To efficiently read, process, and write the YAML front matter of hundreds of Markdown files spread across multiple subdirectories, you can leverage both yq
(a powerful command-line YAML processor) and pandas
(a versatile Python data analysis library). Below is a comprehensive guide to help you accomplish this task:
Overview
- Extract YAML Front Matter: Use
yq
to parse and extract YAML front matter from each Markdown file. - Aggregate Data: Collect the extracted data into a structured format (e.g., JSON or CSV).
- Process with Pandas: Load the aggregated data into a Pandas DataFrame for analysis or manipulation.
- Write Results: Output the processed data as needed, such as updating YAML front matter or generating reports.
Prerequisites
-
Install
yq
:- Using Homebrew (macOS/Linux):
brew install yq
- Using Snap (Linux):
sudo snap install yq
- From Source or Other Methods: Refer to the yq GitHub repository for detailed installation instructions.
- Using Homebrew (macOS/Linux):
-
Install Python and Pandas:
- Ensure you have Python installed (preferably Python 3.6+).
- Install
pandas
andPyYAML
:pip install pandas PyYAML
Directory Structure:
- Assume your Markdown files are organized in various subdirectories under a root directory, e.g.,
./content
.
- Assume your Markdown files are organized in various subdirectories under a root directory, e.g.,
Step-by-Step Guide
1. Extract YAML Front Matter Using yq
First, you’ll need to extract the YAML front matter from each Markdown file. YAML front matter is typically enclosed between ---
lines at the beginning of a Markdown file.
Here’s how you can extract it using yq
:
# Example command to extract YAML front matter from a single file
yq eval 'select(di == 0)' file.md
However, to process multiple files across subdirectories, it’s more efficient to automate this with a Python script.
2. Python Script to Aggregate YAML Front Matter
Below is a Python script that:
- Recursively searches for all Markdown files in the specified directory.
- Uses
yq
to extract YAML front matter from each file. - Aggregates the data into a Pandas DataFrame.
- Processes the data as needed.
- Writes the results back or outputs them as required.
import os
import subprocess
import pandas as pd
import yaml
from pathlib import Path
def find_markdown_files(root_dir):
"""Recursively find all Markdown files in the given directory."""
return list(Path(root_dir).rglob('*.md'))
def extract_yaml_front_matter(file_path):
"""
Extract YAML front matter from a Markdown file using yq.
Assumes that YAML front matter is enclosed between '---' lines.
"""
try:
# Use yq to extract the YAML front matter
# The 'split' function splits the file by '---' and selects the first YAML block
command = f"yq eval 'select(di == 0)' {file_path}"
result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
yaml_content = result.stdout
# Parse YAML content into a Python dictionary
return yaml.safe_load(yaml_content)
except subprocess.CalledProcessError as e:
print(f"Error processing {file_path}: {e.stderr}")
return None
except yaml.YAMLError as e:
print(f"YAML parsing error in {file_path}: {e}")
return None
def aggregate_data(markdown_files):
"""Aggregate YAML front matter from all Markdown files into a list of dictionaries."""
data = []
for file in markdown_files:
yaml_data = extract_yaml_front_matter(file)
if yaml_data:
yaml_data['file_path'] = str(file) # Keep track of the source file
data.append(yaml_data)
return data
def main():
# Define the root directory containing Markdown files
root_dir = './content' # Change this to your directory
# Find all Markdown files
markdown_files = find_markdown_files(root_dir)
print(f"Found {len(markdown_files)} Markdown files.")
# Aggregate YAML front matter
data = aggregate_data(markdown_files)
print(f"Aggregated YAML data from {len(data)} files.")
# Create a Pandas DataFrame
df = pd.DataFrame(data)
print("Initial DataFrame:")
print(df.head())
# Example Processing: Let's say you want to analyze tags
if 'tags' in df.columns:
# Expand tags into separate rows
df_expanded = df.explode('tags')
tag_counts = df_expanded['tags'].value_counts()
print("Tag Counts:")
print(tag_counts)
# Save tag counts to a CSV
tag_counts.to_csv('tag_counts.csv', header=['count'])
print("Tag counts saved to tag_counts.csv")
# Example Processing: Update a field and write back to YAML
# Suppose you want to add a new field 'processed: true' to each YAML front matter
df['processed'] = True
# Iterate over the DataFrame and update YAML front matter in files
for index, row in df.iterrows():
file_path = row['file_path']
# Read the original file
with open(file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
# Find the YAML front matter
if lines[0].strip() == '---':
end_index = lines[1:].index('---\n') + 1
yaml_content = ''.join(lines[1:end_index])
# Update YAML data
yaml_data = yaml.safe_load(yaml_content)
yaml_data.update({'processed': row['processed']})
new_yaml = yaml.dump(yaml_data, sort_keys=False)
# Reconstruct the file with updated YAML
new_content = ['---\n'] + [line for line in new_yaml.splitlines(keepends=True)] + ['---\n'] + lines[end_index+1:]
# Write back to the file
with open(file_path, 'w', encoding='utf-8') as f:
f.writelines(new_content)
print(f"Updated {file_path} with processed=True")
else:
print(f"No YAML front matter found in {file_path}")
# Save the aggregated data to a CSV for further analysis
df.to_csv('aggregated_yaml_data.csv', index=False)
print("Aggregated YAML data saved to aggregated_yaml_data.csv")
if __name__ == "__main__":
main()
3. Explanation of the Script
Finding Markdown Files:
- The
find_markdown_files
function usespathlib
to recursively locate all.md
files within the specified root directory.
- The
Extracting YAML Front Matter:
- The
extract_yaml_front_matter
function invokesyq
viasubprocess
to extract the YAML front matter from each Markdown file. - It handles errors gracefully, printing any issues encountered during extraction or parsing.
- The
Aggregating Data:
- The
aggregate_data
function compiles all extracted YAML data into a list of dictionaries, adding the file path for reference.
- The
Creating a Pandas DataFrame:
- The aggregated data is loaded into a Pandas DataFrame for easy manipulation and analysis.
Processing Data with Pandas:
- Example 1: If there’s a
tags
field, the script counts the occurrence of each tag and saves the results totag_counts.csv
. - Example 2: The script adds a new field
processed: true
to each YAML front matter and writes the updated YAML back to the original Markdown files.
- Example 1: If there’s a
Writing Results:
- Aggregated data is saved to
aggregated_yaml_data.csv
for any additional processing or reporting needs. - Updated YAML front matter is written back to the original Markdown files, ensuring that changes are reflected directly in the source files.
- Aggregated data is saved to
4. Running the Script
Save the Script:
- Save the above Python script to a file, e.g.,
process_markdown_yaml.py
.
- Save the above Python script to a file, e.g.,
Execute the Script:
python process_markdown_yaml.py
Check Outputs:
aggregated_yaml_data.csv
: Contains all extracted YAML data.tag_counts.csv
: (If applicable) Contains counts of each tag.- Updated Markdown files: Each file now includes the
processed: true
field in its YAML front matter.
Additional Tips
Custom Processing:
- Modify the Pandas processing section to suit your specific needs, such as filtering, aggregating, or transforming data.
Backup Files:
- Before writing changes back to your Markdown files, consider creating backups to prevent accidental data loss.
Performance Optimization:
- For a large number of files, consider optimizing the script by parallelizing the YAML extraction process using libraries like
concurrent.futures
.
- For a large number of files, consider optimizing the script by parallelizing the YAML extraction process using libraries like
Error Handling:
- Enhance error handling to manage files without YAML front matter or with malformed YAML gracefully.
Alternative Approaches:
- While this guide uses
yq
for YAML extraction, Python’sfrontmatter
library is another option for handling Markdown front matter directly within Python.
- While this guide uses
Conclusion
By combining the strengths of yq
for YAML processing and pandas
for data manipulation, you can create a robust workflow to manage and analyze YAML front matter across a large collection of Markdown files. The provided Python script serves as a foundation that you can customize further based on your specific requirements.
To read and process the YAML front matter of hundreds of Markdown files located in various subdirectories using yq
and pandas, you can follow a systematic approach that combines shell commands and Python scripting. Here’s a step-by-step guide:
Extract the YAML Front Matter with
yq
The
yq
command-line tool can extract and process the YAML front matter directly from your Markdown files. It has a--front-matter
flag specifically designed for this purpose.Collect Data into Pandas DataFrame
Use Python to traverse your directory structure, invoke
yq
to extract the front matter from each file, and collect this data into a pandas DataFrame for processing.Process the Data with Pandas
Perform any data manipulation or analysis using pandas’ powerful DataFrame operations.
Write Results Back to Files with
yq
After processing, use
yq
again to write the updated front matter back into your Markdown files.
Below is a detailed explanation and example code for each step.
Step 1: Extract the YAML Front Matter with yq
The yq
tool can extract the front matter from Markdown files using the --front-matter=extract
option. It can output the front matter in JSON format, which is convenient for loading into pandas.
Example Command:
yq eval --front-matter=extract -j '.' file.md
--front-matter=extract
: Tellsyq
to extract the front matter.-j
: Outputs the result in JSON format.'.'
: The expression to evaluate (in this case, the entire front matter).
Step 2: Collect Data into Pandas DataFrame
Create a Python script that:
- Walks through all subdirectories to find Markdown files.
- Uses
subprocess
to callyq
and extract the front matter. - Parses the JSON output and collects it into a list.
- Converts the list into a pandas DataFrame.
Python Script:
import os
import subprocess
import pandas as pd
import json
# Initialize a list to store the front matter data
data = []
# Traverse the directory structure
for root, dirs, files in os.walk('.'):
for file in files:
if file.endswith('.md'):
file_path = os.path.join(root, file)
try:
# Use yq to extract front matter in JSON format
result = subprocess.run(
['yq', 'eval', '--front-matter=extract', '-j', '.', file_path],
capture_output=True,
text=True,
check=True
)
front_matter_json = result.stdout
# Load the JSON data
front_matter = json.loads(front_matter_json)
# Add the file path to the data
front_matter['file'] = file_path
data.append(front_matter)
except subprocess.CalledProcessError as e:
print(f"Error processing file {file_path}: {e}")
except json.JSONDecodeError as e:
print(f"Error parsing front matter in file {file_path}: {e}")
# Create a DataFrame from the collected data
df = pd.DataFrame(data)
# Display the first few rows
print(df.head())
Explanation:
- os.walk(‘.’): Recursively traverses the current directory.
- subprocess.run(): Calls
yq
to extract the front matter. - json.loads(): Parses the JSON output from
yq
. - data.append(): Collects each front matter dictionary.
- pd.DataFrame(): Converts the list of dictionaries into a DataFrame.
Step 3: Process the Data with Pandas
Now that you have the data in a DataFrame, you can perform any processing you need. For example, you can filter, modify, or analyze the data.
Example Processing:
# Example: Add a new column based on existing data
df['title_length'] = df['title'].apply(len)
# Example: Filter rows
df_filtered = df[df['tags'].apply(lambda x: 'important' in x)]
# Display the processed DataFrame
print(df_filtered.head())
Step 4: Write Results Back to Files with yq
After processing, you may want to write the results back into the YAML front matter of the Markdown files. You can use yq
with the --front-matter=process
option to update the front matter in place.
Python Script for Writing Back:
for index, row in df.iterrows():
file_path = row['file']
# Prepare the yq command to update multiple fields
yq_expression = ''
if 'title' in row:
yq_expression += f'.title = "{row["title"]}" | '
if 'tags' in row:
tags_yaml = ', '.join(f'"{tag}"' for tag in row['tags'])
yq_expression += f'.tags = [{tags_yaml}] | '
# Remove the trailing ' | '
yq_expression = yq_expression.rstrip(' | ')
try:
# Use yq to update the front matter in place
subprocess.run(
['yq', 'eval', '--front-matter=process', yq_expression, '-i', file_path],
check=True
)
except subprocess.CalledProcessError as e:
print(f"Error updating file {file_path}: {e}")
Explanation:
- yq_expression: Constructs the expression to update fields.
- –front-matter=process: Tells
yq
to process and update the front matter. - -i: Updates the file in place.
Note: Be cautious when constructing yq
expressions, especially with strings that may contain special characters. You may need to sanitize or escape values appropriately.
Handling Special Cases and Errors
- Empty or Missing Front Matter: Handle files that may not have front matter by adding error checking in your script.
- Data Types: Ensure that the data types in your DataFrame match what you expect in the YAML (e.g., lists, strings, numbers).
- Special Characters: Be careful with special characters in strings that may need to be escaped in YAML.
Putting It All Together
Here’s a consolidated version of the script:
import os
import subprocess
import pandas as pd
import json
# Step 1 and 2: Extract front matter and collect data
data = []
for root, dirs, files in os.walk('.'):
for file in files:
if file.endswith('.md'):
file_path = os.path.join(root, file)
try:
result = subprocess.run(
['yq', 'eval', '--front-matter=extract', '-j', '.', file_path],
capture_output=True,
text=True,
check=True
)
front_matter_json = result.stdout
front_matter = json.loads(front_matter_json)
front_matter['file'] = file_path
data.append(front_matter)
except Exception as e:
print(f"Error processing {file_path}: {e}")
df = pd.DataFrame(data)
# Step 3: Process data with pandas
# Example processing
df['title'] = df['title'].str.upper()
# Step 4: Write results back to files
for index, row in df.iterrows():
file_path = row['file']
yq_expression = ''
if 'title' in row:
yq_expression += f'.title = "{row["title"]}" | '
# Add more fields as needed
yq_expression = yq_expression.rstrip(' | ')
try:
subprocess.run(
['yq', 'eval', '--front-matter=process', yq_expression, '-i', file_path],
check=True
)
except Exception as e:
print(f"Error updating {file_path}: {e}")
Alternative: Using Python Libraries Only
If you prefer to avoid subprocess calls and yq
, you can use Python libraries like PyYAML
and python-frontmatter
to read and write the YAML front matter directly.
Example with python-frontmatter
:
import os
import frontmatter
import pandas as pd
data = []
for root, dirs, files in os.walk('.'):
for file in files:
if file.endswith('.md'):
file_path = os.path.join(root, file)
post = frontmatter.load(file_path)
metadata = post.metadata
metadata['file'] = file_path
data.append(metadata)
df = pd.DataFrame(data)
# Process data
df['title'] = df['title'].str.upper()
# Write back to files
for index, row in df.iterrows():
file_path = row['file']
post = frontmatter.load(file_path)
post.metadata['title'] = row['title']
with open(file_path, 'w') as f:
frontmatter.dump(post, f)
Note: While this method avoids external dependencies, you mentioned specifically wanting to use yq
and pandas.
Final Tips:
- Backup Your Files: Before running scripts that modify files in place, make sure to back up your data.
- Test on a Subset: Test your script on a small subset of files to ensure it works as expected.
- Error Handling: Enhance the script with robust error handling to manage files without front matter or with unexpected formats.
By following these steps, you can effectively use yq
and pandas to read, process, and write the YAML front matter of multiple Markdown files spread across various subdirectories.