#Streamlit Data Editing Tutorial

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#Streamlit Data Editing Tutorial Reels - @imaginaryover2 tarafından paylaşılan video - I Made a Form in Streamlit in 30 Seconds : Streamlit day 22 Welcome to the playlist where I explore everything inside Streamlit - the easiest way to
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@imaginaryover2
I Made a Form in Streamlit in 30 Seconds : Streamlit day 22 Welcome to the playlist where I explore everything inside Streamlit – the easiest way to build interactive, data-driven web apps with pure Python. 🚀 If you’re into Python programming, web development, data science, machine learning, AI, computer engineering, or computer science, this channel is for you. I publish short, focused tutorials that showcase Streamlit’s components, layouts, customization options, and hidden tricks — so you can quickly learn how to: ✅ Build interactive apps and dashboards ✅ Replace Jupyter Notebooks with dynamic, shareable apps ✅ Create simple alternatives to Django or Flask for rapid prototyping ✅ Connect Python to data, APIs, and AI models with clean UIs ✅ Design projects with modern, responsive layouts and themes On this channel, you’ll find: 👉 Step-by-step guides to every Streamlit widget (st.button, st.slider, st.text_input, etc.) 👉 Tips for building AI apps, ML dashboards, and data visualization tools 👉 Deployment strategies for sharing apps with teams, clients, or the world 👉 Inspiration to use Streamlit as your go-to framework for Python projects Integrates seamlessly with Python libraries like Pandas, NumPy, Matplotlib, TensorFlow, PyTorch, Hugging Face, and more Whether you’re a student exploring data science, a researcher sharing ML results, a developer building AI apps, or just someone curious about turning Python into powerful web apps, Streamlit makes the process fast, fun, and intuitive. 📌 Subscribe if you want to master Streamlit one step at a time — I’m covering every single feature so you’ll have the complete toolkit to build apps for AI, data, and any Python-based project. 🔔 Don’t miss the next tutorial — new Streamlit Shorts drop regularly! #Python #Streamlit #DataScience #AI #MachineLearning #WebDevelopment #ArtificialIntelligence #React #DjangoAlternative #ComputerScience #ComputerEngineering #JupyterNotebookAlternative #PythonProjects
#Streamlit Data Editing Tutorial Reels - @tom.developer (onaylı hesap) tarafından paylaşılan video - If you're a Python programmer, Streamlit is a really useful open-source tool for demoing your projects without building a complex visual interface. 💻
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@tom.developer
If you’re a Python programmer, Streamlit is a really useful open-source tool for demoing your projects without building a complex visual interface. 💻🤔 Using a few lines of Python, you can build a visual interface for your project, making it much easier to demo, especially to non-programmers! 🚀
#Streamlit Data Editing Tutorial Reels - @mar_antaya (onaylı hesap) tarafından paylaşılan video - Have you used streamlit before? It's one of the best ways to quickly create something for your data science or programming project in my opinion!
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@mar_antaya
Have you used streamlit before? It’s one of the best ways to quickly create something for your data science or programming project in my opinion!
#Streamlit Data Editing Tutorial Reels - @edhillai (onaylı hesap) tarafından paylaşılan video - Comment "Short" to get it, your content pipeline is now completely automated. The tedious, time-consuming work of turning a long video into short, en
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@edhillai
Comment "Short" to get it, your content pipeline is now completely automated. The tedious, time-consuming work of turning a long video into short, engaging clips is a massive grind. But what if you could have a system that does all of that for you, finding and publishing the best moments without any manual effort? This is a Clipping Automation that runs your channels on autopilot. Here's how it works. This automation is set up to automatically watch your long-form videos. It uses intelligent logic to find the most engaging highlights, instantly clips them into short-form videos, and then publishes them directly to your channel. No more tedious editing, hours of searching for highlights, or manually uploading clips. Just a streamlined, automated workflow that frees you to focus on your content. Imagine a single system that handles the entire process, finding, creating, and publishing a consistent stream of content that grows your channel while you sleep. What kind of video clips would this kind of automation create for you?
#Streamlit Data Editing Tutorial Reels - @setupsai (onaylı hesap) tarafından paylaşılan video - Powerful websites you should know (part 889) data visualization tracking effects #effect #animation #visual
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@setupsai
Powerful websites you should know (part 889) data visualization tracking effects #effect #animation #visual
#Streamlit Data Editing Tutorial Reels - @arslanxo_ tarafından paylaşılan video - Turn popular content into your income stream 📹💵" Edit > Post > Earn. That's it. #AIEarning #MakeMoneyOnline #VideoHack #AItools #ShortsIncome #Smar
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@arslanxo_
Turn popular content into your income stream 📹💵" Edit > Post > Earn. That’s it. #AIEarning #MakeMoneyOnline #VideoHack #AItools #ShortsIncome #SmartEditing #FacelessContent #TiktokHustle #EarnWithAI #ContentTrick #youtube #youtubeshort #youtubeautomation #makemoney #makemoneywithai #earnmoney
#Streamlit Data Editing Tutorial Reels - @tread_isaac tarafından paylaşılan video - How to get $9,000 Pitch Design footage with the phone in your pocket ⬇️ 🔥 Step 1 - Set up your phone directly behind pitcher, centered with release
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@tread_isaac
How to get $9,000 Pitch Design footage with the phone in your pocket ⬇️ 🔥 Step 1 - Set up your phone directly behind pitcher, centered with release point Step 2 - Set to SloMo and Zoom to 2.5x - then record video Step 3 - Edit: Exposure (0), Highlights (100), Shadows (0), Filter (Noir) And that’s it! Use this footage to improve your understanding of how you are actually releasing the ball. Adjust grips and intent to get the shapes you want on your pitches 📈 Pair with Trackman/Rapsodo/PitchLogic data for even further insight into your pitches! Or, come train @tread_athletics in-house! (We have edger cameras in every bullpen lane) 👀
#Streamlit Data Editing Tutorial Reels - @techie.sonali tarafından paylaşılan video - DAY 2/100 In a frontend interview, don't just say 'WebSockets.' Explain the Data Stream. You need a bidirectional connection so the server can push GP
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@techie.sonali
DAY 2/100 In a frontend interview, don’t just say ‘WebSockets.’ Explain the Data Stream. You need a bidirectional connection so the server can push GPS coordinates the second the driver moves.”  The Step-by-Step Workflow The “Real-Time Driver Tracking” Response 1. The Connection Strategy (The Pipe) To handle real-time GPS updates, I would implement a bidirectional WebSocket connection. Unlike polling, which is resource-heavy, WebSockets allow for a persistent, low-latency stream. • The Flow: The driver’s app emits a location_update event with a payload of (lat, lng). The server then identifies the specific customer(s) subscribed to that driver’s ID and broadcasts the coordinates only to them to minimize unnecessary traffic. 2. Client-Side Performance (The Optimization) A common mistake is updating the React state or the Map instance for every single incoming socket message. At scale, this causes UI lag. I would implement two key optimizations: • Throttling: I’d throttle the incoming data stream. Even if the driver sends updates every 500ms, the UI only needs to trigger a transition logic at a consistent interval to prevent the main thread from choking. • Direct DOM Manipulation: Instead of a full React re-render of the Map component, I would use the Map API’s native methods (like marker.setPosition()) to update the icon position directly. 3. The Secret Sauce: Smooth Animation (The UX) If you simply “teleport” the marker from Point A to Point B, the movement looks jittery. To achieve a 60fps “Uber-like” experience, I would use Linear Interpolation (Lerp). • The Logic: I’d write a lerp function to calculate intermediate steps between the old coordinates and the new coordinates. • The Execution: Using requestAnimationFrame, I would animate the marker sliding across these intermediate points over the duration of the update interval. This creates a fluid, sliding motion rather than a series of jumps. #javascript #frontend #frontenddeveloper
#Streamlit Data Editing Tutorial Reels - @tmsproductions_ tarafından paylaşılan video - WINNER ANNOUNCEMENT!! Go check out the video on our Youtube to see who won! Link in bio!! Also we are holding a live stream on January 19th to crit
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@tmsproductions_
WINNER ANNOUNCEMENT!! Go check out the video on our Youtube to see who won! Link in bio!! Also we are holding a live stream on January 19th to critique some of your videos that were submitted to the editing contest! Link In Bio to learn more! Thank you @the.shadeinc for helping us pull of this entire editing contest, we couldn't have done it without you!! Check them out if you haven't already! Also... sorry for the bait and switch 😅 #tmsedit2023 #shadepartner #ad
#Streamlit Data Editing Tutorial Reels - @priyal.py tarafından paylaşılan video - 1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn
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@priyal.py
1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn 2. Spotify Audio Feature Analyzer Analyze songs by tempo, energy and danceability using Spotify API. Tech Stack: Python, Spotipy, Matplotlib, Plotly 3. YouTube Trending Video Analyzer Discover what makes a video go viral. Tech Stack: Python, Pandas, BeautifulSoup, Seaborn 4. Resume Scanner using NLP Parse and rank resumes based on job description matching. Tech Stack: Python, SpaCy, NLTK, Streamlit 5. Crypto Price Predictor Predict BTC/ETH prices using historical data. Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib 6. Instagram Hashtag Recommender Suggest hashtags based on image captions or niche. Tech Stack: Python, NLP, TF-IDF, Cosine Similarity 7. Reddit Sentiment Tracker Analyze community sentiment on hot topics using Reddit API. Tech Stack: Python, PRAW, VADER, Plotly 8. AI Job Postings Dashboard Scrape and visualize job trends by tech stack and location. Tech Stack: Python, Selenium/BeautifulSoup, Streamlit 9. Airbnb Price Estimator Predict listing prices based on location and amenities. Tech Stack: Python, Scikit-learn, Pandas, XGBoost 10. Food Calorie Image Classifier Estimate calories from food images using CNNs. Tech Stack: Python, TensorFlow/Keras, OpenCV Each project can be completed in 1-2 weekends. #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects
#Streamlit Data Editing Tutorial Reels - @the.datascience.gal (onaylı hesap) tarafından paylaşılan video - Here's a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you're tired of writing vanilla apps and want to build ML sy
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@the.datascience.gal
Here’s a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you. Step 1 – Learn Python and SQL (not Java, C++, or JavaScript). → Focus on pandas, numpy, scikit-learn, matplotlib → For SQL: use LeetCode or StrataScratch to practice real-world queries → Don’t just write code—learn to think in data Step 2 – Build your foundation in statistics + math. → Start with Practical Statistics for Data Scientists → Learn: probability, hypothesis testing, confidence intervals, distributions → Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule) Step 3 – Learn ML the right way. → Do Andrew Ng’s ML course (Deeplearning.ai) → Master the full pipeline: cleaning → feature engineering → modeling → evaluation → Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper Step 4 – Build 2–3 real, messy projects. → Don’t follow toy tutorials → Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio → Upload everything to GitHub with a clear README Step 5 – Become a storyteller with data. → Read Storytelling with Data by Cole Knaflic → Learn to explain your findings to non-technical teams → Practice communicating precision/recall/F1 in simple language Step 6 – Stay current. Never stop learning. → Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn → Join communities, follow researchers, and keep shipping new experiments ------- Save this for later. Tag a friend who’s trying to make the switch. [software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]

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