Numpy tutorials Part 2

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Numpy tutorials part 1

Numpy tutorials part 2

mean, variance and standard deviation

15. mean

>>>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

16. var

>>>import numpy

>>>my_array = numpy.array([ [1, 2], [3, 4] ])

>>>print numpy.var(my_array, axis = 0)         #Output : [ 1.  1.]

>>>print numpy.var(my_array, axis = 1)         #Output : [ 0.25  0.25]

>>>print numpy.var(my_array, axis = None)      #Output : 1.25

>>>print numpy.var(my_array)                   #Output : 1.25

17. std

>>>import numpy

>>>my_array = numpy.array([ [1, 2], [3, 4] ])

>>>print numpy.std(my_array, axis = 0)         #Output : [ 1.  1.]

>>>print numpy.std(my_array, axis = 1)         #Output : [ 0.5  0.5]

>>>print numpy.std(my_array, axis = None)      #Output : 1.11803398875

>>>print numpy.std(my_array)                   #Output : 1.11803398875

import numpy

a,b = map(int,input().split())

c = []

for i in range(a):

    c.append(list(map(int,input().split())))

print(numpy.mean(c,axis=1))

print(numpy.var(c,axis=0))

print(numpy.std(c,axis=None))

input:

2 2
1 2
3 4

Output:

[1.5 3.5]

[1. 1.]

1.118033988749895

cross product and Dot product

18. cross product

>>>import numpy

>>>A = numpy.array([ 1, 2 ])

>>>B = numpy.array([ 3, 4 ])

>>>print numpy.dot(A, B)       #Output : 11  

19. Dot product

>>>import numpy

>>>A = numpy.array([ 1, 2 ])

>>>B = numpy.array([ 3, 4 ])

>>>print numpy.cross(A, B)     #Output : -2

20. Matrix multiplication of two numpy arrays

matrix multiplication of N*N dimensional array

>>>import numpy

>>>n=int(input())

>>>array1=numpy.array([list(map(int,input().split())) for _ in range(n)])

>>>array2=numpy.array([list(map(int,input().split())) for _ in range(n)])

>>>print(numpy.dot(array1,array2))

Input:

2

1 2

3 4

1 2

3 4

Output:

[[ 7 10]

 [15 22]]

Inner and outer product of two arrays

21. Inner product of two arrays

>>>import numpy

>>>A = numpy.array([0, 1])

>>>B = numpy.array([3, 4])

>>>print numpy.inner(A, B)     #Output : 4 

22. 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]]

import numpy

a = numpy.array(list(map(int,input().split())))
b = numpy.array(list(map(int,input().split())))

print(numpy.inner(a,b))
print(numpy.outer(a,b))

input

0   1

2   3

output

3

[[0  0]

  [2  3]]

Linear algebra

23. Determinant of an array

linalg.det used to find determinant of an array

import numpy

print(numpy.linalg.det([[2 , 3], [3, 2]]))  

output:

-5.000000000000001

Numpy tutorials part 1