LangExtract: Python Library for Structured Data Extraction

Python library for data extraction from Google! LangExtract is a Python library that extracts structured information from unstructured documents with precise source grounding and interactive visualization. What it offers:  - Precise source grounding that maps each extraction to its exact position in the text.  - Reliable structured outputs using schema-based extraction with few-shot examples.  - Optimized for long documents with chunking, parallel processing, and multi-pass extraction.  - Interactive HTML visualization to review entities in original context.  - Domain-agnostic design. Works for any extraction task without fine-tuning. You get verifiable, production-friendly extractions instead of black-box outputs. It's 100% open source. Link to the GitHub repo in the comments!

Web scraping is a powerful technique for extracting data from websites. It is widely used in data science, market research, and competitive analysis. With Python, web scraping becomes more accessible due to its robust libraries such as BeautifulSoup,...

A real Python mistake I see in analytics teams 👇 A Python script was generating numbers that didn’t match the Excel report. Everyone blamed Python. Actual problem ❌ Python script read raw CSV Excel report used cleaned data Missing values treated differently Results never matched Python was correct. Input logic was not aligned. Real-job fix ✅ Standardize data cleaning rules Handle nulls explicitly Match business logic before coding Validate output against source reports Python doesn’t replace business understanding. It amplifies mistakes faster. That’s why: Excel teaches structure SQL teaches logic Python teaches scale Skipping fundamentals makes Python dangerous. This is how Python is actually used in jobs, not tutorial videos. I teach Python after Excel & SQL — because that’s how real teams work.

𝗗𝗮𝘆𝟭𝟱: 𝗦𝗲𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 Python provides several built-in set methods to efficiently manage and manipulate data. 𝗕𝗮𝘀𝗶𝗰 𝗠𝗲𝘁𝗵𝗼𝗱𝘀: 🔹𝗹𝗲𝗻(𝘀𝗲𝘁) → Returns total number of elements 🔹𝗺𝗮𝘅(𝘀𝗲𝘁) → Returns the maximum value 🔹𝗺𝗶𝗻(𝘀𝗲𝘁) → Returns the minimum value 𝗔𝗱𝗱𝗶𝗻𝗴 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀: 🔹𝗮𝗱𝗱(𝘅) → Adds a single element 🔹𝘂𝗽𝗱𝗮𝘁𝗲(𝗶𝘁𝗲𝗿𝗮𝗯𝗹𝗲) → Adds multiple elements and updates the original set 𝗥𝗲𝗺𝗼𝘃𝗶𝗻𝗴 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀: 🔹𝗿𝗲𝗺𝗼𝘃𝗲(𝘅) → Removes element, raises error if not found 🔹𝗱𝗶𝘀𝗰𝗮𝗿𝗱(𝘅) → Removes element, no error if not found 🔹𝗽𝗼𝗽() → Removes and returns a random element (sets are unordered) 🔹𝗰𝗹𝗲𝗮𝗿() → Removes all elements from the set 𝗦𝗲𝘁 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗠𝗲𝘁𝗵𝗼𝗱𝘀: 🔹𝗶𝘀𝘀𝘂𝗯𝘀𝗲𝘁() → Checks if a set is part of another set 🔹𝗶𝘀𝘀𝘂𝗽𝗲𝗿𝘀𝗲𝘁() → Checks if a set contains another set 🔹𝗶𝘀𝗱𝗶𝘀𝗷𝗼𝗶𝗻𝘁() → Returns True if sets have no common elements 📝𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗡𝗼𝘁𝗲𝘀 ✅ Sets automatically remove duplicates ✅ Order is not preserved

Strings hold text in Python. You use them for names, messages, labels, and user input. If you want clean text processing, you start with string basics. Here is the breakdown. • Strings store text inside quotes. • You read characters with indexing. • You slice parts of a string with ranges. • You format output with f strings. • You use methods like upper, lower, split, and strip. Simple examples help you move fast. name = "Shafque" name[0] name[1:4] f"Hello {name}" name.upper() Why does this help you? • You write clean messages. • You clean raw text. • You prepare data for reports or automation. KSR Datavizon Ritesh K Mahesh Desireddy Santosh J. INTURI SUPARNA BABU Md Shoyeb Md Tajuddin Md. Asif Maula Anirban Manna Sree Hare M I Anupriya Singh

Python string methods look simple — but they show up everywhere 🐍🧠 Interviews Data cleaning Automation scripts Real production code If you work with Python, you deal with strings every single day. This infographic breaks down the most commonly used Python string methods with clear inputs and outputs, so you can remember them and apply them faster. You’ll get comfortable with • Changing case (lower(), upper(), capitalize()) • Searching text (find(), index(), count()) • Formatting strings (center(), replace()) • Splitting values (split()) • Validations (isalnum(), isnumeric(), islower(), isupper()) When these methods become second nature, you ✔️ Write cleaner code ✔️ Debug text issues faster ✔️ Solve interview questions with confidence Strings are everywhere. Mastering them is non-negotiable. Courses to strengthen Python fundamentals Microsoft Python Development Professional Certificate https://lnkd.in/dDXX_AHM Google IT Automation with Python Professional Certificate https://lnkd.in/dG67Y8nK Meta Data Analyst Professional Certificate https://lnkd.in/dbqX77F2 Save this infographic Practice each method today Share it with someone learning Python Strong Python starts with mastering the basics.

Tired of complex and clunky LLM integrations? Try 𝗠𝗶𝗿𝗮𝘀𝗰𝗼𝗽𝗲 for Python. It provides powerful LLM abstractions that aren't obstructions. 𝗠𝗶𝗿𝗮𝘀𝗰𝗼𝗽𝗲 simplifies building with models from OpenAI, Google, Anthropic, Cohere, and more. Here's how it helps you build better and faster: ✅ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲: Write code once and run it across different LLM providers ✅ 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: Convert Python functions into tools automatically from docstrings ✅ 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮: Extract structured data with Pydantic for validated outputs ✅ 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗲𝗮𝗱𝘆: Seamlessly integrate images and audio into your applications 🔗Link to repo: github(.)com/Mirascope/mirascope --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

Quick Python Tip: Multithreading Threading vs Multiprocessing: The Python Tip That Finally Made Everything Click Some use threading thinking it would “make things faster.” Sometimes it did… and sometimes it made everything worse Here’s the simple rule they wish they learned earlier: If you’re waiting -> use threading If you’re computing -> use multiprocessing That’s it. That’s the game-changer. Threading is bad for: - Heavy calculations - Data / image / video processing - ML workloads Threading is great for: - API calls - File & DB I/O - Network requests - Web scraping Modern Python move 👇 - Use ThreadPoolExecutor, not manual `Thread()` - Set a smart pool size (10–20 threads for most I/O) - Always use timeouts - Process results with as_completed() - Avoid shared state when possible Big reality check: Python’s GIL means threads don’t run CPU code in parallel. So threading ≠ faster math. It’s just better at waiting. Your decision framework now: • Waiting on I/O → Threading • Crunching CPU → Multiprocessing • Both? → Multiprocessing + threading inside Tip Summary: Stop creating threads manually. Start managing concurrent I/O the right way. Have you ever used threading expecting speed… and got nothing? What was the task?

Python provides a wide range of libraries and frameworks that enable the development of robust and efficient applications. In this project, I developed a simple yet visually engaging application using the Streamlit framework, combined with widely used data analysis libraries such as Pandas and Matplotlib. These tools facilitate data processing and visualization, and can also be integrated with artificial intelligence models to perform analytical consultations, generate insights, and provide recommendations aimed at improving the interpretation and quality of the results.

Quick Python Tip: Argparse Most Python Scripts Have Terrible UX (And We Don’t Even Notice) You write a script. It works. You move on. Then someone else runs it… python script.py Crash. No message. No help. Just pain. The issue isn’t the user. It’s your interface. Here’s the shift that changed how I write scripts: Old way: Positional args, no help, cryptic errors, guessing what goes where. Modern way: Build a real CLI: • --help that explains everything • Named arguments (--file data.csv) • Input validation with clear errors • Smart defaults • Examples in the help text Why it matters: Your script is a product. If people can’t figure out how to run it, they won’t use it. Tools that make this easy: • argparse (built-in) • Click • Typer Lesson: Don’t write scripts only you can use. Build tools that explain themselves. Ever opened a script and had to read the code just to run it? What’s your go-to for building CLIs now? Follow for more