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AI Coding Masterclass

Copy-Paste Prompts, Step-by-Step Tutorials,

ProfessionalsAi Coding

17 min read·3,767 words

AI + CODING

THE PRACTICAL MASTERCLASS

Copy-Paste Prompts, Step-by-Step Tutorials,

and Real-World Examples for Every Coding Task

Stop Learning Syntax. Start Building.

What You'll Learn

The Prompting Framework — Write any code using plain English
Python Fundamentals — The best language for AI-assisted coding
Web Development — Build websites, apps, and interactive tools
Automation Scripts — Automate repetitive tasks in minutes
Data Analysis — Process, analyze, and visualize data
API Integration — Connect to web services and APIs
Debugging & Error Fixing — Fix broken code with AI assistance
Database Operations — Work with SQL and data storage
Real Projects by Role — Marketing, Finance, HR, Operations, Sales
No-Code & Low-Code Tools — Build apps without writing code

Chapter 1: The Art of Prompting for Code

Before we dive into specific tasks, you need to understand how to talk to AI about code. The difference between a broken script and working code often comes down to how you ask.

The Golden Formula for Code Prompts

Every great coding prompt has four components:

CONTEXT: What's the project? What language? What already exists?
GOAL: What specific outcome do you want? Inputs and outputs?
CONSTRAINTS: Any limitations, edge cases, or special conditions?
OUTPUT FORMAT: Code only? With explanation? Step-by-step?

Universal Code Request Template

Copy and customize this template for any coding request:


I'm building: [describe your project/goal] Technical details: - Language: [Python/JavaScript/etc. or "you choose"] - Input: [what the code receives] - Output: [what the code should produce] Requirements: - [specific feature 1] - [specific feature 2] - Handle these edge cases: [list potential problems] Please provide: - Complete, working code - Comments explaining key parts - Example of how to use it

I'm building: [describe your project/goal] Technical details: - Language: [Python/JavaScript/etc. or "you choose"] - Input: [what the code receives] - Output: [what the code should produce] Requirements: - [specific feature 1] - [specific feature 2] - Handle these edge cases: [list potential problems] Please provide: - Complete, working code - Comments explaining key parts - Example of how to use it

Chapter 2: Python - Your First Language

Python is the #1 recommended language for AI-assisted coding. It reads almost like English, works for nearly everything, and AI tools generate excellent Python code.

Your First Script: Hello World

YOUR PROMPT:


Write a simple Python script that: 1. Asks the user for their name 2. Prints a personalized greeting Include comments for a beginner.

AI RETURNS:


# My first Python script # Ask the user for their name name = input("What is your name? ") # Print a personalized greeting print(f"Hello, {name}! Welcome to Python.")

Age Calculator with Error Handling

YOUR PROMPT:


Write a Python script that: 1. Asks for the user's name 2. Asks for their birth year 3. Calculates their age 4. Handles invalid input (non-numbers) gracefully 5. Keeps asking until valid input is provided

AI RETURNS:


import datetime def get_age(): name = input("What's your name? ") while True: try: birth_year = int(input("What year were you born? ")) current_year = datetime.datetime.now().year if birth_year < 1900 or birth_year > current_year: print("Please enter a valid year.") continue age = current_year - birth_year print(f"\nHello {name}! You are {age} years old.") break except ValueError: print("Please enter a valid number.") get_age()

import datetime def get_age(): name = input("What's your name? ") while True: try: birth_year = int(input("What year were you born? ")) current_year = datetime.datetime.now().year if birth_year < 1900 or birth_year > current_year: print("Please enter a valid year.") continue age = current_year - birth_year print(f"\nHello {name}! You are {age} years old.") break except ValueError: print("Please enter a valid number.") get_age()

File Word Counter

YOUR PROMPT:


Write a Python script that: - Reads a text file - Counts total words, lines, and characters - Finds the 5 most common words - Outputs results to console

AI RETURNS:


from collections import Counter def analyze_file(filename): try: with open(filename, 'r') as file: content = file.read() # Count statistics lines = content.split('\n') words = content.split() chars = len(content) # Find most common words word_counts = Counter(words) top_5 = word_counts.most_common(5) # Display results print(f"File: {filename}") print(f"Lines: {len(lines)}") print(f"Words: {len(words)}") print(f"Characters: {chars}") print(f"\nTop 5 words:") for word, count in top_5: print(f" {word}: {count}") except FileNotFoundError: print(f"Error: File '{filename}' not found.") # Usage analyze_file("myfile.txt")

from collections import Counter def analyze_file(filename): try: with open(filename, 'r') as file: content = file.read() # Count statistics lines = content.split('\n') words = content.split() chars = len(content) # Find most common words word_counts = Counter(words) top_5 = word_counts.most_common(5) # Display results print(f"File: {filename}") print(f"Lines: {len(lines)}") print(f"Words: {len(words)}") print(f"Characters: {chars}") print(f"\nTop 5 words:") for word, count in top_5: print(f" {word}: {count}") except FileNotFoundError: print(f"Error: File '{filename}' not found.") # Usage analyze_file("myfile.txt")

Chapter 3: Automation Scripts

One of the most practical uses of AI-generated code: automating boring, repetitive tasks. Here are copy-paste prompts for common automation needs.

Bulk File Renamer

YOUR PROMPT:


Write a Python script that: - Scans a folder for all image files (.jpg, .png, .gif) - Renames them sequentially: photo_001.jpg, photo_002.jpg, etc. - Preserves original extensions - Creates a log showing old name → new name - Asks for confirmation before renaming

AI RETURNS:


import os from pathlib import Path def rename_images(folder_path): extensions = {'.jpg', '.jpeg', '.png', '.gif'} files = [] # Find all image files for f in Path(folder_path).iterdir(): if f.suffix.lower() in extensions: files.append(f) if not files: print("No image files found.") return # Show preview print(f"Found {len(files)} images:") for i, f in enumerate(sorted(files), 1): new_name = f"photo_{i:03d}{f.suffix}" print(f" {f.name} → {new_name}") # Confirm confirm = input("\nProceed with rename? (yes/no): ") if confirm.lower() != 'yes': print("Cancelled.") return # Rename files log = [] for i, f in enumerate(sorted(files), 1): new_name = f"photo_{i:03d}{f.suffix}" new_path = f.parent / new_name f.rename(new_path) log.append(f"{f.name} → {new_name}") # Save log with open('rename_log.txt', 'w') as f: f.write('\n'.join(log)) print(f"\nRenamed {len(files)} files. Log saved.") rename_images('.')

import os from pathlib import Path def rename_images(folder_path): extensions = {'.jpg', '.jpeg', '.png', '.gif'} files = [] # Find all image files for f in Path(folder_path).iterdir(): if f.suffix.lower() in extensions: files.append(f) if not files: print("No image files found.") return # Show preview print(f"Found {len(files)} images:") for i, f in enumerate(sorted(files), 1): new_name = f"photo_{i:03d}{f.suffix}" print(f" {f.name} → {new_name}") # Confirm confirm = input("\nProceed with rename? (yes/no): ") if confirm.lower() != 'yes': print("Cancelled.") return # Rename files log = [] for i, f in enumerate(sorted(files), 1): new_name = f"photo_{i:03d}{f.suffix}" new_path = f.parent / new_name f.rename(new_path) log.append(f"{f.name} → {new_name}") # Save log with open('rename_log.txt', 'w') as f: f.write('\n'.join(log)) print(f"\nRenamed {len(files)} files. Log saved.") rename_images('.')

Email List Cleaner

YOUR PROMPT:


Write a Python script that cleans an email list CSV: - Remove invalid email formats - Remove duplicates (case-insensitive) - Flag disposable email domains (mailinator, tempmail, etc.) - Output: valid.csv, invalid.csv, suspicious.csv - Show statistics at the end

AI RETURNS:


import re import csv def clean_emails(input_file): disposable_domains = {'mailinator.com', 'tempmail.com', 'guerrillamail.com', 'throwaway.com', '10minutemail.com', 'fakeinbox.com'} email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\[a-zA-Z]{2,}$' valid, invalid, suspicious = [], [], [] seen = set() with open(input_file, 'r') as f: reader = csv.reader(f) header = next(reader, None) for row in reader: if not row: continue email = row[0].strip().lower() # Skip duplicates if email in seen: continue seen.add(email) # Check format if not re.match(email_pattern, email): invalid.append([email]) continue # Check disposable domain = email.split('@')[1] if domain in disposable_domains: suspicious.append([email]) continue valid.append([email]) # Save results for name, data in [('valid', valid), ('invalid', invalid), ('suspicious', suspicious)]: with open(f'{name}.csv', 'w', newline='') as f: writer = csv.writer(f) writer.writerow(['email']) writer.writerows(data) print(f"Results:") print(f" Valid: {len(valid)}") print(f" Invalid: {len(invalid)}") print(f" Suspicious: {len(suspicious)}") clean_emails('emails.csv')

import re import csv def clean_emails(input_file): disposable_domains = {'mailinator.com', 'tempmail.com', 'guerrillamail.com', 'throwaway.com', '10minutemail.com', 'fakeinbox.com'} email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\[a-zA-Z]{2,}$' valid, invalid, suspicious = [], [], [] seen = set() with open(input_file, 'r') as f: reader = csv.reader(f) header = next(reader, None) for row in reader: if not row: continue email = row[0].strip().lower() # Skip duplicates if email in seen: continue seen.add(email) # Check format if not re.match(email_pattern, email): invalid.append([email]) continue # Check disposable domain = email.split('@')[1] if domain in disposable_domains: suspicious.append([email]) continue valid.append([email]) # Save results for name, data in [('valid', valid), ('invalid', invalid), ('suspicious', suspicious)]: with open(f'{name}.csv', 'w', newline='') as f: writer = csv.writer(f) writer.writerow(['email']) writer.writerows(data) print(f"Results:") print(f" Valid: {len(valid)}") print(f" Invalid: {len(invalid)}") print(f" Suspicious: {len(suspicious)}") clean_emails('emails.csv')

PDF Merger

YOUR PROMPT:


Write a Python script that: - Takes a folder path as input - Finds all PDF files in that folder - Merges them into one PDF (alphabetical order) - Names output "merged_[timestamp].pdf"

AI RETURNS:


from PyPDF2 import PdfMerger from pathlib import Path from datetime import datetime def merge_pdfs(folder_path): merger = PdfMerger() # Find all PDFs pdfs = sorted(Path(folder_path).glob('*.pdf')) if not pdfs: print("No PDF files found.") return print(f"Merging {len(pdfs)} PDFs:") for pdf in pdfs: print(f" + {pdf.name}") merger.append(str(pdf)) # Create output filename timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') output = f"merged_{timestamp}.pdf" merger.write(output) merger.close() print(f"\nCreated: {output}") # Usage: merge_pdfs('/path/to/folder') merge_pdfs('.')

from PyPDF2 import PdfMerger from pathlib import Path from datetime import datetime def merge_pdfs(folder_path): merger = PdfMerger() # Find all PDFs pdfs = sorted(Path(folder_path).glob('*.pdf')) if not pdfs: print("No PDF files found.") return print(f"Merging {len(pdfs)} PDFs:") for pdf in pdfs: print(f" + {pdf.name}") merger.append(str(pdf)) # Create output filename timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') output = f"merged_{timestamp}.pdf" merger.write(output) merger.close() print(f"\nCreated: {output}") # Usage: merge_pdfs('/path/to/folder') merge_pdfs('.')

Chapter 4: Web Development

AI excels at generating HTML, CSS, and JavaScript. You can build anything from simple landing pages to interactive web applications.

Professional Landing Page

YOUR PROMPT:


Create a modern landing page for a coffee shop called "Bean There": - Hero section with tagline and CTA button - About section - Menu section with 6 items - Contact section with hours - Footer Use HTML and CSS in one file. Warm brown/cream colors. Mobile-friendly.

AI RETURNS:


<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Bean There Coffee Shop</title> <style> * { margin: 0; padding: 0; box-sizing: border-box; } body { font-family: 'Georgia', serif; color: #3d2c1f; } /* Hero / .hero { background: linear-gradient(rgba(0,0,0,0.5), rgba(0,0,0,0.5)), url('coffee-bg.jpg') center/cover; height: 100vh; display: flex; align-items: center; justify-content: center; text-align: center; color: white; } .hero h1 { font-size: 4rem; margin-bottom: 1rem; } .hero p { font-size: 1.5rem; margin-bottom: 2rem; } .btn { background: #c4a35a; color: white; padding: 15px 40px; text-decoration: none; font-size: 1.2rem; border-radius: 5px; } / Sections / section { padding: 80px 20px; max-width: 1200px; margin: 0 auto; } h2 { font-size: 2.5rem; text-align: center; margin-bottom: 40px; } / Menu Grid / .menu-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 30px; } .menu-item { background: #f5f0e8; padding: 30px; border-radius: 10px; } .menu-item h3 { color: #c4a35a; margin-bottom: 10px; } .price { font-weight: bold; color: #3d2c1f; } / Footer */ footer { background: #3d2c1f; color: #f5f0e8; padding: 40px; text-align: center; } </style> </head> <body> <section class="hero"> <div> <h1>Bean There</h1> <p>Life's too short for bad coffee</p> <a href="#menu" class="btn">View Menu</a> </div> </section> <section id="about"> <h2>Our Story</h2> <p style="text-align:center;max-width:600px;margin:0 auto;"> Since 2010, we've been serving the finest locally-roasted coffee... </p> </section> <section id="menu"> <h2>Our Menu</h2> <div class="menu-grid"> <div class="menu-item"> <h3>Espresso</h3> <p>Rich, bold, classic.</p> <p class="price">$3.50</p> </div> <!-- Add 5 more menu items --> </div> </section> <footer> <p>© 2024 Bean There Coffee \| Open Daily 7am-7pm</p> </footer> </body> </html>

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Bean There Coffee Shop</title> <style> * { margin: 0; padding: 0; box-sizing: border-box; } body { font-family: 'Georgia', serif; color: #3d2c1f; } /* Hero */ .hero { background: linear-gradient(rgba(0,0,0,0.5), rgba(0,0,0,0.5)), url('coffee-bg.jpg') center/cover; height: 100vh; display: flex; align-items: center; justify-content: center; text-align: center; color: white; } .hero h1 { font-size: 4rem; margin-bottom: 1rem; } .hero p { font-size: 1.5rem; margin-bottom: 2rem; } .btn { background: #c4a35a; color: white; padding: 15px 40px; text-decoration: none; font-size: 1.2rem; border-radius: 5px; } /* Sections */ section { padding: 80px 20px; max-width: 1200px; margin: 0 auto; } h2 { font-size: 2.5rem; text-align: center; margin-bottom: 40px; } /* Menu Grid */ .menu-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 30px; } .menu-item { background: #f5f0e8; padding: 30px; border-radius: 10px; } .menu-item h3 { color: #c4a35a; margin-bottom: 10px; } .price { font-weight: bold; color: #3d2c1f; } /* Footer */ footer { background: #3d2c1f; color: #f5f0e8; padding: 40px; text-align: center; } </style> </head> <body> <section class="hero"> <div> <h1>Bean There</h1> <p>Life's too short for bad coffee</p> <a href="#menu" class="btn">View Menu</a> </div> </section> <section id="about"> <h2>Our Story</h2> <p style="text-align:center;max-width:600px;margin:0 auto;"> Since 2010, we've been serving the finest locally-roasted coffee... </p> </section> <section id="menu"> <h2>Our Menu</h2> <div class="menu-grid"> <div class="menu-item"> <h3>Espresso</h3> <p>Rich, bold, classic.</p> <p class="price">$3.50</p> </div>  </div> </section> <footer> <p>© 2024 Bean There Coffee | Open Daily 7am-7pm</p> </footer> </body> </html>

Interactive Tip Calculator

YOUR PROMPT:


Create a tip calculator web app: - Input for bill amount - Slider or buttons for tip percentage (15%, 18%, 20%, 25%) - Number of people splitting - Show: tip amount, total, per-person amount - Calculate in real-time as user types - Modern, clean design in single HTML file

AI RETURNS:


<!DOCTYPE html> <html> <head> <title>Tip Calculator</title> <style> body { font-family: Arial; background: #f0f0f0; display: flex; justify-content: center; padding: 40px; } .calculator { background: white; padding: 30px; border-radius: 15px; box-shadow: 0 10px 30px rgba(0,0,0,0.1); width: 350px; } h1 { text-align: center; color: #333; margin-bottom: 25px; } .input-group { margin-bottom: 20px; } label { display: block; margin-bottom: 5px; font-weight: bold; } input { width: 100%; padding: 12px; border: 2px solid #ddd; border-radius: 8px; font-size: 16px; } .tip-buttons { display: flex; gap: 10px; } .tip-btn { flex: 1; padding: 12px; border: 2px solid #00b894; background: white; border-radius: 8px; cursor: pointer; } .tip-btn.active { background: #00b894; color: white; } .results { background: #00b894; color: white; padding: 20px; border-radius: 10px; margin-top: 20px; } .result-row { display: flex; justify-content: space-between; margin: 10px 0; font-size: 18px; } .big { font-size: 28px; font-weight: bold; } </style> </head> <body> <div class="calculator"> <h1>💰 Tip Calculator</h1> <div class="input-group"> <label>Bill Amount</label> <input type="number" id="bill" placeholder="0.00" oninput="calc()"> </div> <div class="input-group"> <label>Tip Percentage</label> <div class="tip-buttons"> <button class="tip-btn" onclick="setTip(15)">15%</button> <button class="tip-btn active" onclick="setTip(18)">18%</button> <button class="tip-btn" onclick="setTip(20)">20%</button> <button class="tip-btn" onclick="setTip(25)">25%</button> </div> </div> <div class="input-group"> <label>Number of People</label> <input type="number" id="people" value="1" min="1" oninput="calc()"> </div> <div class="results"> <div class="result-row"><span>Tip Amount</span><span id="tipAmt">$0.00</span></div> <div class="result-row"><span>Total</span><span id="total">$0.00</span></div> <div class="result-row big"><span>Per Person</span><span id="perPerson">$0.00</span></div> </div> </div> <script> let tipPercent = 18; function setTip(pct) { tipPercent = pct; document.querySelectorAll('.tip-btn').forEach(b => b.classList.remove('active')); event.target.classList.add('active'); calc(); } function calc() { const bill = parseFloat(document.getElementById('bill').value) \|\| 0; const people = parseInt(document.getElementById('people').value) \|\| 1; const tip = bill * (tipPercent / 100); const total = bill + tip; const perPerson = total / people; document.getElementById('tipAmt').textContent = '$' + tip.toFixed(2); document.getElementById('total').textContent = '$' + total.toFixed(2); document.getElementById('perPerson').textContent = '$' + perPerson.toFixed(2); } </script> </body> </html>

<!DOCTYPE html> <html> <head> <title>Tip Calculator</title> <style> body { font-family: Arial; background: #f0f0f0; display: flex; justify-content: center; padding: 40px; } .calculator { background: white; padding: 30px; border-radius: 15px; box-shadow: 0 10px 30px rgba(0,0,0,0.1); width: 350px; } h1 { text-align: center; color: #333; margin-bottom: 25px; } .input-group { margin-bottom: 20px; } label { display: block; margin-bottom: 5px; font-weight: bold; } input { width: 100%; padding: 12px; border: 2px solid #ddd; border-radius: 8px; font-size: 16px; } .tip-buttons { display: flex; gap: 10px; } .tip-btn { flex: 1; padding: 12px; border: 2px solid #00b894; background: white; border-radius: 8px; cursor: pointer; } .tip-btn.active { background: #00b894; color: white; } .results { background: #00b894; color: white; padding: 20px; border-radius: 10px; margin-top: 20px; } .result-row { display: flex; justify-content: space-between; margin: 10px 0; font-size: 18px; } .big { font-size: 28px; font-weight: bold; } </style> </head> <body> <div class="calculator"> <h1>💰 Tip Calculator</h1> <div class="input-group"> <label>Bill Amount</label> <input type="number" id="bill" placeholder="0.00" oninput="calc()"> </div> <div class="input-group"> <label>Tip Percentage</label> <div class="tip-buttons"> <button class="tip-btn" onclick="setTip(15)">15%</button> <button class="tip-btn active" onclick="setTip(18)">18%</button> <button class="tip-btn" onclick="setTip(20)">20%</button> <button class="tip-btn" onclick="setTip(25)">25%</button> </div> </div> <div class="input-group"> <label>Number of People</label> <input type="number" id="people" value="1" min="1" oninput="calc()"> </div> <div class="results"> <div class="result-row"><span>Tip Amount</span><span id="tipAmt">$0.00</span></div> <div class="result-row"><span>Total</span><span id="total">$0.00</span></div> <div class="result-row big"><span>Per Person</span><span id="perPerson">$0.00</span></div> </div> </div> <script> let tipPercent = 18; function setTip(pct) { tipPercent = pct; document.querySelectorAll('.tip-btn').forEach(b => b.classList.remove('active')); event.target.classList.add('active'); calc(); } function calc() { const bill = parseFloat(document.getElementById('bill').value) || 0; const people = parseInt(document.getElementById('people').value) || 1; const tip = bill * (tipPercent / 100); const total = bill + tip; const perPerson = total / people; document.getElementById('tipAmt').textContent = '$' + tip.toFixed(2); document.getElementById('total').textContent = '$' + total.toFixed(2); document.getElementById('perPerson').textContent = '$' + perPerson.toFixed(2); } </script> </body> </html>

Chapter 5: Data Analysis with Python

Python with pandas is the gold standard for data analysis. AI can write sophisticated analysis scripts with simple prompts.

Sales Data Analysis

YOUR PROMPT:


Write a Python script using pandas that analyzes a sales CSV file: Columns: Date, Product, Category, Quantity, UnitPrice, Region Calculate and display: 1. Total revenue and units sold 2. Revenue by category (sorted high to low) 3. Top 5 products by revenue 4. Best performing region 5. Monthly revenue trend Save summary to Excel file with multiple sheets.

AI RETURNS:


import pandas as pd def analyze_sales(filename): # Load data df = pd.read_csv(filename) df['Revenue'] = df['Quantity'] * df['UnitPrice'] df['Date'] = pd.to_datetime(df['Date']) # Total metrics total_revenue = df['Revenue'].sum() total_units = df['Quantity'].sum() print("Total Revenue: ${:,.2f}".format(total_revenue)) print("Total Units Sold: {:,}".format(total_units)) # Revenue by category by_category = df.groupby('Category')['Revenue'].sum().sort_values(ascending=False) print("\nRevenue by Category:") print(by_category) # Top 5 products top_products = df.groupby('Product')['Revenue'].sum().nlargest(5) print("\nTop 5 Products:") print(top_products) # Best region by_region = df.groupby('Region')['Revenue'].sum() best_region = by_region.idxmax() best_value = by_region[best_region] print("\nBest Region: {} (${:,.2f})".format(best_region, best_value)) # Monthly trend df['Month'] = df['Date'].dt.to_period('M') monthly = df.groupby('Month')['Revenue'].sum() # Save to Excel with pd.ExcelWriter('sales_analysis.xlsx') as writer: by_category.to_frame().to_excel(writer, sheet_name='By Category') top_products.to_frame().to_excel(writer, sheet_name='Top Products') by_region.to_frame().to_excel(writer, sheet_name='By Region') monthly.to_frame().to_excel(writer, sheet_name='Monthly Trend') print("\nAnalysis saved to sales_analysis.xlsx") analyze_sales('sales_data.csv')

import pandas as pd def analyze_sales(filename): # Load data df = pd.read_csv(filename) df['Revenue'] = df['Quantity'] * df['UnitPrice'] df['Date'] = pd.to_datetime(df['Date']) # Total metrics total_revenue = df['Revenue'].sum() total_units = df['Quantity'].sum() print("Total Revenue: ${:,.2f}".format(total_revenue)) print("Total Units Sold: {:,}".format(total_units)) # Revenue by category by_category = df.groupby('Category')['Revenue'].sum().sort_values(ascending=False) print("\nRevenue by Category:") print(by_category) # Top 5 products top_products = df.groupby('Product')['Revenue'].sum().nlargest(5) print("\nTop 5 Products:") print(top_products) # Best region by_region = df.groupby('Region')['Revenue'].sum() best_region = by_region.idxmax() best_value = by_region[best_region] print("\nBest Region: {} (${:,.2f})".format(best_region, best_value)) # Monthly trend df['Month'] = df['Date'].dt.to_period('M') monthly = df.groupby('Month')['Revenue'].sum() # Save to Excel with pd.ExcelWriter('sales_analysis.xlsx') as writer: by_category.to_frame().to_excel(writer, sheet_name='By Category') top_products.to_frame().to_excel(writer, sheet_name='Top Products') by_region.to_frame().to_excel(writer, sheet_name='By Region') monthly.to_frame().to_excel(writer, sheet_name='Monthly Trend') print("\nAnalysis saved to sales_analysis.xlsx") analyze_sales('sales_data.csv')

Customer Segmentation

YOUR PROMPT:


Using pandas, analyze customer data and segment customers: Columns: CustomerID, TotalPurchases, TotalSpent, DaysSinceLastPurchase Create segments: - VIP: Top 10% by spending - Regular: Purchased in last 90 days - At-Risk: No purchase in 90-180 days - Churned: No purchase in 180+ days Output summary stats for each segment.

AI RETURNS:


import pandas as pd import numpy as np def segment_customers(filename): df = pd.read_csv(filename) # Calculate spending threshold for VIP (top 10%) vip_threshold = df['TotalSpent'].quantile(0.90) # Create segments def assign_segment(row): if row['TotalSpent'] >= vip_threshold: return 'VIP' elif row['DaysSinceLastPurchase'] <= 90: return 'Regular' elif row['DaysSinceLastPurchase'] <= 180: return 'At-Risk' else: return 'Churned' df['Segment'] = df.apply(assign_segment, axis=1) # Summary statistics summary = df.groupby('Segment').agg({ 'CustomerID': 'count', 'TotalSpent': ['mean', 'sum'], 'TotalPurchases': 'mean', 'DaysSinceLastPurchase': 'mean' }).round(2) summary.columns = ['Count', 'Avg Spent', 'Total Spent', 'Avg Purchases', 'Avg Days Inactive'] print("Customer Segmentation Summary:") print(summary) # Save segmented data df.to_csv('customers_segmented.csv', index=False) print("\nSegmented data saved to customers_segmented.csv") segment_customers('customers.csv')

import pandas as pd import numpy as np def segment_customers(filename): df = pd.read_csv(filename) # Calculate spending threshold for VIP (top 10%) vip_threshold = df['TotalSpent'].quantile(0.90) # Create segments def assign_segment(row): if row['TotalSpent'] >= vip_threshold: return 'VIP' elif row['DaysSinceLastPurchase'] <= 90: return 'Regular' elif row['DaysSinceLastPurchase'] <= 180: return 'At-Risk' else: return 'Churned' df['Segment'] = df.apply(assign_segment, axis=1) # Summary statistics summary = df.groupby('Segment').agg({ 'CustomerID': 'count', 'TotalSpent': ['mean', 'sum'], 'TotalPurchases': 'mean', 'DaysSinceLastPurchase': 'mean' }).round(2) summary.columns = ['Count', 'Avg Spent', 'Total Spent', 'Avg Purchases', 'Avg Days Inactive'] print("Customer Segmentation Summary:") print(summary) # Save segmented data df.to_csv('customers_segmented.csv', index=False) print("\nSegmented data saved to customers_segmented.csv") segment_customers('customers.csv')

Chapter 6: Debugging & Error Fixing

AI is remarkably good at debugging. When code doesn't work, describe the problem clearly and AI will identify the issue.

The Debugging Template

Copy this whenever you need help fixing code:


My code isn't working. Here's the situation: The Code: [paste your code here] Error Message: [paste the exact error] Expected Behavior: [what should happen] Actual Behavior: [what's happening instead] Please: 1. Identify the problem 2. Explain why it's happening 3. Provide the fixed code 4. Explain what you changed

Common Python Errors


Error	What It Means
SyntaxError	Typo, missing colon, unmatched parentheses
NameError	Variable doesn't exist (typo or not defined)
TypeError	Wrong data type (adding string to number)
IndexError	Accessing list item that doesn't exist
KeyError	Dictionary key doesn't exist
FileNotFoundError	File path is wrong or file doesn't exist

Ask AI to Explain Code

YOUR PROMPT:


Explain this code line by line. I'm a beginner. def fibonacci(n): if n <= 1: return n return fibonacci(n-1) + fibonacci(n-2) For each line, explain what it does and why.

AI RETURNS:


This is a recursive function that calculates Fibonacci numbers: Line 1: def fibonacci(n): - Defines a function named "fibonacci" that takes one parameter "n" - "n" represents which Fibonacci number we want (1st, 2nd, 5th, etc.) Line 2: if n <= 1: - Base case: if n is 0 or 1, we stop recursing - The first two Fibonacci numbers are 0 and 1 Line 3: return n - If n is 0, return 0. If n is 1, return 1 - These are our "known answers" that don't need calculation Line 4: return fibonacci(n-1) + fibonacci(n-2) - For any other number, the Fibonacci value is the sum of the previous two Fibonacci numbers - The function calls itself twice (recursion) - Example: fibonacci(5) = fibonacci(4) + fibonacci(3) The Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21...

This is a recursive function that calculates Fibonacci numbers: Line 1: def fibonacci(n): - Defines a function named "fibonacci" that takes one parameter "n" - "n" represents which Fibonacci number we want (1st, 2nd, 5th, etc.) Line 2: if n <= 1: - Base case: if n is 0 or 1, we stop recursing - The first two Fibonacci numbers are 0 and 1 Line 3: return n - If n is 0, return 0. If n is 1, return 1 - These are our "known answers" that don't need calculation Line 4: return fibonacci(n-1) + fibonacci(n-2) - For any other number, the Fibonacci value is the sum of the previous two Fibonacci numbers - The function calls itself twice (recursion) - Example: fibonacci(5) = fibonacci(4) + fibonacci(3) The Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21...

Chapter 7: Working with APIs

APIs let your code talk to other services—get weather data, send messages, access databases, and more.

Weather Data Fetcher

YOUR PROMPT:


Write a Python script that: - Gets current weather for a city using OpenWeatherMap API - User inputs city name - Shows temperature (F and C), conditions, humidity, wind - Handles errors gracefully - Note: I'll add my API key where indicated

AI RETURNS:


import requests def get_weather(city, api_key): url = f"http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: response = requests.get(url, params=params) response.raise_for_status() data = response.json() # Extract data temp_c = data['main']['temp'] temp_f = (temp_c * 9/5) + 32 conditions = data['weather'][0]['description'] humidity = data['main']['humidity'] wind_speed = data['wind']['speed'] # Display print(f"\nWeather in {city}:") print(f" Temperature: {temp_f:.1f}°F ({temp_c:.1f}°C)") print(f" Conditions: {conditions.capitalize()}") print(f" Humidity: {humidity}%") print(f" Wind: {wind_speed} m/s") except requests.exceptions.HTTPError: print(f"City '{city}' not found.") except requests.exceptions.ConnectionError: print("Connection error. Check your internet.") # Usage API_KEY = "your_api_key_here" # Get free key from openweathermap.org city = input("Enter city name: ") get_weather(city, API_KEY)

import requests def get_weather(city, api_key): url = f"http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: response = requests.get(url, params=params) response.raise_for_status() data = response.json() # Extract data temp_c = data['main']['temp'] temp_f = (temp_c * 9/5) + 32 conditions = data['weather'][0]['description'] humidity = data['main']['humidity'] wind_speed = data['wind']['speed'] # Display print(f"\nWeather in {city}:") print(f" Temperature: {temp_f:.1f}°F ({temp_c:.1f}°C)") print(f" Conditions: {conditions.capitalize()}") print(f" Humidity: {humidity}%") print(f" Wind: {wind_speed} m/s") except requests.exceptions.HTTPError: print(f"City '{city}' not found.") except requests.exceptions.ConnectionError: print("Connection error. Check your internet.") # Usage API_KEY = "your_api_key_here" # Get free key from openweathermap.org city = input("Enter city name: ") get_weather(city, API_KEY)

Simple REST API Creation

YOUR PROMPT:


Create a simple REST API using Python Flask: - GET /tasks - list all tasks - POST /tasks - create new task - PUT /tasks/<id> - update task - DELETE /tasks/<id> - delete task Store tasks in memory. Include error handling.

AI RETURNS:


from flask import Flask, request, jsonify app = Flask(__name__) tasks = [] next_id = 1 @app.route('/tasks', methods=['GET']) def get_tasks(): return jsonify(tasks) @app.route('/tasks', methods=['POST']) def create_task(): global next_id data = request.json if not data or 'title' not in data: return jsonify({'error': 'Title required'}), 400 task = { 'id': next_id, 'title': data['title'], 'completed': False } tasks.append(task) next_id += 1 return jsonify(task), 201 @app.route('/tasks/<int:task_id>', methods=['PUT']) def update_task(task_id): task = next((t for t in tasks if t['id'] == task_id), None) if not task: return jsonify({'error': 'Not found'}), 404 data = request.json task['title'] = data.get('title', task['title']) task['completed'] = data.get('completed', task['completed']) return jsonify(task) @app.route('/tasks/<int:task_id>', methods=['DELETE']) def delete_task(task_id): global tasks tasks = [t for t in tasks if t['id'] != task_id] return '', 204 if __name__ == '__main__': app.run(debug=True)

from flask import Flask, request, jsonify app = Flask(__name__) tasks = [] next_id = 1 @app.route('/tasks', methods=['GET']) def get_tasks(): return jsonify(tasks) @app.route('/tasks', methods=['POST']) def create_task(): global next_id data = request.json if not data or 'title' not in data: return jsonify({'error': 'Title required'}), 400 task = { 'id': next_id, 'title': data['title'], 'completed': False } tasks.append(task) next_id += 1 return jsonify(task), 201 @app.route('/tasks/<int:task_id>', methods=['PUT']) def update_task(task_id): task = next((t for t in tasks if t['id'] == task_id), None) if not task: return jsonify({'error': 'Not found'}), 404 data = request.json task['title'] = data.get('title', task['title']) task['completed'] = data.get('completed', task['completed']) return jsonify(task) @app.route('/tasks/<int:task_id>', methods=['DELETE']) def delete_task(task_id): global tasks tasks = [t for t in tasks if t['id'] != task_id] return '', 204 if __name__ == '__main__': app.run(debug=True)

Chapter 8: Real Projects by Department

Here are complete, copy-paste prompts for practical projects organized by department.

📊 Marketing: Campaign Performance Tracker


Write a Python script that: - Reads marketing campaign data from CSV (columns: Campaign, Channel, Spend, Impressions, Clicks, Conversions, Revenue) - Calculates for each campaign: CTR, Conversion Rate, CPA, ROAS - Identifies top 3 campaigns by ROAS - Identifies underperforming campaigns (ROAS < 1) - Creates a summary HTML report with tables - Save to "campaign_report.html"

💰 Finance: Budget vs Actual Analyzer


Write a Python script that: - Reads two CSVs: budget.csv and actual.csv - Both have columns: Category, Jan, Feb, Mar, Apr, May, Jun - Calculates variance (actual - budget) for each month - Calculates % variance - Flags categories where actual exceeds budget by >10% - Creates Excel report with conditional formatting (red for over budget, green for under)

👥 HR: Employee Tenure Report


Write a Python script that: - Reads employee data CSV (Name, Department, HireDate, Status) - Calculates tenure for each employee - Groups by department and calculates: - Average tenure - Number of employees - Turnover rate (if Status field exists) - Identifies employees with anniversaries this month - Outputs formatted report to console and saves to Excel

📦 Operations: Inventory Reorder Alert


Write a Python script that: - Reads inventory CSV (SKU, ProductName, CurrentStock, ReorderLevel, LeadTimeDays, DailyUsage) - Calculates days until stockout for each item - Creates three lists: - Critical (stockout < lead time) - Warning (stockout < 2x lead time) - OK (everything else) - Generates email-ready HTML report - Saves list of items to reorder as CSV

🛒 Sales: Lead Scoring Calculator


Write a Python script that scores leads based on criteria: - Company size: Enterprise(+30), Mid-market(+20), SMB(+10) - Engagement: Downloaded whitepaper(+15), Attended webinar(+20), Requested demo(+30) - Budget: Confirmed(+25), Exploring(+10), Unknown(0) - Timeline: This quarter(+20), Next quarter(+10), No timeline(0) Read leads from CSV, calculate total score, categorize as: - Hot (80+), Warm (50-79), Cold (<50) Output prioritized list sorted by score.

Write a Python script that scores leads based on criteria: - Company size: Enterprise(+30), Mid-market(+20), SMB(+10) - Engagement: Downloaded whitepaper(+15), Attended webinar(+20), Requested demo(+30) - Budget: Confirmed(+25), Exploring(+10), Unknown(0) - Timeline: This quarter(+20), Next quarter(+10), No timeline(0) Read leads from CSV, calculate total score, categorize as: - Hot (80+), Warm (50-79), Cold (<50) Output prioritized list sorted by score.

Chapter 9: Best Practices & Tips

The 5 Rules of AI-Assisted Coding

Always test the code. AI generates plausible code that may have subtle bugs. Run it, test edge cases, verify results.
Be specific in prompts. The more detail you provide (inputs, outputs, edge cases), the better the output.
Iterate and refine. First attempt not perfect? Ask AI to modify, fix, or improve specific parts.
Ask for explanations. If you don't understand the code, ask AI to explain it. This builds your knowledge.
Save working prompts. Build a library of prompts that work well. Reuse and adapt them.

Power User Prompting Techniques

Chain prompts: "Now modify the code to also handle X"
Request review: "Review this code for bugs, security issues, and improvements"
Ask for alternatives: "Show me 3 different ways to solve this"
Request tests: "Write unit tests for this function"
Optimize: "Make this code more efficient/readable/maintainable"

Start Building Today

You now have everything you need to start building with AI-assisted code. Let's recap:

The prompting framework: Context + Goal + Constraints + Format
Python basics and real scripts you can use immediately
Automation scripts that save hours of manual work
Web development for sites and interactive tools
Data analysis with pandas for business insights
Debugging techniques to fix any error
API integration to connect with web services
Department-specific projects ready to customize

Your First Assignment: Pick one project from this guide and build it today. Don't wait until you "know more." The best way to learn is by doing.

Start building. The future is yours to code.