NumPy Basics : Part 2

This article is a continuation of my previous post on NumPy here.

NumPy Operations

1.Adding an element to an array
Use numpy.append() to add to the end of the array.

array1 = np.append(array_x, 1)
array1

Output:
array([ 5, 6, 7, 8, 9, 1])

Note: A numpy array does not support append() method directly.

2.Removing an element in the array
Use numpy.delete() to remove the element at a particular index.

array2 = np.delete(array1,2)
array2

Output:
array([ 5, 6, 8, 9, 1])

3.Sorting an array.

array3 = np.sort(array2)
array3

Output:
array([1, 5, 6, 8, 9])

4.Reshaping an array
Use np.reshape(). The array you intend to output must have the same number of elements.
For instance if an 3x4 array has 12 elements, then you can reshape it to 6x2 or 4x3 or 2x6.
You can use numpy.reshape() directly on a numpy array.

arr2 = np.reshape(array_A,(3,2))
arr2

Output:
array([[2, 4],[6, 1],[3, 5]])
5.Flattening an array
Use flatten() to return a one dimension array.

arr2.flatten()

Output:
array([2, 4, 6, 1, 3, 5])

You can also use ravel() to flatten the array.

array_A.ravel()

Output:
array([2, 4, 6, 1, 3, 5])

Note: reshape(), flatten(),ravel() does not affect the original array.

NumPy Arithmetic Operations

In this section, we will cover specific functions in NumPy used in arithmetic operations.
**Note: **You can only perform arithmetic operations if the arrays have the same structure and dimensions.

1.Add
Addition can be done using np.add() or using the + arithmetic operator. The output is an ndarray object.

import numpy as np
a = np.array([20,30,40,50,60])
b = np.array([4,5,6,7,8])
np.add(a, b)

Output:
array([24, 35, 46, 57, 68])

c = a+b
print(c)
print(type(c))

Output:
[24 35 46 57 68]

2.Subtract
Subtraction can be done using np.subtract() or using the -arithmetic operator.

import numpy as np
a = np.array([20,3,40,50,60])
b = np.array([4,5,6,7,8])
c = a-b
print(c)

Output: [16 -2 34 43 52]
Using np.subtract() the second argument is subtracted from the first.

import numpy as np
a = np.array([20,30,40,50,60])
b = np.array([4,5,6,7,8])
np.subtract(b, a)

Output:
array([-16, -25, -34, -43, -52])

3.Multiply
Multiplication can be done using np.multiply() or using the * arithmetic operator.

import numpy as np
a = np.array([20,30,40,50,60])
b = np.array([4,5,6,7,8])
print(np.multiply(b, a))
print(a*b)

Output:
[ 80 150 240 350 480]
[ 80 150 240 350 480]

4.Divide
Division can be done using np.divide() or using the / arithmetic operator.

import numpy as np
a = np.array([20,30,40,50,60])
b = np.array([4,5,6,7,8])
print(np.divide(b, a))
print(a/b)

Output:
[5. 6. 6.66666667 7.14285714 7.5]
[5. 6. 6.66666667 7.14285714 7.5]

5.Statistical Functions
You can get the sum,mean, average and variance of all the elements in an array.

import numpy as np
a = np.array([2.5,3.5 ,4.3,5.4,6.5])
print('The sum is ', np.sum(a))
print('The mean is ', np.mean(a))
print('The average is ', np.average(a))
print('The variance is ', np.var(a))

Output:

The sum is  22.200000000000003
The mean is  4.44
The average is  4.44
The variance is  1.9664000000000001

6.Power function
The power() function performs the power of two arrays where the first argument is the base raised to the power of the second argument.

import numpy as np
a = np.array([2,3,4,5,6])
b = np.array([5,4,3,2,1])
print(np.power(a, b))

Output:
[32 81 64 25 6]

7.Remainder and Modulus
The remainder() function gives the remainder of the two arrays similar to the mod() function.

import numpy as np
a = np.array([2,3,4,5,6])
b = np.array([5,4,3,2,1])
print(np.mod(a, b))
print(np.remainder(a,b))

Output:

[2 3 1 1 0]
[2 3 1 1 0]

8.Reciprocal
The reciprocal() function returns the reciprocal of each element in the array.

import numpy as np
a = np.array([2.5,3.5 ,4.3,5.4,6.5])
print(np.reciprocal(a))

Output:
array([ 1., 3.25, 5.5 , 7.75, 10.])

9.Minimum and Maximum
You can get the minimum using min() function and maximum using max() function.

array_x = np.array([5,6,7,8,9])
print('The max is' ,array_x.max())
print('The min is' ,array_x.min())

Output:

The max is 9
The min is 5

This is wraps up the summary of some of the most popularly used NumPy operations.