Simple Arithmetic

You could use arithmetic operators + - * / directly between NumPy arrays, but this section discusses an extension of the same where we have functions that can take any array-like objects e.g. lists, tuples etc. and perform arithmetic conditionally.

Arithmetic Conditionally: means that we can define conditions where the arithmetic operation should happen.

All of the discussed arithmetic functions take a where parameter in which we can specify that condition.

Addition

The add() function sums the content of two arrays, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 11, 12, 13, 14, 15])
arr2 = np.array([20, 21, 22, 23, 24, 25])

newarr = np.add(arr1, arr2)

print(newarr)

The example above will return [30 32 34 36 38 40] which is the sums of 10+20, 11+21, 12+22 etc.

Subtraction

The subtract() function subtracts the values from one array with the values from another array, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([20, 21, 22, 23, 24, 25])

newarr = np.subtract(arr1, arr2)

print(newarr)

The example above will return [-10 -1 8 17 26 35] which is the result of 10-20, 20-21, 30-22 etc.

Multiplication

The multiply() function multiplies the values from one array with the values from another array, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([20, 21, 22, 23, 24, 25])

newarr = np.multiply(arr1, arr2)

print(newarr)

The example above will return [200 420 660 920 1200 1500] which is the result of 10*20, 20*21, 30*22 etc.

Division

The divide() function divides the values from one array with the values from another array, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([3, 5, 10, 8, 2, 33])

newarr = np.divide(arr1, arr2)

print(newarr)

Power

The power() function rises the values from the first array to the power of the values of the second array, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([3, 5, 6, 8, 2, 33])

newarr = np.power(arr1, arr2)

print(newarr)

The example above will return [1000 3200000 729000000 6553600000000 2500 0] etc.

Remainder

Both the mod() and the remainder() functions return the remainder of the values in the first array corresponding to the values in the second array, and return the results in a new array.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([3, 7, 9, 8, 2, 33])

newarr = np.mod(arr1, arr2)

print(newarr)

You get the same result when using the remainder() function:

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([3, 7, 9, 8, 2, 33])

newarr = np.remainder(arr1, arr2)

print(newarr)

Quotient and Mod

The divmod() function return both the quotient and the mod. The return value is two arrays, the first array contains the quotient and second array contains the mod.

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50, 60])
arr2 = np.array([3, 7, 9, 8, 2, 33])

newarr = np.divmod(arr1, arr2)

print(newarr)

Returns: (array([3, 2, 3, 5, 25, 1]), array([1, 6, 3, 0, 0, 27])) - The first array represents quotients, the second represents remainders.

Absolute Values

Both the absolute() and the abs() functions do the same absolute operation element-wise but we should use absolute() to avoid confusion with python's inbuilt math.abs().

import numpy as np

arr = np.array([-1, -2, 1, 2, 3, -4])

newarr = np.absolute(arr)

print(newarr)

The example above will return [1 2 1 2 3 4].