Introduction to SciPy
Introduction to SciPy
SciPy, pronounced “Sigh Pi”, is an open-source scientific Python framework, released under the BSD license, for performing mathematical, scientific, and engineering computations.
The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array operations. The SciPy library is built to work with NumPy arrays and provides many user-friendly and efficient numerical routines, such as numerical integration and optimization. Together, they run on all popular operating systems, install quickly, and are free. NumPy and SciPy are easy to use, yet powerful enough to be relied upon by some of the world’s leading scientists and engineers.
SciPy Subpackages
SciPy is organized into subpackages covering different areas of scientific computing. The following table summarizes these subpackages –
| scipy.cluster | Vector Quantization/Kmeans |
|---|---|
| scipy.constants | Physical and Mathematical Constants |
| scipy.fftpack | Fourier Transform |
| scipy.integr https://docs.scipy.org/doc/scipy/reference/integrate.html#module-scipy.integrateation | Integration Routines |
| scipy.interpolate | Interpolation |
| scipy.io | Data Input and Output |
| scipy.linalg | Linear Algebra Programs |
| scipy.ndimage | N-Dimensional Image Package |
| scipy.odr | Orthogonal Distance Regression |
| scipy.optimization | Optimization |
| scipy.signal | Signal Processing |
| scipy.sparse | Sparse matrices |
| scipy.spatial | Spatial data structures and algorithms |
| scipy.special | Any special mathematical function |
| Statistics |
Data Structures
The fundamental data structure used by SciPy is the multidimensional array provided by the NumPy module. NumPy provides functions for linear algebra, Fourier transforms, and random number generation, but these functions lack the generality of their SciPy counterparts.