Python Program to Read YAML

Differences between JSON, XML, and YAML

Let’s take a look at some of the key differences between JSON, XML, and YAML.

Now we will look at the same data represented in all the 3 formats to illustrate the difference between them.

person:
  name: John Doe
  age: 30
  email: john@example.com
  phone: 123-456-7890

{
  "person": {
    "name": "John Doe",
    "age": 30,
    "email": "john@example.com",
    "phone": "123-456-7890"
  }
}

<person>
  <name>John Doe</name>
  <age>30</age>
  <email>john@example.com</email>
  <phone>123-456-7890</phone>
</person>

As you can see, YAML is significantly easier to read in comparison to XML and JSON.

In the following sections, you will learn how to read YAML data in Python using PyYAML.

Installing PyYAML

We must use a third-party library called PyYAML as Python lacks built-in support for YAML. This library lets you parse YAML files and convert them into Python data structures.

If you use pip or pip3, you can install PyYAML using the following commands:-

pip3 install pyyaml
pip install pyyaml

On successful installation, you will see an output similar to the following.

For Linux distributions, you can use the following commands depending on your package manager:-

sudo apt-get install PyYAML
sudo yum install PyYAML

Python Program to Read YAML

Before getting started, we will create a demo YAML file.

Creating a YAML File

YAML files have the “.YAML” extension; create a “demo.YAML” file and add the following content.

name: John Doe
age: 30
email: johndoe@example.com
interests:
  - Reading
  - Hiking
  - Cooking

For this example, we have used VSCode for Python notebooks. You may use other software such as Jupyter. Also, make sure to set the Python kernel correctly.

Reading YAML

Create a notebook cell and paste the following code inside it.

• Python

Python

import yaml
with open('data.yaml', 'r') as yaml_file:
    yaml_data = yaml.safe_load(yaml_file)
print(yaml_data)

You can also try this code with Online Python Compiler

Run Code

In the code snippet above, we have used the load function with the SafeLoader to convert the contents of our YAML file into corresponding Python data structures. When you run the notebook cell, you should see the following output.

Always ensure that your YAML files are correctly formatted and indented, as YAML relies on it for structuring the data.

Types of Loaders Supported by the load() Function

The load() function supports the following loaders:-

• SafeLoader: This loader only recognizes standard YAML tags and cannot construct arbitrary objects, making it safe to use with untrusted data.
   
• BaseLoader: This loader can only construct basic Python objects such as lists, dictionaries, and Unicode strings.
   
• Loader: This loader supports all predefined tags and can construct an arbitrary Python object, making it unsafe to use with untrusted data.
   

In the next section, you will learn about the basics of YAML.

Basics of YAML

The following are some basics of YAML:-

Indentation

YAML uses indentations to indicate nested elements. It is important to use them correctly as it determines the structure of your data.

Scalar

Scalars are single values such as strings, numbers, or booleans. They are usually written without quotation marks; however, you can use them if necessary.

Example

name: John Doe
age: 30
email: johndoe@example.com

List

List elements are represented by hyphens followed by a space. Each element can be of different types, such as scalars or other data structures.

Example

interests:
  - Reading
  - Hiking
  - Cooking

Dictionary

This data structure is used for storing data in the form of key-value pairs.

Example

person:
  name: John Doe
  age: 30

Comment

You can create comments in a YAML file using the ‘#’ symbol, similar to Python.

Example

name: John Doe #This is a comment
age: 30
email: johndoe@example.com

The following section will teach you how to read key-value pairs from a YAML file.

Reading Values and Keys From a YAML File

Paste the following YAML into the data.YAML file we created earlier. 

- name: John Doe
  age: 30
  email: john@example.com
  address:
    street: 123 Main St
    city: Anytown
    country: USA
- name: Jane Smith
  age: 25
  email: jane@example.com
  address:
    street: 456 Elm St
    city: Another City
    country: Canada
- name: Alex Johnson
  age: 28
  email: alex@example.com
  address:
    street: 789 Oak Rd
    city: Someplace
    country: Australia

Create a new cell in your Python notebook and paste the following code.

• Python

Python

import yaml
with open('data.yaml', 'r') as yaml_file:
    yaml_data = yaml.load(yaml_file, Loader=yaml.SafeLoader)
for obj in yaml_data:
    for key, value in obj.items():
        print(key, ":", value)
    print()

You can also try this code with Online Python Compiler

Run Code

In the code snippet above, we use the outer loop to traverse over the array and the inner loop traverses over each key-value pair. When you run the notebook cell, you should see the following output.

Also read,  python filename extensions

Frequently Asked Questions

What is data serialization?

Data serialization is the process of converting complex data structures into a format that can be easily transmitted and reconstructed from the serialized form. Some common data serialization formats are JSON, XML, and YAML.

Is YAML better than JSON?

YAML is better than JSON in terms of readability and simpler syntax. However, JSON has wider support across different programming languages due to its consistency and strict specification.

How to define types in YAML?

In YAML, data types are inferred based on the content and structure of your data. However, you can explicitly define a type using the ‘!!’ symbol followed by a type name such as int, float, string, or boolean.

Conclusion

In this article, you learned how to read data from a YAML file using Python, some basics of YAML, and some differences between YAML, JSON, and XML.

If you want to learn more about Python and YAML, go through the following articles:-

• YAML for DevOps
   
• Python Introduction

Happy Learning!