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
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.]
import numpy
my__1D_array = numpy.array([1, 2, 3, 4, 5])
print my_1D_array.shape #(5,) -> 1 row and 5 columns
my__2D_array = numpy.array([[1, 2],[3, 4],[6,5]])
print my_2D_array.shape #(3, 2) -> 3 rows and 2 columns
import numpy
a = numpy.array([1,2,3,4], float)
b = numpy.array([5,6,7,8], float)
print a + b #[ 6. 8. 10. 12.]
print numpy.add(a, b) #[ 6. 8. 10. 12.]
print a - b #[-4. -4. -4. -4.]
print numpy.subtract(a, b) #[-4. -4. -4. -4.]
print a * b #[ 5. 12. 21. 32.]
print numpy.multiply(a, b) #[ 5. 12. 21. 32.]
print a / b #[ 0.2 0.33333333 0.42857143 0.5 ]
print numpy.divide(a, b) #[ 0.2 0.33333333 0.42857143 0.5 ]
print a % b #[ 1. 2. 3. 4.]
print numpy.mod(a, b) #[ 1. 2. 3. 4.]
print a**b #[ 1.00000000e+00 6.40000000e+01 2.18700000e+03 6.55360000e+04]
print numpy.power(a, b) #[ 1.00000000e+00 6.40000000e+01 2.18700000e+03 6.55360000e+04]
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