Python Projects for Practice: Beginner to Advanced - Textnotes

Python Projects for Practice: Beginner to Advanced

Practice Python programming with real-world projects. Build beginner projects like Calculator, intermediate projects like Web Scraper, and advanced projects like Chatbots, E-commerce websites, and ML model deployment.

Objective:

Apply Python knowledge to real-world scenarios through hands-on projects, improving problem-solving, coding skills, and project implementation experience.

Projects by Level:

1. Beginner Projects

Start practicing with simple, manageable projects to reinforce basic Python concepts.

  1. Calculator:
  2. Create a simple calculator that can perform addition, subtraction, multiplication, and division.

def calculator(a, b, op):
if op == '+':
return a + b
elif op == '-':
return a - b
elif op == '*':
return a * b
elif op == '/':
return a / b
else:
return "Invalid operator"

print(calculator(5, 3, '+')) # 8
  1. To-Do App:
  2. Command-line to-do list to add, view, and delete tasks.

tasks = []
tasks.append("Buy groceries")
tasks.append("Study Python")
print(tasks)
tasks.remove("Buy groceries")
print(tasks)
  1. Dice Rolling Simulator:
  2. Simulate rolling a dice using random numbers.

import random
dice_roll = random.randint(1, 6)
print(f"You rolled: {dice_roll}")

2. Intermediate Projects

Projects that involve working with data, APIs, and web interaction.

  1. Web Scraper:
  2. Scrape data from websites using BeautifulSoup or Scrapy.

import requests
from bs4 import BeautifulSoup

url = "https://www.example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
for link in soup.find_all("a"):
print(link.get("href"))
  1. REST API:
  2. Create an API using Flask or Django REST Framework.

from flask import Flask, jsonify
app = Flask(__name__)

@app.route("/api/users")
def users():
return jsonify([{"name": "Chinmaya", "age": 25}])

if __name__ == "__main__":
app.run(debug=True)
  1. Data Analysis Project:
  2. Analyze datasets with Pandas and visualize with Matplotlib/Seaborn.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv")
df['age'].hist()
plt.show()

3. Advanced Projects

Complex projects requiring integration of multiple skills, suitable for portfolio building.

  1. Chatbot:
  2. Build a rule-based or AI chatbot using Python libraries.

from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer

bot = ChatBot('MyBot')
trainer = ChatterBotCorpusTrainer(bot)
trainer.train("chatterbot.corpus.english")
response = bot.get_response("Hello")
print(response)
  1. E-commerce Website:
  2. Build a full-stack e-commerce app using Django, with product listing, cart, and payment integration.
  3. Machine Learning Model Deployment:
  4. Train a ML model with scikit-learn or TensorFlow and deploy it as a REST API using Flask/Django.

from flask import Flask, request, jsonify
import pickle

app = Flask(__name__)
model = pickle.load(open("model.pkl", "rb"))

@app.route("/predict", methods=["POST"])
def predict():
data = request.get_json()
prediction = model.predict([data["features"]])
return jsonify({"prediction": prediction.tolist()})

if __name__ == "__main__":
app.run(debug=True)

This section provides hands-on Python projects for all skill levels, helping learners solidify their knowledge and gain practical experience in coding, data handling, web development, and AI.