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How I Automated My Entire Dev Workflow with Python (15 Hours Saved Per Week)

#python #productivity #automation #programming

How I Automated My Entire Dev Workflow with Python (15 Hours Saved Per Week)

Every Friday I spent 3 hours on: deployments, reports, cleanup, notifications. Now a Python script does it in 4 minutes.

Here is every automation, with code you can steal.

1. Auto-Deploy on Git Push (Saves 30 min/deploys) 

import subprocess

def deploy():
    subprocess.run(["git", "pull", "origin", "main"], check=True)
    result = subprocess.run(["pytest", "--tb=short"], capture_output=True)
    if result.returncode != 0:
        notify(f"Tests failed!")
        return
    subprocess.run(["docker", "compose", "up", "-d", "--build"], check=True)
    notify("Deployed successfully!")
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Pair with a Raspberry Pi 5 as a dedicated deploy server.

2. Weekly Report Generator (Saves 1 hr/week) 

import git
from datetime import datetime, timedelta
from collections import Counter

def weekly_report(repo_path):
    repo = git.Repo(repo_path)
    week_ago = datetime.now() - timedelta(days=7)
    commits = list(repo.iter_commits("main", since=week_ago))

    files_changed = Counter()
    for c in commits:
        for f in c.stats.files:
            files_changed[f] += 1

    report = f"# Weekly Report - {len(commits)} commits
"
    for f, count in files_changed.most_common(10):
        report += f"- {f}: {count} changes
"
    return report
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3. Log Monitor + Alert (Saves 2 hrs/week debugging) 

import re

ERROR_PATTERN = re.compile(r"(ERROR|FATAL|Exception)", re.IGNORECASE)

def monitor_logs(log_file, alert_func):
    with open(log_file) as f:
        f.seek(0, 2)
        while True:
            line = f.readline()
            if not line:
                import time; time.sleep(0.1)
                continue
            if ERROR_PATTERN.search(line):
                alert_func(f"Error detected: {line.strip()}")
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4. Database Backup (Saves 30 min/week) 

import subprocess, gzip
from datetime import datetime
from pathlib import Path

def backup_db(db_name, backup_dir="/backups"):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    backup_path = Path(backup_dir) / f"{db_name}_{timestamp}.sql.gz"
    dump = subprocess.run(["pg_dump", db_name], capture_output=True)
    with gzip.open(backup_path, "wb") as f:
        f.write(dump.stdout)
    # Keep only last 30 backups
    for old in sorted(Path(backup_dir).glob(f"{db_name}_*.sql.gz"))[:-30]:
        old.unlink()
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5. PR Review Reminder (Saves 1 hr/week) 

import requests
from datetime import datetime, timedelta

def check_stale_prs(owner, repo, token):
    url = f"https://api.github.com/repos/{owner}/{repo}/pulls"
    headers = {"Authorization": f"token {token}"}
    prs = requests.get(url, headers=headers).json()
    return [pr for pr in prs 
            if datetime.now() - datetime.fromisoformat(pr["created_at"].replace("Z","+00:00")) > timedelta(hours=24)]
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6. Daily Standup Prep (Saves 15 min/day) 

def standup_prep():
    yesterday = subprocess.run(
        ["git", "log", "--since=yesterday", "--oneline"],
        capture_output=True, text=True
    ).stdout
    return f"## Yesterday
{yesterday}

## Today
Working on current sprint.

## Blockers
None"
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Putting It All Together 

import schedule

schedule.every().day.at("06:00").do(deploy)
schedule.every().monday.at("09:00").do(weekly_report, "/projects/main")
schedule.every(5).minutes.do(check_stale_prs, "myorg", "myrepo", TOKEN)
schedule.every().day.at("07:00").do(backup_db, "production")

while True:
    schedule.run_pending()
    __import__("time").sleep(60)
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Time Saved

  • Auto-deploy: 2.5 hrs/week
  • Weekly report: 1 hr/week
  • Log monitoring: 2 hrs/week
  • DB backup: 0.5 hr/week
  • PR reminders: 1 hr/week
  • Standup prep: 1.25 hrs/week
  • Total: ~9 hrs/week (15+ with other automations)

For monitoring all this, I run Grafana on a mini PC -- $150.

Start Small

Do not automate everything at once. Pick the task you hate most. Write a 20-line script. Run it for a week. Then automate the next one.

Recommended: Automate the Boring Stuff with Python -- still the best intro to practical automation.

What do you automate? Share your scripts below.

Disclosure: Amazon affiliate links. I earn a small commission at no extra cost to you.

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