## Mean, Var, and Std python

mean The mean tool computes the arithmetic mean along the specified axis. import numpy my_array = numpy.array([ [1, 2], [3, 4] ]) print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] print numpy.mean(my_array, axis = None) #Output : 2.5 print numpy.mean(my_array) #Output : 2.5 By default, the axis is None. Therefore, it computes the mean of the flattened array. var The var tool

## Dot and Cross python

dot The dot tool returns the dot product of two arrays. import numpy A = numpy.array([ 1, 2 ]) B = numpy.array([ 3, 4 ]) print numpy.dot(A, B) #Output : 11 cross The cross tool returns the cross product of two arrays. import numpy A = numpy.array([ 1, 2 ]) B = numpy.array([ 3, 4 ]) print numpy.cross(A, B) #Output : -2 Task You are given two arrays A and B. Both have dimensions of N X M. Your task is to compute their matrix product. Input Format

## Inner and Outer python

inner The inner tool returns the inner product of two arrays. import numpy A = numpy.array([0, 1]) B = numpy.array([3, 4]) print numpy.inner(A, B) #Output : 4 outer The outer tool returns the outer product of two arrays. import numpy A = numpy.array([0, 1]) B = numpy.array([3, 4]) print numpy.outer(A, B) #Output : [[0 0] # [3 4]] Task You are given two arrays: A and B. Your task is to compute their inner and outer

## Polynomials python

poly The poly tool returns the coefficients of a polynomial with the given sequence of roots. print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10] roots The roots tool returns the roots of a polynomial with the given coefficients. print numpy.roots([1, 0, -1]) #Output : [-1. 1.] polyint The polyint tool returns an antiderivative (indefinite integral) of a polynomial. print numpy.polyint([1, 1, 1]) #Output : [ 0.33333333 0.5

## Linear Algebra python

The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg. linalg.det The linalg.det tool computes the determinant of an array. print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0 linalg.eig The linalg.eig computes the eigenvalues and right eigenvectors of a square array. vals, vecs = numpy.linalg.eig([[1 , 2], [2, 1]]) print vals #Output : [ 3. -1.]