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🕸️ Web Scraping in Python: A Practical Guide for Data Scientists
- Web scraping is a powerful tool for extracting data from websites automatically.
- Web scraping involves extracting data from websites using automated processes.
- Legal considerations for web scraping include checking robots.txt files, reading Terms of Service, and avoiding overloading servers.
- Popular Python libraries for web scraping include requests, BeautifulSoup, pandas, lxml, Selenium, and playwright.
- A step-by-step example of web scraping involves sending requests, parsing HTML, extracting quotes and authors, and storing data using pandas.
- Scraping multiple pages can involve iterating over pages and storing data in a structured format.
- Bonus: Scraping JavaScript-rendered sites using Selenium may require installing Selenium, WebDriver, and utilizing appropriate drivers.
- Best practices for web scraping include using headers, adding delays, handling exceptions, respecting terms of use, and using proxies for large-scale scraping.
- Real-world use cases for web scraping include news monitoring, e-commerce price tracking, competitor research, NLP/ML projects, job listings, and market analysis.
- Web scraping is a foundational tool for data scientists with endless possibilities for custom datasets and AI model empowerment.
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Dev
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Machine Learning Fundamentals: a/b testing with python
- A/B testing with Python is a critical aspect of modern machine learning systems ensuring robustness and scalability.
- It involves controlled testing of multiple model versions in production environments with traffic allocation based on predefined rules.
- Various use cases include fraud detection, recommendation engines, medical diagnosis, autonomous driving, and search ranking.
- The architecture involves features like data sources, a feature store, traffic splitter, model versions, prediction service, and monitoring.
- Implementation strategies include Python orchestration for traffic routing and Kubernetes deployment for traffic splitting.
- Failure modes encompass stale models, feature skew, latency spikes, data corruption, and traffic routing errors.
- Performance tuning techniques focus on metrics like latency, throughput, accuracy, and cost optimization.
- Monitoring and observability tools like Prometheus, Grafana, and Datadog are essential for tracking critical metrics.
- Security, policy, and compliance aspects emphasize adherence to regulations, audit logging, secure data access, and governance tools.
- CI/CD integration automates A/B testing processes, enforces quality checks, and supports automated rollback strategies.
- Common engineering pitfalls include ignoring feature skew, lack of monitoring, complex traffic routing, and insufficient automated rollback mechanisms.
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Medium
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Simple AI Projects Students Can Build with Python
- Students with basic Python knowledge can build simple AI projects.
- These projects are easy to complete in a weekend and offer valuable real-world AI insights.
- 10 simple AI project ideas are outlined, emphasizing using Python.
- Projects include building chatbots, image recognition models, sentiment analysis tools, recommendation engines, digit recognition models, sentiment analysis on tweets, HR tool simulations, object detection models, reinforcement learning projects, and a startup project.
- Chatbots start with rule-based bots and progress to NLP-powered bots.
- Image recognition project teaches image preprocessing, CNNs, and more.
- Sentiment analysis project combines machine learning and NLP, offers a bonus idea of creating a web interface.
- Recommendation engine project teaches similarity metrics and presents a bonus idea of creating a fun quiz.
- Digit recognition project is visual and satisfying, with a bonus idea of creating a live prediction feature.
- Sentiment analysis on tweets project involves text cleaning, polarity scores, and real-world APIs use, with a bonus idea of visualizing results with charts.
- HR tool simulation project teaches NLP concepts like Named Entity Recognition, with a bonus idea of extracting insights from resumes.
- Object detection project is real-time and visual, offering a bonus idea of implementing face recognition.
- Reinforcement learning project offers a gentle introduction to the concept, with a bonus idea of recording gameplay.
- Startup project combines computer vision and web scraping, with a bonus idea of adding a product classifier.
- These projects can enhance Python skills and are beneficial for both learning and showcasing abilities.
- Students can further customize these projects by adding bonus features and functionalities.
- Building these AI projects can be a great addition to a student's resume or GitHub profile.
- The projects cover a wide range of AI concepts and tools, making them a comprehensive learning experience.
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Dev
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Python Fundamentals: asgi
- ASGI (Asynchronous Server Gateway Interface) goes beyond web frameworks to support asynchronous, distributed Python applications with long-lived connections like WebSockets and Server-Sent Events.
- It defines callable objects and an event loop interface, decoupled from specific frameworks, leveraging asyncio for concurrency.
- Real-world use cases include FastAPI for REST APIs, Celery for async job queues, Server-Sent Events for real-time data streaming, WebSockets for collaborative editing, and ML model serving.
- Integration involves type safety, Pydantic for data validation, configuration via pyproject.toml, and logging for observability.
- Code examples demonstrate how ASGI handles WebSocket connections and the importance of testing, debugging, performance tuning, security considerations, and best practices.
- Challenges like blocking operations, incorrect handling of disconnects, and lack of type hints can be mitigated by following best practices, embracing type safety, and modular design.
- Key aspects include separation of concerns, defensive coding, modularity, config layering, and automation for testing, deployment, and monitoring.
- Mastering ASGI is essential for scalability and reliability, emphasizing the importance of refactoring legacy WSGI code, performance monitoring, writing tests, and enforcing type safety.
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Dev
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📝 Beginner-Friendly Guide "Divide a String Into Groups of Size k" - LeetCode 2138 (C++ | Python | JavaScript)
- LeetCode 2138 presents an easy string manipulation problem that involves dividing a string into groups of a specified size k.
- The task includes dividing the string into groups of size k and padding the last group if it has fewer than k characters with a specified fill character.
- The C++ solution involves iterating through the string and adding substrings of size k to a list while padding the last group if necessary.
- Key notes include checking for remaining characters, padding using the fill character, and the time complexity being O(n) where n is the length of the string.
- The JavaScript solution utilizes slicing to grab groups and repeats the fill character if padding is required.
- The Python code demonstrates a similar approach using string slicing and handling edge cases with padding if the chunk size is less than k.
- Overall, the problem focuses on string slicing, iteration, and padding for edge cases, making it a practical exercise for understanding these concepts.
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Dev
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Python Fundamentals: arrays
- Arrays in Python can be handled with built-in lists, the array module, and numpy for optimized numerical operations and performance.
- Real-world use cases include fast API request handling, async job queues, type-safe data models, CLI tools, and ML preprocessing using numpy arrays.
- Integration with Python tooling involves specifying dependencies in pyproject.toml and utilizing tools like mypy for type safety.
- Code examples showcase pre-allocation, vectorized operations with numpy, and considerations for using dataclasses with numpy arrays.
- Failure scenarios and debugging involve handling shape mismatches, using tools like pdb, cProfile, and memory_profiler, and ensuring proper deallocation of numpy arrays.
- Performance and scalability considerations include benchmarking, avoiding unnecessary allocations, controlling concurrency, and using C extensions for critical operations.
- Security considerations highlight the importance of secure deserialization, validating array shapes, and avoiding untrusted input with numpy.fromstring.
- Testing involves unit tests with pytest, property-based testing with hypothesis, and integrating type validation with mypy into CI pipelines.
- Common pitfalls and anti-patterns to avoid include repeated appending to lists, ignoring array shapes, unnecessary type conversions, and lack of vectorization.
- Best practices emphasize type-safety, separation of concerns, defensive coding, modularity, configuration layering, dependency injection, automation, reproducible builds, and documentation.
- Mastering array handling in Python is crucial for scalable systems, performance optimization, and avoiding common pitfalls by following best practices and leveraging numpy.
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Dev
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Python Fundamentals: argparse
- argparse is Python's module for parsing command-line arguments, providing help messages, and ensuring argument validity.
- It integrates with key Python tools like mypy, pytest, pydantic, logging, and dataclasses.
- Real-world use cases include FastAPI request handling, async job queues, type-safe data models, CLI tools for data science, and ML preprocessing pipelines.
- Code examples demonstrate defining arguments and validating them using pydantic models.
- Failure scenarios with argparse include incorrect type coercion and unhandled default values.
- Performance tips for argparse include avoiding global state, reducing allocations, caching, and profiling for bottlenecks.
- Security considerations involve insecure deserialization, code injection, and privilege escalation risks.
- Testing strategies encompass unit tests, integration tests, property-based tests, and mypy type validation.
- Common pitfalls include ignoring type hints, complex argument structures, lack of validation, and hardcoding default values.
- Best practices suggest type-safety, separation of concerns, defensive coding, modularity, config layering, and dependency injection.
- argparse is crucial for building reliable and scalable Python systems, emphasizing performance, security, testing, and best practices.
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Dev
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Python Fundamentals: __repr__
- The article discusses the importance of mastering __repr__ in Python for system observability, security, and data governance.
- __repr__ defines the official string representation of an object in Python, aiming to provide an unambiguous developer-focused representation.
- Real-world use cases of __repr__ include logging incoming requests, asynchronous task queues, data validation, and CLI tools.
- __repr__ integrates with key Python tools such as mypy, pytest, pydantic, logging, dataclasses, and asyncio.
- Code examples show how to customize __repr__, mask sensitive data, and enhance immutability.
- Performance considerations involve optimizing __repr__ for high-volume systems, efficient string interpolation, and caching computed values.
- __repr__ must be carefully implemented to avoid security vulnerabilities by masking sensitive data and preventing code injection.
- Testing __repr__ involves unit tests, property-based testing, and static type checking with mypy.
- Common pitfalls like infinite recursion, exposing sensitive data, and lack of testing are highlighted.
- Best practices for __repr__ include type-safety, separation of concerns, defensive coding, modularity, automation, and documentation.
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Educba
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Career as a Python Developer
- Python continues to be popular in 2025 for various programming tasks.
- No computer science degree is required to start a career as a Python developer.
- Key steps to becoming a job-ready Python developer include understanding roles, mastering fundamentals, building real projects, choosing a specialization, setting up a development environment, joining the Python community, creating a strong portfolio, and gaining real experience.
- Learning platforms like Codegnan, freeCodeCamp, and W3Schools are recommended for Python learning.
- Project ideas range from beginner-level tasks like a Calculator to intermediate projects such as a Blog Platform.
- Choosing a career path in Python can focus on web development, data science, machine learning, or automation.
- Tools like VS Code, Git, GitHub, and Jupyter Notebook are essential for a developer environment.
- Participating in Python communities like Reddit, Stack Overflow, and Python Discord can aid in learning and networking.
- Creating a strong portfolio on platforms like GitHub is crucial for showcasing skills as a Python developer.
- Internships and freelance gigs provide real-world experience and opportunities to apply Python skills.
- The guide suggests further reading on software development, machine learning, Python mobile app development, and SQL careers.
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Medium
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The 3 Lies I Told Myself About Learning Python (And How I’m Finally Getting It Right)
- The author reflects on the lies they told themselves about learning Python and how it hindered their progress.
- Lie 1: Believed learning Python required long, uninterrupted hours of focus when the real issue was time mismanagement.
- Lie 2: Was scared of not being good at Python compared to others, leading to procrastination.
- Lie 3: Delayed practice when concepts became challenging, falling into a loop of procrastination.
- Restarted Python journey with a focus on progress over perfection, emphasizing consistency and self-kindness.
- Learning is a continuous effort that demands discipline and curiosity.
- The author aims to complete the course by showing up daily without rushing to finish.
- Emphasizes the importance of honesty and kindness towards oneself in the learning process.
- Encourages others on similar learning paths to keep going and not strive for perfection.
- Reminds that mental battles in learning are often tougher than the learning itself.
- Urges individuals to be understanding and compassionate towards themselves while continuing forward.
- Self-acceptance and perseverance are highlighted as crucial elements for success in learning endeavors.
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Dev
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Unlocking Speed: Mastering High-Performance Data Structures for Python Data Science
- Python's built-in data structures like lists, dictionaries, and tuples show performance limitations for large-scale numerical operations due to memory overhead and type checking.
- The data science community shifted to 'vectorized operations' for efficiency by applying operations on entire arrays or columns at once, leveraging NumPy arrays' homogenous data and contiguous memory storage.
- NumPy arrays outperform Python lists significantly in numeric computations, serving as a foundation for numerical computing in Python and various scientific libraries.
- Pandas DataFrames and Series, built on NumPy arrays, provide high-level data structures for structured data manipulation, leveraging NumPy's efficiency for tasks like filtering and aggregation.
- Apache Arrow introduces a standardized in-memory columnar format for efficient data exchange across systems and languages, enhancing performance and reducing serialization overhead.
- Polars, a powerful DataFrame library written in Rust, integrates natively with Apache Arrow, offering blazing speed, lazy evaluation, and an expressive API for data manipulation.
- Narwhals project aims to unify DataFrame APIs across libraries like Pandas and Polars, simplifying code development and improving interoperability in the Python data ecosystem.
- Understanding specialized data structures like NumPy arrays, Pandas DataFrames, Apache Arrow, and Polars is crucial for optimizing data processing workflows in Python data science.
- Each tool, from Python's built-in structures to high-performance libraries, offers unique advantages catering to specific use cases in data manipulation and numerical computation.
- Choosing the right tool, whether for flexibility, speed, or memory efficiency, can significantly impact performance and efficiency in data processing tasks.
- Exploring these foundational data structures in Python provides insights for developers to optimize data workflows and address complex data challenges effectively.
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Dev
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377
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📝 Beginner-Friendly Guide "Minimum Deletions to Make String K-Special" LeetCode 3085 (C++ | Python | JavaScript)
- LeetCode 3085 is a medium-level problem that involves minimizing the number of deletions required to make a given string k-special.
- To make a string k-special, the difference between the maximum and minimum frequency of any two letters should be ≤ k.
- The problem involves counting the frequency of each character, normalizing frequencies, adjusting values greedily, and finding the configuration with minimal deletions.
- In C++, a solution is provided that sorts frequencies for easier analysis and scans for minimum deletions with a time complexity of O(26^2) and space complexity of O(26).
- The JavaScript solution follows a similar approach as the C++ solution but is implemented in JavaScript, sorting frequencies and calculating minimum deletions.
- A Python solution is also presented, using frequency arrays and greedy optimization to find the minimum deletions required to make the string k-special.
- This problem showcases the benefits of frequency analysis and greedy optimizations, transforming a global condition into local transformations via range loops.
- The problem provides good practice for frequency array manipulation and greedy analysis on sorted data.
- This article offers algorithm insights and optimizations for the LeetCode 3085 problem, highlighting the importance of frequency analysis and local transformations.
- The content provides valuable information for algorithm enthusiasts and demonstrates the power of frequency-based optimizations.
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Medium
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243
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Building My Own Workflow Automation Framework in Python
- The author developed a core loop in Python to listen for events and dispatch them, akin to a personalized 'Zapier'.
- Utilizing watchdog, the framework reacts immediately when a file is added to a folder, eliminating the need for polling.
- The framework replaced crontab setups, consolidating all automation tasks within a unified Python process.
- Features include sending Slack notifications via Webhooks and writing to Excel with openpyxl.
- An addition for responding to low-priority emails was incorporated, enhancing productivity when combined with IMAP polling.
- A lightweight Flask interface was integrated, allowing manual triggering of workflows through '/trigger' using tools like Postman or dashboards.
- This setup enables retries, debugging, and the generation of weekly activity reports for enhanced efficiency.
- The entire system can be run as a single container, easily restarted with Docker Compose, ensuring no dependency-related concerns.
- The framework empowers users with total ownership and has significantly boosted the author's productivity with Python.
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PlanetPython
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The Python Coding Stack: I Want to Remove Duplicates from a Python List ��� How Do I Do It?
- The article explores different methods to remove duplicates from a Python list.
- The manual method involves iterating through the list and creating a new list without duplicates.
- The article then discusses using sets to easily remove duplicates by converting the list into a set.
- It compares the output of the manual method with the set method that removes duplicates.
- It highlights that sets remove duplicates but do not maintain the original order of elements.
- Another method discussed is using dictionaries to remove duplicates while maintaining order.
- It points out the efficiency of the set and dictionary methods over manual approaches.
- The article concludes with recommendations on when to use sets or dictionaries to remove duplicates.
- The limitations of set and dictionary methods due to the requirement of hashable objects are also mentioned.
- Various code blocks are provided throughout the article demonstrating the different approaches.
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Dev
5d
302
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Part 1: Your Python Gateway to Blockchain – Getting Started with `web3.py`
- Python with web3.py is a great choice for blockchain development, simplifying interaction with Ethereum nodes.
- web3.py translates Python commands into JSON-RPC calls understood by blockchain networks, enabling querying and interactions.
- Installation involves setting up a Python project, creating a virtual environment, and installing web3 and python-dotenv.
- Connecting to an Ethereum node can be done through services like Infura or by running a local blockchain using Ganache.
- Keep sensitive information secure with environment variables and avoid committing API keys by adding them to .gitignore.
- The provided Python code initializes web3, connects to the Ethereum node, and reads blockchain data like block numbers and gas prices.
- Unit conversions such as from Wei to Gwei or Ether are automatically handled by web3, simplifying data interpretation.
- This article offers a clear guide for Python developers interested in blockchain integration, showcasing a practical example with web3.py.
- Overall, the article details the process of connecting Python to blockchain, querying live data, and utilizing clean Python code for interaction.
- The presented code allows for exploring blockchain functionalities like checking balances, retrieving block details, and handling conversions effortlessly.
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