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PostHeaderIcon Boosting AI Reliability: Uncertainty Quantification with MAPIE

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Introduction

Thierry Cordier and Valentin Laurent introduce MAPIE, a Python library within scikit-learn-contrib, designed for uncertainty quantification in machine learning models.

MAPIE on GitHub

Managing Uncertainty in Machine Learning

In AI applications — from autonomous vehicles to medical diagnostics — understanding prediction uncertainty is crucial. MAPIE uses conformal prediction methods to generate prediction intervals with controlled confidence, ensuring safer and more interpretable AI systems.

Key Features

MAPIE supports regression, classification, time series forecasting, and complex tasks like multi-label classification and semantic segmentation. It integrates seamlessly with scikit-learn, TensorFlow, PyTorch, and custom models.

Real-World Use Cases

By generating calibrated prediction intervals, MAPIE enables selective classification, robust decision-making under uncertainty, and provides statistical guarantees critical for safety-critical AI systems.

Conclusion

MAPIE empowers data scientists to quantify uncertainty elegantly, bridging the gap between predictive power and real-world reliability.

PostHeaderIcon [PyData Paris 2024] Exploring Quarto Dashboard for Impactful and Visual Communication

Exploring Quarto Dashboard for Impactful and Visual Communication

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Introduction

Christophe Dervieux introduces us to Quarto Dashboard, a powerful open-source scientific and technical publishing system. Designed to create impactful visual communication directly from Jupyter Notebooks, Quarto enables the seamless creation of interactive charts, dashboards, and dynamic narratives.

Building Visual Communication with Quarto

Quarto extends standard markdown with advanced features tailored for scientific writing. It offers support for multiple computation engines, allowing narratives and executable code to merge into various outputs: PDF, HTML pages, websites, books, and especially dashboards. The dashboard format enhances data communication by organizing visual metrics in an efficient and impactful layout.

Using Quarto, rendering a Jupyter notebook becomes simple: with just a command-line instruction (quarto render), users can output polished, shareable dashboards. Additional extensions, such as those available in VS Code, JupyterLab, and Positron IDEs, streamline this experience further.

Dashboard Features and Design

Dashboards in Quarto organize content using components like cards, rows, columns, sidebars, and tabs. Each element structures visual outputs like plots, tables, and value boxes, allowing maximum clarity. Customization is straightforward, leveraging YAML configuration and Bootstrap-based theming. Users can create multi-page navigation, interactivity through JavaScript libraries, and adapt layouts for specific audiences.

Recent updates even enable branding dashboards easily with SCSS themes, making Quarto ideal for both scientific and corporate environments.

Conclusion

Quarto revolutionizes technical communication by enabling scientists and analysts to produce professional-grade dashboards and publications effortlessly. Christophe’s session at PyData Paris 2023 showcased the simplicity, power, and flexibility Quarto brings to modern data storytelling.

PostHeaderIcon [PyConUS 2024] Demystifying Python Decorators: A Comprehensive Tutorial

At PyConUS2024, Reuven M. Lerner, an esteemed independent trainer and consultant from Lerner Consulting, presented an exhaustive tutorial titled “All About Decorators.” This session aimed to strip away the perceived complexity surrounding Python’s decorators, revealing their inherent power and versatility. Reuven’s approach was to guide attendees through the fundamental principles, practical applications, and advanced techniques of decorators, empowering developers to leverage this elegant feature for cleaner, more maintainable code. The tutorial offered a deep dive into what decorators are, their internal mechanics, how to construct them, and when to employ them effectively in various programming scenarios.

Functions as First-Class Citizens: The Foundation of Decorators

At the heart of Python’s decorator mechanism lies the concept of functions as first-class objects. Reuven Lerner began by elucidating this foundational principle, demonstrating how functions in Python are not merely blocks of code but entities that can be assigned to variables, passed as arguments to other functions, and returned as values from functions. This flexibility is pivotal, as it allows for the dynamic manipulation and extension of code behavior without altering the original function definition.

He illustrated this with simple examples, such as wrapping print statements with additional lines of text. Initially, this might involve manually calling a “wrapper” function that takes another function as an argument. This manual wrapping, while functional, quickly becomes cumbersome when applied repeatedly across numerous functions. Reuven showed how this initial approach, though verbose, laid the groundwork for understanding the more sophisticated decorator syntax. The ability to treat functions like any other data type in Python empowers developers to create highly modular and adaptable code structures, a cornerstone for building robust and scalable applications.

The Power of Closures: Functions Returning Functions

Building upon the concept of first-class functions, Reuven delved into the powerful notion of closures. A closure is a function that remembers the environment in which it was created, even after the outer function has finished executing. This is achieved when an inner function is defined within an outer function, and the outer function returns this inner function. The inner function retains access to the outer function’s local variables, forming a “closure” over that environment.

Lerner’s explanations made it clear that closures are a critical stepping stone to understanding how decorators work. The decorator pattern fundamentally relies on an outer function (the decorator) that takes a function as input, defines an inner “wrapper” function, and then returns this wrapper. This wrapper function “closes over” the original function and any variables from the decorator’s scope, allowing it to execute the original function while adding pre- or post-processing logic. This concept is essential for functions that need to maintain state or access context from their creation environment, paving the way for more sophisticated decorator implementations.

Implementing the Decorator Pattern Manually

Before introducing Python’s syntactic sugar for decorators, Reuven walked attendees through the manual implementation of the decorator pattern. This hands-on exercise was crucial for demystifying the @ syntax and showing precisely what happens under the hood. The manual approach involves explicitly defining a “decorator function” that accepts another function (the “decorated function”) as an argument. Inside the decorator function, a new “wrapper function” is defined. This wrapper function contains the additional logic to be executed before or after the decorated function, and it also calls the decorated function. Finally, the decorator function returns this wrapper.

The key step, as Reuven demonstrated, is then reassigning the original function’s name to the returned wrapper function. For instance, my_function = decorator(my_function). This reassignment effectively replaces the original my_function with the new, enhanced wrapper function, without changing how my_function is called elsewhere in the code. This explicit, step-by-step construction revealed the modularity and power of decorators, highlighting how they can seamlessly inject new behavior into existing functions while preserving their interfaces. Understanding this manual process is fundamental to debugging and truly mastering decorator usage.

Python’s Syntactic Sugar: The @ Operator

Once the manual mechanics of decorators were firmly established, Reuven introduced Python’s elegant and widely adopted @ syntax. This syntactic sugar simplifies the application of decorators significantly, making code more readable and concise. Instead of the explicit reassignment, my_function = decorator(my_function), the @ symbol allows developers to place the decorator name directly above the function definition:

@decorator
def my_function():
#### ...

Lerner emphasized that this @ notation is merely a convenience for the manual wrapping process discussed earlier. It performs the exact same operation of passing my_function to decorator and reassigning the result back to my_function. This clarity was vital, as many developers initially find the @ syntax magical. Reuven illustrated how this streamlined syntax enhances code readability, especially when multiple decorators are applied to a single function, or when creating custom decorators for specific tasks. The @ operator makes decorators a powerful and expressive tool in the Python developer’s toolkit, promoting a clean separation of concerns and encouraging reusable code patterns.

Practical Applications of Decorators

The tutorial progressed into a series of practical examples, showcasing the diverse utility of decorators in real-world scenarios. Reuven presented various use cases, from simple enhancements to more complex functionalities:

  • “Shouter” Decorator: A classic example where a decorator modifies the output of a function, perhaps by converting it to uppercase or adding exclamation marks. This demonstrates how decorators can alter the result returned by a function.
  • Timing Function Execution: A highly practical application involves using a decorator to measure the execution time of a function. This is invaluable for performance profiling and identifying bottlenecks in code. The decorator would record the start time, execute the function, record the end time, and then print the duration, all without cluttering the original function’s logic.
  • Input and Output Validation: Decorators can be used to enforce constraints on function arguments or to validate the return value. For instance, a decorator could ensure that a function only receives positive integers or that its output adheres to a specific format. This promotes data integrity and reduces errors.
  • Logging and Authentication: More advanced applications include decorators for logging function calls, handling authentication checks before a function executes, or implementing caching mechanisms to store and retrieve previously computed results.

Through these varied examples, Reuven underscored that decorators are not just an academic curiosity but a powerful tool for injecting cross-cutting concerns (like logging, timing, validation) into functions in a clean, non-intrusive manner. This approach adheres to the “separation of concerns” principle, making code more modular, readable, and easier to maintain.

Decorators with Arguments and Stacking Decorators

Reuven further expanded the attendees’ understanding by demonstrating how to create decorators that accept arguments. This adds another layer of flexibility, allowing decorators to be configured at the time of their application. To achieve this, an outer function is required that takes the decorator’s arguments and then returns the actual decorator function. This creates a triple-nested function structure, where the outermost function handles arguments, the middle function is the actual decorator that takes the decorated function, and the innermost function is the wrapper.

He also covered the concept of “stacking decorators,” where multiple decorators are applied to a single function. When decorators are stacked, they are executed from the bottom up (closest to the function definition) to the top (furthest from the function definition). Each decorator wraps the function that results from the application of the decorator below it. This allows for the sequential application of various functionalities to a single function, building up complex behaviors from smaller, modular units. Reuven carefully explained the order of execution and how the output of one decorator serves as the input for the next, providing a clear mental model for understanding chained decorator behavior.

Preserving Metadata with functools.wraps

A common side effect of using decorators is the loss of the decorated function’s original metadata, such as its name (__name__), docstring (__doc__), and module (__module__). When a decorator replaces the original function with its wrapper, the metadata of the wrapper function is what becomes visible. This can complicate debugging, introspection, and documentation.

Reuven introduced functools.wraps as the standard solution to this problem. functools.wraps is itself a decorator that can be applied to the wrapper function within your custom decorator. When used, it copies the relevant metadata from the original function to the wrapper function, effectively “wrapping” the metadata along with the code.

from functools import wraps

def my_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
##### ... decorator logic ...
        return func(*args, **kwargs)
    return wrapper

This simple yet crucial addition ensures that decorated functions retain their original identity and documentation, making them behave more like their undecorated counterparts. Reuven stressed the importance of using functools.wraps in all custom decorators to avoid unexpected behavior and maintain code clarity, a best practice for any Python developer working with decorators.

Extending Decorator Concepts: Classes as Decorators and Decorating Classes

Towards the end of the tutorial, Reuven touched upon more advanced decorator patterns, including the use of classes as decorators and the application of decorators to classes themselves.

  • Classes as Decorators: While functions are the most common way to define decorators, classes can also serve as decorators. This is achieved by implementing the __call__ method in the class, making instances of the class callable. The __init__ method typically takes the function to be decorated, and the __call__ method acts as the wrapper, executing the decorated function along with any additional logic. This approach can be useful for decorators that need to maintain complex state or have more intricate setup/teardown procedures.
  • Decorating Classes: Decorators can also be applied to classes, similar to how they are applied to functions. When a class is decorated, the decorator receives the class object itself as an argument. The decorator can then modify the class, for example, by adding new methods, altering existing ones, or registering the class in some way. This is often used in frameworks for tasks like dependency injection, ORM mapping, or automatically adding mixins.

Reuven’s discussion of these more advanced scenarios demonstrated the full breadth of decorator applicability, showcasing how this powerful feature can be adapted to various architectural patterns and design needs within Python programming. This segment provided a glimpse into how decorators extend beyond simple function wrapping to influence the structure and behavior of entire classes, offering a flexible mechanism for meta-programming.

Hashtags: #Python #Decorators #PyConUS2024 #Programming #SoftwareDevelopment #Functions #Closures #PythonTricks #CodeQuality #ReuvenMLerner #LernerConsulting #LernerPython

PostHeaderIcon Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age

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Introduction

The team from INSEE presents Onyxia, an open-source, Kubernetes-based platform designed to offer flexible, collaborative, and powerful cloud environments for data scientists.

Rethinking Data Science Infrastructure

Traditional local development faces issues like configuration divergence, data duplication, and limited compute resources. Onyxia solves these by offering isolated namespaces, integrated object storage, and a seamless user interface that abstracts Kubernetes and S3 complexities.

Versatile Deployment

With a few clicks, users can launch preconfigured environments — including Jupyter notebooks, VS Code, Postgres, and MLflow — empowering fast innovation without heavy IT overhead. Organizations can extend Onyxia by adding custom services, ensuring future-proof, evolvable data labs.

Success Stories

Adopted across French universities and research labs, Onyxia enables students and professionals alike to work in secure, scalable, and fully-featured environments without managing infrastructure manually.

Conclusion

Onyxia democratizes access to powerful cloud tools for data scientists, streamlining collaboration and fostering innovation.

PostHeaderIcon [PyConUS 2024] How Python Harnesses Rust through PyO3

David Hewitt, a key contributor to the PyO3 library, delivered a comprehensive session at PyConUS 2024, unraveling the mechanics of integrating Rust with Python. As a Python developer for over a decade and a lead maintainer of PyO3, David provided a detailed exploration of how Rust’s power enhances Python’s ecosystem, focusing on PyO3’s role in bridging the two languages. His talk traced the journey of a Python function call to Rust code, offering insights into performance, security, and concurrency, while remaining accessible to those unfamiliar with Rust.

Why Rust in Python?

David began by outlining the motivations for combining Rust with Python, emphasizing Rust’s reliability, performance, and security. Unlike Python, where exceptions can arise unexpectedly, Rust’s structured error handling via pattern matching ensures predictable behavior, reducing debugging challenges. Performance-wise, Rust’s compiled nature offers significant speedups, as seen in libraries like Pydantic, Polars, and Ruff. David highlighted Rust’s security advantages, noting its memory safety features prevent common vulnerabilities found in C or C++, making it a preferred choice for companies like Microsoft and Google. Additionally, Rust’s concurrency model avoids data races, aligning well with Python’s evolving threading capabilities, such as sub-interpreters and free-threading in Python 3.13.

PyO3: Bridging Python and Rust

Central to David’s talk was PyO3, a Rust library that facilitates seamless integration with Python. PyO3 allows developers to write Rust code that runs within a Python program or vice versa, using procedural macros to generate Python-compatible modules. David explained how tools like Maturin and setup-tools-rust simplify project setup, enabling developers to compile Rust code into native libraries that Python imports like standard modules. He emphasized PyO3’s goal of maintaining a low barrier to entry, with comprehensive documentation and a developer guide to assist Python programmers venturing into Rust, ensuring a smooth transition across languages.

Tracing a Function Call

David took the audience on a technical journey, tracing a Python function call through PyO3 to Rust code. Using a simple word-counting function as an example, he showed how a Rust implementation, marked with PyO3’s @pyfunction attribute, mirrors Python’s structure while offering performance gains of 2–4x. He dissected the Python interpreter’s bytecode, revealing how the CALL instruction invokes PyObject_Vectorcall, which resolves to a Rust function pointer via PyO3’s generated code. This “trampoline” handles critical safety measures, such as preventing Rust panics from crashing the Python interpreter and managing the Global Interpreter Lock (GIL) for safe concurrency. David’s step-by-step breakdown clarified how arguments are passed and converted, ensuring seamless execution.

Future of Rust in Python’s Ecosystem

Concluding, David reflected on Rust’s growing adoption in Python, citing over 350 projects monthly uploading Rust code to PyPI, with downloads exceeding 3 billion annually. He predicted that Rust could rival C/C++ in the Python ecosystem within 2–4 years, driven by its reliability and performance. Addressing concurrency, David discussed how PyO3 could adapt to Python’s sub-interpreters and free-threading, potentially enforcing immutability to simplify multithreaded interactions. His vision for PyO3 is to enhance Python’s strengths without replacing it, fostering a symbiotic relationship that empowers developers to leverage Rust’s precision where needed.

Hashtags: #Rust #PyO3 #Python #Performance #Security #PyConUS2024 #DavidHewitt #Pydantic #Polars #Ruff

PostHeaderIcon Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Mastering how dependencies are handled can define your project’s success or failure. Let’s explore the nuances across today’s major development ecosystems.

Introduction

Every modern application relies heavily on external libraries. These libraries accelerate development, improve security, and enable integration with third-party services. However, unmanaged dependencies can lead to catastrophic issues — from version conflicts to severe security vulnerabilities. That’s why understanding dependency management and resolution is absolutely essential, particularly across different programming ecosystems.

What is Dependency Management?

Dependency management involves declaring external components your project needs, installing them properly, ensuring their correct versions, and resolving conflicts when multiple components depend on different versions of the same library. It also includes updating libraries responsibly and securely over time. In short, good dependency management prevents issues like broken builds, “dependency hell”, or serious security holes.

Java: Maven and Gradle

In the Java ecosystem, dependency management is an integrated and structured part of the build lifecycle, using tools like Maven and Gradle.

Maven and Dependency Scopes

Maven uses a declarative pom.xml file to list dependencies. A particularly important notion in Maven is the dependency scope.

Scopes control where and how dependencies are used. Examples include:

  • compile (default): Needed at both compile time and runtime.
  • provided: Needed for compile, but provided at runtime by the environment (e.g., Servlet API in a container).
  • runtime: Needed only at runtime, not at compile time.
  • test: Used exclusively for testing (JUnit, Mockito, etc.).
  • system: Provided by the system explicitly (deprecated practice).

<dependency>
  <groupId>junit</groupId>
  <artifactId>junit</artifactId>
  <version>4.13.2</version>
  <scope>test</scope>
</dependency>
    

This nuanced control allows Java developers to avoid bloating production artifacts with unnecessary libraries, and to fine-tune build behaviors. This is a major feature missing from simpler systems like pip or npm.

Gradle

Gradle, offering both Groovy and Kotlin DSLs, also supports scopes through configurations like implementation, runtimeOnly, testImplementation, which have similar meanings to Maven scopes but are even more flexible.


dependencies {
    implementation 'org.springframework.boot:spring-boot-starter'
    testImplementation 'org.springframework.boot:spring-boot-starter-test'
}
    

Python: pip and Poetry

Python dependency management is simpler, but also less structured compared to Java. With pip, there is no formal concept of scopes.

pip

Developers typically separate main dependencies and development dependencies manually using different files:

  • requirements.txt – Main project dependencies.
  • requirements-dev.txt – Development and test dependencies (pytest, tox, etc.).

This manual split is prone to human error and lacks the rigorous environment control that Maven or Gradle enforce.

Poetry

Poetry improves the situation by introducing a structured division:


[tool.poetry.dependencies]
requests = "^2.31"

[tool.poetry.dev-dependencies]
pytest = "^7.1"
    

Poetry brings concepts closer to Maven scopes, but they are still less fine-grained (no runtime/compile distinction, for instance).

Node.js: npm and Yarn

JavaScript dependency managers like npm and yarn allow a simple distinction between regular and development dependencies.

npm

Dependencies are declared in package.json under different sections:

  • dependencies – Needed in production.
  • devDependencies – Needed only for development (e.g., testing libraries, linters).

{
  "dependencies": {
    "express": "^4.18.2"
  },
  "devDependencies": {
    "mocha": "^10.2.0"
  }
}
    

While convenient, npm’s dependency management lacks Maven’s level of strictness around dependency resolution, often leading to version mismatches or “node_modules bloat.”

Key Differences Between Ecosystems

When switching between Java, Python, and Node.js environments, developers must be aware of the following fundamental differences:

1. Formality of Scopes

Java’s Maven/Gradle ecosystem defines scopes formally at the dependency level. Python (pip) and JavaScript (npm) ecosystems use looser, file- or section-based categorization.

2. Handling of Transitive Dependencies

Maven and Gradle resolve and include transitive dependencies automatically with sophisticated conflict resolution strategies (e.g., nearest version wins). pip historically had weak transitive dependency handling, leading to issues unless careful pinning is done. npm introduced better nested module flattening with npm v7+ but conflicts still occur in complex trees.

3. Lockfiles

npm/yarn and Python Poetry use lockfiles (package-lock.json, yarn.lock, poetry.lock) to ensure consistent dependency installations across machines. Maven and Gradle historically did not need lockfiles because they strictly followed declared versions and scopes. However, Gradle introduced lockfile support with dependency locking in newer versions.

4. Dependency Updating Strategy

Java developers often manually manage dependency versions inside pom.xml or use dependencyManagement blocks for centralized control. pip requires updating requirements.txt or regenerating them via pip freeze. npm/yarn allows semver rules (“^”, “~”) but auto-updating can lead to subtle breakages if not careful.

Best Practices Across All Languages

  • Pin exact versions wherever possible to avoid surprise updates.
  • Use lockfiles and commit them to version control (Git).
  • Separate production and development/test dependencies explicitly.
  • Use dependency scanners (e.g., OWASP Dependency-Check, Snyk, npm audit) regularly to detect vulnerabilities.
  • Prefer stable, maintained libraries with good community support and recent commits.

Conclusion

Dependency management, while often overlooked early in projects, becomes critical as applications scale. Maven and Gradle offer the most fine-grained controls via dependency scopes and conflict resolution. Python and JavaScript ecosystems are evolving rapidly, but require developers to be much more careful manually. Understanding these differences, and applying best practices accordingly, will ensure smoother builds, faster delivery, and safer production systems.

Interested in deeper dives into dependency vulnerability scanning, SBOM generation, or automatic dependency update pipelines? Subscribe to our blog for more in-depth content!

PostHeaderIcon [PyConUS 2023] Fixing Legacy Code, One Pull Request at a Time

At PyCon US 2023, Guillaume Dequenne from Sonar presented a compelling workshop on modernizing legacy codebases through incremental improvements. Sponsored by Sonar, this session focused on integrating code quality tools into development workflows to enhance maintainability and sustainability, using a Flask application as a practical example. Guillaume’s approach, dubbed “Clean as You Code,” offers a scalable strategy for tackling technical debt without overwhelming developers.

The Legacy Code Conundrum

Legacy codebases often pose significant challenges, accumulating technical debt that hinders development efficiency and developer morale. Guillaume illustrated this with a vivid metaphor: analyzing a legacy project for the first time can feel like drowning in a sea of issues. Traditional approaches to fixing all issues at once are unscalable, risking functional regressions and requiring substantial resources. Instead, Sonar advocates for a pragmatic methodology that focuses on ensuring new code adheres to high-quality standards, gradually reducing technical debt over time.

Clean as You Code Methodology

The “Clean as You Code” approach hinges on two principles: ownership of new code and incremental improvement. Guillaume explained that developers naturally understand and take responsibility for code they write today, making it easier to enforce quality standards. By ensuring that each pull request introduces clean code, teams can progressively refurbish their codebase. Over time, as new code replaces outdated sections, the overall quality improves without requiring a massive upfront investment. This method aligns with continuous integration and delivery (CI/CD) practices, allowing teams to maintain high standards while delivering features systematically.

Leveraging SonarCloud for Quality Assurance

Guillaume demonstrated the practical application of this methodology using SonarCloud, a cloud-based static analysis tool. By integrating SonarCloud into a Flask application’s CI/CD pipeline, developers can automatically analyze pull requests for issues like bugs, security vulnerabilities, and code smells. He showcased how SonarCloud’s quality gates enforce standards on new code, ensuring that only clean contributions are merged. For instance, Guillaume highlighted a detected SQL injection vulnerability due to unsanitized user input, emphasizing the tool’s ability to provide contextual data flow analysis to pinpoint and resolve issues efficiently.

Enhancing Developer Workflow with SonarLint

To catch issues early, Guillaume introduced SonarLint, an IDE extension for PyCharm and VSCode that performs real-time static analysis. This tool allows developers to address issues before committing code, streamlining the review process. He demonstrated how SonarLint highlights issues like unraised exceptions and offers quick fixes, enhancing productivity. Additionally, the connected mode between SonarLint and SonarCloud synchronizes issue statuses, ensuring consistency across development and review stages. This integration empowers developers to maintain high-quality code from the outset, reducing the burden of post-commit fixes.

Sustaining Codebase Health

The workshop underscored the long-term benefits of the “Clean as You Code” approach, illustrated by a real-world project where issue counts decreased over time as new rules were introduced. By focusing on new code and leveraging tools like SonarCloud and SonarLint, teams can achieve sustainable codebases that are maintainable, reliable, and secure. Guillaume’s presentation offered a roadmap for developers to modernize legacy systems incrementally, fostering a culture of continuous improvement.

Hashtags: #LegacyCode #CleanCode #StaticAnalysis #SonarCloud #SonarLint #Python #Flask #GuillaumeDequenne #PyConUS2023

PostHeaderIcon No handlers could be found for logger…

Case

You run a Python script, and you get an error similar to:
[python]No handlers could be found for logger &quot;stomp.py&quot;[/python]

Fix

Edit the script, and add the following block:
[python]import logging
logging.basicConfig()[/python]
This will produce explicit log that will allow you to identity the source of the actual issue.