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Articles by Niharika Aitam
Page 14 of 14
Compute the mean, standard deviation, and variance of a given NumPy array
Mean, Standard Deviation and Variance are the statistical measures which are used to describe the distribution of the given dataset data. In Numpy library, we have the functions to calculate the mean, standard deviation and variance of the arrays. Let’s see one by one in detail. Mean Mean is also known as average, which is the sum of all the elements in the array divided by the total number of elements. It is used to represent the central tendency of the data. Syntax Following is the syntax for applying the mean function on the arrays - numpy.mean(arr) ...
Read MoreCompute the inverse of a matrix using inv() function of NumPy
The inverse matrix is a matrix that gives a multiplicative identity when multiplied with its original matrix. It is denoted by A-1. The inverse matrix can be calculated for the square matrices with the n x n size. The mathematical formula given for calculating the inverse matrix is as follows. A-1 . A = A . A-1 = I Where, A is the original matrix. A-1 is the inverse of the original matrix A. I is the identity matrix. Let’s take the original matrix as A with the size 2 x 2 and elements as ...
Read MoreCompute the covariance matrix of two given NumPy arrays
Covariance is the measure of two variables that defines how they are related to each other. In other words, it measures the extent to which one variable is associated with the changes in the other variable. When the covariance of the variables is positive, it implies that both variables are moving in the same direction i.e. if one variable tends to increase, as a result, the value of the other variable also increases. When the covariance of the variables is negative then it represents the two variables are moving in opposite direction i.e. if one variable increases the value ...
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