# 4.16. Array Arithmetic

Vectorized Operations

Single statement without a loop that explains a looping concept. Applies operation to each element.

import numpy as np

a = np.array([1, 2, 3])

a ** 2
# array([1, 4, 9])


1. Operations between multiple array objects are first checked for proper shape match

2. Mathematical operators (+, -, *, /, exp, log, ...) apply element by element, on values

3. Reduction operations (mean, std, skew, kurt, sum, prod, ...) apply to whole array, unless an axis is specified

4. Missing values propagate, unless explicitly ignored (nanmean, nansum, ...)

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a + 2
# array([[3, 4, 5],
#        [6, 7, 8]])

a + a
# array([[ 2,  4,  6],
#        [ 8, 10, 12]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a + b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.3. Subtraction

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a - 2
# array([[-1,  0,  1],
#        [ 2,  3,  4]])

a - a
# array([[0, 0, 0],
#        [0, 0, 0]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a - b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.4. Division

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a / 2
# array([[0.5, 1. , 1.5],
#        [2. , 2.5, 3. ]])

a / a
# array([[1., 1., 1.],
#        [1., 1., 1.]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a / b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.5. Square Root

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

np.sqrt(a)
# array([[1., 1.41421356, 1.73205081],
#        [2., 2.23606798, 2.44948974]])


## 4.16.6. Modulo

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a % 2
# array([[1, 0, 1],
#        [0, 1, 0]])

a % a
# array([[0, 0, 0],
#        [0, 0, 0]])

a // a
# array([[1, 1, 1],
#        [1, 1, 1]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a % b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)

a // b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.7. Multiplication

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a * 2
# array([[ 2,  4,  6],
#        [ 8, 10, 12]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a * b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.8. Power

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

a ** 2
# array([[ 1,  4,  9],
#        [16, 25, 36]])

a * a
# array([[ 1,  4,  9],
#        [16, 25, 36]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5])

a ** b
# ValueError: operands could not be broadcast together with shapes (3,) (2,)


## 4.16.9. Array Multiplication

Warning

For two-dimensional arrays, multiplication * remains elementwise and does not correspond to matrix multiplication.

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

b = np.array([[4, 5, 6],
[7, 8, 9]])

a * b
# array([[ 4, 10, 18],
#        [ 7, 16, 27]])


## 4.16.10. Matrix Multiplication

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

b = np.array([[4, 5, 6],
[7, 8, 9]])

a.dot(b)
# ValueError: shapes (3,) and (2,3) not aligned: 3 (dim 0) != 2 (dim 0)

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

b = np.array([[1, 2],
[3, 4],
[5, 6]])

a.dot(b)
# array([[22, 28],
#        [49, 64]])

import numpy as np

a = np.array([[1, 2, 3],
[4, 5, 6]])

b = np.array([[1, 2],
[3, 4],
[5, 6]])

a @ b
# array([[22, 28],
#        [49, 64]])

import numpy as np

a = np.array([1, 2, 3])
b = np.array([[4, 5, 6],
[7, 8, 9]])

a @ b
# ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)

a.dot(b)
# ValueError: shapes (3,) and (2,3) not aligned: 3 (dim 0) != 2 (dim 0)

• np.dot()

• If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).

• If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

• If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.

• If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b: dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])

import numpy as np

a = np.array([1, 2, 3], float)
b = np.array([0, 1, 1], float)

np.dot(a, b)
# 5.0

import numpy as np

a = np.array([[0, 1], [2, 3]], float)
b = np.array([2, 3], float)
c = np.array([[1, 1], [4, 0]], float)

a
# array([[ 0., 1.],
#        [ 2., 3.]])

np.dot(b, a)
# array([ 6., 11.])

np.dot(a, b)
# array([ 3., 13.])

np.dot(a, c)
# array([[ 4., 0.],
#        [ 14., 2.]])

np.dot(c, a)
# array([[ 2., 4.],
#        [ 0., 4.]])


## 4.16.11. Assignments

### 4.16.11.1. Arithmetic operations

• Complexity level: easy

• Lines of code to write: 10 lines

• Estimated time of completion: 5 min

English
1. For given: a: ndarray, b: ndarray, c: ndarray (see below)

2. Calculate square root of each element in a and b

3. Calculate second power (square) of each element in c

4. Add elements from a to b

5. Multiply the result by c

Polish
1. Dla danych: a: ndarray, b: ndarray, c: ndarray (patrz sekcja input)

2. Oblicz pierwiastek kwadratowy każdego z elementu w a i b

3. Oblicz drugą potęgę (kwadrat) każdego z elementu w c

4. Dodaj elementy z a do b

5. Przemnóż wynik przez c

Input
a = np.array([[0, 1], [2, 3]], float)
b = np.array([2, 3], float)
c = np.array([[1, 1], [4, 0]], float)

Output
array([[ 1.41421356,  2.73205081],
[45.254834  ,  0.        ]])


• Complexity level: easy

• Lines of code to write: 2 lines

• Estimated time of completion: 5 min

English
1. For given: a: ndarray, b: ndarray (see below)

2. Add a and b

3. Add b and a

4. What happened?

Polish
1. Dla danych: a: ndarray, b: ndarray (patrz sekcja input)

2. Dodaj a i b

3. Dodaj b i a

4. Co się stało?

import numpy as np

a = np.array([[1, 0], [0, 1]])
b = [[4, 1], [2, 2]]


### 4.16.11.3. Array Multiplication

• Complexity level: easy

• Lines of code to write: 2 lines

• Estimated time of completion: 5 min

English
1. For given: a: ndarray, b: ndarray (see below)

2. Multiply a and b using scalar multiplication

3. Multiply a and b using matrix multiplication

4. Multiply b and a using scalar multiplication

5. Multiply b and a using matrix multiplication

6. Discuss results

Polish
1. Dla danych: a: ndarray, b: ndarray (patrz sekcja input)

2. Przemnóż a i b używając mnożenia skalarnego

3. Przemnóż a i b używając mnożenia macierzowego

4. Przemnóż b i a używając mnożenia skalarnego

5. Przemnóż b i a używając mnożenia macierzowego

6. Omów wyniki

import numpy as np

a = np.array([[1,0,1,0],
[0,1,1,0],
[3,2,1,0],
[4,1,2,0]])

b = np.array([[4,1],
[2,2],
[5,1],
[2,3]])