What is Python Programming for Beginners
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## **Design Philosophy: The Zen of Python**
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- **Simple is Better Than Complex:** Python encourages simplicity in both code design and implementation. It favors straightforward solutions over convoluted ones.
- **Explicit is Better Than Implicit:** Code should be explicit and not rely on hidden or magical behavior. This principle promotes code clarity and predictability.
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Table of Contents
Introduction to Python
Python's History and Evolution
Python's Key Features
3.1. Readability and Simplicity
3.2. High-level Language
3.3. Interpreted and Dynamic
3.4. Cross-platform Compatibility
3.5. Rich Standard Library
3.6. Community Support
Python's Application Domains
4.1. Web Development
4.2. Data Science and Machine Learning
4.3. Scientific Computing
4.4. Automation and Scripting
4.5. Game Development
4.6. Desktop Applications
Python Development Environments
5.1. IDLE
5.2. PyCharm
5.3. Jupyter Notebook
5.4. Visual Studio Code
Getting Started with Python
6.1. Installing Python
6.2. Your First Python Program
Python Syntax and Basic Concepts
7.1. Variables and Data Types
7.2. Conditional Statements
7.3. Loops
7.4. Functions
7.5. Exception Handling
Working with Python Libraries
8.1. NumPy
8.2. Pandas
8.3. Matplotlib
8.4. Scikit-Learn
Python and Web Development
9.1. Frameworks (Django, Flask)
9.2. Front-end Integration (HTML/CSS)
9.3. Database Interaction (SQL, NoSQL)
Python in Data Science
10.1. Data Analysis with Pandas
10.2. Data Visualization with Matplotlib and Seaborn
10.3. Machine Learning with Scikit-Learn
10.4. Deep Learning with TensorFlow and PyTorch
Scientific Computing with Python
11.1. Scientific Libraries (SciPy, SymPy)
11.2. Plotting and Visualization (Matplotlib)
Automation and Scripting
12.1. Automating Tasks
12.2. Scripting for System Administration
Game Development with Python
13.1. Pygame
13.2. Unity and Unreal Engine Integration
Desktop Applications with Python
14.1. Tkinter
14.2. PyQt
Python's Ecosystem and Package Management
Python Best Practices
16.1. Code Readability (PEP 8)
16.2. Documentation and Comments
16.3. Testing (Unit Testing, pytest)
16.4. Version Control (Git)
Python's Future and Trends
Conclusion
1. Introduction to Python
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The language has gained immense popularity due to its versatility and a rich ecosystem of libraries and frameworks. Python is renowned for its vibrant community and extensive documentation, making it in p
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Python's Ecosystem and Package Management
Python Best Practices
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...
Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...AxisTechnolabs Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business
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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
How can one start with crypto wallet development.pptx
How can one start with crypto wallet development.pptxlaravinson24 This presentation is a beginner-friendly guide to developing a crypto wallet from scratch. It covers essential concepts such as wallet types, blockchain integration, key management, and security best practices. Ideal for developers and tech enthusiasts looking to enter the world of Web3 and decentralized finance.
Not So Common Memory Leaks in Java Webinar
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Landscape of Requirements Engineering for/by AI through Literature Review
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FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categorie...
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Societal challenges of AI: biases, multilinguism and sustainability
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Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
Creating Automated Tests with AI - Cory House - Applitools.pdf
Creating Automated Tests with AI - Cory House - Applitools.pdfApplitools In this fast-paced, example-driven session, Cory House shows how today’s AI tools make it easier than ever to create comprehensive automated tests. Full recording at https://applitools.info/5wv
See practical workflows using GitHub Copilot, ChatGPT, and Applitools Autonomous to generate and iterate on tests—even without a formal requirements doc.
Implementing promises with typescripts, step by step
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This slides were presented at Negev Web Development meetup
Secure Test Infrastructure: The Backbone of Trustworthy Software Development
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The Significance of Hardware in Information Systems.pdf
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Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
Kubernetes_101_Zero_to_Platform_Engineer.pptx
Kubernetes_101_Zero_to_Platform_Engineer.pptxCloudScouts Presentacion de la primera sesion de Zero to Platform Engineer
Who Watches the Watchmen (SciFiDevCon 2025)
Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5
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What is Python Programming for Beginners
- 1. Python Programming An Absolute Beginners Guide
- 2. What is Python? • Python is a high-level, interpreted programming language known for its readability, simplicity, and versatility. • It was created by Guido van Rossum and first released in 1991. • Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
- 3. Why to Learn Python? • Python is simple and so easy to learn • Python is Open Source which means its available free of cost. • Python is versatile and can be used to create many different things. • Python has powerful development libraries include AI, ML , Web development , GUI etc.
- 4. Characteristics of Python • Easy-to-learn − Python has few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language quickly. • A broad standard library − Python's bulk of the library is very portable and cross-platform compatible on UNIX, Windows, and Macintosh. • Interactive Mode − Python has support for an interactive mode which allows interactive testing and debugging of snippets of code. • Easy-to-read − Python code is more clearly defined and visible to the eyes. • Easy-to-maintain − Python's source code is fairly easy-to-maintain.
- 5. Characteristics of Python • GUI Programming − Python supports GUI applications that can be created and ported to many system • Scalable − Python provides a better structure and support for large programs than shell scripting. • Portable − Python can run on a wide variety of hardware platforms and has the same interface on all platforms. • Extendable − You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient. • Databases − Python provides interfaces to all major commercial databases.
- 6. Careers in Python Full-stack developer Software engineer DevOps engineer Data analyst Data scientist Machine learning engineer Data engineer Game developer
- 7. What you are going to learn ? Python setup Basic Syntax Variables & Comments Input & Output Data Types Operators Flow Control Functions Modules File Handling Exception Handling OOPs Basics
- 8. Thank You !