A Review of the Implementation of NumPy and SciPy Packages in Science and Math

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Raed Waheed Kadhim , Muna jaffar Raheem , Yasmin Makki Mohialden , Nadia Mahmood Hussien


In the Python programming language, there are a number of simple case studies of scientific computing. It gives you a multidimensional array object, a lot of organisms (like arrays and masked arrays), and many fast ways to work with arrays. SciPy, which is also called "Sigh Pie," is free math, science, and engineering software. The NumPy library is what the SciPy library is built on. This makes it easy and quick to work with arrays with N dimensions. The SciPy library is made to work with NumPy arrays in particular. It has a lot of easy-to-use and effective numerical methods, like scalar optimization and integration. They work well together, are free, and are easy to set up on all common operating systems. NumPy and SciPy are both easy to learn and use. This paper explains the most popular application of these packages in math-focused scientific research

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