#Streamlit Data Editor Features

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#Streamlit Data Editor Features Reel by @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
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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 Editor Features Reel by @tom.developer (verified account) - 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 Editor Features Reel by @aifortechies (verified account) - Python devs, meet your new best friend Streamlit! 🐍⚡ With just a few lines of code, you can turn your project into a slick, shareable web app no fron
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@aifortechies
Python devs, meet your new best friend Streamlit! 🐍⚡ With just a few lines of code, you can turn your project into a slick, shareable web app no frontend headache required. Perfect for demos, even your non-tech friends will get it! 💻✨ . . . #streamlit #python #opensource #pythonprojects #webapp #uidesign #codinglife #developerlife #pythonprogrammer #techtools #projectdemo #nocode #programmingtips #pythontools #devtools . . [streamlit, python, open source, python projects, build web app, demo tools, no code, visual interface, developer tools, python framework, tech stack, frontend alternative, data apps, machine learning demo, easy ui for python]
#Streamlit Data Editor Features Reel by @laskentatechltd - Streamlit - Build an Interactive Data Visualization App in Python! #python #coding #programming
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@laskentatechltd
Streamlit - Build an Interactive Data Visualization App in Python! #python #coding #programming
#Streamlit Data Editor Features Reel by @mar_antaya (verified account) - 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 Editor Features Reel by @maggieindata (verified account) - Has anyone heard of TaiPy? 👀 apparently it's coming for streamlit 

📥 what data science topics do you want to see next? 

Follow @maggieindata to br
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@maggieindata
Has anyone heard of TaiPy? 👀 apparently it’s coming for streamlit 📥 what data science topics do you want to see next? Follow @maggieindata to break into your dream data job with everything you need to know or just stay up to date with tech shenanigans 👩🏻‍💻 #datascience #cs #datascientist #ai #aiapplications #dashboard #powerbi #tableau #datavisualization #datascienceprojects #dataanalytics #dataanalysis
#Streamlit Data Editor Features Reel by @hadi.khezrpour - ⏬️ اولین ویدیو آموزش ساخت ورکفلو در n8n

با این ورکفلو در تعداد زیاد می تونید عنوان سئو بنویسید بدون اینکه خودتون رو درگیر کنید و وقت بزارید 😍

ویدیو
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@hadi.khezrpour
⏬️ اولین ویدیو آموزش ساخت ورکفلو در n8n با این ورکفلو در تعداد زیاد می تونید عنوان سئو بنویسید بدون اینکه خودتون رو درگیر کنید و وقت بزارید 😍 ویدیو آموزش کاملش رو داخل کانال تلگرام و یوتیوب گذاشتم. اگه فیدبک های خوبی بگیرم ویدیو بعدی رو براتون رکورد میکنم کاراتون راحت تر بشه ✌️ #سئو #اتومیشن #n8n
#Streamlit Data Editor Features Reel by @dr.james.utley (verified account) - Skip the frontend heartache and use streamlit or gradio for all your UI needs. 

#ai #buildwithai #streamlit #gradio #ml
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@dr.james.utley
Skip the frontend heartache and use streamlit or gradio for all your UI needs. #ai #buildwithai #streamlit #gradio #ml
#Streamlit Data Editor Features Reel by @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
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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 Editor Features Reel by @priyal.py - 1. Personalized Workout & Nutrition Recommendation System
Collects wearable fitness tracker data + food logs to suggest daily workouts and meals.
Stac
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@priyal.py
1. Personalized Workout & Nutrition Recommendation System Collects wearable fitness tracker data + food logs to suggest daily workouts and meals. Stack: Fitbit API / Apple HealthKit (data), PyTorch (recommendation model), FastAPI (backend), Streamlit (UI), MLflow (tracking). 2. AI-Powered Meeting Minutes Generator Records meeting audio, transcribes it, and generates structured summaries + action points. Stack: Whisper (speech-to-text), BART/T5 (summarization), Hugging Face Transformers, FastAPI (API), React (frontend). 3. Smart Hiring Interview Analyzer Analyzes candidate interview videos for sentiment, speech clarity, and confidence scoring. Stack: OpenCV (video), Whisper (speech-to-text), PyTorch (sentiment + emotion detection), FastAPI (API), Streamlit (dashboard). 4. Intelligent Traffic Management System Uses live camera feeds to detect congestion and optimize traffic light timings. Stack: YOLOv8 (object detection), OpenCV (video processing), Kafka (streaming), FastAPI (API), Grafana (monitoring). 5. AI Tutor for Code Debugging Users paste buggy code → system explains errors and suggests fixes automatically. Stack: Tree-Sitter (code parsing), LLM (OpenAI GPT / CodeLlama), LangChain (prompt orchestration), Streamlit (UI). 6. Mental Health Chat Companion A chatbot that detects emotional states in conversation and gives wellness recommendations. Stack: Emotion classification with RoBERTa, RAG pipeline with domain knowledge, LangChain + FAISS, Gradio (UI). 7. Energy Consumption Predictor for Smart Homes Forecasts household electricity usage and suggests optimization strategies. Stack: Smart meter datasets, LSTMs/Transformers (forecasting), Flask (API), Streamlit (interactive dashboard), Airflow (automation). 8. AI-Driven Career Path Advisor Takes a resume and recommends next career roles, required skills, and learning paths. Stack: BERT embeddings for skill extraction, Scikit-learn / PyTorch (matching), Streamlit (UI), Hugging Face Hub (deployment). #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
#Streamlit Data Editor Features Reel by @datasciencewithsantosh - 🔢 Day 87 - Data Science Journey(Project-3)
📊 Topic: Project - WhatsApp Chat Analyzer using Python & Streamlit-(Part 3)
 🎥 [1-min video demo attache
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@datasciencewithsantosh
🔢 Day 87 – Data Science Journey(Project-3) 📊 Topic: Project - WhatsApp Chat Analyzer using Python & Streamlit-(Part 3) 🎥 [1-min video demo attached] 📌 Key Learnings: Today, I extended my WhatsApp Chat Analyzer project by adding data visualization and user-level analysis using Python and Streamlit 🔍 What’s new in Part 2? ✅ Displays top-level stats like: Total messages Word count Media shared Links shared ✅ Shows most active users in group chats ✅ Identifies most common words used (excluding stop words) ✅ Visualizes key insights using Matplotlib charts ✅ Clean and interactive UI with Streamlit 📌 This part helped me practice: Creating visual insights with matplotlib User-based filtering Handling group notifications/media Data storytelling with code 💡 Technologies Used: Python Pandas Streamlit Matplotlib Regex URLExtract 📢 Hashtags: #DataScienceTrends2025 #DataScience #DataInsights #CareerInDataScience #AIRevolution #Python #datasciencewithsantosh . . . . . @datasciencewithsantosh @datasciencewithsantosh @datasciencewithsantosh

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