How to Convert a Python Dictionary to YAML

Overview of YAML Serialization Format

YAML (rhymes with “camel”) stands for Yet Another Markup Language. It emerged in the early 2000s as a human-friendly alternative to XML for transmitting or storing structured data in text files and streams.

Some key characteristics of YAML include:

• Human Readable – Uses minimal syntax and intuitive indenting for easy hand editing
• Portable Data – Can be used for cross-language data files and API payloads
• Great for Configs – Often used for configuration files and storing preferences
• Multiple Stylings – Supports JSON-like or more flow-based styles

Unlike formats like JSON, YAML allows more flexibility in how data schemas can be represented – from strict key-value pairs to tabbed outlines and beyond.

This versatility explains YAML’s popularity for cross-platform configuration and data transport.

Python Dictionary Refresher

Before converting, let’s recap how Python dictionaries work.

Dictionaries in Python map unique keys to associated values, similar to hash maps in other languages:

person = {
  "first_name": "John",
  "last_name": "Doe",
  "age": 30  
}

The main operations for interacting with dictionary data include:

• Access Value – person["first_name"] => “John”
• Add New – person["job"] = "Programmer"
• Update – person["age"] = 31
• Delete – del person["last_name"]
• Length – len(person) => 2

Dictionaries are extremely useful for organizing structured information in Python, making them great candidates for YAML conversion.

Using PyYAML to Encode Dictionaries

The PyYAML package provides full YAML support for parsing and emitting content using Python objects.

To install:

pip install pyyaml

Now let’s look at utilizing PyYAML for converting dictionaries:

import yaml

dict_data = {
  "name": "John Smith",
  "age": 30,
  "skills": [
    "Python",
    "JavaScript" 
  ]
}

yaml_output = yaml.dump(dict_data)
print(yaml_output)

Outputs:

name: John Smith
age: 30
skills:
- Python  
- JavaScript

By default, yaml.dump() encodes a Python dict as a YAML string output.

The data is converted following YAML stylistic conventions:

• Key/value pairs on separate lines
• List items indented with hyphens
• Two spaces for nesting/indentation

That covers the basics of translating a dictionary to YAML format!

Customizing YAML Output

You can further configure how your dictionary is styled in the resulting YAML by passing additional arguments to yaml.dump().

For example, to use a more JSON-esque flow style syntax:

yaml_output = yaml.dump(dict_data, default_flow_style=False) 

print(yaml_output)

Gives output:

{name: John Smith, age: 30, skills: [Python, JavaScript]}

By toggling default_flow_style we make the YAML emitter use inline formatting without linebreaks.

Other available options include:

• sort_keys – Sort diction keys alphabetically
• indent – Control spacing for indentation level
• width – Max line width before wrapping

See the complete list of dump() arguments for customizing export.

Handling YAML Parsing Issues

When exporting more complex nested dictionaries, you may occasionally encounter exceptions like:

yaml.serializer.SerializerError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/object/new:...`

This occurs when the YAML emitter struggles to serialize a specific Python object type.

To help handle these serialization issues, you can pass custom representer functions that know how to translate tricky objects.

For example:

def dict_representer(dumper, data):
    return dumper.represent_dict(data.items())

yaml.add_representer(SomeClass, dict_representer)

yaml_output = yaml.dump(obj)

Here we defined a representer to explicitly serialize SomeClass instances as normal dictionaries. The function gets registered to handle translating that object type properly.

With appropriate representers you can dump nearly any custom Python object or class structure using PyYAML.

Complete Python Dictionary to YAML Examples

Let’s now step through some end-to-end examples of converting Python dictionaries to formatted YAML strings for real world usage.

Basic YAML String Conversion

This script accepts a dictionary on command line arguments, encodes it to YAML, and prints the output:

#!/usr/bin/env python
import yaml
import sys
import ast

dict_arg = ast.literal_eval(sys.argv[1])  

yaml_output = yaml.dump(dict_arg, default_flow_style=False)

print(yaml_output)

Save as dict_to_yaml.py and run it:

python dict_to_yaml.py '{"name": "John", "age": 30}' 

Output:
name: John
age: 30

By parsing command line arguments as a dictionary, we can easily accept Python dict data and export as YAML from the terminal.

Multi-Document YAML Output

You can also use the yaml.dump_all() method to export multiple dictionaries as a YAML multi-document stream:

docs = []

doc1 = {"name": "John"}
doc2 = {"age": 30}

docs.append(doc1)  
docs.append(doc2)

yaml_output = yaml.dump_all(docs)

print(yaml_output)

Now the output contains the two YAML documents together:

---
name: John

--- 
age: 30

The document separator --- gets added between each dict represented.

Multi-doc YAML is useful for breaking up larger data flows like API responses while keeping parsing straightforward.

Configuring YAML Styling

Lastly, here is an example making use of YAML’s width and indent arguments to configure document styling:

import yaml

data = {
  "name": {
    "first": "Nancy",
    "last": "Anderson" 
  },
  "job": "Developer",
  "skills": [
    "Python",
    "Go", 
    "JavaScript"
  ]
}

yaml_str = yaml.dump(data, default_flow_style=False, indent=4, width=70)

print(yaml_str)

Output:

    name: 
        first: Nancy
        last: Anderson
    job: Developer 
    skills:
       - Python
       - Go  
       - JavaScript

The increased indent spacing and smaller width makes for an easier to read hierarchical structure.

Tweak these settings until you get YAML output tailored to your application needs.

In Summary

Working with dictionaries is built into any Python developer’s toolbox. Knowing how to properly convert dict data into clean YAML strings helps expand possibilities for portable serialization.

Using the PyYAML library, translating Python dictionaries to YAML is straightforward:

• Import yaml
• Pass dict to yaml.dump()
• Output human friendly YAML
• Customize styling as needed

Beyond plain text usage, portable YAML outputs enable you to efficiently store configuration data, emit API payloads, produce dynamic documentation and much more from common Python data types.

Hope it will Solve Your Problem, Thank You For Reading This Article.