Having used Jupyter Notebook for quite some time, I’ve always appreciated its flexibility, especially for data science and machine learning tasks. Some benefits I’ve enjoyed with Jupyter Notebook include:𝘐𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘷𝘦 𝘊𝘰𝘥𝘪𝘯𝘨: 𝘈𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘳𝘶𝘯 𝘤𝘰𝘥𝘦 𝘤𝘦𝘭𝘭-𝘣𝘺-𝘤𝘦𝘭𝘭, 𝘮𝘢𝘬𝘪𝘯𝘨 𝘪𝘵 𝘦𝘢𝘴𝘺 𝘵𝘰 𝘵𝘦𝘴𝘵 𝘢𝘯𝘥 𝘪𝘵𝘦𝘳𝘢𝘵𝘦 𝘰𝘯 𝘴𝘮𝘢𝘭𝘭 𝘤𝘩𝘶𝘯𝘬𝘴 𝘰𝘧 𝘤𝘰𝘥𝘦. 𝘙𝘪𝘤𝘩 𝘖𝘶𝘵𝘱𝘶𝘵: 𝘐𝘵’𝘴 𝘨𝘳𝘦𝘢𝘵 𝘧𝘰𝘳 𝘷𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘪𝘯𝘨 𝘥𝘢𝘵𝘢, 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘱𝘭𝘰𝘵𝘴 𝘪𝘯-𝘭𝘪𝘯𝘦, 𝘢𝘯𝘥 𝘦𝘷𝘦𝘯 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘮𝘢𝘳𝘬𝘥𝘰𝘸𝘯 𝘢𝘭𝘰𝘯𝘨𝘴𝘪𝘥𝘦 𝘤𝘰𝘥𝘦. 𝘌𝘢𝘴𝘦 𝘰𝘧 𝘚𝘩𝘢𝘳𝘪𝘯𝘨: 𝘛𝘩𝘦 𝘯𝘰𝘵𝘦𝘣𝘰𝘰𝘬 𝘧𝘰𝘳𝘮𝘢𝘵 𝘪𝘴 𝘦𝘢𝘴𝘺 𝘵𝘰 𝘴𝘩𝘢𝘳𝘦 𝘸𝘪𝘵𝘩 𝘱𝘦𝘦𝘳𝘴 𝘢𝘯𝘥 𝘤𝘰𝘭𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘴, 𝘮𝘢𝘬𝘪𝘯𝘨 𝘪𝘵 𝘢 𝘨𝘰-𝘵𝘰 𝘧𝘰𝘳 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘤𝘰𝘭𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘪𝘰𝘯. However, recently I tried the Python interactive window in VS Code, and it's been a total game-changer for me! Here are a few things I like about it:𝘍𝘭𝘦𝘹𝘪𝘣𝘪𝘭𝘪𝘵𝘺: 𝘛𝘩𝘦 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘷𝘦 𝘸𝘪𝘯𝘥𝘰𝘸 𝘤𝘰𝘮𝘣𝘪𝘯𝘦𝘴 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘰𝘧 𝘣𝘰𝘵𝘩 𝘸𝘰𝘳𝘭𝘥𝘴—𝘑𝘶𝘱𝘺𝘵𝘦𝘳-𝘭𝘪𝘬𝘦 𝘤𝘦𝘭𝘭𝘴 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘱𝘰𝘸𝘦𝘳 𝘢𝘯𝘥 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘝𝘚 𝘊𝘰𝘥𝘦’𝘴 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵. 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦𝘥 𝘋𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵: 𝘐 𝘤𝘢𝘯 𝘴𝘦𝘢𝘮𝘭𝘦𝘴𝘴𝘭𝘺 𝘴𝘸𝘪𝘵𝘤𝘩 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘸𝘳𝘪𝘵𝘪𝘯𝘨 𝘴𝘤𝘳𝘪𝘱𝘵𝘴, 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘤𝘰𝘥𝘦, 𝘢𝘯𝘥 𝘶𝘴𝘪𝘯𝘨 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭 𝘝𝘚 𝘊𝘰𝘥𝘦 𝘦𝘹𝘵𝘦𝘯𝘴𝘪𝘰𝘯𝘴, 𝘢𝘭𝘭 𝘸𝘪𝘵𝘩𝘪𝘯 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘸𝘰𝘳𝘬𝘴𝘱𝘢𝘤𝘦. 𝘌𝘯𝘩𝘢𝘯𝘤𝘦𝘥 𝘋𝘦𝘣𝘶𝘨𝘨𝘪𝘯𝘨: 𝘝𝘚 𝘊𝘰𝘥𝘦’𝘴 𝘥𝘦𝘣𝘶𝘨𝘨𝘪𝘯𝘨 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴 𝘢𝘳𝘦 𝘧𝘢𝘳 𝘮𝘰𝘳𝘦 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘤𝘰𝘮𝘱𝘢𝘳𝘦𝘥 𝘵𝘰 𝘑𝘶𝘱𝘺𝘵𝘦𝘳, 𝘮𝘢𝘬𝘪𝘯𝘨 𝘪𝘵 𝘦𝘢𝘴𝘪𝘦𝘳 𝘵𝘰 𝘵𝘳𝘰𝘶𝘣𝘭𝘦𝘴𝘩𝘰𝘰𝘵 𝘤𝘰𝘥𝘦. 𝘉𝘦𝘵𝘵𝘦𝘳 𝘗𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: 𝘛𝘩𝘦 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦𝘥 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵 𝘢𝘭𝘭𝘰𝘸𝘴 𝘮𝘦 𝘵𝘰 𝘮𝘢𝘯𝘢𝘨𝘦 𝘭𝘢𝘳𝘨𝘦𝘳 𝘱𝘳𝘰𝘫𝘦𝘤𝘵𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘴𝘭𝘰𝘸𝘪𝘯𝘨 𝘥𝘰𝘸𝘯 𝘮𝘺 𝘴𝘺𝘴𝘵𝘦𝘮 𝘢𝘴 𝘮𝘶𝘤𝘩 𝘢𝘴 𝘑𝘶𝘱𝘺𝘵𝘦𝘳 𝘴𝘰𝘮𝘦𝘵𝘪𝘮𝘦𝘴 𝘥𝘰𝘦𝘴. The beauty of the Python Interactive Window is that you can execute your code lines cell-by-cell, visualize the plot, and quickly modify the code—all without leaving the VS Code editor. It also allows for in-line visualizations just like in Jupyter but with more control over the overall environment:If you’ve been using Jupyter Notebook for your Python work, I highly recommend giving the Python Interactive Window a try in VS Code. #Python #VSCode #JupyterNotebook #DataScience #MachineLearning hashtag#Productivity #PythonDevelopment #CodeSnippet #DevTools #TechInnovation

Wednesday funny

The irony is that it was this comment that pushed me over the threshold and into level 6: "I can teach you how to turn your $250 into $6,500 in 2hours without sending money to anyone or interrupting your daily activities and it's 100% legitimate and secure TEXT ME IF YOU ARE INTERESTED FOR MORE INFORM WITH THE DETAILS BELOW $$$$." Ah well.. for once it was useful 😂

While not easy, bootstrapping your data science career is possible. Here's an overview of the most important skills and resource suggestions for learning them. https://www.kdnuggets.com/bootstrapping-your-data-science-career-a-guide-to-self-learning-pathways

Hey guys again me. I really love to share my achievement in this platform because we are all like minded people (: https://www.kaggle.com/datasets/alicemtopcu/top-1500-games-on-steam-by-revenue-09-09-2024/code?datasetId=5679841&sortBy=dateRun&tab=profile&excludeNonAccessedDatasources=false this is my first %100 self made data analysis project. it still goes on but I've made many plots and explanations on it. I hope you guys like it. Data Frame is collected by me from https://gamalytic.com and uploaded to kaggle. I opened to public share, if any of you cannot reach or have problem with it, also want to give advice and ask questions I am here for your shots (: Have a great day (: