Since most people have a job to do and tend to copy-paste some example code quickly, I wanted them to use the safest method of parsing YAML with Python. I intentionally didn’t tell you about the load() function until now. You will encounter many examples of PyYAML usage where load() is used instead of safe_load(). How to convert JSON to YAML with Python PyYAML safe_load() vs load() Here’s the same example, but interactive, so you can experiment with this for yourself: This means all the regular dictionary features can be used, like using get() with a default value. The YAML parser returns a regular Python object that best fits the data. Please note that I changed the output a little to make it more readable for you: > import yaml Next, we parse it with the yaml.safe_load() function. Loading, parsing, and using this configuration file is similar to loading JSON with the Python JSON library. Let’s work with the following example YAML file called config.yaml: rest: url: "" port: 8443 prime_numbers: Code language: YAML ( yaml ) Once we have the YAML parser imported, we can load a YAML file and parse it. Note that you don’t import pyyaml, but simply yaml: import yaml Code language: Python ( python ) Reading and parsing a YAML file with Python To use PyYAML in your scripts, import the module as follows. On some systems, you need to use pip3: pip3 install pyyaml Code language: Bash ( bash ) Use the following command to install PyYAML, preferably in a virtual environment: pip install pyyaml Code language: PowerShell ( powershell ) PyYAML is not part of the standard Python library, meaning you must install it with Pip. However, PyYAML is the most prevalent and complete implementation for parsing YAML. Multiple Python packages can parse YAML data. If you’re looking for a suitable data format for data exchange and storage, I recommend JSON, XML, or other more efficient formats like protocol buffers and Avro. It’s perhaps too versatile for simple use cases, like data exchange of simple objects.Its dependence on indentation is frustrating sometimes (however, Python developers are used to that, right?).YAML is not part of the standard Python library, while XML and JSON are.There are some downsides to using YAML with Python, too: ini files, but is still nice on the eyes and simple to write and parse. It has a richer syntax than the often used alternative. Others seem to agree, as many large projects, like Docker and Kubernetes, use YAML to define deployments. That’s exactly how I, and many other developers, use it the most. If you ask me, YAML is perfect for configuration files. A feature often used in Kubernetes definitions.ġ) Fun fact: the official YAML website is written as valid YAML files. You can store multiple documents in one YAML file with the - separator.These are the most prominent features of YAML: At the same time, it’s also easy to parse YAML, especially with Python and the PyYAML library! Its human-friendliness and readability is YAML’s biggest advantage over other formats, like JSON and XML. YAML is easy to write and read for humans, even for non-programmers. YAML is most often used for configuration files, but it’s also used for data exchange. According to the official site (1), YAML is a human-friendly data serialization language for all programming languages.
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