This article is a continuation of my previous post on NumPy here.
The NumPy ndarray class is used to represent both matrices and vectors.
A vector is a one-dimensional array.
A matrix is a two-dimensional array.
A tensor is a 3-D or higher dimensional array.
NumPy Operations
- Adding an element to an array
Use numpy.append() to add an element to the end of an array.
It takes an array and the value to be appended and returns a new array with the value appended to the end.
array_x = np.array([5,6,7,8,9]))
array1 = np.append(array_x, 1)
array1
Output: array([ 5, 6, 7, 8, 9, 1])
- Removing an element from an array
Use numpy.delete() to remove the element at a particular index.
The method takes in an array and the index of the value to be deleted and retuns a new aray with the value deleted.
array2 = np.delete(array1,2)
array2
Output:array([ 5, 6, 8, 9, 1])
- Sorting an array.
array3 = np.sort(array2)
array3
Output:array([1, 5, 6, 8, 9])
- Reshaping an array
Use np.reshape() which takes the array to be reshaped and a tuple of the new shape and returns a copy of the new shaped array.
The output must have the same number of elements.
For instance, a 3x4 array with 12 elements,can be reshaped 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]])
- Flattening an array
Use flatten() to return a one-dimensional 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() do 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.Addition
Use np.add() or 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]
<class 'numpy.ndarray'>
2.Subtraction
Use np.subtract() or 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.Multiplication
Use np.multiply() or 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.Division
Use 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]
- 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:
[0.4,0.28571429, 0.23255814, 0.18518519, 0.15384615]
9.Minimum and Maximum
Use the min() function to get the minimum and the max() function to get the maximum.
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 commonly used NumPy operations.
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