5 Python Projects
to Build Today
Stop watching, start building. Each project takes 1-3 hours, teaches real skills, and gives you something you can actually show off.
Create a program that generates secure, random passwords. The user picks a length and which character types to include (letters, numbers, symbols) — then gets a strong password instantly. Simple to build, genuinely useful, and a great intro to working with randomness in Python.
import random import string def generate_password(length, use_symbols=True): characters = string.ascii_letters + string.digits if use_symbols: characters += string.punctuation password = "" for i in range(length): password += random.choice(characters) return password # Ask the user length = int(input("Password length: ")) symbols = input("Include symbols? (y/n): ") result = generate_password(length, symbols == "y") print(f"Your password: {result}")
Try using random.shuffle() on a list of characters to make the password even more random. You can also add a check that ensures at least one uppercase letter, one digit, and one symbol are always included.
Add a "password strength" checker that rates the generated password as Weak, Medium, or Strong based on its length and character variety. Then generate multiple passwords and let the user pick their favourite.
Build a multiple-choice quiz game that asks questions, tracks your score, and tells you how you did at the end. You'll store the questions as a list of dictionaries — one of the most common data structures you'll use in real Python work.
questions = [
{
"question": "What does HTML stand for?",
"options": ["A) Hyper Text Markup Language",
"B) Hot Mail",
"C) How To Make Lasagna"],
"answer": "A"
},
{
"question": "Which planet is closest to the Sun?",
"options": ["A) Earth", "B) Mercury", "C) Venus"],
"answer": "B"
}
]
score = 0
for q in questions:
print(q["question"])
for option in q["options"]:
print(f" {option}")
user_answer = input("Your answer: ").upper()
if user_answer == q["answer"]:
print("Correct!\n")
score += 1
else:
print(f"Wrong! The answer was {q['answer']}\n")
print(f"You scored {score}/{len(questions)}")Add more questions to the list — the loop handles any number automatically. Try using random.shuffle(questions) at the start to randomise the order each time.
Add a timer using the time module to track how long the quiz takes. Show the final time alongside the score. Bonus: add difficulty levels that change how many options each question has.
Build a command-line app that logs expenses, categorises them, and shows you a summary. You'll save everything to a CSV file so nothing gets lost — and you'll learn how real applications store and retrieve data. This is the kind of project that makes a portfolio look serious.
import csv from datetime import datetime def add_expense(expenses): amount = float(input("Amount: £")) category = input("Category (food/transport/other): ") note = input("Note: ") expenses.append({ "date": datetime.now().strftime("%Y-%m-%d"), "amount": amount, "category": category, "note": note }) print("Expense added!\n") def show_summary(expenses): total = sum(e["amount"] for e in expenses) print(f"\nTotal spent: £{total:.2f}") categories = set(e["category"] for e in expenses) for cat in categories: cat_total = sum(e["amount"] for e in expenses if e["category"] == cat) print(f" {cat}: £{cat_total:.2f}") expenses = [] while True: action = input("(a)dd / (s)ummary / (q)uit: ") if action == "a": add_expense(expenses) elif action == "s": show_summary(expenses) elif action == "q": break
To save expenses permanently, write them to a CSV file using csv.DictWriter. Load them back in when the program starts with csv.DictReader. This way your data survives between sessions.
Use pandas to read the CSV and create a monthly spending breakdown. Which category do you spend the most on? Can you spot any patterns?
Write a script that visits a website and extracts useful data automatically. You'll use the requests library to fetch pages and BeautifulSoup to parse the HTML. Then you'll scrape multiple pages, clean up the data, and save it all to a CSV file. This is one of the most in-demand Python skills — every data role mentions it.
# First, install: pip install requests beautifulsoup4 pandas import requests from bs4 import BeautifulSoup import pandas as pd # Step 1: Scrape 5 pages of quotes all_data = [] for page in range(1, 6): url = f"https://quotes.toscrape.com/page/{page}/" response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser") for quote in soup.find_all("div", class_="quote"): text = quote.find("span", class_="text").text author = quote.find("small", class_="author").text all_data.append({"quote": text, "author": author}) print(f"Page {page} done — {len(all_data)} quotes so far") # Step 2: Save to CSV df = pd.DataFrame(all_data) df.to_csv("quotes.csv", index=False) print(f"\nSaved {len(df)} quotes to quotes.csv") # Step 3: Quick analysis print(f"\nTop 5 most quoted authors:") print(df["author"].value_counts().head(5))
Install the libraries first: pip install requests beautifulsoup4 pandas. The site used here is built specifically for scraping practice, so you won't get blocked. Use your browser's "Inspect Element" to understand the HTML structure before writing your selectors.
Find which tags appear most often across all quotes. Create a bar chart of the top 10 tags using matplotlib. Then try scraping a different website — like a news homepage or a product listing page.
Load a real CSV dataset with pandas, explore it, clean it up, and answer questions about it. You'll calculate averages, group data, find patterns, and create visualisations. This is what data scientists do every single day — and it's more satisfying than you'd think.
# First, install: pip install pandas matplotlib import pandas as pd import matplotlib.pyplot as plt # Step 1: Load a real dataset (restaurant tips) url = ("https://raw.githubusercontent.com/mwaskom/" "seaborn-data/master/tips.csv") df = pd.read_csv(url) # Step 2: Explore the data print("First 5 rows:") print(df.head()) print(f"\nShape: {df.shape[0]} rows, {df.shape[1]} columns") print(f"\nColumn names: {list(df.columns)}") # Step 3: Basic statistics print(f"\nAverage bill: ${df['total_bill'].mean():.2f}") print(f"Average tip: ${df['tip'].mean():.2f}") print(f"Highest tip: ${df['tip'].max():.2f}") # Step 4: Create a new column — tip percentage df["tip_pct"] = df["tip"] / df["total_bill"] * 100 print(f"\nAverage tip percentage: {df['tip_pct'].mean():.1f}%") # Step 5: Group by day — which day gets the best tips? by_day = df.groupby("day")["tip_pct"].mean().sort_values() print(f"\nAverage tip % by day:") print(by_day) # Step 6: Do smokers tip differently? by_smoker = df.groupby("smoker")["tip_pct"].mean() print(f"\nAverage tip % by smoker status:") print(by_smoker) # Step 7: Visualise — bar chart of tips by day fig, axes = plt.subplots(1, 2, figsize=(12, 5)) by_day.plot(kind="bar", color="#4CAF50", ax=axes[0]) axes[0].set_title("Average Tip % by Day") axes[0].set_ylabel("Tip %") # Scatter plot — bill vs tip axes[1].scatter(df["total_bill"], df["tip"], alpha=0.6, color="#4CAF50") axes[1].set_title("Bill Amount vs Tip") axes[1].set_xlabel("Total Bill ($)") axes[1].set_ylabel("Tip ($)") plt.tight_layout() plt.savefig("tips_analysis.png") print("\nCharts saved as tips_analysis.png")
Install pandas and matplotlib first: pip install pandas matplotlib. The dataset loads directly from a URL — no file downloads needed. Use df.describe() to get a quick statistical overview of every numeric column. If you want to use your own data, just replace the URL with a path to your Excel or CSV file.
Does table size affect tip percentage? Group by size and find out. Then create a third chart — a histogram showing the distribution of tip percentages. What's the most common tip range?