Scalar

In Physics, a scalar is a mathematical entity that is fully described by a magnitude (or numerical value) alone.
For e.g., a = 10.5, is a scalar. There is no other value associated with a scalar, just one single numerical value.

Vector

In Physics, the official definition of a Vector is a mathematical entity that is fully described by both a magnitude and a direction.
E.g., wind speed = 100 miles/hour, North East direction. In simple mathematical terms, vectors have multiple values. For e.g., b = [10,20] is a vector. From this definition any array of size greater than 1 is a vector

One-dimensional Array

A one-dimensional array is a type of linear array which has all elements in either one row or one column. Accessing its elements involves a single index position which can either represent a row or column index.
E.g., a=[1, 2] is a one dimensional array. Note this array can be having elements either in one row or one column. Using a[0] provides the value of the first element.

Multidimensional Array

A multidimensional array is a type of array structure which has elements represented in both rows and columns. Accessing its elements involves a 'n' number of indices where 'n' denotes the dimension of the array.
E.g., b = ([[1, 2, 4], [4, 4, 6]]) is a 2 dimensional array as it has two rows and three columns. To access the element in the first row, third column you use the notation b[0][2]

Array- rank

The number of dimensions is the rank of the array.
For e.g., if a = ([1, 2, 3]), then 'a' is of rank 1; If b = ([[1, 2, 4], [4, 4, 6]]) then b is of rank 2 and so on

Array- shape

The shape of an array is a tuple of integers giving the size of the array along each dimension.
For e.g., if a = ([1, 2, 3]), then 'a' has a shape of 1x3; If b = ([[1, 2, 4], [4, 4, 6]]) then it has a shape of 2x3

Array- size

The number of elements in an array determines the size of the array.
For e.g., a = [1, 2] has a sizs of 2; b= [[2, 3, 4],[5, 6, 7]] has a size of 6

KDE - Kernel Density Estimation

Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths. In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. Reference:https://en.wikipedia.org/wiki/Kernel_density_estimation

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