re going to look at some small segments of Python code that make refactoring for cleaner code easier. In most cases, these use simple, standard library functions and don’t obfuscate intent behind exceedingly complex algorithms or dense one-liners. When you’re refactoring code, you want a good balance between being concise and readable. Let’s take a look at a few examples.,To build a machine learning model the first step is to prepare the dataset. Data visualization, feature engineering, and extracting important features can be an important part of data preparation. Null values management is also a very crucial step specifically in the small datasets. We imputed missing data in this dataset using ML prediction to save more data. The second main important step is to build a model and validate it. Hyper-parameters are also important to be chosen carefully for efficient model performances. We employed a grid search to find out the optimized parameters. Finally, model evaluation is the most important task in ML model production. We mainly start with simple evaluation metrics and then narrow down to specific and more detailed metrics to understand our model’s strengths and weaknesses.,There are a bunch of APIs out there (I listed my favorites here, and there is a great list of free public APIs here). While there is no end to the streams of ideas and projects that could stem from utilizing these APIs, if you want to start getting into the wonders of consuming data from APIs then you should read on to learn how to use them.,This is sloppy and hides intent if you ever reference it more than once. Referencing person[0] all over the place in your code will make it difficult to interpret what value that should be. There is a very simple fix for this that does not hide your intent and makes your code more readable. Simply assign this to a variable and, if possible, destructure the entire list into appropriately named variables as well. Let’s look at an example of putting this into practice:,Now if you need to reference the name later, you will be doing so via a named variable vs. an index value. This works well for response data that you’ll likely know the structure of. As long as the person’s information is always in the same order, you can destructure and assign everything over and over again to avoid using index values.,You’ve probably heard about this concept a million times before, but it bears repeating: Use destructuring assignment where possible to display values and convey intent more clearly. When you access a list or an array by index values, you inadvertently hide your intent. Referencing values by a clear variable name assigned through destructuring assignment makes your code easier to read.,There are a few nuances to explore when it comes to approaching APIs for the first time. When APIs are created, they can be secured using forms of authentication and unique codes that identify who is calling the API. These are put in place so that the owners of the API can keep track of who is using and/or consuming from the API, thereby preventing malicious use and abuse. The API that you plan to use will determine what you will need to do in order to get access. Most APIs will require you to register for an API key and use this as an identifier for every call that you make to the API. Note: see below for a brief on API keys.,If you encounter sections of code where you won’t necessarily know the name of a function you wish to invoke until later, this is the answer. By using the built-in Python function getattr, you can call a function by name using a string. This is great for reducing tightly coupled code and improving extensibility. If you reduce hard-coded function names and allow more flexibility, then there is a strong chance your code will break less.,Using this simple method also reduces the overhead for other developers to add functionality to existing code. If all you have to do is add a new function to a class and it becomes immediately usable, that reduces complexity and makes code easier work with.,Secondly, API owners sometimes enforce a limit on the number of requests that can be made to the API in a given time-frame in order to prevent users from overwhelming the service. This is called a ‘rate-limit’, and it varies from API to API.