#Python Etk Library Documentation

Useful Python Libraries.!! @rengatechnologies #python #pythonlibraries #learnpython #kovilpatti #sivakasi

Did you knew about this?

Your AI coding assistant might be a career bottleneck in disguise. Anthropic just released a new research paper to investigate how AI impact skill formation. The study focused on engineers learning Trio, a Python library for asynchronous programming, to see if AI speed comes at the cost of technical mastery. The Experiment Design Engineers were split into 2 groups. The control group coded by hand using traditional documentation. The treatment group had access to an AI assistant with full codebase context and a nudge to move as fast as possible. Both groups were evaluated on four specific criteria: debugging, code reading, low level code writing, and conceptual understanding. The Technical Results The AI group finished only two minutes faster on average which was not statistically significant. However, their comprehension scores were a disaster. The AI group averaged 50 percent on the evaluation versus 67 percent for the manual group. The Cohen d effect size was 0.738, indicating a substantial negative impact on learning. The biggest failure point was debugging. Scores in this category plummeted because users who offloaded the logic to AI never developed the mental model required to find and fix errors when the code failed. The Patterns they observed: Anthropic identified distinct ways developers interact with AI that determine if they actually learn Low scoring patterns included AI Delegation where users let the bot write everything, Progressive Reliance where they started manual but gave up, and Iterative Debugging where they let the AI fix its own mistakes without understanding why. High scoring patterns included Conceptual Inquiry where users only asked the AI to explain principles, and Generation then Comprehension where they generated code but then grilled the AI on the logic afterward. The takeaway is clear. Efficiency without engagement is a trap. If you are not using AI to explain the why you are effectively de-skilling yourself in real time. PS- If you are still reading this, comment “Old School Coder” so that I know you’re the real deal! #ai #vibecoding #anthropic #artificialintelligence #technews

Python trending song #python #programming #coding #java #javascript #snake #programmer #developer #html #computerscience #reptile #snakes #technology #reptiles #ballpython #pythonprogramming #coder #datascience #snakesofinstagram #css #linux #code #machinelearning #reptilesofinstagram #software #webdevelopment #php #webdeveloper #tech #pythons

For PYHON NOTES DM PAID ONLY..! I’ve turned my personal notes into a PDF. Follow me and get the next update! Python notes PDF Learn Python fast Python tips and tricks Python for beginners Python learning guide Python study notes Python coding PDF Python tutorial PDF Easy Python learning Python cheat sheet

Follow (@codingpythonic) for more Python script to convert a PDF file to a Word document using the pypandoc or pdfplumber library Ensure the required libraries are installed using the command pip install pypandoc pdfplumber Script Using pypandoc import pypandoc def pdf_to_word(pdf_file, word_file): try: output = pypandoc.convert_file(pdf_file, 'docx', outputfile=word_file) print(f"Conversion successful! Word file saved at: {word_file}") except Exception as e: print(f"Error: {e}") # Input and output files pdf_file = "sample.pdf" word_file = "output.docx" # Convert PDF to Word pdf_to_word(pdf_file, word_file) Script Using pdfplumber The pdfplumber library extracts text and does not preserve formatting. Here's an example: import pdfplumber from docx import Document def pdf_to_word(pdf_file, word_file): try: # Create a Word document doc = Document() # Open and read the PDF with pdfplumber.open(pdf_file) as pdf: for page in pdf.pages: text = page.extract_text() if text: doc.add_paragraph(text) # Save the Word document doc.save(word_file) print(f"Conversion successful! Word file saved at: {word_file}") except Exception as e: print(f"Error: {e}") # Input and output files pdf_file = "sample.pdf" word_file = "output.docx" # Convert PDF to Word pdf_to_word(pdf_file, word_file) Notes: 1. pypandoc maintains formatting better but requires Pandoc installed. Install it using: sudo apt install pandoc # Linux brew install pandoc # macOS 2. pdfplumber is ideal for extracting plain text but doesn't handle images or complex layouts.

Complete Python Notes 📝

3 Python coding practice tools that you need to know 👩‍💻✨ Follow @sundaskhalidd for data science, tech, and AI educational content✨ #sundaskhalid #python #coding #codingtips #codingtools #upskilling #hackerrank #stratascratch #pythontutor

Propose Using Python Open Cv Media Pipe library For Python Coders Whatsapp status #reels #love #iloveyou #python #engineer #engineering #opencv #coding #reelsindia

Want to download genetic data from NCBI using Python? 🧬📂 Say goodbye to manual downloads and hello to automation with Bio.Entrez from Biopython! 🚀 Watch this reel to learn how to fetch sequences, articles, and more with just a few lines of code! #Bioinformatics #NCBI #PythonForBioinformatics #Biopython #Genomics #ComputationalBiology #DataScience #PythonAutomation #Biotech #ScienceExplained

📌Top 10 Python Libraries - AI/ML/Data 👩🏻‍💻☕️ 1. ⁠MCP Python SDK & FastMCP - connect LLMs to external data sources 2. ⁠Token-Oriented Object Notation (TOON) - compact JSON encoding for LLMs 3. ⁠Deep Agents - framework for building sophisticated LLM agents 4. ⁠smolagents - agent framework that executes actions as code 5. ⁠LlamaIndex Workflows - building complex AI workflows with ease 6. ⁠Batchata - unified batch processing for AI providers 7. ⁠MarkItDown - convert any file to clean Markdown 8. ⁠Data Formulator - AI-powered data exploration through natural language 9. ⁠LangExtract - extract key details from any document 10. ⁠GeoAI - bridging AI and geospatial data analysis Source: tryolabs.com . . . #datascience #python #setup #fyp #datascientist

Diagrams A Python-based open-source library that turns code into beautiful system architecture Key Benefits * Supports a vast number of cloud and DevOps icons out of the box. * Easy to use for developers familiar with Python. * Good integration with CI/CD tools for automated doc generation. Drawbacks * Limited customisation in layout and styling. * No support for real-time collaboration or GUIs. Mermaid Mermaid is a JavaScript-based diagramming and charting tool that uses Markdown-inspired syntax. Key Benefits * Works seamlessly with markdown files. * Easy syntax for beginners.
Supported natively on many platforms. * Can be embedded Notion, GitHub, or GitLab. Drawbacks * Not suitable for large, complex diagrams. * Limited visual customisation options. PlantUML Supports sequence, use case, class, component diagrams, and more,suitable for both software development & infrastructure modelling. Key Benefits * Text-based and version control-friendly. * Works well with markdown, documentation tools, and IDEs. * Flexible and supports many diagram types. Drawbacks * Requires learning its syntax. * Can become verbose for large-scale diagrams. AWS Diagram-as-Code (by AWS Labs) Key Benefits * Simple and declarative YAML syntax. * Tailored for AWS services. * Lightweight and CLI-driven. Drawbacks * Limited to AWS only * Customisation and layout control are minimal. Structurizr DSL Focuses on conceptual clarity than visual perfection. Key Benefits * Fully supports C4 modelling standard. * Clear separation of model and view. * Encourages best practices in architecture. Drawbacks * Steeper learning curve * Requires understanding of the C4 model. D2 Key Benefits * Simple syntax with auto-layout.
Themes and annotations are supported. * Fast rendering, great developer experience. Drawbacks * The community and ecosystem are still growing. * Lacks out-of-the-box AWS-specific icons. #coding #tech #programming #ai #softwaredevelopment #dev #git #python