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Python
Feedspot
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Java vs Python? Which Programming Language You Should Learn?
- The debate between Java and Python is similar to 'tabs vs spaces' or 'light mode vs dark mode' in the programming world.
- The choice between Java and Python depends on what you want to build, development speed, and career goals.
- Java, created in 1995, is used for enterprise applications, backend systems, and Android development with core values of reliability, portability, and security.
- Python, introduced in 1991, focuses on making programming accessible and enjoyable with a clean syntax and ease of learning.
- Java is a compiled language with the 'Write Once, Run Anywhere' feature, while Python is an interpreted language known for its simplicity and readability.
- Compiled languages like Java prioritize speed and efficiency, while interpreted languages like Python offer convenience and quick iteration.
- Java follows a two-step execution process converting code into bytecode running on the JVM, while Python executes code line by line.
- Java is faster due to JIT compilation, ideal for high-performance applications, while Python is slower but excels in AI, data science, and web development.
- Java requires static typing for variables, enhancing reliability, while Python's dynamic typing speeds up development.
- Java's memory management via JVM and Python's reliance on reference counting differ in efficiency, affecting large-scale applications.
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RealPython
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228
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How Can You Structure Your Python Script?
- Transitioning from interactive environments to structured scripts in Python is crucial for promoting readability and robust development practices.
- This tutorial helps transform messy scripts into well-organized, shareable code, incorporating standard Python practices and tools.
- Key learnings include organizing scripts logically, managing script state efficiently, enhancing interactivity with command-line arguments, and creating self-contained scripts with inline dependencies.
- The tutorial guides through steps like setting up the initial script, integrating external libraries, handling command-line arguments, structuring internal data, and enhancing feedback and robustness.
- A concrete script interacting with a web server to work with the Iris dataset is used to demonstrate these structuring concepts.
- Starting with the importance of the Shebang line for Unix-like systems, the tutorial covers organizing import statements following PEP 8 recommendations.
- The Shebang line, like #!/usr/bin/env python3, makes Python scripts directly executable from the command line without typing 'python'.
- Organizing import statements into standard library, third-party, and local imports helps in clarifying dependencies and improving code readability.
- Minimizing dependencies for easy sharing and structuring scripts for better collaboration are essential scripting practices emphasized in the tutorial.
- The tutorial offers a comprehensive guide on structuring Python scripts efficiently to enhance readability, collaboration, and development practices.
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Dev
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1.9k
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What is a coroutine object in python
- A coroutine is a special kind of function that can pause and resume its execution, allowing asynchronous programming.
- Coroutine objects represent tasks that haven’t run yet and must be awaited inside an async function to actually execute the code and get the result.
- When you call an async function, it returns a coroutine object, which needs to be awaited to run the code and obtain the result.
- In a FastAPI app, async functions return coroutine objects that need to be awaited so that FastAPI can send the actual data in the response.
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Dev
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13
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"Python Hangman: Guess the Word, Learn the Code"
- CLI games are a fun way to reinforce programming concepts, like creating a Hangman game in Python.
- The game involves guessing a word within a limited number of attempts by suggesting letters.
- The game structure includes a hardcoded target word, maintaining state using lists and sets, and 8 attempts limit.
- Input validation ensures only alphabetic characters are accepted, and guessed letters are tracked to prevent repetition.
- Design considerations like using sets for lookup operations and updating lists in place contribute to effective coding.
- The game's simplicity allows for practice in input validation, data structure manipulation, and logical segmentation.
- Possible improvements for future versions include randomizing word selection, adding ASCII visuals, and support for whole word guessing.
- The Hangman game serves as a learning tool, a showcase of basic Python structures, and a foundation for more advanced programming.
- Overall, the game provides interactive learning and demonstrates how straightforward code can be engaging and educational.
- The Hangman game code demonstrates the implementation of game logic, input validation, and state management in Python.
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PlanetPython
3w
364
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Python Bytes: #434 Most of OpenAI���s tech stack runs on Python
- Most of OpenAI's tech stack runs on Python, using FastAPI for building APIs quickly, C for optimizations, and Temporal for asynchronous workflows.
- PyPI's test suite was made 81% faster by steps such as parallelizing test execution, using Python 3.12's features, optimizing test discovery, and eliminating unnecessary imports.
- Python enthusiasts can find interesting PyCon talks on YouTube covering topics from FOSS work to JIT compiler building and design pressure in code.
- Optimizing Python import performance involves techniques like lazy imports, avoiding circular imports, and optimizing __init__.py files for faster loading.
- PEPs & Co. and updates to pythontest.com are some of the extras shared, along with resources like git-bug and a Pyrefly follow-up.
- Python enthusiasts can catch up on Python-related updates and jokes including a humorous take on programmer humor shared in the episode.
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PlanetPython
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342
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Talk Python to Me: #507: Agentic AI Workflows with LangGraph
- Agentic AI Workflows with LangGraph empower LLMs to use tools and take further action based on what they have learned.
- Frameworks like LangGraph provide building blocks for Python apps with features such as long-term memory and durable resumability.
- Sydney Runkle discusses building Python apps with LangChain and LangGraph on the Talk Python podcast.
- Links related to LangGraph, LangChain, tutorials, and episodes mentioned in the show are available for further reference.
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PlanetPython
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412
Zato Blog: Enterprise Python: Integrating with Salesforce
- Salesforce connections are now available in Zato 3.2 for managing Salesforce records and business data with basic REST APIs and Python objects.
- The process includes creating Salesforce credentials, defining a Salesforce connection in Zato, authoring an integration service in Python, and testing the integration.
- Credentials needed for the integration are username, password, consumer key, and consumer secret, usually obtained from a Salesforce connected app.
- Zato handles authentication and authorization tokens based on the provided credentials, simplifying the integration process for users.
- Creating a Salesforce connection in Zato Dashboard involves inputting the obtained credentials and setting up the connection details.
- An integration service like creating Salesforce campaigns can be developed in Python using REST methods to interact with Salesforce endpoints.
- A SimpleIO definition can be used to structure the input and output for the service, facilitating the handling of data between the service and Salesforce.
- HTTP Basic Auth credentials can be assigned to the REST channel in Zato to enable client authentication, ensuring secure communication with Salesforce.
- Testing the integration can be done by invoking the REST channel using tools like curl, allowing interaction with Salesforce and verifying the functionality.
- By following the steps outlined, users can effectively integrate their applications with Salesforce using Zato and expose REST channels for seamless integration.
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Dev
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426
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Comprehension in Python (2)
- Python offers comprehensions like list comprehension, set comprehension, dictionary comprehension, and generator comprehension.
- Generator comprehensions are created using (). Generators can be passed to a function without ().
- 1D generator comprehensions produce a sequence of values based on an iterable. The end result is a generator object.
- 2D generator comprehensions create a sequence of generators based on nested iterables. The outcome is a generator object of generator objects.
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Dev
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430
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Comprehension in Python (1)
- Explanation of generator comprehension and tuple comprehension in Python.
- Comprehension is used to create iterables in a concise way, including list comprehension, set comprehension, dict comprehension, and generator expression.
- Examples of 1D and 2D list, set, and dict comprehensions provided, showcasing how to generate data in a more compact form compared to traditional loops.
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Dev
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303
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🧒🍬 Beginner-Friendly Guide to Solving "Distribute Candies Among Children" | LeetCode 135 Explained (C++ | JavaScript | Python)
- LeetCode 135 - Candy problem involves distributing candies to children based on their ratings in a line, following certain rules.
- The problem requires each child to receive at least one candy and a child with a higher rating than adjacent child gets more.
- Two strategies discussed in the guide are a naive two-pass greedy approach and an optimized greedy algorithm.
- The optimized greedy solution reduces space complexity from O(n) to O(1) by tracking peaks, valleys, and previous candies given.
- Code implementations provided in C++, JavaScript, and Python demonstrate the optimized approach.
- Test cases cover various scenarios to validate the correctness of the implemented solutions.
- Key takeaways include understanding the intuition behind both approaches and the significance of optimizing space for large datasets.
- Overall, mastering this problem helps in grasping greedy algorithms and efficient space utilization in coding interviews and DSA practice.
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Medium
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65
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Pulling Options Data with Python and yFinance — and Saving it Like a Pro
- The goal of the code is to retrieve options data for a specific stock ticker and expiration date using the yfinance library in Python.
- The code formats the options chain data neatly and saves the data into two CSV files:
_calls.csv and _puts.csv. - Each row in the CSV files includes details such as contract symbol, last trade timestamp, strike price, bid/ask prices, volume, open interest, implied volatility, among others.
- This script can be useful for options backtesting, volatility screening, and automated reporting by leveraging real-time options data and Python's capabilities.
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Dev
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158
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Visualizing Options Market Data in Python: Implied Volatility, Open Interest, and Max Pain
- This post covers visualizing options market data in Python focusing on implied volatility, open interest, and max pain.
- Options traders analyze implied volatility and open interest for market sentiment, liquidity, and price expectations. The Max Pain theory helps predict stock price behavior near expiry.
- The code provided loads and cleans options data, calculates key metrics like ATM strike and expected price move, and visualizes open interest and implied volatility.
- This analysis requires exporting options data to CSV files, running a Python script for processing, and interpreting charts to understand market activity and sentiment.
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Dev
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215
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Getting Started with FastAPI: A Beginner’s Guide Using Python 🐍
- FastAPI is a modern, high-performance web framework for building APIs with Python.
- FastAPI is fast, uses Python type hints for validation, and is asynchronous by design.
- FastAPI is more scalable than Flask and more minimal and lightning fast than Django REST Framework.
- FastAPI is used in production for GenAI backends, microservices, ML model deployment, and startups.
- To get started with FastAPI, ensure Python is installed and create a virtual environment.
- Using PyCharm for development, install FastAPI and create your first FastAPI app.
- FastAPI generates interactive Swagger docs and uses Pydantic for data validation.
- A mini project is demonstrated - building a Simple Calculator API with addition and subtraction endpoints.
- The code creates endpoints for adding and subtracting numbers, using Pydantic for data validation.
- The FastAPI automatically parses JSON input, validates data, and generates interactive docs.
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PlanetPython
3w
17
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The Python Coding Stack: What's the Difference Between Zipping and Unzipping Your Jacket? ��� Unzipping in Python
- Python's zip() function is commonly used, but there's an unzipping method as well.
- The unzipping tool in Python helps separate data previously zipped together with zip().
- Zip() in Python connects elements from different lists together, similar to a zipper on clothing.
- Zipping multiple lists creates groupings of related data.
- Sorting by age can be achieved by utilizing the zip object and the sorted() function.
- Unzipping involves reversing the zipping process to retrieve separate lists.
- To unzip, the unpacking operator * is used to separate zipped data back into original lists.
- Python's zip() and unzip() processes are akin to using a zipper to zip and unzip clothing.
- Zip() creates tuples of related elements, while unzipping separates them back into individual lists.
- Understanding both zipping and unzipping in Python is essential for handling grouped data efficiently.
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Dev
3w
92
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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.
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