Blog Word Counter

Blog Word Counter - README

• The github repo: Github Repo
• You can see the current updating status via here!

Overview

The Blog Word Counter is a Python utility designed to help bloggers and content creators track their writing progress by counting words in Markdown (.md) files. This tool is particularly useful for:

• Tracking writing milestones
• Measuring content production over time
• Analyzing writing habits
• Maintaining consistency in blog post lengths

The tool handles both English and Chinese content, providing accurate word counts for multilingual bloggers. It generates detailed logs of word counts per article and summary statistics of your entire blog repository.

Features

1. Bilingual Support: Accurately counts words in both English and Chinese
2. Directory Scanning: Processes all Markdown files in a directory recursively
3. Progress Tracking: Maintains historical logs of your word counts
4. Statistics Generation: Provides total article count and aggregate word count
5. JSON Output: Produces machine-readable output for further analysis

Installation

Just clone the directory via:

git clone https://github.com/xiyuanyang-code/Blog-word-counting.git
cd blog-word-counting

Usage

By default, the script looks for Markdown files in /mnt/d/Blog/source/_posts. You need to replace it with your own directory.

run the scripts via:

python main.py

Expected Output

Let's see how many words you have typed in your Blog!
Finished

Output Files

The script generates two types of output files:

1. Individual Article Logs: Timestamped JSON files in the log/ directory containing word counts for each article
2. Summary Statistics: A total.json file with aggregate statistics

Code Explanation

Core Functions

1. count_words(text: str = "")

This function handles the actual word counting logic.

Key Features:

• Uses regular expressions to identify Chinese characters ([\u4e00-\u9fff])
• Counts English words using word boundary detection (\b[a-zA-Z]+\b)
• Combines counts for bilingual content

Implementation Details:

def count_words(text: str = ""):
    text = text.strip()
    chinese_count = len(re.findall(r"[\u4e00-\u9fff]", text))
    english_count = len(re.findall(r"\b[a-zA-Z]+\b", text))
    return chinese_count + english_count

2. count_words_in_directory(directory: str)

Processes all Markdown files in a directory recursively.

Key Features:

• Walks through directory tree using os.walk()
• Filters for .md files
• Returns a list of dictionaries with filename and word count

Implementation Details:

def count_words_in_directory(directory: str):
    blog_word_counts = []
    for root, _, files in os.walk(directory):
        for file in files:
            if file.endswith(".md"):
                file_path = os.path.join(root, file)
                with open(file_path, "r", encoding="utf-8") as opened_file:
                    content = opened_file.read()
                    count_num = count_words(content)
                    blog_word_counts.append(
                        {"blog_name": file, "word_count": count_num}
                    )
    return blog_word_counts

3. Logging and Statistics Functions

get_log_filename(log_dir="log")

• Creates timestamped log filenames
• Ensures log directory exists

save_to_json(blog_word_counts)

• Saves individual article counts to JSON

do_calculate(blog_word_counts)

• Computes total articles and words
• Includes timestamp in output

Main Execution Flow

1. Directory scanning and word counting
2. Log file generation
3. Summary statistics calculation
4. Output file creation

Example Outputs

Individual Article Log (log/log_20250421_190058.txt)

[
    {
        "blog_name": "getting_started_with_python.md",
        "word_count": 1245
    },
    {
        "blog_name": "machine_learning_basics.md",
        "word_count": 2876
    }
]

Summary Statistics (total.json)

{
    "time": "2025-04-21",
    "total_articles": 42,
    "total_word_count": 87654
}