Recommended Python Office Automation Code

Here are more advanced application cases of Python in office scenarios, organized according to the latest technological trends and practical needs:

1. GUI Automation Process (Cross-Application Operations)

import pyautogui
import time
def auto_fill_form():
    # Open the target application (assumed to be Excel)
    pyautogui.hotkey('win', 'r')
    pyautogui.write('excel')
    pyautogui.press('enter')
    time.sleep(3)
    # Simulate operation process
    pyautogui.click(x=100, y=200)  # Locate input box
    pyautogui.write('Automated Test Data', interval=0.1)
    pyautogui.press('tab')
    pyautogui.write('2025-09-16')
    pyautogui.hotkey('ctrl', 's')  # Save file
    pyautogui.write('Auto_Generated_Report.xlsx')
    pyautogui.press('enter')
# Safety reminder: Screen coordinates need to be calibrated in advance (use pyautogui.position())

2. Deep Parsing of PDF Forms

import camelot
import pandas as pd
def process_pdf_tables():
    # Extract all tables
    tables = camelot.read_pdf('report.pdf', pages='all', flavor='lattice')
    # Merge multi-page tables
    combined = pd.concat([t.df for t in tables])
    # Data cleaning
    cleaned = combined.dropna(how='all').reset_index(drop=True)
    # Generate standardized report
    cleaned.to_excel('parsed_report.xlsx', index=False)

3. Enterprise-Level Task Orchestration

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from datetime import datetime
def data_pipeline():
    # Data processing logic
    pass
with DAG('enterprise_workflow', start_date=datetime(2025,9,1), schedule_interval='0 8 * * *') as dag:
    # Task dependencies
    t1 = BashOperator(task_id='backup_db', bash_command='aws s3 sync /db s3://backup')
    t2 = PythonOperator(task_id='generate_report', python_callable=data_pipeline)
    t3 = BashOperator(task_id='send_alert', bash_command='curl -X POST alert-system')
    t1 >> t2 >> t3

4. Deep Integration with Office Suite

import win32com.client
def outlook_automation():
    outlook = win32com.client.Dispatch("Outlook.Application")
    namespace = outlook.GetNamespace("MAPI")
    # Access inbox
    inbox = namespace.GetDefaultFolder(6)  # 6 represents inbox
    messages = inbox.Items
    latest = messages.GetLast()
    # Automatically categorize emails
    if "Urgent" in latest.Subject:
        latest.Categories = "Urgent"
        latest.Save()

5. Intelligent Test Report Generation

import pytest
from allure_commons.types import AttachmentType
def test_login():
    # Test logic
    assert login_successful()
    # Automatic screenshot
    pytest.allure.attach(
        "screenshot.png",
        name="Login Screen",
        attachment_type=AttachmentType.PNG
    )

6. Batch Image Processing System

from PIL import Image
import os
def batch_image_process():
    input_dir = 'raw_images'
    output_dir = 'optimized_images'
    for img_file in os.listdir(input_dir):
        img = Image.open(os.path.join(input_dir, img_file))
        # Automatic processing flow
        img = img.convert('RGB')
        img.thumbnail((800,600))
        img.save(os.path.join(output_dir, img_file), 'WEBP', quality=85)

Recommended Learning Path

Basic Construction: Master os/shutil file operations, datetime time handling

Data Processing: Proficient in Pandas data operations, Matplotlib visualization

Office Integration: In-depth knowledge of openpyxl/python-docx document processing

Automation Advancement: Learn Selenium browser automation, PyAutoGUI GUI operations

Enterprise Applications: Master Airflow task orchestration, Celery distributed tasks