Write a NumPy program to compute the mean, standard deviation, and variance of a given array along the second axis

Write a NumPy program to compute the mean, standard deviation, and variance of a given array along the second axis.

From Wikipedia: There are several kinds of means in various branches of mathematics (especially statistics).
For a data set, the arithmetic mean, also called the mathematical expectation or average, is the central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers $x_{1}, x_{2}, . . . . ., x_{n}$ is typically denoted by $\bar{x}$ , pronounced " $x$ bar". If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is the sample mean (denoted $\bar{x}$ ) to distinguish it from the mean of the underlying distribution.
In probability and statistics, the population mean, or expected value, are a measure of the central tendency either of a probability distribution or of the random variable characterized by that distribution. In the case of a discrete probability distribution of a random variable $X$ , the mean is equal to the sum over every possible value weighted by the probability of that value; that is, it is computed by taking the product of each possible value $x$ of $X$ and its probability $p (x)$ , and then adding all these products together, giving $μ = \sum_{} x p (x)$ . An analogous formula applies to the case of a continuous probability distribution. Not every probability distribution has a defined mean; see the Cauchy distribution for an example. Moreover, for some distributions the mean is infinite.

import numpy as np x = np.arange(6) print("\nOriginal array:") print(x) r1 = np.mean(x) r2 = np.average(x) assert np.allclose(r1, r2) print("\nMean: ", r1) r1 = np.std(x) r2 = np.sqrt(np.mean((x - np.mean(x)) ** 2 )) assert np.allclose(r1, r2) print("\nstd: ", 1) r1= np.var(x) r2 = np.mean((x - np.mean(x)) ** 2 ) assert np.allclose(r1, r2) print("\nvariance: ", r1)

Write a NumPy program to compute the mean, standard deviation, and variance of a given array along the second axis

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