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I Used Python to Solve a Real-Life Problem. Here’s How

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Have you ever felt overwhelmed by everyday tasks that seem to steal your time and energy? I certainly did. Between arguing over rent splits, manually tracking expenses, and missing out on product deals because I wasn’t alerted in time, life felt a bit out of control. That’s when I decided to pick up Python and turn these challenges into manageable—and even fun—projects. In this article, I’m sharing my personal coding journey and how I used Python to solve three common problems. More than just a tutorial, this is a story about taking charge of your daily hassles and making life a little simpler with some well-crafted code.

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1. Automating Rent Calculations for Roommates

Living with roommates has its perks, but splitting the rent and utilities can be a major headache. I used to spend way too much time calculating who owed what and double-checking numbers on my phone. One day, I thought, “There has to be a better way.” That’s when I turned to Python.

The Problem

Every month, our rent bill would arrive, and we'd all scramble to figure out our shares—considering factors like the total rent, shared utilities, and even who had a slightly bigger room. This wasn’t just annoying; it led to misunderstandings and wasted hours.

The Python Solution

I wrote a Python script that did all the math for me. The idea was simple: input the total rent, add any extra costs, and then divide by the number of roommates. I even added a feature to adjust for uneven room sizes by applying a custom factor to each share.

Detailed Explanation & Code

Collect Your Data: Gather your total rent, utility bills, and any additional expenses.
Factor in Adjustments: If one room is larger, you can assign a weight factor.
Perform the Calculations: Use Python to calculate each share based on these factors.
Output the Results: Display the final amounts or save them to a file for record keeping.

Here’s an extended version of the code:

# Total amounts and expenses
total_rent = 1200       # Monthly rent in dollars
utilities = 150         # Monthly utility bills
extra_expenses = 50     # Any extra shared expenses
total_cost = total_rent + utilities + extra_expenses

# Number of roommates
roommates = 3

# Custom adjustment factors (e.g., based on room size)
# These can be dynamically adjusted based on further inputs or measurements.
adjustments = [1.1, 1.0, 0.9]  # Roommate 1 pays 10% more, Roommate 3 pays 10% less

# Calculate the total adjustment factor
total_adjustment = sum(adjustments)

# Compute each roommate's share
shares = [(adjust / total_adjustment) * total_cost for adjust in adjustments]

# Display the results in a formatted manner
for i, share in enumerate(shares, start=1):
    print(f"Roommate {i} should pay: ${share:.2f}")

# Optional: Save results to a file for future reference
with open("rent_split.txt", "w") as f:
    for i, share in enumerate(shares, start=1):
        f.write(f"Roommate {i}: ${share:.2f}\n")

info: "According to recent surveys, over 60% of shared household disputes stem from unclear financial splits. Automating rent calculations can reduce these conflicts significantly."

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2. Tracking Personal Expenses with Python and Google Sheets API

Keeping track of every expense manually can be overwhelming and often leads to budgeting woes. I struggled with maintaining accurate records until I automated the process using Python and the Google Sheets API.

The Problem

Manually entering expenses into spreadsheets is tedious and error-prone. Missing an entry means your budget might be off, and reviewing your spending habits becomes nearly impossible.

The Python Solution

I created a personal finance dashboard that automatically updates a Google Sheet with my daily expenses. This script logs each expense entry, calculates totals, and even provides insights into spending trends.

Detailed Explanation & Code

Set Up Your Google Sheet: Create a new sheet with columns like Date, Description, and Amount.
Obtain API Credentials: Set up your project in the Google Developer Console and download the credentials as a JSON file.
Install Required Libraries: Use gspread to interact with Google Sheets and pandas for data analysis.
Write and Automate the Script: Connect to your Google Sheet and append new rows with each expense entry.

Below is a more detailed script:

import gspread
from oauth2client.service_account import ServiceAccountCredentials
import datetime
import pandas as pd

# Define the scope and authenticate
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
client = gspread.authorize(creds)

# Open your Google Sheet (ensure the sheet name matches)
sheet = client.open("Personal Finance Dashboard").sheet1

def log_expense(description, amount):
    today = datetime.datetime.now().strftime("%Y-%m-%d")
    # Append the expense data to the sheet
    sheet.append_row([today, description, amount])
    print("Expense logged successfully!")

# Example usage: Log a daily expense
log_expense("Coffee at Cafe", 3.75)

# For detailed analysis: Read the entire sheet into a pandas DataFrame
data = sheet.get_all_records()
df = pd.DataFrame(data)
print("Current Expenses:")
print(df)

# Optionally, calculate total monthly expenditure
df['Amount'] = pd.to_numeric(df['Amount'])
monthly_total = df.groupby(pd.to_datetime(df['Date']).dt.month)['Amount'].sum()
print("Monthly Expense Totals:")
print(monthly_total)

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3. A Simple Python Script That Alerts When Your Favorite Product Goes on Sale

Missing out on a great deal because you weren’t online at the right time can be frustrating. I built a Python script that monitors the price of a product and alerts me when it drops below a desired threshold.

The Problem

Manually checking product prices is inefficient and often results in missed opportunities. I needed a solution that would automatically notify me when the price was right.

The Python Solution

The solution was to create a script that periodically checks the product page, extracts the current price, and sends an alert if the price falls below my set threshold. This script uses web scraping to retrieve data and an SMTP server to send email alerts.

Detailed Explanation & Code

Identify the Product URL: Choose the product you want to track.
Set the Desired Price Threshold: Define the maximum price you’re willing to pay.
Scrape the Product Data: Use requests and BeautifulSoup to fetch and parse the webpage.
Extract the Price and Compare: Convert the price string to a float and compare it to your threshold.
Send an Alert: If the price meets your criteria, send an email alert.

Here’s a more detailed version of the script:

import requests
from bs4 import BeautifulSoup
import smtplib
from email.mime.text import MIMEText

def check_price(url, threshold):
    headers = {"User-Agent": "Mozilla/5.0"}  # Mimic a real browser
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')

    # This selector may need adjustment based on the website structure
    price_element = soup.find("span", class_="price")
    if not price_element:
        print("Price element not found!")
        return

    price_text = price_element.get_text().strip()
    # Convert the price string to a float (remove currency symbols, commas, etc.)
    price = float(price_text.replace("$", "").replace(",", ""))

    print(f"Current price: ${price:.2f}")

    if price < threshold:
        send_alert(price, url)
    else:
        print("Price is still above the threshold.")

def send_alert(price, url):
    msg = MIMEText(f"Great news! The product is now ${price:.2f}.\nCheck it out here: {url}")
    msg['Subject'] = "Price Alert: Your Product is on Sale!"
    msg['From'] = "[email protected]"
    msg['To'] = "[email protected]"

    # Connect to your SMTP server (adjust server and port as needed)
    with smtplib.SMTP('smtp.example.com', 587) as server:
        server.starttls()
        server.login("[email protected]", "your_password")
        server.send_message(msg)
    print("Alert sent successfully!")

# Example usage: Monitor a product
product_url = "http://example.com/product-page"
price_threshold = 50.00  # Set your desired threshold here
check_price(product_url, price_threshold)

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Bringing It All Together

What these projects taught me is that even small, everyday problems can be tackled with the right mindset and a bit of code. Python allowed me to automate tedious tasks, reduce daily stress, and free up time to focus on what really matters—whether it’s spending quality time with friends or pursuing new hobbies.

Key Takeaways

Break Down the Problem: Clearly define what you need to solve. For me, it was calculating rent splits, tracking expenses, and monitoring product prices.
Plan Your Approach: Outline the steps needed. Identify your inputs (data you have) and desired outputs (the solution you want).
Start Small: Build a basic version first, then iterate and improve.
Automate to Save Time: Use Python libraries (like gspread, pandas, requests, and BeautifulSoup) to connect various tools (like Google Sheets) and automate your tasks.
Learn and Adapt: Every project is a learning opportunity. Experiment with new libraries and techniques to improve your solution.

info: "Automation is not a luxury—it’s a necessity in today’s fast-paced world. With Python, you can streamline repetitive tasks and gain valuable insights into your daily operations."

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