Python Real-Time Examples and Solutions

Scenario 1: Automating Excel Reporting

Problem: You receive a CSV file of daily sales every morning. You need to filter out cancelled orders, calculate total revenue per region, and save it as an Excel file with formatting.

Solution: Use the pandas library.

import pandas as pd

# 1. Read Data
df = pd.read_csv('daily_sales.csv')

# 2. Filter Data
# Keep only orders where Status is NOT 'Cancelled'
df_clean = df[df['Status'] != 'Cancelled']

# 3. Aggregate Data
# Group by Region and sum the Amount
report = df_clean.groupby('Region')['Amount'].sum().reset_index()

# 4. Save to Excel
report.to_excel('daily_report.xlsx', index=False)
print("Report generated successfully.")

Scenario 2: Building a High-Performance API

Problem: You need to build a microservice that serves machine learning predictions. It needs to be fast and handle concurrent requests.

Solution: Use FastAPI (modern, async).

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post("/predict/")
async def predict_price(item: Item):
    # Simulate a prediction model
    predicted_value = item.price * 1.1
    return {"item": item.name, "predicted_price": predicted_value}

# Run with: uvicorn main:app --reload

Scenario 3: Web Scraping for Market Research

Problem: You need to track the price of a competitor's product on their website daily.

Solution: Use requests and BeautifulSoup.

import requests
from bs4 import BeautifulSoup

url = 'https://example-competitor.com/product/123'
response = requests.get(url)

soup = BeautifulSoup(response.content, 'html.parser')
# Find the price element (inspect the HTML to find the class or ID)
price_element = soup.find('span', class_='product-price')

if price_element:
    print(f"Current Price: {price_element.text.strip()}")
else:
    print("Price not found.")