如何将含唯一标识符的JSON批量解析为表格格式？

If you need to parse a folder of JSON files (each containing top-level unique block IDs) into a structured table, here's a straightforward Python solution that extracts key fields and outputs a Markdown table (or CSV if preferred).

Step 1: Understand the JSON Structure #

From your example, each JSON file has top-level keys as unique block IDs, with each value containing:

• category: Type of block (e.g., chapter)
• children: List of child block IDs
• metadata: Includes display_name and start timestamp

Step 2: Python Script to Process Files & Generate Table #

This script will iterate over all JSON files in your target folder, extract relevant data, and generate a Markdown table. No external libraries are required (though pandas can simplify things if you need more advanced table handling).

import os
import json

def process_json_files(folder_path):
    """Process all JSON files in the folder and collect table data."""
    table_rows = []
    
    for filename in os.listdir(folder_path):
        if not filename.endswith(".json"):
            continue
        
        file_path = os.path.join(folder_path, filename)
        with open(file_path, "r", encoding="utf-8") as f:
            try:
                json_data = json.load(f)
            except json.JSONDecodeError:
                print(f"Skipping invalid JSON file: {filename}")
                continue
            
            # Iterate over each top-level block in the JSON
            for block_id, block_details in json_data.items():
                # Extract fields, handle missing values gracefully
                row = {
                    "Block ID": block_id,
                    "Category": block_details.get("category", "N/A"),
                    "Display Name": block_details["metadata"].get("display_name", "N/A"),
                    "Start Date": block_details["metadata"].get("start", "N/A"),
                    "Children": ", ".join(block_details.get("children", [])) or "N/A"
                }
                table_rows.append(row)
    
    return table_rows

def generate_markdown_table(data):
    """Convert collected data into a Markdown table string."""
    if not data:
        return "No valid data found in JSON files."
    
    # Get column headers from the first row
    headers = list(data[0].keys())
    
    # Build Markdown table
    markdown_table = "| " + " | ".join(headers) + " |\n"
    markdown_table += "| " + " | ".join(["---"] * len(headers)) + " |\n"
    
    for row in data:
        # Convert each value to string to handle missing data
        row_values = [str(row[header]) for header in headers]
        markdown_table += "| " + " | ".join(row_values) + " |\n"
    
    return markdown_table

# ----------------------
# Usage Example
# ----------------------
if __name__ == "__main__":
    # Replace with your actual folder path
    target_folder = "/path/to/your/json/files"
    
    # Process files and generate table
    table_data = process_json_files(target_folder)
    markdown_output = generate_markdown_table(table_data)
    
    # Print the table to console
    print(markdown_output)
    
    # Or save to a Markdown file
    with open("converted_table.md", "w", encoding="utf-8") as f:
        f.write(markdown_output)

Step 3: Example Output #

Based on your sample JSON snippet, the generated Markdown table would look like this:

Notes #

• The script handles invalid JSON files by skipping them and printing a warning.
• Missing fields are replaced with N/A to keep the table consistent.
• If you prefer a CSV output instead of Markdown, you can use Python's csv module to write the table_rows list directly to a .csv file.

内容的提问来源于stack exchange，提问作者pooja kosala