I. yaml file introduction

yaml is a special language for writing configuration files.

1. yaml file rules

• Case sensitive;
• Use indents to represent hierarchical relationships;
• Indent with spacebar instead of Tab
• The number of indented spaces is not fixed, only the left side of elements of the same level needs to be aligned;
• The strings in the file do not need to be marked with quotation marks, but if the strings contain special characters, they need to be marked with quotation marks;
• Note identification is#

2. yaml file data structure

• Object: set of key value pairs (referred to as "map or dictionary")
• Key value pairs are represented by a colon ":" structure, and the colon and value are separated by spaces
• Array: a set of ordered values (sequence or list for short)
• The array is preceded by a "-" symbol, which is separated from the value by a space
• Scalars: a single, indivisible value (such as string, bool value, integer, floating-point number, time, date, null, etc.)
• The value of None can be represented by null ~

II. Reading yaml configuration file in python

1. Prerequisites

You need to install pyyaml and import yaml module before reading yaml file in python:

• Pyyaml (pip3 install pyyaml) is the module to be installed when using yaml;
• The imported module is yaml (import yaml)

2. Read yaml file data

python reads the file data through the open mode, and then converts the data into a list or dictionary through the load function;

import yaml
import os

def get_yaml_data(yaml_file):
    # Open yaml file
    print("***Obtain yaml file data***")
    file = open(yaml_file, 'r', encoding="utf-8")
    file_data = file.read()
    file.close()
    
    print(file_data)
    print("Type:", type(file_data))

    # Convert a string to a dictionary or list
    print("***conversion yaml Data is dictionary or list***")
    data = yaml.full_load(file_data)
    print(data)
    print("Type:", type(data))
    return data
current_path = os.path.abspath(".")
yaml_path = os.path.join(current_path, "config.yaml")
get_yaml_data(yaml_path)

"""
***Obtain yaml file data***
# yaml key value pair: dictionary in python
usr: my
psw: 123455
//Type: < class' STR '>
***conversion yaml Data is dictionary or list***
{'usr': 'my', 'psw': 123455}
//Type: < class' dict '>
"""

3. yaml file data is key value pair

(1) yaml file contains key value pairs:

# yaml key value pair: dictionary in python
usr: my
psw: 123455
s: " abc\n"

Data obtained by python after parsing yaml file:

{'usr': 'my', 'psw': 123455, 's': ' abc\n'}

(2) the content of yaml file is "key value pair 'nesting" key value pair "

# yaml key value pair nesting: Dictionary nesting dictionary in python
usr1:
  name: a
  psw: 123
usr2:
  name: b
  psw: 456

Data obtained by python after parsing yaml file:

{'usr1': {'name': 'a', 'psw': 123}, 'usr2': {'name': 'b', 'psw': 456}}

(3) nesting "array" in "key value pair" in yaml file

# Nested array in yaml key value pair
usr3:
  - a
  - b
  - c
usr4:
  - b

Data obtained by python after parsing yaml file:

{'usr3': ['a', 'b', 'c'], 'usr4': ['b']}

4. yaml file data is an array

(1) contents in yaml file are arrays

# yaml array
- a
- b
- 5

Data obtained by python after parsing yaml file:

['a', 'b', 5]

(2) nesting key value pairs in yaml file array

# Nested key value pairs in yaml array
- usr1: aaa
- psw1: 111
  usr2: bbb
  psw2: 222

Data obtained by python after parsing yaml file:

[{'usr1': 'aaa'}, {'psw1': 111, 'usr2': 'bbb', 'psw2': 222}]

5. Basic data types in yaml file:

# Scalar quantity
s_val: name              # String: {'s_val': 'name'}
spec_s_val: "name\n"    # Special string: {'spec_s_val': 'name\n'
num_val: 31.14          # Number: {'num_val': 31.14}
bol_val: true           # Boolean value: {'bol_val': True}
nul_val: null           # null value: {'nul_val': None}
nul_val1: ~             # null value: {'nul_val1': None}
time_val: 2018-03-01t11:33:22.55-06:00     # Time value (iso8601 format): {'time_val': datetime.datetime(2018, 3, 1, 17, 33, 22, 550000)}
date_val: 2019-01-10    # Date value: {'date_val': datetime.date(2019, 1, 10)}

6. Reference in yaml file

Content in yaml file

animal3: &animal3 fish
test: *animal3

Data read by python

{'animal3': 'fish', 'test': 'fish'}

III. reading multiple yaml documents in python

1. Multiple documents in a yaml file are segmented by the separation method

For example: data in yaml file

# Multiple documents in segmented yaml file
---
animal1: dog
age: 2
---
animal2: cat
age: 3

2. python script reading multiple documents in a yaml file

When python obtains yaml data, it needs to use the load ﹣ all function to parse all documents, and then read the data in the object

# When a yaml file contains multiple documents, the data in the documents are obtained separately
def get_yaml_load_all(yaml_file):
    # Open yaml file
    file = open(yaml_file, 'r', encoding="utf-8")
    file_data = file.read()
    file.close()
    all_data = yaml.full_load_all(file_data)
    for data in all_data:
        print(data)
current_path = os.path.abspath(".")
yaml_path = os.path.join(current_path, "config.yaml")
get_yaml_load_all(yaml_path)
"""Result
{'animal1': 'dog', 'age': 2}
{'animal2': 'cat', 'age': 3}
"""

IV. generating yaml documents from python objects

Convert list or dictionary data to yaml standard schema through yaml.dump() method

# Generating yaml documents from python objects
import yaml
def generate_yaml_doc(yaml_file):
    py_object = {'school': 'zhang',
                 'students': ['a', 'b']}
    file = open(yaml_file, 'w', encoding='utf-8')
    yaml.dump(py_object, file)
    file.close()
current_path = os.path.abspath(".")
yaml_path = os.path.join(current_path, "generate.yaml")
generate_yaml_doc(yaml_path)
"""Result
school: zhang
students:
-a
-b
"""

PS: in many online tutorials, it is said that the result of pyyaml library writing yaml file is not in standard yaml format, but after my personal test, I found that there is no such problem, so the ruamel module will not be introduced. My test environment is python 3.7, PyYAML 5.1.2.