Standardizing Filename Conventions

Legacy project directories often contain inconsistently named files. Normalizing these names ensures predictable path resolution and sequential processing. The following routine scans a target directory, isolates the week identifier, and applies zero-padding for single-digit entries.

import os
from pathlib import Path

target_dir = Path("./legacy_planning_files")
current_items = list(target_dir.iterdir())

# Normalize base naming pattern
for item in current_items:
    if not item.is_file():
        continue
    
    stem = item.stem
    if "Week" not in stem and "周" not in stem:
        continue
        
    # Extract numeric week portion
    week_marker = "周" if "周" in stem else "Week"
    prefix = stem.split(week_marker)[0]
    proposed_name = f"{prefix}{week_marker} Information_Window.doc"
    
    old_path = target_dir / item.name
    new_path = target_dir / proposed_name
    
    if old_path != new_path:
        old_path.replace(new_path)

print("Naming convention updated.")

After basic alignment, single-digit week numbers require padding. Checking file character counts prevents over-padding longer identifiers.

for item in target_dir.iterdir():
    if not item.name.endswith(".doc"):
        continue
        
    # Target single-digit week patterns
    if len(item.stem) == 11 and "第0" not in item.stem:
        parts = item.stem.split("第")
        padded_name = f"{parts[0]}第0{parts[1]}.doc"
        item.rename(target_dir / padded_name)

Migrating Legacy .doc Files to .docx

Modern document pipelines rely on the Office Open XML standard. Automating the conversion from legacy .doc formats eliminates manual export steps. The script leverages pywin32 to launch an invisible Word instance, iterate through source files, and persist them as .docx.

import os
import win32com.client as win32
from pathlib import Path

def convert_doc_to_docx(source_folder: str, dest_folder: str):
    src_path = Path(source_folder)
    dst_path = Path(dest_folder)
    dst_path.mkdir(exist_ok=True)
    
    # Initialize COM object for background execution
    word_app = win32.Dispatch("Word.Application")
    word_app.Visible = False
    
    try:
        for doc_file in src_path.glob("*.doc"):
            if doc_file.name.startswith("~$"): 
                continue
                
            doc_obj = word_app.Documents.Open(str(doc_file))
            filename_xlsx = dst_path / f"{doc_file.stem}x"
            
            # SaveAs constant 16 corresponds to WordML/OpenXML format
            doc_obj.SaveAs(Filename=str(filename_xlsx), FileFormat=16)
            doc_obj.Close()
            print(f"Converted: {doc_file.name}")
    finally:
        word_app.Quit()
        del word_app

convert_doc_to_docx("./input_docs", "./output_docsx")

Purging Unnecessary Line Breaks

Scraped or templated documents frequently contain orphaned carriage returns. These disrupt spacing and table layouts. The python-docx library allows direct manipulation of the underlying XML tree to strip empty paragraphs safely.

from docx import Document
from pathlib import Path

def cleanse_empty_paragraphs(directory_path: str):
    target = Path(directory_path)
    
    for doc_file in target.glob("*.docx"):
        doc = Document(doc_file)
        paragraphs_to_remove = []
        
        for para in doc.paragraphs:
            if not para.text.strip():
                paragraphs_to_remove.append(para._element)
        
        for elem in paragraphs_to_remove:
            parent = elem.getparent()
            if parent is not None:
                parent.remove(elem)
        
        doc.save(doc_file)
        print(f"Cleaned: {doc_file.name}")

# Example usage:
# cleanse_empty_paragraphs("./cleaned_documents")

Extracting Content and Populating Spreadsheet Structures

Once documents are standardized, extracting structured text allows population of a master tracking sheet. This routine reads each processed file, skips header metadata, aggregates body text, and writes it into designated Excel columns.

import glob
from docx import Document
from openpyxl import Workbook

def extract_and_populate_spreadsheet(input_dir: str, output_excel: str):
    workbook = Workbook()
    sheet = workbook.active
    sheet.title = "Weekly_Contents"
    
    # Reserve headers
    sheet.cell(row=1, column=1, value="Sequence_ID")
    sheet.cell(row=1, column=2, value="Planned_Content")
    
    row_counter = 1
    doc_pattern = f"{input_dir}\\*.docx"
    
    for file_path in glob.glob(doc_pattern):
        doc = Document(file_path)
        extracted_lines = []
        
        # Iterate skipping potential header rows
        for idx, para in enumerate(doc.paragraphs):
            text = para.text.strip()
            if idx == 0 and ("header" in text.lower() or "class" in text.lower()):
                continue
                
            if not text:
                continue
                
            formatted_text = f"    {text}"
            extracted_lines.append(formatted_text)
        
        row_counter += 1
        merged_content = "\n".join(extracted_lines)
        
        sheet.cell(row=row_counter, column=1, value=row_counter - 1)
        sheet.cell(row=row_counter, column=2, value=merged_content)
    
    workbook.save(output_excel)
    print(f"Data exported to {output_excel}")

# extract_and_populate_spreadsheet("./processed", "./master_schedule.xlsx")

Merging Data with Word Templates via Dynamic Rendering

With a consolidated dataset, individual planning sheets can be generated programmatical. Using docxtpl alongside pandas enables seamless injection of cell values in to predefined placeholder tags within a base template.

import pandas as pd
from docxtpl import DocxTemplate
from pathlib import Path

def render_weekly_templates(data_source: str, template_path: str, output_dir: str):
    df = pd.read_excel(data_source)
    
    # Clean whitespace across relevant columns
    text_cols = ["week_title", "main_topic", "teacher_name", "class_group", "date_range"]
    for col in text_cols:
        df[col] = df[col].astype(str).str.strip()
        
    out_path = Path(output_dir)
    out_path.mkdir(exist_ok=True)
    
    tpl = DocxTemplate(template_path)
    
    for _, row in df.iterrows():
        context_map = {
            "title": row["week_title"],
            "content": row["main_topic"],
            "educator": row["teacher_name"],
            "group": row["class_group"],
            "schedule": row["date_range"]
        }
        
        tpl.render(context_map)
        safe_filename = f"Week_{row['week_title']}_{row['class_group']}_Plan.docx"
        tpl.save(out_path / safe_filename)
        tpl = DocxTemplate(template_path) # Reset template for next iteration

# render_weekly_templates("./master_schedule.xlsx", "./base_template.docx", "./generated_plans")

Batch Processing Documents into Image Assets

For digital publishing platforms that restrict file uploads, converting finished .docx artifacts to rasterized images is often required. This pipeline copies assets, renders them as PDFs via Word Automation, and uses pymupdf to extract high-resolution page snapshots.

import os
import shutil
import win32com.client as win32
import fitz
from pathlib import Path

def prepare_image_assets(source_plans: str, staging_area: str):
    src = Path(source_plans)
    tmp = Path(staging_area)
    tmp.mkdir(exist_ok=True)
    
    # 1. Copy DOCX files to isolated working directory
    for item in src.glob("*.docx"):
        shutil.copy2(item, tmp / item.name)
    
    # 2. Convert to PDF via COM
    word = win32.Dispatch("Word.Application")
    word.Visible = False
    pdf_format = 17
    
    for docx_file in tmp.glob("*.docx"):
        doc = word.Documents.Open(str(docx_file))
        pdf_out = docx_file.with_suffix(".pdf")
        doc.SaveAs(Filename=str(pdf_out), FileFormat=pdf_format)
        doc.Close()
        docx_file.unlink() # Remove source DOCX post-conversion
        
    word.Quit()
    del word
    
    # 3. Rasterize PDF pages to PNG
    zoom_factor = 2.0
    
    for pdf_file in tmp.glob("*.pdf"):
        pdf_doc = fitz.open(pdf_file)
        for page_num in range(len(pdf_doc)):
            page = pdf_doc.load_page(page_num)
            matrix = fitz.Matrix(zoom_factor, zoom_factor)
            pix = page.get_pixmap(matrix=matrix)
            img_out = tmp / f"{pdf_file.stem}_page_{page_num + 1}.png"
            pix.save(str(img_out))
        pdf_doc.close()
        pdf_file.unlink() # Clean up PDFs
    
    print(f"Image assets generated in {staging_area}")

# prepare_image_assets("./generated_plans", "./web_ready_images")

Implementation Considerations

Automating document workflows yields measurable operational improvements when designed with modularity and consistency in mind:

• Standardized Metadata Injection: Centralizing parameters such as class identifiers, educator names, and scheduling dates guarantees typographic uniformity and eliminates repetitive manual entry errors.
• Legacy Content Reuse: Repurposing established templates preserves institutional formatting standards (font families, line spacing, margin ratios) while accelerating production cycles.
• Decoupled Architecture: Separating naming normalization, format migration, content parsing, and asset export into discrete functions simplifies debugging and accommodates varying input structures across academic terms.
• Output Flexibility: Maintaining separate routing for printable documents and web-ready image exports ensures compliance with diverse distribution channels without compromising source fidelity.