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Sanskar
Keen Learner and Exp... • 20h
Day 7 of learning Data Science as a beginner. Topic: Indexing and Slicing NumPy arrays Since a past few days I have been learning about NumPy arrays I have learned about creating arrays from list and using other numpy functions today I learned about how to perform Indexing and Slicing on these numpy arrays. Indexing and slicing in numpy arrays is mostly similar to slicing a python list however the only major difference is that array slicing does not create a new array instead it just takes a view from the original one meaning that if you change the new sliced array its effect will also be shown in the original array. To tackle this we often use a .copy() function while slicing as this will create a new array of that particular slice. Then there are some fancy slicing where you can slice a array using multiple indices for example for array ([1, 2, 3, 4, 5, 6, 7, 8, 9]) you can also slice it like flat[[1, 5, 6]] please note that flat here is the name of the array and the output will be array([2, 6, 7]). Then there is Boolean masking which helps you to slice the array using a condition like flat[flat>8] (meaning print all those elements which are greater than 8). I must also say that I have been receiving many DM asking me for my resources so I would like to share them here as well for you amazing people. I am following CodeWithHarry's data science course and also use some modern AI tools like ChatGPT (only for understanding errors and complexities). I also use perplexity's comet browser (I have started using this recently) for brainstorming algorithms and bugs in the program I only use these tools for learning and writes my own code. Also here's my code and its result. Also here's the link of resources I use if you are searching 1. CWH course I am following: https://www.codewithharry.com/courses/the-ultimate-job-ready-data-science-course 2. Perplexity's Comet browser: https://pplx.ai/sanskar08c81705 Note: I am not forcing or selling to anyone I am just sharing my own resources for interested people.
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