How to Extract Insights from Amazon Prime Video Data

📊 Performed Practical on Data Visualization using Python! Explored different ways to represent data using Matplotlib — creating meaningful graphs, charts, and visual insights to make data-driven decisions more effective. This helped me understand how visualization plays a key role in analyzing patterns and trends in datasets. 🔗 GitHub Repository: https://lnkd.in/gsPj_hxs 👨🏫 Under the Guidance of: Ashish Sawant #Python #DataVisualization #Matplotlib #Seaborn #DataScience #MachineLearning #CodingJourney #LearningEveryday

📚 Libraries in Python Libraries are pre-written collections of code that simplify tasks — from data analysis to web development. Popular ones include NumPy, Pandas, and Matplotlib. They help save time and boost productivity. ⚡ #DataAnalytics #Python #Libraries #Learningjourney

Blog by Gerhard Brueckl! "Using Custom Libraries in Microsoft Fabric Data Engineering" Learn how to extend your data engineering workflows with your own Python libraries in Fabric. A must-read for advanced users! 🔗 https://lnkd.in/e8aFXVpB #MicrosoftFabric #DataEngineering #Python #Synapse #MicrosoftFabricCommunity

🔹 Python Arrays in a Nutshell Arrays help store multiple values in one variable. In Python, we use: ✅ list → flexible, mixed types ✅ array (from array module) → type-specific ✅ numpy.array → best for data analysis 🧠 Quick Tips: Indexing starts at 0 Use append(), pop(), insert() for changes NumPy arrays are faster & memory-efficient Avoid mixing types unless using lists 💡 Example: import array arr = array.array('i', [1, 2, 3]) 📌 Arrays are the backbone of data tasks—mastering them sets the stage for analysis, ML, and beyond. ecisions. 💥 Ready to elevate your journey? ✅ Join Our Community for More Info 👉 https://lnkd.in/g88h8xEF ✅ Fill This Form for 1:1 Counseling 🔗 https://lnkd.in/gbMpt6r8 ✅ Visit Our Website 🌐 https://lnkd.in/gVpcfM9q Let’s build careers, not just code. #Python #Arrays #DataAnalysis #CareerSwitch #TechTips #LinkedInLearning #CodingBasics #DataScience #DataAnalysis #Analytics #PayWhenYouGetHired #CupuleGwalior #CupuleChicago

Day 7: Python — The Power Tool for Data Analytics 🐍📊 Python has become the backbone of modern data analytics due to its versatility, simplicity, and powerful libraries. From handling data with Pandas to visualizing insights using Matplotlib and Seaborn, Python offers everything needed to explore and understand data effectively. With Scikit-learn for machine learning 🤖, SQL integration for database connectivity 🗄️, and automation capabilities ⚙️, Python helps transform raw data into meaningful insights faster and smarter. #DataAnalytics #Python #DataVisualization #MachineLearning #Automation #DataScience

📊 Experiment No. 06 – Data Visualization using Matplotlib Transforming raw data into meaningful insights through visual representation. Learning how to tell stories with data using Matplotlib, one of the most powerful Python libraries for visualization. Under The Guidance of Ashish Sawant Sir https://lnkd.in/dgYDvsV5 #DataScience #Matplotlib #DataVisualization #Python #Analytics

Simplifying Pandas vs PySpark ✅ I created a colorful cheat sheet with the correct syntax for both—perfect for quick reference while working with data! #Pandas #PySpark #DataScience #Python #BigData #DataEngineering

🟣 Day 3 — Python for Data Engineering I spent today learning Python from scratch — variables, loops, data structures, functions, classes, and multi-threading. 💡 Key takeaway: Python automates, scales, and empowers every data process. Next: ETL (Extract, Transform, Load) #Python #DataEngineering #LearningInPublic

🚀 Python File Handling — Simplify Data Storage! In Python, file handling helps us store, read, write, and manage data permanently — beyond program execution. It’s one of the most important parts of any real-world project, especially for working with reports, logs, or user data. 🧠 Key Functions: open() → Open a file read() / write() → Read or write data close() → Close the file after use 💻 Example: file = open("data.txt", "w") file.write("Learning file handling in Python!") file.close() file = open("data.txt", "r") print(file.read()) file.close() 🔍 Output: Learning file handling in Python! #Python #PythonProgramming #Coding #PythonDeveloper #FileHandling #LearnPython #ProgrammingBasics #PythonCode #DeveloperCommunity #DataScience #TechLearning

Evolving Python ecosystem 🌱 Our favorite snake keeps growing. Beyond staples like pandas, NumPy and scikit‑learn, new libraries are blooming: auto‑ML frameworks, data validation tools, and integrations with big data platforms like Spark and Dask. The Python community never stops. Stay curious, keep experimenting, and let your code evolve with the ecosystem. #Python #AutoML #BigData #ML