SCIPY

SciPy is an open-source Python library used for scientific and technical computing, providing tools for optimization, integration, interpolation, and more.

SciPy Getting Started refers to the process of learning how to install and use SciPy, a Python library for scientific and technical computing, including various functions for optimization, integration, interpolation, and more.

Checking SciPy version refers to the process of verifying the installed version of the SciPy library in your Python environment using scipy.__version__.

SciPy constants are predefined mathematical constants, such as pi and Euler's number, provided in the scipy.constants module for use in scientific computations.

Metric (SI) prefixes are standardized symbols used to represent powers of ten for units of measurement, such as kilo (k), mega (M), and milli (m), in the International System of Units (SI).

An angle is the space between two intersecting lines or planes, measured in degrees or radians.

Pressure is the force exerted per unit area, commonly measured in pascals (Pa) or other units such as atmospheres (atm) or bar.

Temperature is a measure of the average kinetic energy of the particles in a substance, typically expressed in degrees Celsius (°C), Fahrenheit (°F), or Kelvin (K).

SciPy optimizers are algorithms provided in the scipy.optimize module for finding the minimum or maximum of a function, including methods for both unconstrained and constrained optimization problems.

Minimizing a function involves finding the input values that result in the lowest possible output of the function.

SciPy sparse data refers to data structures provided by the scipy.sparse module for efficiently storing and manipulating large datasets with mostly zero or empty elements.

Sparse matrix methods are operations and functions in the scipy.sparse module designed for efficient manipulation and analysis of sparse matrices.

SciPy graphs are data structures provided by the scipy.sparse.csgraph module for representing and analyzing sparse graphs and networks efficiently.

Dijkstra's algorithm is a shortest-path algorithm that finds the minimum distance from a source node to all other nodes in a weighted graph.

Bellman-Ford is an algorithm used to find the shortest path from a single source node to all other nodes in a graph, even with negative edge weights.

SciPy Spatial Data refers to data structures and algorithms in the scipy.spatial module used for geometric and spatial operations, such as distance calculations, nearest neighbors, and spatial indexing.

A convex hull is the smallest convex boundary that encloses a set of points in a geometric space.

A distance matrix is a square matrix that represents the pairwise distances between a set of points or elements.

Cosine distance measures the dissimilarity between two vectors by calculating the cosine of the angle between them.

SciPy Matlab arrays are data structures in SciPy that mimic MATLAB arrays, enabling efficient numerical computations and matrix operations in Python.

Importing data from a MATLAB format involves loading MATLAB .mat files into Python for further analysis or processing.

SciPy interpolation refers to the process of estimating values between known data points using various interpolation methods provided in the scipy.interpolate module.

Spline interpolation is a method of estimating values between data points using piecewise polynomial functions, typically cubic splines, to ensure smoothness and continuity.

Interpolation with Radial Basis Function (RBF) involves using radial basis functions to estimate values at unmeasured points, typically providing smooth, multi-dimensional interpolation.

SciPy statistical significance tests are methods provided in the scipy.stats module to assess whether the results of a hypothesis test are statistically significant, typically using p-values.

The alternate hypothesis is a statement suggesting there is an effect, difference, or relationship in the population, contrary to the null hypothesis.

The alpha value is the threshold for statistical significance, representing the probability of rejecting a true null hypothesis, commonly set at 0.01, 0.05, or 0.1.

A T-test is a statistical test used to determine if there is a significant difference between the means of two groups.

The KS-test (Kolmogorov-Smirnov test) is a statistical test used to compare two samples or a sample with a reference distribution to determine if they are drawn from the same distribution.

Normality tests (Skewness and Kurtosis) are used to assess whether a dataset follows a normal distribution by evaluating its symmetry (skewness) and the peakedness (kurtosis) of its distribution.