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Python
Dev
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itertools in Python (7)
- The post explains various itertools functions like count(), cycle(), repeat(), accumulate(), batched(), chain(), chain.from_iterable(), compress(), filterfalse(), takewhile(), dropwhile(), groupby(), islice(), pairwise(), starmap(), tee(), zip_longest(), product(), and permutations().
- It mentions that itertools has functions to create iterators and more-itertools offers additional functions by installing with pip.
- The post demonstrates the usage of combinations() and combinations_with_replacement() functions with different arguments and examples.
- combinations() function returns unique combinations from an iterable.
- combinations_with_replacement() function returns non-unique combinations from an iterable allowing for repetition.
- The arguments for both functions include the iterable and the length of the returned tuple.
- The post includes code examples and output for both combinations() and combinations_with_replacement() functions for different scenarios.
- It concludes with examples showcasing how to iterate over combinations and combinations with replacement for specific lengths.
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PlanetPython
1w
412
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Python Engineering at Microsoft: Python in Visual Studio Code ��� June 2025 Release
- Microsoft announces Python, Pylance, and Jupyter extensions for Visual Studio Code in June 2025.
- New features include Python chat tools, Language Server completions in the terminal for the Python REPL, and support for PyEnv and Poetry.
- Python chat tools provide functionalities like getting Python environment information, installing Python packages, and configuring Python environments.
- Language server completions are now available in the terminal for Python REPL sessions, improving interactive coding experience.
- The Create Project feature in Python Environments extension allows users to create projects for Python packages and scripts quickly.
- Support for PyEnv and Poetry for environment and package management has been added to the Python Environments extension.
- Controlled rollout of the Python Environments extension has started, making it an optional dependency with the Python extension for some pre-release users.
- Users can try out the preview version of the Python Environments extension from the Extension Marketplace for early feedback.
- Special thanks to contributors for their contributions to the Python extensions in this release.
- Users can download the Python and Jupyter extensions from the Marketplace or directly from Visual Studio Code for these new improvements.
- For any issues or suggestions, users can file them on the Python VS Code GitHub page.
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Dev
1w
755
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🧠 5 Python Scripts That Solve Problems You Didn't Know You Had
- Python scripts can solve everyday problems efficiently without much fanfare.
- One script organizes and uploads screenshots automatically, handling clutter in the Downloads folder.
- Another script summarizes browsing history and generates TL;DR summaries for visited pages.
- A Python script intelligently builds resumes by extracting information from GitHub, LinkedIn, etc., resulting in real-world success.
- A maintenance bot script automates system health fixes and offers tech support solutions.
- A calendar AI script interprets natural language for scheduling events efficiently.
- Each script addresses practical issues and streamlines tasks effectively.
- The article emphasizes solving small problems to yield significant benefits over time.
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Dev
1w
109
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🪂 import antigravity — A Deep Dive into Python's Most Uselessly Brilliant Module
- Python's 'import antigravity' module is a playful feature in the language, not meant for defying gravity through code.
- The module, existing since Python version 3.0, humorously opens the xkcd comic #353 when imported.
- Import antigravity is Python's way of injecting humor and light-heartedness amidst serious programming tasks.
- In a world of intense coding, this module serves as a gentle reminder to take a breather and enjoy the process.
- Python includes other playful Easter eggs like 'import this' and 'import __hello__' for developers to explore.
- These features, though not essential for coding tasks, add a touch of fun and creativity to the development process.
- Software development can be challenging, but Python's quirky modules bring moments of joy and unnecessary brilliance.
- Importing antigravity is more than just a technical action; it embodies a light-hearted spirit within the programming community.
- Python developers are encouraged to embrace the unconventional and appreciate the playful side of coding.
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Medium
1w
331
FROM ZERO TO HERO: My Python Journey Through the Lens of Zen
- Embracing simplicity over complexity in programming concepts leads to elegant solutions.
- Prioritizing code readability in Python contributes to better communication and understanding.
- Focusing on mastering one clear path in learning before exploring alternatives is crucial.
- Taking action promptly rather than aiming for perfection encourages progress and growth.
- Aspiring to excel in data science and cybersecurity using Python's versatility and problem-solving skills.
- Seeking financial independence and professional fulfillment through technical expertise and community impact.
- Dreaming of mentoring others, contributing to open-source projects, and initiating local tech solutions.
- Joining a supportive community of learners and practitioners to collaborate and make a difference.
- Recognizing the significance of structured learning with mentorship for personal and technical growth.
- Transitioning from a beginner to an expert with dedication, support, and community-guided learning.
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Dev
1w
409
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OAuth client IDs dirty little secrets: old & new Python auth libraries
- Google has two auth libraries for Python, an 'OG' and its replacement, causing confusion for developers due to varying code samples online.
- The deprecation of the oauth2client library in 2017 led to the introduction of google-auth and google_auth_oauthlib.
- Differences between the old and new libraries include the lack of OAuth2 token storage support in the current libraries, requiring manual implementation.
- Both old and new libraries require the Google API client library for Python (google-api-python-client).
- The old auth library installation includes 'pip install -U pip google-api-python-client oauth2client'.
- The new auth library installation includes 'pip install -U pip google-api-python-client google-auth-httplib2 google-auth-oauthlib'.
- The code samples using the old and new auth libraries are discussed, showcasing differences in authentication flows.
- Implementing OAuth token storage is crucial to prevent users from facing repeated permissions requests due to expired access tokens.
- Differences in security mechanisms between old and new auth libraries lie in the OAuth flow part of the boilerplate code.
- Understanding the differences between the old and current Python auth libraries is essential for modernizing code and ensuring a consistent authentication process.
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Medium
1w
151
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Stop writing okay Python functions. Start writing good ones
- To split items bought according to quantities, a function named 'split_items_and_quantities' will be created.
- Type hinting is used to specify expected data types for variables, function arguments, and return values.
- Documentation strings provide information relevant for using the function, including definition, arguments, and expected output.
- The function rejects incorrect input types before execution and includes error handling.
- Following the Single Responsibility principle, the function focuses on separating items from quantities using regex and appending to columns.
- Efficient code includes error handling using try-catch blocks to highlight and capture runtime errors.
- The function combines multiple tasks within its logic, including parsing items from quantities and creating a data frame.
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Dev
1w
325
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Sets - Python
- Sets in Python are a built-in data type that represents an unordered collection of unique elements.
- Sets are mutable and do not maintain any specific order.
- Duplicates are automatically removed from sets.
- Add elements to a set using methods like add() and update().
- Removing elements using remove() can lead to a KeyError if the element is not present.
- To safely remove elements, use the discard() method.
- Set operations like intersection, difference, and symmetric_difference are available.
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Medium
1w
109
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TNFR in Python: a resonant structural AI
- The Theory of Resonant Fractal Nature (TNFR) proposes that reality is made up of patterns of coherence that stabilize over time.
- TNFR is a resonant-symbolic framework that models the emergence of form from interaction, allowing symbolic networks to resonate, reorganize, and mutate in response to structured input.
- Each node in a TNFR-based network evolves over time through structural perturbations mediated by gliphs, symbolic transitions that alter structure.
- A Python library called tnfr has been developed to implement this framework, enabling nodes in a network to enter resonance and trigger structural reorganizations in response to input words.
- TNFR differs from traditional models by responding structurally rather than assigning labels or outputs, leading to dynamic reorganizations and symbolic evolution.
- The framework introduces breakthroughs in inducing structural coherence and is adaptable for integration into larger systems.
- TNFR serves as an operating system for structural thought, fostering structural transformation and coherence within dynamic systems.
- The TNFR engine in Python offers a language of transformation, encoding intelligence as structural evolution rather than mere computation.
- TNFR is positioned as a symbolic substrate that fosters coherence and meaning transformation, emphasizing resonance over optimization in system development.
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Medium
1w
199
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Convert n8n Workflows to Python FastAPI and Self-Host with Ease Using n8n2py.me
- Many individuals are creating automation stacks using cloud platforms like n8n Cloud, Zapier, or Make.com, which raises concerns about data ownership and privacy.
- Dependence on third-party servers for processing business logic poses risks, especially with critical data passing through black-box APIs.
- For those who value privacy, compliance, and control, self-hosting becomes essential. However, setting up and maintaining n8n servers can be challenging.
- To address this challenge, n8n2py.me was developed, a tool that converts exported n8n JSON files into Python FastAPI apps that can be self-hosted.
- Benefits of using n8n2py include working with real Python without limitations, the ability to connect with various services, and avoiding usage-based fees.
- Users of n8n2py can convert up to 100 n8n workflows per month, access production-ready Python code, and deploy their workflows independently.
- By leveraging n8n2py, individuals can overcome the risks associated with entrusting data to external SaaS tools and securely self-host their workflows.
- n8n2py offers the flexibility of converting workflows, obtaining Python code, and deploying workflows without relying on external servers.
- n8n2py was created during the Bolt.new Hackathon for developers, privacy-conscious founders, and teams focused on intentional building.
- Feedback, feature suggestions, and collaboration opportunities can be directed to [email protected] or explored on GitHub at https://github.com/aditybdev.
- Some features are pending deployment due to bolt.new limitations, but they will be introduced once the restrictions are lifted.
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Dev
1w
35
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🧮 Beginner’s Guide to "Maximum Difference by Remapping a Digit" – LeetCode 2566 (C++ | JavaScript | Python)
- LeetCode problem 2566: Maximum Difference by Remapping a Digit involves remapping one digit in a number to find the maximum difference between the largest and smallest number possible.
- The problem allows Bob to change one digit (0–9) to any other digit, including itself, and aims to maximize the difference between the resulting numbers.
- The key approach is to change the leftmost significant digits to impact the number the most, ensuring a larger shift for maximum difference.
- In C++, the solution involves finding the first non-'9' digit and the first digit, then replacing them to compute the maximum and minimum values for the difference.
- The JavaScript and Python versions also follow similar logic to find and replace digits to calculate the difference.
- Test cases demonstrate how the algorithm works for different inputs, producing the correct maximum differences.
- The time complexity for this problem is O(n), where n is the number of digits, as it involves a single pass for replacement.
- Space complexity is O(1) as only a few variables are used during the computation.
- This problem is a great exercise for understanding greedy digit manipulation and string traversal techniques, emphasizing maximizing by changing the first non-'9' digit to '9' and minimizing by changing the first digit to '0'.
- Developers can drop a ❤️ if they found this breakdown helpful and can look forward to more concise explanations in the future.
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Medium
1w
97
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Applications of Python Programming: Versatile Tools for Science, Industry, and Innovation
- Python programming finds applications in various academic and practical contexts, offering strategic advantages to professionals and institutions.
- In data science, Python is indispensable with libraries like NumPy, pandas, SciPy, and StatsModels for data manipulation, statistical modeling, and numerical computation.
- Python supports reproducible research in academic research through Jupyter Notebooks, integrating code, equations, visualization, and narrative.
- Python is extensively used in machine learning and artificial intelligence due to its modular design and support from libraries like scikit-learn, TensorFlow, and PyTorch.
- For web development, Python frameworks like Django, Flask, and FastAPI enable secure, scalable web applications quickly.
- Python excels in automation tasks with standard libraries and third-party tools like Selenium and PyAutoGUI for file management, system tasks, and automation.
- In engineering disciplines, Python tools like SymPy, NumPy, and OpenCV support simulation, modeling, and computation in various domains.
- Academically, Python interfaces like FEniCS, Cantera, and PySPH aid in finite element analysis, chemical kinetics, and computational science, making it more collaborative.
- Python is widely used in cybersecurity for scripting penetration tests, automation of vulnerability assessments, and developing custom tools using libraries like Scapy and Paramiko.
- Python's simplicity and readability make it an ideal teaching language in introductory programming courses and higher education, covering algorithms, artificial intelligence, and interdisciplinary curricula.
- Platforms like edX, Coursera, and MIT OpenCourseWare democratize computational education globally with Python-based coursework.
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Dev
1w
212
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How to use Functions in OCI (Python)
- Oracle Cloud's Functions offer a serverless architecture solution like AWS Lambda and allow short functions to be performed on-demand in the cloud.
- OCI Functions support multiple programming languages like Java, Python, Node, Go, Ruby, and C#, and can even use custom Dockerfiles and Graal VM for advanced cases.
- To create a Function in OCI using Python, you need to start by creating an application within the OCI UI console.
- Prerequisites include setting up OCI API config profiles and installing the fn CLI using respective commands for macOS, Linux, or Windows.
- The process involves creating and deploying a sample Python function using commands provided in the OCI UI Console under Getting Started.
- After deploying, the function can be invoked using the Fn CLI or OCI CLI with signed HTTP requests by copying the Invoke endpoint.
- The Python function includes files like func.py, func.yaml, and requirements.txt to define the function logic, runtime, and dependencies.
- References for creating and using OCI Functions are available for further details and guidance.
- Information provided about OCI Functions is intended for informational purposes and should not be solely relied upon for purchasing decisions.
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Analyticsindiamag
1w
391
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Over 20% of Indians on GitHub Used AI to Write Python Functions: Study
- A study conducted by multiple institutions outlined the use of AI to write code globally and its economic impact.
- The study trained a neural classifier to detect AI-generated Python functions in 80 million GitHub commits by 200,000 developers over six years.
- AI was estimated to have written 30.1% of Python functions by US contributors, with varying percentages in other countries.
- Newer GitHub users tend to use more AI than veteran users, and using AI 30% of the time can raise quarterly commits by 2.4%.
- Combining wage data with programming estimates, the study calculates that AI generates $9.6-14.4 billion annually in the US software sector.
- Researchers ensured the neural classifier correctly identifies AI-generated Python functions by collecting human-written functions and synthetic AI-written functions.
- They used GraphCodeBERT, a cutting-edge model, to tokenize and embed the code for classification, achieving a high detection precision.
- The study explored AI's impact on coding practices and economic implications in various countries and sectors.
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Arxiv
1w
63
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Deep Learning-Based Digitization of Overlapping ECG Images with Open-Source Python Code
- Researchers have developed a deep learning-based method to digitize paper-based electrocardiogram (ECG) recordings, specifically addressing signal overlaps in single leads.
- A two-stage pipeline was proposed: first using a U-Net based segmentation network to isolate the primary ECG trace and then converting it into a time-series signal.
- The segmentation network achieved an IoU of 0.87 for fine-grained segmentation, outperforming existing methodologies.
- The digitization method showed superior performance to a baseline technique for both non-overlapping and overlapping ECG samples, with lower Mean Squared Error and higher Pearson Correlation Coefficient.
- For non-overlapping signals, the method achieved an MSE of 0.0010 and a rho of 0.9644 compared to the baseline's 0.0015 and 0.9366.
- On overlapping samples, the method achieved an MSE of 0.0029 and a rho of 0.9641, whereas the baseline had 0.0178 and 0.8676.
- This work presents an effective strategy to enhance digitization accuracy, particularly in the presence of signal overlaps in ECG recordings, enabling the conversion of analog ECG records into digital data for research and clinical use.
- The implementation code is available on GitHub at this repository: https://github.com/masoudrahimi39/ECG-code.
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