SciPy - Fundamental Algorithms for Scientific Computing in Python
SciPy provides fundamental algorithms for scientific computing in Python, offering optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and more.
SciPy Product Information
What is SciPy?
SciPy extends NumPy, providing additional tools for array computing and specialized data structures like sparse matrices and k-dimensional trees. Its algorithms and data structures are broadly applicable across various domains and are designed to be performant by wrapping highly-optimized implementations written in low-level languages like Fortran, C, and C++. The platform is open source and distributed under a liberal license, developed and maintained by a vibrant, responsive, and diverse community.
How to Use SciPy?
Core Features of SciPy
- Optimization
- Integration
- Interpolation
- Eigenvalue Problems
- Algebraic Equations
- Differential Equations
- Statistics
Use Cases of SciPy
- Scientific Research
- Data Analysis
- Algorithm Development
- Statistical Modeling
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