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.
print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0
print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10]
import numpy
A = numpy.array([0, 1])
B = numpy.array([3, 4])
print numpy.inner(A, B) #Output : 4
import numpy
A = numpy.array([ 1, 2 ])
B = numpy.array([ 3, 4 ])
print numpy.dot(A, B) #Output : 11
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
import numpy
my_array = numpy.array([[2, 5],
[3, 7],
[1, 3],
[4, 0]])
print numpy.min(my_array, axis = 0) #Output : [1 0]
print numpy.min(my_array, axis = 1) #Output : [2 3 1 0]
print numpy.min(my_array, axis = None) #Output : 0
print numpy.min(my_array) #Output : 0
import numpy
my_array = numpy.array([ [1, 2], [3, 4] ])
print numpy.sum(my_array, axis = 0) #Output : [4 6]
print numpy.sum(my_array, axis = 1) #Output : [3 7]
print numpy.sum(my_array, axis = None) #Output : 10
print numpy.sum(my_array) #Output : 10
import numpy
my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])
print numpy.floor(my_array) #[ 1. 2. 3. 4. 5. 6. 7. 8. 9.]
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