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Sanskar
Keen Learner and Exp... • 24d
Day 5 of learning Data Science as a beginner Topic: Using NumPy in Data Science Python despite having much advantages (like being beginner friendly, easy to read) is also famous for its one limitation i.e. it is slow. We don't really feel much about it as a beginner because at the beginning stage all we are doing is learning through coding a few lines or a couple hundreds however once you start working with large data sets this limitation makes its presence felt. Python is slow because it offers incredible flexibility like being able to write multiple type items like integer, strings, float, Boolean, dictionary and even tuples in a single therefore in order to offer such flexibilities python has to compromise with speed. However to tackle this limitation we use a python library named NumPy which is created using C as base and because C is very close to hardware it offers great speed for computing numbers. NumPy has a great speed however it is used only on numerical arrays. NumPy is also very efficient in storing the data i.e. it uses less memory to store data. It also offers vectorized operation i.e. it avoids using loops explicitly this also makes it much more cleaner and readable. In the coming days I will focus on learning NumPy from basics. And also here's my code and its result.
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