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Python
Dev
1w
64
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PIDM - python internet download manager
- PIDM is a modern open-source download manager built with Python, offering control over downloads and queues.
- Features include scheduling downloads, smart download worker, browser extension integration, speed limiter, download cache system, and multi-language support.
- It also offers flexible queues, prioritization, metadata awareness, local database with smart upgrades, and is currently built for Windows (macOS/Linux support coming).
- PIDM focuses on transparency, being open-source with no telemetry or unwanted background services, making it ideal for power users.
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Dev
1w
273
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How I Monitored My Lithium Battery Pack Using Python and a Raspberry Pi
- Monitoring lithium-ion battery packs is crucial for safety, health, and efficiency in various applications.
- A detailed guide on setting up a battery monitoring system using Python on a Raspberry Pi is provided.
- Components like Raspberry Pi 4, INA219 current sensor, and DS18B20 temperature sensor are used in the project.
- The article covers wiring instructions for INA219 and DS18B20 to the Raspberry Pi.
- Software setup involves installing necessary packages, enabling interfaces, and running a Python script for monitoring.
- Data visualization and analysis using Pandas and Matplotlib help track battery metrics over time.
- Building a web dashboard with Flask for remote data access is also outlined as an optional step.
- The article emphasizes accuracy, calibration, safety measures, and potential advanced extensions for the monitoring system.
- Lessons learned include the importance of sampling rate, cell balancing awareness, data normalization, and thermally-aware design.
- Monitoring lithium batteries with Python and Pi provides valuable insights for optimizing device performance and longevity.
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Dev
1w
64
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Namespace vs Regular Packages in Python — And Why mypy Might Be Failing You
- Python developers working on AI systems, data pipelines, or backend services may face issues with mypy not detecting types or imports correctly, often due to the use of namespace packages.
- Regular packages in Python require an init.py file, reside in a single folder, and are easier to process for tools like mypy, linters, and IDEs.
- Namespace packages, on the other hand, do not need an init.py, can span multiple folders or repositories, and are common in plugin systems and modular AI/ML tools.
- Namespace packages are beneficial for scalability but can be challenging for static analysis tools like mypy unless configured correctly.
- AI developers and data teams should pay attention to namespace packages for modular pipelines, plugin systems, and shared AI tooling across different libraries.
- One key issue with mypy and namespace packages is that mypy may struggle to analyze them correctly by default.
- To address mypy issues with namespace packages, developers can enable namespace support using commands like 'mypy --namespace-packages' or by setting it in mypy.ini.
- Developers should use package names instead of just folder names when running mypy commands and set additional options like MYPYPATH and --explicit-package-bases for non-standard layouts.
- If still facing challenges, adding dummy init.py or init.pyi files can help tools infer the package structure more accurately.
- Understanding how namespace packages and tools like mypy work together is crucial for maintaining bug-free and reliable code when building AI pipelines and shared tooling.
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Dev
1w
55
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🎯"Maximum Difference Between Adjacent Elements in a Circular Array" LeetCode 3423 (C++ | JavaScript | Python)
- LeetCode problem 3423 focuses on finding the maximum absolute difference between adjacent elements in a circular array.
- The task involves considering the circular nature of the array, including the first and last elements as adjacent.
- The strategy includes initializing the answer with the absolute difference between the first and last elements and then iterating through to find the maximum absolute difference between adjacent elements.
- C++ solution involves iterating through the array elements and updating the maximum absolute difference.
- JavaScript solution follows a similar approach to find the maximum adjacent difference.
- Python solution uses a loop to calculate the maximum adjacent difference.
- Test cases demonstrate the correctness of the solutions for various input arrays.
- The time complexity for the solutions is O(n) as they traverse the array once, while the space complexity is O(1) due to constant extra space usage.
- This problem helps in understanding circular connections and edge conditions, offering a simple yet effective way to find adjacent element differences.
- It serves as a good exercise for beginners and emphasizes clean coding practices.
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PlanetPython
1w
209
Image Credit: PlanetPython
The Python Coding Stack: Are Python Dictionaries Ordered Data Structures?
- Dictionaries in Python versions before 3.6 are not ordered, but in Python 3.6 and later, they maintain the order of insertion.
- Python dictionaries now guarantee the order of insertion from Python 3.7 onwards.
- Although dictionaries preserve the order of insertion, the order itself is not a key characteristic of a dictionary.
- OrderedDict from collections module is a dictionary that is also ordered, distinct from standard dictionaries.
- Different data structures serve different purposes, and the order of items is crucial in sequences but not in dictionaries.
- Sequences like lists rely on the order of items, while dictionaries focus on the mapping between keys and values.
- Support for OrderedDict may still be relevant due to additional distinct features compared to standard dictionaries.
- Using an OrderedDict maintains the importance of order, unlike standard dictionaries where order does not impact equality.
- The preservation of insertion order in dictionaries is a useful feature but not a central aspect of their functionality.
- Different needs require different data structures, and there isn't a single data structure that fits all scenarios.
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PlanetPython
1w
197
Python Insider: Python 3.13.5 is now available!
- Python 3.13.5 is now available as the fifth maintenance release of Python 3.13.
- This release addresses significant issues that were present in the previous version, 3.13.4.
- Some of the issues fixed include problems building extension modules on Windows and errors with generator expressions and int-like objects.
- The release includes various bug fixes that were expedited to resolve issues promptly.
- Upgrading to Python 3.13.5 is highly recommended for all users of Python 3.13.
- The Python Development team encourages users to stay safe and upgrade to the latest release.
- Volunteers play a crucial role in Python Development, and support from the community is appreciated.
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RealPython
2w
210
Image Credit: RealPython
Defining Your Own Python Function
- A Python function is a named block of code that performs specific tasks and can be reused in other parts of your code.
- To define a function in Python, you use the def keyword, followed by the function name and an optional list of parameters enclosed in a pair of parentheses.
- Functions help organize code, make it more modular, reusable, and easier to maintain.
- You can call a function using its name and necessary arguments.
- Functions in Python are self-contained blocks of code designed for specific tasks, improving code readability and efficiency.
- Defining and using functions is essential for managing complexity in Python programming.
- Functions in programming are analogous to mathematical functions, mapping inputs to outputs.
- Python supports user-defined functions along with built-in functions.
- User-defined functions in Python allow developers to create custom functions with specified arguments and internal logic.
- The article provides examples of Python built-in functions like id() and len().
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PlanetPython
2w
394
Martin Fitzpatrick: [Early Access] 6th Edition - Create GUI Applications with Python & Qt, Released ��� PyQt6 & PySide6 Books updated for 2025 with model view controller architecture, new Python/Qt features and more examples
- The 6th edition of 'Create GUI Applications with Python & Qt' book is now available for PyQt6 & PySide6.
- The update includes the latest changes in PyQt6 & PySide6, and utilizes new features in Python.
- The book's chapters have been extended with more examples of form layouts, built-in dialogs, and the Model View Controller (MVC) architecture.
- You can purchase the latest editions for PyQt6 and PySide6.
- Previous buyers can access these updates for free by entering the email used for the purchase on the account downloads page.
- Buyers who purchased the book elsewhere can register for updates by emailing their receipt to [email protected].
- Enjoy the new updates!
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Medium
2w
223
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If You Can’t Explain These Python Concepts, You’re Not a Real Developer.
- Focus on problem-solving and sharpening basics when prepping for DSA interviews or LeetCode.
- Advanced Python involves mindset upgrade rather than a checklist completion.
- Companies utilize advanced Python concepts such as generators, decorators, magic methods, and metaclasses.
- Decorators add custom behavior without altering core function logic, akin to upgrading a basic pizza.
- Generators yield values one at a time, enhancing memory efficiency and performance.
- Magic methods (dunder methods) like __init__, __str__, etc., are automated hooks in Python for specific events.
- Metaclasses control how classes are created, with popular frameworks like Django leveraging metaclasses for ease.
- Metaclasses provide power but necessitate caution to avoid complexity; simpler alternatives like decorators are advisable.
- Master advanced Python tools after solidifying basics through practice, project building, and error handling.
- Continuous learning is essential in Python programming due to the abundance of concepts waiting to be explored.
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PlanetPython
2w
51
Image Credit: PlanetPython
Python GUIs: 6th Edition - Create GUI Applications with Python & Qt, Released ��� PyQt6 & PySide6 Books updated for 2025 with model view controller architecture, new Python/Qt features and more examples
- The 6th edition of the book 'Create GUI Applications with Python & Qt' for PyQt6 & PySide6 has been released.
- Updates include bringing the book in line with the latest changes in PyQt6 & PySide6, utilizing new features in Python, and extending chapters with more examples.
- The book now emphasizes Model View Controller (MVC) architecture for applications.
- The latest editions for PyQt6 and PySide6 are available for purchase.
- Existing book owners can receive these updates for free by entering their email on the account downloads page.
- Owners who bought the book elsewhere can also register for updates by emailing their receipt to [email protected].
- Readers are encouraged to enjoy the updated content.
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PlanetPython
2w
175
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Talk Python to Me: #509: GPU Programming in Pure Python
- NVIDIA has developed Python SDKs to leverage modern GPUs for data science and ML with nearly native performance.
- Bryce Adelstein Lelbach discusses programming GPUs in pure Python on Talk Python episode #509.
- Sponsors of the episode include Posit, Agntcy, and Talk Python Courses.
- Links mentioned in the show: Bryce Adelstein Lelbach on Twitter, NVIDIA CUDA Python API, Numba, Applied Data Science Podcast, NVIDIA Accelerated Computing Hub, CUDA Cooperative Groups, Numba CUDA User Guide, CuPy, RAPIDS User Guide, and more.
- Resources such as Google Colab, Compiler Explorer, and NVIDIA's First Desktop AI PC were shared.
- Viewers can find additional resources, deep-dive content, and episode transcripts on the Talk Python website.
- Ways to stay in touch include subscribing to Talk Python on YouTube, following Talk Python and Michael Kennedy on Bluesky and Mastodon.
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Dev
2w
266
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What is Django? Exploring the Power of Python’s Top Web Framework
- Django is a high-level, open-source web framework written in Python known for rapid development and clean design.
- It offers ready-to-use components like authentication systems, admin interfaces, and URL routing.
- Django's 'batteries-included' philosophy reduces reliance on third-party libraries for common tasks.
- It is scalable, performance-oriented, and secure, making it popular for high-traffic applications.
- Key features include a Model-Template-View architecture, ORM, admin interface, URL dispatcher, and form handling.
- Django is used by major platforms like Instagram, Pinterest, and Mozilla due to its robustness.
- Ideal for Python developers, beginners, backend developers, and startup teams, Django streamlines web development.
- To start with Django, install Python, pip, create a virtual environment, and install the Django library.
- Django's documentation, community support, and features make it a top choice for web development.
- From models to views and templates, Django offers a comprehensive toolkit for building web applications efficiently.
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Medium
2w
72
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Jupyter Notebooks vs Python Scripts — What’s the Difference and When to Use Each?
- Understanding the difference between using Jupyter Notebooks and Python scripts is crucial for beginners in coding.
- Choosing between the two impacts your learning process, debugging efficiency, and the ease of sharing your work.
- Jupyter Notebooks and Python scripts are compared in terms of functionality, use cases, and importance for beginners in this beginner-friendly guide.
- This guide aims to clarify the distinction between Jupyter Notebooks and Python scripts with real-world examples and without using technical jargon.
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Dev
2w
8
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🔢Beginner-Friendly Guide "Maximum Difference Between Even and Odd Frequency II" LeetCode 3445 (C++ | JavaScript | Python)
- The LeetCode problem 3445, 'Maximum Difference Between Even and Odd Frequency II,' involves finding the maximum difference between the frequencies of two characters in a substring of a given string of digits.
- The task is to identify a substring of size at least k where one character has an odd frequency and the other has an even frequency, with the requirement to maximize the difference between their frequencies.
- A strategy is devised to iterate over digit pairs, use prefix counts, maintain a window of at least size k with both characters present, and track frequency parity using a bitmasking approach.
- The C++ solution involves sliding pointers to optimize substring selection and comparing frequencies to compute the maximum difference between even and odd frequency characters.
- The JavaScript and Python versions follow a similar approach using different syntax, showing how the same logic can be implemented in multiple languages.
- Test cases are provided to demonstrate the expected outputs based on different input scenarios.
- The time complexity of the solution is O(n) due to iterating over fixed pairs of digits, and the space complexity is O(1) as it only uses fixed-sized counters and arrays.
- This problem combines parity logic, prefix optimizations, and window comparisons, offering a challenging yet enriching experience in handling frequencies and substring conditions.
- If the breakdown of this problem was beneficial, consider liking, saving, and following for more content to enhance your problem-solving skills.
- Happy coding and exploring the intricacies of algorithms!
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Dev
2w
206
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Understanding List Comprehension in Python: A Cleaner Way to Build Lists
- List comprehension in Python offers a cleaner and more concise way to work with lists.
- It involves expressing operations like filtering, transforming, or skipping items in a single readable statement.
- List comprehension has three main parts: expression, source, and an optional condition for filtering.
- While list comprehension simplifies code for simple tasks, it may not be suitable for complex logic involving nested loops or multiple conditionals.
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