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.]