#Python Matplotlib

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#Python Matplotlib Reel by @michaellin250 - 🐍🚀 30 Days Python Roadmap 🚀🐍 📅 Day 1: Dive into Python basics - variables, data types, and loops! 💻🔤 📅 Day 5: Explore functions , conditiona
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@michaellin250
🐍🚀 30 Days Python Roadmap 🚀🐍 📅 Day 1: Dive into Python basics - variables, data types, and loops! 💻🔤 📅 Day 5: Explore functions , conditional statements , data structures - building blocks of Python! 🧱🔁 📅 Day 10: Get hands-on with Python libraries - NumPy, Pandas, and Matplotlib! 📈📊🎉 📅 Day 15: Master the art of working with files and handling exceptions! 📂🚫💾 📅 Day 20: Level up your skills with Object-Oriented Programming (OOP) in Python! 🧬🔄👨‍💻 📅 Day 25: Unleash the power of Python web frameworks - Flask or Django! 🌐🌟💻 📅 Day 30: Congratulations, you’ve completed the Python roadmap! 🎓🎉 Now build something awesome! 🚀💡 ( Eg : virtual pet , email automator etc ) Still having problems ? Follow this github repo : https://github.com/Asabeneh/30-Days-Of-Python #softwareengineer #softwaredeveloper #python #30daysofpythob #30daysofcoding #developers #java #coding #programmerslife #coding #coders #github #githubrepository #100daysofcodechallenge
#Python Matplotlib Reel by @barghstyle - 🚦بهترین نرم افزارهای نقشه کشی صنعت برق🚦 خلاصه مفید #ایپلن #اتوکد #دیگسایلنت #نرم_افزارایپلن #اتوکدالکتریکال
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@barghstyle
🚦بهترین نرم افزارهای نقشه کشی صنعت برق🚦 خلاصه مفید #ایپلن #اتوکد #دیگسایلنت #نرم_افزارایپلن #اتوکدالکتریکال
#Python Matplotlib Reel by @codeandcrush - 🐍 Top 15 Python Libraries Every Data Analyst Must Know 📊 If you are starting your Data Analytics journey, the right Python libraries can save you h
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@codeandcrush
🐍 Top 15 Python Libraries Every Data Analyst Must Know 📊 If you are starting your Data Analytics journey, the right Python libraries can save you hours of effort and make your projects 10x more powerful. 🚀 Here’s a quick breakdown of the must-know libraries: ✅ Pandas → Data cleaning & manipulation ✅ NumPy → Fast numerical computing ✅ Matplotlib & Seaborn → Stunning visualizations ✅ Plotly → Interactive dashboards ✅ Scikit-learn → Easy machine learning ✅ Statsmodels & SciPy → Statistical analysis ✅ TensorFlow / PyTorch → Advanced AI & analytics ✅ OpenPyXL, Dask, BeautifulSoup, NLTK, SQLAlchemy → Excel automation, big data, web scraping, text analytics, and databases! 💡 Whether you’re preparing for a job, building projects, or just learning, these libraries are the backbone of Data Analytics. 👉 Save this reel for quick reference 🔖 👉 Share it with your data friends 🔄 👉 Follow @codeandcrush for more daily Data Analytics tips, tricks & career hacks 🚀 #python #dataanalytics #pythonlibraries #datascience #machinelearning #sql #powerbi #dataanalyst #learnpython #learnandgrow #careergoals #instagram #pythonprogramming #reelsi̇nstagram #trendings
#Python Matplotlib Reel by @sandreke99 - Plantamos un jardín de flores con código? 👨‍💻 El código en el canal de Telegram: 🔗 t.me/sandreke99canal #matplotlib #3d #3dflower #python #python
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@sandreke99
Plantamos un jardín de flores con código? 👨‍💻 El código en el canal de Telegram: 🔗 t.me/sandreke99canal #matplotlib #3d #3dflower #python #pythonprogramming #programacion #programming #graficos #tecnologia #sandreke
#Python Matplotlib Reel by @askdatadawn (verified account) - Tbh after being a Data Scientist for 6 years, I still don't know some stuff on that 2nd list 😅 Trying to learn ALL of Python at once is so intimidat
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@askdatadawn
Tbh after being a Data Scientist for 6 years, I still don’t know some stuff on that 2nd list 😅 Trying to learn ALL of Python at once is so intimidating Don’t put that pressure on yourself. Instead only focus on these must-know concepts, and you can ignore stuff on the “Not Now” list for now. MUST KNOW PYTHON CONCEPTS • Basic syntax: variables, data types, loops • Writing custom functions • Lists, tuples, dictionaries • List comprehensions • String manipulation • Reading and writing files • Try/except error handling • Importing and using libraries • Pandas basics – Series vs DataFrame • Selecting and filtering data • Groupby and aggregations • Merging or joining data • Sorting and ranking data • Handling missing values • Basic plotting – matplotlib • Working with dates – e.g. pd.to_datetime, .dt NOT NOW • Object oriented programming – classes, inheritance • Generators and decorators • Custom context managers • Writing modules or packages • Virtual environments and dependency management • Multiprocessing or multithreading • Async programming • Advanced pandas tuning – eval, query • Unit testing and CI/CD • Custom exception classes • Functional programming tricks – map, reduce, lambdas everywhere • Building web APIs – Flask, FastAPI #python #datascience #datascientist #datascienceinterview
#Python Matplotlib 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
#Python Matplotlib Reel by @aidataverse.in - 🚀 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐨𝐫 𝐄𝐕𝐄𝐑𝐘𝐓𝐇𝐈𝐍𝐆! 🐍 Whether you're a beginner exploring tech or a pro building solutions - Python is your best companion.
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@aidataverse.in
🚀 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐨𝐫 𝐄𝐕𝐄𝐑𝐘𝐓𝐇𝐈𝐍𝐆! 🐍 Whether you're a beginner exploring tech or a pro building solutions — Python is your best companion. Here's how Python powers multiple domains with just the right libraries: 🔹 Python + Pandas → Data Manipulation Handle, clean, and analyze large datasets efficiently. Perfect for data wrangling and preprocessing. 🔹 Python + Scikit-Learn → Machine Learning Build predictive models, classification systems, recommendation engines, and more with ease. 🔹 Python + TensorFlow → Deep Learning Train neural networks, work with image recognition, NLP, and cutting-edge AI systems. 🔹 Python + Matplotlib → Data Visualization Create beautiful plots, charts, and graphs to tell impactful data stories. 🔹 Python + Seaborn → Advanced Visualization Enhance Matplotlib with cleaner, more detailed statistical visualizations. 🔸 Python + Flask → Web Development Build powerful backends and APIs for web apps — lightweight, fast, and secure. 🔸 Python + Pygame → Game Development Bring your gaming ideas to life. Great for prototypes and learning game logic. 🔸 Python + Kivy → Mobile App Development Create multi-platform mobile apps using Python. One codebase for Android and iOS. 🔸 Python + Tkinter → GUI Development Design desktop applications with interactive interfaces. Simple and beginner-friendly. #Python #CodingChallenge #LearnPython #PythonTips #PythonBeginner #CodeNewbie #100DaysOfCode #ProgrammersLife #PythonProgramming #DeveloperHumor #CodeWithFun #CodingCommunity
#Python Matplotlib Reel by @code_helping - Free Source Code in Bio | You need Python and two modules: NumPy and Matplotlib installed. . For more such content, Join Telegram from profile highlig
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@code_helping
Free Source Code in Bio | You need Python and two modules: NumPy and Matplotlib installed. . For more such content, Join Telegram from profile highlights. . Follow for more such content. . . . #python #animation #numpy #matplotlib #coding #software #cse #computerscience #bca #panda #pythonprogramming #softwareengineering #codehelping.com #projectsourcecode
#Python Matplotlib Reel by @shakra.shamim (verified account) - Data Analytics interviews does not require complex Python Programming knowledge. I have created a PDF which contains required Python syllabus and free
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@shakra.shamim
Data Analytics interviews does not require complex Python Programming knowledge. I have created a PDF which contains required Python syllabus and free resources to learn. Please comment “Python” to get the PDF directly to your DM !! #python #coding #programming #2025 #tech #datascience #dataanalytics
#Python Matplotlib Reel by @kaabil.engineer (verified account) - 3 months Roadmap here 👇 . . Month 1 - Foundations & Python 1. Master Python (loops, OOPs, libraries). 2. Learn NumPy, Pandas, Matplotlib, Seaborn.
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@kaabil.engineer
3 months Roadmap here 👇 . . Month 1 – Foundations & Python 1. Master Python (loops, OOPs, libraries). 2. Learn NumPy, Pandas, Matplotlib, Seaborn. 3. Practice data cleaning and visualization. 4. Study statistics & linear algebra basics. 5. Explore Git & push small projects to GitHub. 6. Join Kaggle, start beginner competitions. 7. Build mini data analysis projects. Month 2 – ML & DL Core 1. Learn ML algorithms (regression, SVM, trees). 2. Understand bias-variance & evaluation metrics. 3. Work with Scikit-learn on datasets. 4. Start Deep Learning (Neural Nets, CNN, RNN). 5. Use TensorFlow/PyTorch for small models. 6. Share 2 ML projects on GitHub. 7. Participate in 1 Kaggle competition. Month 3 – GenAI & Deployment 1. Learn LLM concepts & prompt engineering. 2. Explore Hugging Face transformers. 3. Fine-tune small models (BERT, GPT). 4. Learn API deployment with FastAPI/Flask. 5. Integrate models into simple web apps. 6. Build GenAI project (chatbot, summarizer). 7. Create strong GitHub + Kaggle portfolio. . . #students #engineering #roadmap #python #ai #learning #tools #opportunity #programming
#Python Matplotlib Reel by @its.anu.sharma (verified account) - Hey, I'm Anu, I'm learning AI before I get left behind. And here's the roadmap I'm following and you can get a 20-30LPA job as a fresher AI engineer.
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@its.anu.sharma
Hey, I'm Anu, I'm learning AI before I get left behind. And here's the roadmap I'm following and you can get a 20-30LPA job as a fresher AI engineer. Month 1: Python, Basic Syntax+ OOPs. Tools like Google Colab, VS code. Month 2: Maths for AI ( linear algebra, probability) + learn numpy, matplotlib, pandas Month 3: ML Basics - Regression, Supervised and unsupervised learning. Scikit learn. Month 4: Deep Learning + Neural Networks. Use Tensorflow or PyTorch. You can learn from deeplearning.ai site. Month 5: Build projects - Few examples like house price prediction, sentiment analysis, chatbot with RNN or AI that plays a game. Month 6: Portfolio + Apply + Learn advanced topics. GitHub projects, Linkedin optimised for AI roles, personal portfolio website. Learn about LangChain+ LLMs, huggingface, and streamlit/gradio for UI. If you want a detailed reel on what exact projects to build. ✅ Comment "projects" below. If you’ve reached here, follow @its.anu.sharma for more such content. I help you to crack big tech. [software, coder, developer, google, hiring, interviews, tips, personal, story, experience, algorithms, cs students, computer science, ai, ai engineer, artificial intelligence] #SoftwareEngineer #TechJourney #ai #ml #aiml #roadmap #CodingLife #GoogleIntern #CSFundamentals #FullStackDeveloper #CodingContests #TechInterviews #WomenInTech #CareerTips

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