Pydantic validators are great! The validation is performed as soon as you instantiate your model. But how do we perform validation on modification of a particular attribute or field of the (model) instance after its creation? When we instantiate our model, all the validators run. But when we change the value of a particular (model) […]
Let’s say we have a simple Pydantic model that looks like this: When you instantiate a Pydantic model – post = Post(**data) – all the required attributes defined must be passed, i.e., id, title, body. What if you had one or more of the following requirements: This would mostly require us to have an attribute […]
Dnspython is a robust DNS toolkit for Python that you can use to make DNS queries, resolve CNAMEs, test nameservers, do zone transfers, perform dynamic updates, and much more than you can imagine. In this tutorial, I will cover the basic usage for querying certain types of DNS records for a particular domain (or subdomains) […]
Our Python source code files that contain the Airflow DAG definitions are parsed and processed at certain intervals. It can be useful to know the time intervals and conditions around when new and updated DAG files are (re-)processed by Airflow. Before learning about them and how they can be configured, I’d first suggest reading the […]
Once you’ve caught an exception, you can capture and log a message with the exception info (including full stack traceback), using logging.exception() or logging.error(). With logging.exception(): With logging.error() we must set the exc_info keyword argument to True (default is False): In both cases, the Error occurred message is logged with level ERROR on the root […]
At times you will want to catch an exception and extract the stack trace from the exception object to capture it somewhere (for instance log it). In our codebase, we generally aim to handle all kinds of unexpected or incorrect input and output by putting validations and expecting certain kinds of exceptions (try/except). But there […]
Let’s look at a couple of quick ways to uninstall all the packages that we installed with pip in our environment or system. First of all, to list all the packages that were installed via pip (in requirements format): If we can list all the packages installed, then we can pass that list to pip […]
When we install a package like this – pip install Flask – pip needs to work out Flask’s dependencies, the dependencies of those dependencies, and so on. This recursive process builds a dependency tree or graph where multiple nodes (packages) can point to the same dependency, but different versions. Flask may depend on packages A […]
If you are using Pydantic in Python, which is an excellent data parsing and validation library, you’ll often want to do one of the following three things with extra fields or attributes that are passed in the input data to build the models: Ignore the extra fields or attributes, i.e., they should not be present […]
Trying to print(var) in your pytest tests and wondering why you cannot see the output in your terminal’s standard output? This is because by default pytest captures all output sent to stdout and stderr, i.e., it intercepts the data going into the low-level file descriptors 1 and 2. This is a good idea because pytest […]