Top 15 Python Projects for 2024 - Code Available
Scientific computing, machine learning, and data analysis all heavily rely on Python, making it an essential tool for data science experts.
Python remains the second most-used programming language on GitHub. Interestingly, Python's use grew more than 22% year over year, with more than four million developers on GitHub. source
{% module_block module "widget_11ecd362-512c-49ee-bd11-e1207b10d60f" %}{% module_attribute "child_css" is_json="true" %}{% raw %}{}{% endraw %}{% end_module_attribute %}{% module_attribute "css" is_json="true" %}{% raw %}{}{% endraw %}{% end_module_attribute %}{% module_attribute "definition_id" is_json="true" %}{% raw %}null{% endraw %}{% end_module_attribute %}{% module_attribute "field_types" is_json="true" %}{% raw %}{"link":"text","text":"text"}{% endraw %}{% end_module_attribute %}{% module_attribute "label" is_json="true" %}{% raw %}null{% endraw %}{% end_module_attribute %}{% module_attribute "link" is_json="true" %}{% raw %}"https://www.odinschool.com/blog/python-jobs-2024"{% endraw %}{% end_module_attribute %}{% module_attribute "module_id" is_json="true" %}{% raw %}135590387735{% endraw %}{% end_module_attribute %}{% module_attribute "path" is_json="true" %}{% raw %}"/OdinSchool_V3/modules/Blog/blog - source links"{% endraw %}{% end_module_attribute %}{% module_attribute "schema_version" is_json="true" %}{% raw %}2{% endraw %}{% end_module_attribute %}{% module_attribute "smart_objects" is_json="true" %}{% raw %}null{% endraw %}{% end_module_attribute %}{% module_attribute "smart_type" is_json="true" %}{% raw %}"NOT_SMART"{% endraw %}{% end_module_attribute %}{% module_attribute "tag" is_json="true" %}{% raw %}"module"{% endraw %}{% end_module_attribute %}{% module_attribute "text" is_json="true" %}{% raw %}"Python Jobs [2024]"{% endraw %}{% end_module_attribute %}{% module_attribute "type" is_json="true" %}{% raw %}"module"{% endraw %}{% end_module_attribute %}{% module_attribute "wrap_field_tag" is_json="true" %}{% raw %}"div"{% endraw %}{% end_module_attribute %}{% end_module_block %}The process of improving your Python programming abilities never ends, regardless of your level of experience. Hence, its important to join a comprehensive data science course whose curriculum is always updated as per the industry standards.
The best example would be that of Sourav Karmakar, who used his bench time at TCS to practice Python, and here he is, a successful data scientist with Tiger Analytics, drawing almost 6 times the salary he used to get before the transition.
This article will help you practice more by providing code along with the top Python projects.
Beginner-level Python Projects
#1. Personal Budget Manager
A command-line application to manage your personal finances. You can add expenses and income, categorize them, and see summaries of your financial activities.
import json
def load_transactions():
try:
with open('transactions.json', 'r') as file:
return json.load(file)
except FileNotFoundError:
return []
def save_transactions(transactions):
with open('transactions.json', 'w') as file:
json.dump(transactions, file, indent=4)
def add_transaction(transactions, type, amount, category):
transactions.append({"type": type, "amount": amount, "category": category})
save_transactions(transactions)
def show_summary(transactions):
income = sum(t['amount'] for t in transactions if t['type'] == 'income')
expense = sum(t['amount'] for t in transactions if t['type'] == 'expense')
balance = income - expense
print(f"Total Income: {income}, Total Expense: {expense}, Balance: {balance}")
transactions = load_transactions()
# Example usage
add_transaction(transactions, 'income', 500, 'salary')
add_transaction(transactions, 'expense', 100, 'groceries')
show_summary(transactions)
#2. Simple Chatbot
A simple chatbot that can perform basic interactions with the user, using predefined responses to simulate conversation.
def chatbot_response(message):
message = message.lower()
if "hello" in message:
return "Hello there! How can I help you?"
elif "how are you" in message:
return "I'm a bot, so I'm always doing well, thanks!"
elif "bye" in message:
return "Goodbye! Have a nice day!"
else:
return "I'm not sure how to respond to that. Can you ask something else?"
# Example interaction with the chatbot
user_message = input("You: ")
print("Bot:", chatbot_response(user_message))
#3. Email Slicer
An application that takes an email address and extracts the username and domain name.
Key Concepts Covered
-
String manipulation and methods
-
Working with user input
def email_slicer(email):
username, domain = email.split('@')
return username, domain
email = input("Enter your email: ").strip()
username, domain = email_slicer(email)
print(f"Username: {username}, Domain: {domain}")
#4. Desktop Notifier App
A desktop notifier app that sends you a notification at scheduled intervals.
Key Concepts Covered
-
Working with external libraries (plyer)
-
Scheduling tasks
from plyer import notification
import time
def send_notification(title, message):
notification.notify(
title=title,
message=message,
timeout=10
)
while True:
# Send a notification every hour
send_notification("Reminder", "Stretch your legs! It's been an hour.")
time.sleep(3600) # Sleep for 1 hour
#5. Currency Converter
A simple currency converter that uses an API to fetch the latest exchange rates and perform currency conversion.Key Concepts Covered
-
API requests
-
Handling JSON data
-
User input and error handling
import requests
def convert_currency(amount, from_currency, to_currency, api_key):
url = f"https://api.exchangerate-api.com/v4/latest/{from_currency}"
response = requests.get(url)
data = response.json()
rates = data['rates']
converted_amount = amount * rates[to_currency]
return converted_amount
# Example usage (You'll need a valid API key)
amount = 100
from_currency = 'USD'
to_currency = 'EUR'
api_key = 'YOUR_API_KEY_HERE'
print(f"{amount} {from_currency} is equal to {convert_currency(amount, from_currency, to_currency, api_key)} {to_currency}")
Steps to get API key
-
Visit the ExchangeRate-API website: Go to the ExchangeRate-API website.
-
Sign Up for an Account: You will need to sign up for an account by providing your email address and creating a password. ExchangeRate-API usually offers a free tier, which might be sufficient for basic usage or testing purposes.
-
Get Your API Key: After signing up, you will be given an API key. This is a unique identifier that allows you to make requests to their service.
-
Use Your API Key in the Code: Replace 'YOUR_API_KEY_HERE' in your code with the actual API key you received. Note, however, that your current code snippet does not utilize the api_key variable in the request. To properly use the API key, you'll likely need to adjust your code according to the API's documentation, as some APIs require the key to be included in the request headers or as a query parameter.
#6. Simple File Organizer
A script that organizes files in a directory into subdirectories based on file type.
Key Concepts Covered
-
File and directory operations
-
Working with paths
-
Automating routine tasks
import os
from pathlib import Path
def organize_directory(path):
for item in os.listdir(path):
item_path = Path(path) / item
if item_path.is_file():
file_type = item.split('.')[-1]
directory = Path(path) / file_type
directory.mkdir(exist_ok=True)
item_path.rename(directory / item)
# Example usage
organize_directory('path/to/your/directory')
Python Projects for Intermediate Level
#1. Real-time Face Recognition System
Develop a Python application that can identify and label faces in a video stream in real-time.
import face_recognition
import cv2
import numpy as np
# Load a sample picture and learn how to recognize it.
your_image = face_recognition.load_image_file("your_image.jpg")
your_face_encoding = face_recognition.face_encodings(your_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [your_face_encoding]
known_face_names = ["Your Name"]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# Capture video from the first webcam
video_capture = cv2.VideoCapture(0)
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# Use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
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#2. Sentiment Analysis Tool
The goal of this project is to create a Python application that can determine the sentiment of a given piece of text. The application will analyze text inputs (such as sentences, paragraphs, or entire documents) and classify the sentiment as positive, negative, or neutral.
Skills Developed:
-
Natural Language Processing (NLP): Learn the basics of NLP, including how to work with textual data, tokenize words, and analyze sentiment.
-
Data Analysis: Practice handling and processing data, potentially working with large datasets of text.
-
Libraries and APIs: Gain experience with Python libraries like textblob and pandas, and learn how to leverage them for sentiment analysis and data manipulation.
from textblob import TextBlob
# Example text
text = "Python is amazing, but learning it can be challenging."
# Analyze sentiment
blob = TextBlob(text)
print(blob.sentiment)
# Output: Sentiment(polarity=0.21666666666666667, subjectivity=0.5833333333333334)
# For a dataset, read using pandas and apply sentiment analysis on each row
# df = pd.read_csv('your_dataset.csv')
# df['sentiment'] = df['text_column'].apply(lambda x: TextBlob(x).sentiment.polarity)
#3. Custom Command-Line Tool
Develop a versatile command-line tool for a specific task, such as batch processing files, text manipulation, or generating reports from datasets. This tool will accept various command-line arguments to perform its functions dynamically.
Skills Developed:
-
Command-Line Interface (CLI) Design: Learn how to design and implement a user-friendly CLI, including parsing command-line arguments and providing useful feedback to users.
-
File and Data Processing: Enhance your skills in reading from and writing to files, as well as performing operations on data (e.g., filtering, sorting, transforming).
-
Automation: Understand how to automate repetitive tasks, improving efficiency and productivity.
import sys
def process_command(command):
# Example functionality: print reversed command
print(command[::-1])
if __name__ == "__main__":
if len(sys.argv) > 1:
process_command(sys.argv[1])
else:Related Articles