NumPy vs SciPy: What are the differences?
NumPy: Fundamental package for scientific computing with Python. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases; SciPy: Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
NumPy and SciPy can be primarily classified as "Data Science" tools.
NumPy and SciPy are both open source tools. It seems that NumPy with 11.1K GitHub stars and 3.67K forks on GitHub has more adoption than SciPy with 6.01K GitHub stars and 2.85K GitHub forks.
According to the StackShare community, NumPy has a broader approval, being mentioned in 63 company stacks & 34 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.
What is NumPy?
What is SciPy?
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Why do developers choose SciPy?
What are the cons of using NumPy?
What are the cons of using SciPy?
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We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools.