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Python
Medium
7d
79
How to Self-Learn Python in 2025 — The Ultimate Beginner’s Guide
- Python is a popular language for beginners and job-seekers alike, known for its readability and versatility.
- To start learning Python in 2025, one can download the latest Python version from Python.org and use tools like Visual Studio Code, Replit, Google Colab, GitHub, and Jupyter Notebooks.
- Begin by mastering foundational concepts, practicing daily, and working on small projects.
- For data science or machine learning, consider using Anaconda.
- Progress by picking a specific path and engaging with relevant resources.
- Understand code rather than just copying and pasting it, and utilize tools like Replit, Kaggle Learn, LeetCode Python, and Official Python Docs for interactive learning.
- Python offers not just programming skills, but a way to bring ideas to life.
- In 2025, self-learning Python can lead to job-ready skills for various career paths, emphasizing the importance of curiosity, continuous learning, and hands-on practice.
- Self-learners are encouraged to start coding, no matter how small, as a way to progress and create meaningful projects.
- The article provides tools, resources, and guidance for anyone interested in self-learning Python.
- Readers are invited to follow the author for more self-learning tips on tech, creativity, and DIY coding projects, and to ask any questions in the comments section.
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Medium
7d
245
Image Credit: Medium
The Road to Becoming a Python 10x Engineer
- Python is a popular programming language known for its ease of use and versatility.
- While Python allows for quick code bootstrapping and is often used for proof-of-concepts and machine learning, maintaining Python code can be challenging due to its lack of static typing.
- An example code snippet is provided to illustrate how readability and understanding can be improved by implementing type hints and classes in Python.
- Type hints, indicated by adding a ":" followed by the type of an object, provide context and make code more explicit.
- By incorporating type hints, the code becomes more readable and easier to work with, especially when dealing with AI libraries.
- Classes, which are traditionally used in object-oriented languages, are now available in Python and can improve modularity and readability of the code.
- By introducing classes, the code becomes more structured and reusable, enhancing its readability and maintainability.
- Implementing type hints and classes in Python code can elevate it to be more maintainable, scalable, and easily comprehensible without the need for extensive documentation.
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Medium
7d
133
Python Pitfall: The Mutable Default Argument Trap You MUST Avoid
- A common Python pitfall involves mutable default arguments leading to unexpected behavior when functions are called.
- When a mutable default argument like a list is used in a function, it retains its state across multiple function calls, leading to unintended consequences.
- This issue can cause the list to grow with each function call instead of resetting to an empty list as expected.
- In real-world applications, this behavior can result in unwanted state accumulation, especially when functions are called repeatedly.
- To avoid this pitfall and prevent shared mutable default arguments, it is recommended to use a safe pattern by setting the default argument as None and initializing it as needed within the function.
- Using this safe pattern may require an extra step but helps in maintaining the expected behavior of functions and prevents unexpected side effects.
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PlanetPython
1w
296
Python Insider: Python 3.14.0 beta 3 is here!
- Python 3.14.0 beta 3 is now available, as part of the Python 3.14 development cycle.
- Beta releases like 3.14.0b3 allow the community to test new features and bug fixes before the final release.
- Maintainers of third-party Python projects are encouraged to test with 3.14 and report any issues.
- Python 3.14 is feature-complete in the beta phase, with minimal changes expected after the first release candidate.
- Key features of Python 3.14 include support for free-threaded Python, deferred type annotation evaluation, and template string literals.
- Other additions in Python 3.14 are multiple interpreters in the standard library, Zstandard compression support, and improved UUID module.
- Python 3.14 also introduces enhancements in debugging, error messages, HMAC implementation, and remote attaching in the pdb module.
- Build changes include using Sigstore for verifying release artifacts and providing an experimental JIT compiler in macOS and Windows binaries.
- Incompatible changes, removals, and deprecations are documented for Python 3.14.
- The Python installer for Windows is replaced by a new install manager, available from the Windows Store or FTP page.
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RealPython
1w
150
Image Credit: RealPython
Exploring Python's list Data Type With Examples
- The list class is a fundamental built-in data type in Python, offering a variety of features for organizing and manipulating data.
- Understanding lists is essential for Python developers, given their versatility and usefulness in managing different types of data.
- Lists are commonly utilized in various real-world coding scenarios due to their wide range of applications.
- A video course is available to explore Python lists in depth and gain a comprehensive understanding of their functionalities.
- The course covers creating new lists, accessing items in lists, copying, updating, resizing, and concatenating lists.
- Additionally, sorting, reversing, and iterating through lists are explained to improve coding efficiency.
- Other advanced features of Python lists are also discussed in the course.
- The course aims to enhance Python skills through practical examples and exercises.
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PlanetPython
1w
250
Image Credit: PlanetPython
Mike Driscoll: Python 201 ��� All About the TypedDict
- Python supports type hinting but does not enforce it, requiring external tools like Mypy.
- TypedDict allows adding type hinting to heterogeneous dictionaries with values of different types.
- Type hinting a regular dictionary involves using dict[key_type, value_type].
- Python versions before 3.9 required 'from typing import Dict' for type hinting.
- TypedDict was introduced in Python 3.8 for dictionaries with different value types.
- TypedDict helps in situations where a dictionary has values of various types.
- Using TypedDict ensures better type checking and prevents errors.
- Creating a TypedDict involves defining a class that inherits from TypedDict.
- Dealing with keys containing spaces in a TypedDict requires using TypedDict constructor instead of subclassing.
- TypedDict is useful for type-hinting complex dictionaries, even within other data structures like lists or tuples.
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Dev
1w
288
Image Credit: Dev
6 Ways AI Can Improve Your Python Code(Tested!)
- AI is transforming how software is built and maintained, offering automation and bug detection for flawless Python code.
- Automated AI-powered code reviews streamline the review process, detect bugs, and enforce coding standards efficiently.
- Advanced static analysis with AI identifies subtle bugs and security flaws, enhancing code quality and security.
- AI-generated testing automates test case generation, improving code coverage and catching bugs early in Python applications.
- AI-driven documentation maintains code consistency, generates explanatory docstrings, and simplifies code understanding.
- AI-powered developer assistants provide code completion suggestions and accelerate coding, leading to cleaner Python code.
- AI aids code refactoring by analyzing and recommending systematic refactors, ensuring code consistency and efficiency.
- AI is essential for writing high-quality Python code at scale, enhancing processes without disruption.
- By embracing AI tools, teams can improve code quality, catch bugs early, and streamline development workflows.
- Experimenting with AI tools can lead to smoother processes, fewer bugs, and increased confidence in software releases.
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PlanetPython
1w
142
Image Credit: PlanetPython
Python Software Foundation: The PSF's 2024 Annual Impact Report is here!
- The Python Software Foundation had a remarkable year of growth in 2024.
- Python became the most popular language on GitHub, and community engagement reached record levels.
- Key highlights of the year include welcoming a PyPI Support Specialist, revival of workgroups, and continued investment in grants and infrastructure.
- The Annual Impact Report includes a letter from the Executive Director and updates from various key figures.
- Developers-in-Residence achievements and PyPI updates are featured in the report.
- The report also covers PyCon US 2024, fiscal sponsees, sponsors, and PSF financials.
- The PSF encourages sharing the report with the Python community and providing feedback.
- The report encapsulates significant numbers, details, and insights from the past year.
- Updates on PyPI Safety & Security Engineer's work are included in the report.
- Readers can find information about PyCon US 2025, achievements of Developers-in-Residence, and PyPI stats.
- Sponsors who supported the PSF and the Python ecosystem are highlighted in the report.
- PSF Financials, financial statements, and grants data are part of the Annual Impact Report.
- The PSF recommends engaging with them on social media platforms or through the Python Discuss thread.
- The report provides a comprehensive overview of the Python Software Foundation's activities and accomplishments in 2024.
- Community members are encouraged to review the report and share their thoughts with the PSF.
- The Python Software Foundation's growth and achievements are highlighted in the 2024 Annual Impact Report.
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8 Likes
Dev
1w
16
Image Credit: Dev
Setting up a Python project with CMake
- CMake can be used to set up and run Python applications in mixed-language projects.
- The unique approach allows for running any application with a single command.
- Project structure includes separate directories for each application, each with its own CMakeLists.txt file.
- CMakeLists.txt file includes functions to create Python virtual environments and install dependencies from requirements.txt.
- Individual app directories contain CMakeLists.txt files to setup virtual environments and run applications.
- Running 'cmake .' in the build folder initiates the setup of virtual environments and downloads dependencies.
- Build all targets using 'cmake --build .' command.
- Applications can be run using 'make run_app1', 'make run_app2', and 'make run_app3' commands.
- Targets can also be executed in VS Code using the CMake GUI interface.
- Ninja can be used as an alternative to 'make' for generating configuration.
- Generate Ninja configuration with 'cmake .. -G Ninja' and build with 'cmake --build .'.
- Run applications using 'ninja run_app1', 'ninja run_app2', and 'ninja run_app3' commands.
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Leadgrowdevelop
1w
305
Image Credit: Leadgrowdevelop
The True Cost of Hiring Python Developers: Budgeting for 2025
- Python remains highly sought after for web development, AI, and data analysis, leading to increased demand and higher pay rates for Python developers by 2025.
- Factors influencing the cost of hiring Python developers include location, experience level, engagement model, and additional perks and benefits.
- Key cost areas include recruitment and onboarding expenses, turnover costs, and the quality vs. cost balancing act when selecting talent.
- Factors affecting hiring costs such as project complexity, duration, location, and skill set, contribute to the overall cost of hiring Python developers.
- Global salary comparisons for Python developers in countries like India, the United States, Germany, Canada, and Australia showcase varied pay scales based on experience and location.
- Trends shaping Python development in 2025 include AI/ML integration, low-code solutions, automation, and sustainability initiatives.
- Careful planning and consideration are essential when hiring Python developers, focusing on skills, working relationships, potential costs, and long-term team improvements.
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18 Likes
Dev
1w
326
Image Credit: Dev
🔢 Count the Number of Arrays with K Matching Adjacent Elements LeetCode 3405 (C++ | Python | JavaScript)
- LeetCode 3405 is a hard problem related to combinatorics.
- The problem involves finding the total number of 'good arrays' meeting specific criteria.
- Criteria: Elements in the array lie in the range [1, m] and exactly k adjacent indices have equal values.
- The result needs to be returned modulo 10⁹ + 7 due to large results.
- Approach: Choose k positions to be equal, and for the rest, have m - 1 options.
- Formula: Number of such arrays = C(n - 1, k) x m x (m - 1)^(n - 1 - k)
- C++ and Python code solutions are provided for the problem.
- The C++ solution involves precomputing factorials and inversions, and utilizes combinatorial functions.
- The JavaScript solution also precomputes factorials and inversions, employing modular exponentiation.
- Python code initializes values, calculates modular inverse, and implements combinatorial functions.
- The problem showcases the use of modular combinatorics, fast exponentiation, and factorial precomputation with inverse modulo.
- It requires mathematical insight rather than just raw implementation.
- The problem serves as a good template for combinatorics-based questions with constraints up to 10⁵.
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Dev
1w
402
Image Credit: Dev
My Journey with the Zen of Python: Learning to Code, Learning to Grow
- Signing up for the 'From Zero to Hero' Python Mentorship Program was more than just about learning to code, it was a journey of personal growth and new beginnings.
- Discovering the Zen of Python provided a mindset shift, emphasizing simplicity, readability, and taking action in coding.
- Embracing the principle that 'simple is better than complex,' the focus shifted towards clear, understandable, and trustworthy code.
- The importance of 'readability counts' highlighted the need to make code understandable not only to machines but also to humans.
- Realizing that 'now is better than never' motivated a start despite imperfections, emphasizing progress over perfection.
- Striving for 'one obvious way to do it' helped in making code not just functional but solid and comprehensible.
- Personal growth goals include applying Python to cybersecurity and data analysis, aiming to build impactful skills and gaining confidence.
- The desire is to progress in focus, resilience, and belief, envisioning a career path where tech enables personal and creative freedom.
- Joining the program was a decision to actively participate, seeking growth, and opportunities to learn and contribute to the tech field.
- Acknowledging the support of Black Python Dev for providing this transformative mentorship experience and encouraging beginners to pursue their coding journey.
- Encouragement is given to those considering learning to code to begin now, emphasizing the importance of starting despite any perceived imperfections.
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24 Likes
Medium
1w
142
Image Credit: Medium
Advanced Python Type Hints for Scalable Codebases
- Type hints in Python provide numerous benefits for developers in large and distributed teams.
- Type hints are simple and readable, but real-world projects often require more complexity.
- Type hints are crucial for a caching utility used for multiple data types and API responses with known fields of different types or optional fields.
- They help in accepting any object with specific methods and restricting function inputs to known constants.
- Type hints are useful for passing custom logic to utility functions, validation libraries, and documentation tools.
- Advanced type hints make Python more maintainable, readable, and scalable, essential for data engineering, backend development, and large-scale APIs.
- Using generics, protocols, literals, and unions in type hints improves correctness and enhances team collaboration.
- Starting with basic type hints and gradually adopting advanced concepts, with the help of tooling, can make code more robust and understandable.
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8 Likes
Dev
1w
104
Image Credit: Dev
Ditch Zapier: Automate Sales Follow-Ups with Python
- The article discusses automating sales follow-ups using Python as an alternative to using tools like Zapier.
- The author details how to replace parts of a CRM and automation stack with Python, Gmail API, and Google Sheets.
- The automation involves monitoring a Google Sheet for new leads and sending customized follow-up emails via Gmail, with logging and timestamps.
- Prerequisites include a Google Sheet with lead data, a Gmail account with API access, and basic Python knowledge.
- The process involves connecting to Google Sheets using gspread and OAuth2 for retrieving lead data.
- Sending emails via Gmail is done using smtplib and MIMEText for creating and sending personalized messages.
- A full automation loop is described, where emails are sent based on criteria from the Google Sheet data.
- Options for running the automation regularly include PythonAnywhere for scheduled tasks.
- The article suggests replacing tools like Zapier, Make, or Pabbly with Python automation for cost savings.
- Key takeaways for indie hackers include the versatility of Python for basic automation needs and the benefits of controlling data and reducing costs.
- The author provides bonus resources for working with Google Sheets, sending emails with Gmail in Python, and using PythonAnywhere Scheduler.
- The article ends with a call for suggestions on other tools to automate and a promotion for a local business website launch system.
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