A naukri.com initiative
Python
Dev
3w
92
Image Credit: Dev
Build a Full-Featured Hangman Game in Python Using Amazon Q CLI — From Scratch to Launch in Under 10 Minutes 🚀
- Amazon Q CLI is an AI-powered command-line assistant designed to enhance developer productivity and learning while building projects like games quickly.
- The author showcases building a full-featured Hangman game in Python using Pygame, with support for categories, difficulty levels, animations, sound effects, and more.
- Reasons for using Amazon Q CLI included speed, productivity, simplifying complex features, real-time learning, and not requiring external assets.
- The Hangman game featured classic rules, category selection, difficulty levels, animated elements like hangman drawing and backgrounds, sound effects, and a scoring system.
- Amazon Q CLI streamlined the development process by generating the game codebase quickly based on detailed prompts provided by the author.
- The output was a well-structured Python project handling UI, animations, input logic, sound generation, and more, all accomplished in under 10 minutes.
- The blog provides instructions on running the game from the GitHub repository and highlights key learnings, such as avoiding external files and leveraging Pygame for game development.
- Future steps suggested include adding online multiplayer or scoreboards, creating a mobile-friendly version, and encouraging open-source contributions for new categories or themes.
- The author recommends trying out Amazon Q CLI for accelerating game development workflows or coding projects due to its efficiency and capabilities.
- To connect with the author, their portfolio, GitHub, and LinkedIn profiles are shared for further interaction.
Read Full Article
5 Likes
Medium
3w
127
Image Credit: Medium
Mastering Context Managers in Python
- Context managers in Python are used to allocate and release resources like files, database connections, or locks efficiently.
- They are commonly used with the with statement to simplify resource management, ensuring proper handling of resources.
- Context managers use __enter__() and __exit__() methods to manage resources effectively, providing a cleaner and error-safe approach in Python.
- By automating resource cleanup and management, context managers help in writing more readable and efficient code, reducing the risk of resource leaks.
Read Full Article
7 Likes
Medium
3w
176
Image Credit: Medium
Top 5 Python Mistakes Beginners Should Avoid (From My Personal Experience)
- Start solving problems early-on instead of trying to memorize every detail of Python syntax. Use resources like Google and documentation for help.
- Jump into using powerful Python libraries early, like Pandas, Numpy, Scikit-Learn, Matplotlib, and Plotly, to save time and increase productivity.
- Focus on building projects to apply your learning effectively, as projects are where real learning happens.
- Stick to one comprehensive course and documentation rather than switching between multiple tutorials to avoid confusion and stay focused.
- Learn the basics of Git even if it seems technical, as it helps in organizing files, tracking changes, and collaborating efficiently.
Read Full Article
10 Likes
Dev
3w
176
Image Credit: Dev
🐍 Python Built-in Functions - Ultimate Cheat Sheet
- The article provides a comprehensive overview of Python's built-in functions, categorized alphabetically with explanations and examples for each function.
- Examples include abs() for absolute value, all() for checking if all items are True, and any() for checking if at least one item is True.
- Other functions covered are chr() for ASCII characters, dict() for dictionaries, and divmod() for division and modulus operations.
- The article also explains functions like eval() for evaluating Python code, filter() for filtering iterables, and float() for float conversion.
- Notable functions discussed are globals() for global variables, hash() for hash values, and input() for user input processing.
- Additionally, functions such as map() for applying a function, max() for finding maximum values, and min() for minimum values are outlined.
- The article also covers functions like object() for base class objects, open() for file handling, and print() for console output.
- Lastly, functions like sum() for summation, super() for calling parent class methods, and vars() for object attributes are detailed.
- The cheat sheet concludes with special functions like __import__() for module importing, offering a well-organized reference for Python developers.
- This cheat sheet serves as a valuable resource for Python programmers looking to enhance their understanding and utilization of built-in functions.
Read Full Article
10 Likes
Discover more
Medium
3w
415
Image Credit: Medium
Python Polars is Faster than Pandas
- Polars is a high-performance DataFrame library written in Rust that outpaces Pandas by using parallel, memory-safe operations.
- Unlike Pandas, Polars is built with performance and scalability in mind, utilizing parallelism, vectorization, and memory-safe operations without relying on the Python runtime.
- Polars supports both lazy and eager evaluation, with lazy mode optimizing across entire workflows like SQL engines or Spark.
Read Full Article
24 Likes
Medium
3w
398
Image Credit: Medium
What if AI Took an Indian Exam? — Mastering Prompt Engineering with Python
- Prompting in AI is essential for providing context and instructions to the system to generate relevant responses.
- Different prompt engineering techniques such as system_prompt, CoT Prompting, Persona Prompting, and Self-Consistency are explored and explained using Python and OpenAI API.
- System_prompt sets the tone and persona for AI responses, CoT Prompting structures tasks, Persona Prompting defines character roles, and Self-Consistency ensures consistent and logical responses.
- By utilizing these techniques, AI can be trained to provide more accurate, context-aware, and reliable answers, similar to preparing for an Indian exam.
Read Full Article
23 Likes
Dev
3w
225
Image Credit: Dev
10 Genius Python Tricks Every Beginner Should Be Using
- Python has many hidden gems that can make your code cleaner, faster, and more Pythonic.
- Highlighted 10 Python tricks for beginners with code examples like swapping variables without a temporary variable, using list comprehensions, merging dictionaries, using enumerate() instead of range + index, using zip() to iterate over multiple lists, etc.
- These small Python tricks can improve code readability, efficiency, and style.
- Keep experimenting and building projects to master Python.
Read Full Article
13 Likes
Dev
3w
199
Image Credit: Dev
Life Path Number using Python
- Numerology is an ancient metaphysical science that explores the mystical relationship between numbers and events, often associated with spiritual significance.
- Numerology is used to discover personality traits, life paths, and more based on names and birth dates.
- This blog post explores bringing numerology into the modern era using Python to calculate Life Path Number and Name Number.
- Python script calculates Life Path Number from birth date and Name Number from full name following Pythagorean system of letter to number mapping.
Read Full Article
11 Likes
Nycdatascience
3w
278
Image Credit: Nycdatascience
2024/2025 NBA Season Team & Player Analysis (Python Project)
- The 2024-2025 NBA season analysis focuses on team and player statistics to determine key factors contributing to game outcomes and team success.
- Key questions addressed include the impact of overall team performance versus star player performance and the statistics most correlated with team success.
- Data analysis is based on partial season data, focusing on averages due to varying game counts per team, with a breakdown of player and team statistics for each game.
- Top teams such as the Cleveland Cavaliers, Boston Celtics, and Oklahoma City Thunder are analyzed based on points scored, points allowed, turnovers, and points differential per game.
- League-wide trends reveal that teams scoring 120+ points or allowing 105 points or fewer have higher winning percentages, emphasizing the importance of points and turnovers.
- Individual player impact analysis shows that team success is not solely reliant on star players but on a balanced team chemistry and performance.
- Analysis also compares top players across offensive and defensive categories, highlighting the importance of team dynamics over individual player performance.
- The key takeaway is that basketball is a team sport, emphasizing the significance of team-specific goals and balanced team play for greater success.
- Future work involves deeper analysis on specific teams, opponents' influence on player statistics, and comparing regular season trends with playoff performances.
- The analysis provides valuable insights into team and player dynamics in the 2024-2025 NBA season, highlighting the importance of team cohesion and balanced performance in achieving success.
Read Full Article
16 Likes
Medium
3w
332
Image Credit: Medium
Learn about python basics
- Python was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation.
- Indentation in Python is used to define blocks of code, indicating that a group of statements belongs to a specific block.
- Understanding input and output operations is fundamental in Python programming, with functions like print() for output and input() for user input.
- Python's print() function allows text, variables, and expressions to be displayed on the console easily.
- Variables are essential in Python to store and manipulate data, acting as placeholders that can be referenced and reused.
- Operators in Python are symbols used for logical and arithmetic operations, including Arithmetic and Comparison Operators.
- Python keywords are reserved words with special meanings, serving specific purposes in the language syntax.
- Python offers a variety of data types that categorize data items, allowing specific operations on different data.
- Conditional statements and loops in Python are used to control the flow of a program, executing blocks of code based on conditions or for repetitive tasks.
- Python uses indentation for code structuring, with blocks of code being defined by the number of indented character spaces.
Read Full Article
19 Likes
Dev
4w
248
Image Credit: Dev
Article Outline: Data Visualization with Python — Make Interactive Dashboards with Plotly & Dash!!
- Data visualization is crucial in today’s data-driven world to gain better insights.
- Interactive dashboards allow users to explore data dynamically, enhancing the understanding of information.
- Plotly and Dash are powerful Python tools for creating interactive dashboards.
- To get started, basic Python knowledge is required along with installation of Python packages like plotly and dash.
Read Full Article
14 Likes
Ubuntu Handbook
4w
22
How to Install Python 3.14 Beta 2 in Ubuntu
- Python 3.14 Beta 2 was released recently, with the official release planned for October 07, 2025, and will be supported until 2030.
- New features of Python 3.14 include deferred evaluation of annotations, t-strings, new module compression.zstd, and syntax highlighting in PyREPL.
- To install Python 3.14 on Ubuntu, you can either use a PPA or compile from the source code.
- For PPA installation, add the Deadsnakes PPA and run 'sudo apt install python3.14' to install Python 3.14 or python3.14-full to include Tkinter IDE.
- To compile from source, download the source tarball, install dependencies, configure, compile, and install Python 3.14.
- It's not advisable to set non-pre-installed Python as default, but you can do so by managing alternatives using the 'update-alternatives' command.
- Using virtual environments for Python 3.14 installation is recommended to avoid conflicts with system packages, especially with the introduction of PEP 668.
- To uninstall Python 3.14, simply remove it using 'sudo apt remove --autoremove python3.14' for PPA installation or manually delete the installed files for source-built Python.
Read Full Article
1 Like
Medium
4w
363
Image Credit: Medium
Top 5 Game-Changing Python Libraries Every Machine Learning Enthusiast NEEDS to Know in 2025
- SDV: Unlock synthetic data magic and turbocharge ML projects with privacy-preserving innovation.
- PyCaret: Build and deploy state-of-the-art ML models with minimal code through automation.
- Yellowbrick: Enhance model evaluation with visual diagnostics for better AI development.
- Alibi: Gain transparency in complex AI models with cutting-edge interpretability tools.
- Optuna: Achieve top-tier model performance efficiently using Bayesian optimization and pruning algorithms.
Read Full Article
21 Likes
Dev
4w
53
Image Credit: Dev
Why Use Python for Data Analysis?
- Python stands out for data analysis due to its ease of use with clear, concise, and readable syntax.
- Python offers a rich ecosystem of data analysis libraries like Pandas, Numpy, Matplotlib, and Scikit-learn.
- The scalability and performance of Python are enhanced by tools like Dask and PySpark for processing large datasets and distributed computing.
- Python is versatile across various data tasks including data wrapping, cleaning, statistical analysis, and building machine learning models.
Read Full Article
3 Likes
Medium
4w
3k
Image Credit: Medium
How to Automate Tasks with Python
- Python provides a wide range of libraries for automation in tasks like web scraping, GUI interaction, and task scheduling.
- With Python, tasks such as sending personalized emails, scheduling WhatsApp messages, triggering SMS alerts, or notifying teams on Slack can be automated with minimal code.
- To automate tasks with Python, start by installing Python and necessary libraries, then create scripts for automation and run them continuously in the background for scheduled executions.
- Automation with Python not only saves time but also allows for smarter work practices, from sending daily email updates to managing files efficiently.
Read Full Article
10 Likes
For uninterrupted reading, download the app