I Automated My Entire Job With 200 Lines of Python (Boss Still Doesn’t Know)
Six months ago, I was drowning in repetitive tasks. As a data analyst at a mid-sized marketing firm, my days looked identical: generate morning reports, respond to the same client emails, schedule endless meetings, process invoices, and post updates to our social media channels.
I spent 8 hours doing work that required maybe 90 minutes of actual thinking.
The breaking point came on a Tuesday afternoon when I caught myself manually copying data from one spreadsheet to another for the third time that day. I had a degree in computer science and was using my brain like a very expensive copy-paste machine.
That night, I started writing Python scripts. Not to get fired or slack off, but because I was genuinely frustrated at how much human potential was being wasted on tasks a computer could handle in milliseconds.
Six months later, those scripts handle about 70% of my daily workload. My boss thinks I am the most productive analyst on the team. My colleagues wonder how I deliver everything so fast and still leave on time.
The truth is simpler than they think.
Why Automation Felt Like Survival
How I Automated My Entire Job With 200 Lines of Python (Boss Still Doesn’t Know)
Before I share the code, let me explain why this was not about laziness. It was about sanity.
My typical day started at 8 AM with generating three reports from our database. Same queries, same format, just different date ranges. This took 45 minutes because our legacy system was slow and required multiple manual steps.
Then came emails. Clients asking for the same metrics, teammates requesting the same files, management wanting status updates. I could predict 80% of my inbox before opening it.
Meetings were worse. Coordinating schedules across four time zones, sending calendar invites, following up with people who did not respond. Each meeting required 15-20 minutes of administrative overhead.
Invoice processing was mind-numbing. Download PDFs, extract data, enter into our accounting system, send confirmation emails. Rinse and repeat for 30-40 invoices per week.
Social media posting rounded out my day. Publishing the same type of content on LinkedIn, Twitter, and Facebook at optimal times. Each post required logging into three platforms and manually scheduling.
I was not working. I was executing a daily algorithm that happened to require a human, so i thought automate my entire job with 200 lines of python (Boss Still Doesn’t Know)
The Five Scripts That Changed Everything
I did not automate everything overnight. I started with the most painful task and built from there. Here is what I automated and how.
Script 1: Morning Reports (Saved 45 Minutes Daily)
The first script generates three daily reports and emails them to stakeholders before I even log in.
What It Does: Connects to our MySQL database, runs predefined queries, formats the results into Excel files with charts, and sends emails with the reports attached.
The Core Logic:
import pandas as pd
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders
import mysql.connector
from datetime import datetime, timedelta
def generate_daily_report():
# Database connection
conn = mysql.connector.connect(
host='company-db.internal',
user='analyst_readonly',
password=os.getenv('DB_PASSWORD'),
database='marketing_data'
)
# Query for yesterday's metrics
yesterday = (datetime.now() - timedelta(1)).strftime('%Y-%m-%d')
query = f"""
SELECT campaign_name, impressions, clicks, conversions, spend
FROM campaign_metrics
WHERE date = '{yesterday}'
ORDER BY spend DESC
"""
df = pd.read_sql(query, conn)
# Create Excel with formatting
output_file = f'daily_report_{yesterday}.xlsx'
with pd.ExcelWriter(output_file, engine='xlsxwriter') as writer:
df.to_excel(writer, sheet_name='Metrics', index=False)
workbook = writer.book
worksheet = writer.sheets['Metrics']
# Add chart
chart = workbook.add_chart({'type': 'column'})
chart.add_series({
'values': f'=Metrics!$E$2:$E${len(df)+1}',
'categories': f'=Metrics!$A$2:$A${len(df)+1}',
})
worksheet.insert_chart('G2', chart)
conn.close()
return output_file
The Email Component:
def send_report(report_file):
msg = MIMEMultipart()
msg['From'] = 'analytics@company.com'
msg['To'] = 'management@company.com'
msg['Subject'] = f'Daily Marketing Report - {datetime.now().strftime("%Y-%m-%d")}'
# Attach file
with open(report_file, 'rb') as f:
part = MIMEBase('application', 'octet-stream')
part.set_payload(f.read())
encoders.encode_base64(part)
part.add_header('Content-Disposition', f'attachment; filename={report_file}')
msg.attach(part)
# Send via company SMTP
server = smtplib.SMTP('smtp.company.com', 587)
server.starttls()
server.login('analytics@company.com', os.getenv('EMAIL_PASSWORD'))
server.send_message(msg)
server.quit()
I run this on a cron job at 7:30 AM. By the time management arrives at 8:30, the report is in their inbox. They think I am an early bird. I am usually still asleep.
Script 2: Email Response Automation (Saved 90 Minutes Daily)
I do not use AI to write emails from scratch. That would be obvious and potentially problematic. Instead, I built a smart template system that handles the repetitive 80%.
What It Does: Monitors my inbox, categorizes emails by type, and drafts responses using predefined templates with dynamic data insertion.
The Pattern Matching:
import imaplib
import email
from email.header import decode_header
def categorize_email(subject, body):
patterns = {
'metric_request': ['can you send', 'need the numbers', 'latest metrics'],
'file_request': ['please share', 'send me the file', 'need the report'],
'meeting_request': ['schedule', 'meeting time', 'available for'],
'status_update': ['status on', 'progress update', 'how is']
}
text = f"{subject} {body}".lower()
for category, keywords in patterns.items():
if any(keyword in text for keyword in keywords):
return category
return 'manual_review'
def draft_response(category, original_email):
templates = {
'metric_request': """Hi {sender},
Here are the metrics you requested for {period}:
- Total Impressions: {impressions}
- Click-through Rate: {ctr}%
- Conversion Rate: {conversion_rate}%
Full report attached. Let me know if you need anything else.
Best regards""",
'file_request': """Hi {sender},
I've attached the {file_type} you requested. The data covers {date_range}.
Please let me know if you need any clarification.
Best regards"""
}
if category in templates:
# Extract relevant data and fill template
return templates[category].format(**extract_email_data(original_email))
return None
The script saves drafts to my email client. I review them in batches, make minor edits if needed, and send. What used to take 90 minutes now takes 15.
Script 3: Meeting Scheduler (Saved 60 Minutes Weekly)
Coordinating meetings across time zones was eating my life. This script handles it.
What It Does: Integrates with Google Calendar API, checks availability across participants, finds optimal meeting times, and sends calendar invites automatically.
The Scheduling Logic:
from googleapiclient.discovery import build
from datetime import datetime, timedelta
import pytz
def find_available_slots(attendees, duration_minutes, days_ahead=7):
service = build('calendar', 'v3', credentials=get_credentials())
# Get busy times for all attendees
body = {
"timeMin": datetime.now().isoformat() + 'Z',
"timeMax": (datetime.now() + timedelta(days=days_ahead)).isoformat() + 'Z',
"items": [{"id": email} for email in attendees]
}
events_result = service.freebusy().query(body=body).execute()
# Find gaps in schedules
available_slots = []
current_time = datetime.now().replace(hour=9, minute=0)
while current_time < datetime.now() + timedelta(days=days_ahead):
if current_time.hour >= 17: # After 5 PM
current_time += timedelta(days=1)
current_time = current_time.replace(hour=9, minute=0)
continue
is_available = True
for attendee in attendees:
busy_times = events_result['calendars'][attendee]['busy']
for busy in busy_times:
busy_start = datetime.fromisoformat(busy['start'].replace('Z', '+00:00'))
busy_end = datetime.fromisoformat(busy['end'].replace('Z', '+00:00'))
if busy_start <= current_time < busy_end:
is_available = False
break
if is_available:
available_slots.append(current_time)
current_time += timedelta(minutes=30)
return available_slots[:5] # Return top 5 options
When someone emails asking for a meeting, the script finds three available time slots, and I respond with options. No more endless back-and-forth.
Script 4: Invoice Processing (Saved 3 Hours Weekly)
This one borders on magic. It processes invoices from PDF to accounting system without human intervention.
What It Does: Downloads invoice PDFs from email, extracts data using OCR, validates the information, and enters it into our accounting system via API.
The Extraction Pipeline:
import PyPDF2
import re
from pdf2image import convert_from_path
import pytesseract
def extract_invoice_data(pdf_path):
# Try text extraction first
with open(pdf_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
text = ''
for page in reader.pages:
text += page.extract_text()
# If text extraction fails, use OCR
if len(text.strip()) < 50:
images = convert_from_path(pdf_path)
text = pytesseract.image_to_string(images[0])
# Extract key fields with regex
invoice_data = {
'invoice_number': re.search(r'Invoice #?:?\s*(\w+)', text).group(1),
'date': re.search(r'Date:?\s*(\d{1,2}/\d{1,2}/\d{4})', text).group(1),
'amount': re.search(r'Total:?\s*\$?([\d,]+\.?\d*)', text).group(1),
'vendor': re.search(r'From:?\s*([^\n]+)', text).group(1).strip()
}
return invoice_data
def post_to_accounting_system(invoice_data):
import requests
response = requests.post(
'https://accounting.company.com/api/invoices',
headers={'Authorization': f'Bearer {os.getenv("ACCOUNTING_TOKEN")}'},
json={
'invoice_number': invoice_data['invoice_number'],
'date': invoice_data['date'],
'amount': float(invoice_data['amount'].replace(',', '')),
'vendor': invoice_data['vendor'],
'status': 'pending_approval'
}
)
return response.status_code == 201
The script runs every morning. I review the processed invoices for accuracy (takes 10 minutes), approve them, and move on. Accounting thinks I have superhuman speed.
Script 5: Social Media Posting (Saved 45 Minutes Daily)
The last piece was social media. Same content, three platforms, specific timing.
What It Does: Takes content from a Google Sheet, formats it for each platform, and posts at optimal times using platform APIs.
The Multi-Platform Publisher:
import tweepy
from linkedin_api import Linkedin
import facebook
def post_to_all_platforms(content, image_path=None):
# Twitter/X
twitter_auth = tweepy.OAuthHandler(
os.getenv('TWITTER_KEY'),
os.getenv('TWITTER_SECRET')
)
twitter_auth.set_access_token(
os.getenv('TWITTER_TOKEN'),
os.getenv('TWITTER_TOKEN_SECRET')
)
twitter_api = tweepy.API(twitter_auth)
if image_path:
twitter_api.update_status_with_media(content[:280], image_path)
else:
twitter_api.update_status(content[:280])
# LinkedIn
linkedin = Linkedin(
os.getenv('LINKEDIN_EMAIL'),
os.getenv('LINKEDIN_PASSWORD')
)
linkedin.post(content)
# Facebook
graph = facebook.GraphAPI(os.getenv('FACEBOOK_TOKEN'))
graph.put_object("me", "feed", message=content)
return True
I batch-create content once a week, load it into a spreadsheet, and the script handles distribution. Each platform gets the post at its optimal engagement time.
The Technical Stack (Total: 187 Lines)
Here is what made this possible:
Core Libraries:
- pandas (data manipulation)
- mysql-connector-python (database queries)
- smtplib (email sending)
- google-api-python-client (calendar integration)
- PyPDF2 and pytesseract (invoice processing)
- tweepy, linkedin-api, facebook-sdk (social media)
Infrastructure:
- Python 3.11
- Cron jobs for scheduling
- Environment variables for credentials
- Error logging to Slack for failures
The entire codebase is 187 lines across five scripts. I spent three weeks building and testing them. They have run flawlessly for six months.
What I Actually Do Now
This is where it gets interesting. I did not automate my job to do nothing. I automated the boring parts so I could do the work that actually matters.
Now I spend my time on:
- Deep analysis that requires human judgment
- Strategy development for campaigns
- Exploring new data sources and tools
- Mentoring junior analysts
- Proposing process improvements
Ironically, automating the routine work made me better at my actual job. My performance reviews improved because I finally had time to think strategically instead of executing mechanical tasks.
I discovered insights in our data that led to a 23% improvement in campaign performance. I built relationships with clients because I was not buried in administrative work. I proposed a new attribution model that changed how we measure success.
None of that would have happened if I was still manually generating reports at 8 AM every morning.
The Ethical Question Nobody Wants to Ask
Here is the uncomfortable truth: I am being paid for 40 hours of work but only working about 15 hours. Is that fraud? Am I stealing from my company?
I have thought about this constantly over the past six months. Here is my conclusion.
My employment contract does not specify that I must spend 40 hours performing tasks. It specifies that I must deliver certain outcomes: reports generated, emails answered, invoices processed, metrics analyzed, and insights provided.
I am delivering all of those outcomes. In fact, I am delivering them better and faster than before. My manager is happy. My clients are happy. The company is getting exactly what they are paying for.
The only difference is the method.
When factory automation replaced assembly line workers, we did not accuse the factories of fraud for producing goods faster. We recognized it as progress. Why should knowledge work be different?
But I understand the counterargument. If my company knew I could do my job in 15 hours, they might hire me part-time or give me additional responsibilities. By not disclosing the automation, I am arguably hiding information that could affect my employment terms.
This is the gray area where I live.
Why I Have Not Told My Boss
I have three reasons for keeping this quiet, and they are more complex than simple self-preservation.
First, institutional resistance to change. My company moves slowly. If I revealed these scripts, they would enter a six-month review process involving IT security, compliance, legal, and management. During that time, I would likely be told to stop using them and return to manual processes. The scripts might eventually be approved, but only after being watered down or implemented company-wide in a way that loses their effectiveness.
I have seen this happen to other automation initiatives. Good ideas get bureaucratized to death.
Second, job security concerns. If management realizes these tasks can be automated, what stops them from eliminating my position? Or worse, asking me to automate everyone else’s jobs before eliminating the entire department?
I am not being paranoid. I have watched companies automate away entire teams and celebrate it as efficiency gains. The people who built the automation rarely benefited from it.
Third, and most honestly, the status quo works for me. I have work-life balance for the first time in years. I leave at 5 PM. I do not work weekends. I have time for my family, my health, and my side projects. Why would I volunteer to give that up?
This is selfish, and I acknowledge that.
What This Says About Modern Work
My situation reveals something uncomfortable about how we structure employment in knowledge work.
We are still operating under an industrial-age model where time spent equals value created. You sit at a desk for eight hours, therefore you did eight hours of work. This made sense when work was physical and measurable.
But knowledge work is different. The value I create comes from insights, decisions, and solutions. Not from the mechanical process of generating a report.
A developer who writes elegant code in two hours creates more value than one who writes messy code in eight hours. An analyst who identifies a critical insight in 20 minutes creates more value than one who processes data all day without finding anything meaningful.
We know this intellectually, but our employment structures have not caught up.
The result is a system that incentivizes appearing busy over being effective. People stretch tasks to fill time. They schedule unnecessary meetings. They create work to justify their existence.
I did the opposite. I eliminated the waste and focused on value. But I had to hide it because the system punishes efficiency.
The Coming Automation Wave
My situation is not unique. It is a preview of what is coming for millions of knowledge workers.
AI and automation tools are getting better every month. Tasks that required human intelligence last year can be automated this year. The pace is accelerating.
Within five years, I predict that most knowledge workers will have the tools to automate 40-60% of their jobs. The question is what we do with that capability.
Do we pretend nothing has changed and keep executing manual processes to fill time? Do we disclose the automation and risk job elimination? Do we quietly automate and use the freed time for higher-value work?
There is no obvious right answer. But the conversation needs to happen before millions of workers are forced to make this choice individually and in secret.
How to Start Automating Your Job
If you are intrigued by this idea, here is how to begin responsibly.
Start small. Do not try to automate everything at once. Pick the single most repetitive task you do and automate just that. Learn from the process before scaling up.
Focus on personal productivity first. Automate tasks that only affect you. Do not automate processes that involve other people or systems until you understand the implications.
Document everything. Keep clear records of what you automated, how it works, and what would happen if it stopped working. You might need to hand this over someday.
Test extensively. Automated mistakes happen faster and at larger scale than manual mistakes. Build in error checking and monitoring. I check my automated reports every day before they go out.
Know your limits. Some tasks should not be automated. Anything requiring genuine human judgment, empathy, or creative thinking should remain manual. Automation is for mechanical tasks, not for thinking.
Consider the ethics. Think through the implications for your team, your company, and your own integrity. There are no universal rules here, but you should at least consider the questions.
The Skills That Matter
If you want to build automation like I Automated My Entire Job With 200 Lines of Python (Boss Still Doesn’t Know) I still do not know if what I am doing is right. Some days I feel like I am working smarter. Other days I feel like I am deceiving my employer.
this, you need three core skills.
Python fundamentals. You do not need to be an expert, but you need to understand loops, functions, error handling, and basic data structures. Most of my scripts use fairly simple Python.
API literacy. Modern automation is about connecting systems. Learn how to work with REST APIs, handle authentication, and parse JSON responses.
Problem decomposition. The hard part is not writing code. It is breaking down a messy real-world task into steps a computer can execute. This requires analytical thinking more than technical skill.
You can learn all of this in three to six months of dedicated practice. There are countless free resources online. The return on investment is extraordinary.
Final Thoughts
I Automated My Entire Job With 200 Lines of Python (Boss Still Doesn’t Know) I still do not know if what I am doing is right. Some days I feel like I am working smarter. Other days I feel like I am deceiving my employer.
What I do know is that the nature of work is changing faster than our systems and ethics can keep up. We are going to see millions of variations of my situation in the coming years.
Maybe the answer is radical transparency. Maybe companies should celebrate employees who automate themselves and find higher-value work. Maybe we need to rethink how we measure productivity entirely.
Or maybe I should just tell my boss and deal with the consequences.
For now, I keep running my scripts. My reports go out at 7:30 AM. My emails get answered. My meetings get scheduled. The work gets done.
And I try not to think too hard about what it all means.
The automation is easy. The ethics are hard.
That might be the most important lesson of all.
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