SciPy

SciPy is a free and open-source Python library used for scientific computing and technical computing.

Look at the problem above carefully. The constraint is a fixed volume. Think of that as a business deliverable (aka commitment to the customer).

But the goal of the problem is to find the minimum material needed (in terms of the surface area). Think of that as related to the profit margin of the producer (the less material is needed, the less production cost for the same selling price, and hence a higher profit margin).

Mathematical optimization is at the heart of solutions to major business problems in engineering, finance, healthcare, socioeconomic affairs. Pretty much all business problems boil down to minimization of some kind of resource cost or maximization of some kind of profit given other constraints.

An optimization process is also the soul of operation research, which is intimately related to modern data-driven business analytics. In this manner, it is also closely related to the data science pipeline, employed in virtually all businesses today.

Modules

SciPy 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.

SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website.

The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science.

The SciPy package is at the core of Python’s scientific computing capabilities. Available sub-packages include:

• cluster: hierarchical clustering, vector quantization, K-means
• constants: physical constants and conversion factors
• fft: Discrete Fourier Transform algorithms
• fftpack: Legacy interface for Discrete Fourier Transforms
• integrate: numerical integration routines
• interpolate: interpolation tools
• io: data input and output
• linalg: linear algebra routines
• misc: miscellaneous utilities (e.g. example images)
• ndimage: various functions for multi-dimensional image processing
• ODR: orthogonal distance regression classes and algorithms
• optimize: optimization algorithms including linear programming
• signal: signal processing tools
• sparse: sparse matrices and related algorithms
• spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, Convex hulls, etc.
• special: special functions
• stats: statistical functions
• weave: tool for writing C/C++ code as Python multiline strings.

Data structures

The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code.

SciPy is a python library that is useful in solving many mathematical equations and algorithms. It is designed on the top of Numpy library that gives more extension of finding scientific mathematical formulae like Matrix Rank, Inverse, polynomial equations, LU Decomposition, etc. Using its high level functions will significantly reduce the complexity of the code and helps in better analyzing the data. SciPy is an interactive Python session used as a data-processing library that is made to compete with its rivalries such as MATLAB, Octave, R-Lab,etc. It has many user-friendly, efficient and easy-to-use functions that helps to solve problems like numerical integration, interpolation, optimization, linear algebra and statistics.

Benefits

The benefit of using SciPy library in Python while making ML models is that it also makes a strong programming language available for use in developing less complex programs and applications.

SciPy is a scientific computation library that uses NumPy underneath. SciPy stands for Scientific Python. It provides more utility functions for optimization, stats and signal processing. Like NumPy, SciPy is open source so we can use it freely. SciPy was created by NumPy’s creator Travis Olliphant.

If SciPy uses NumPy underneath, why can we not just use NumPy? SciPy has optimized and added functions that are frequently used in NumPy and Data Science.

Extends NumPy providing additional tools for array computing and provides specialized data structures, such as sparse matrices and k-dimensional trees.

SciPy is predominantly written in Python, but a few segments are written in C.

The source code for SciPy is located at this GitHub repository https://github.com/scipy/scipy