Numpy

NumPy is a one of the Data Science library for the Python programming language,
adding support for large, multi-dimensional arrays and
matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers

Section Goals:

Understand NumPy
Create Arrays With NumPy
Retrieve Information From A NumPy Array Through Slicing And Indexing
Learn Basic NumPy Operations

What Is NumPy?

Python Library For Creating N-Dimensional Arrays

Ability To Quickly Broadcast functions

Built-in Linear Algebra, Statistical Distributions, Trigonometric And Random Number Capabilities

Why Use NumPy?

While NumPy Structures Look Similar To Standerf Python Lists, They Are Much More Efficient!!!

The Broadcasting Capabilities Are Also Exreaely Useful For Quickly Applying Functions To Out Data Structures

Create Array With NumPy:

import numpy as np
myList =[1,2,3,4,5,6,7,8,9,0]
myArray =np.array(myList)
print(myArray) 

Output:

[1 2 3 4 5 6 7 8 9 0]

Create N-Dimensional Array With NumPy:

import numpy as np 
 
myArray =np.array([[1,2,3], 
            [4,5,6], [7,8,9]])
print(myArray) 

Output:

[[1 2 3]
[4 5 6]
[7 8 9]]

Create Array Using NumPy arange Method:

import numpy as np

myArray1 =np.arange(0, 10)
myArray2 =np.arange(0, 20, 2)
print(myArray1)
print(myArray2)

Output:

[0 1 2 3 4 5 6 7 8 9]
[ 0 2 4 6 8 10 12 14 16 18]

Create Zeros Array Using NumPy zeros Method:

import numpy as np

myArray1 =np.zeros(10)
myArray2 =np.zeros((10, 5))
print(myArray1)
print(myArray2)

Output:

[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]

Create Ones Array Using NumPy ones Method:

import numpy as np

myArray1 =np.ones(10)
myArray2 =np.ones((10, 5))
print(myArray1)
print(myArray2)

Output:

[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]]

Create LinSpace Array Using NumPy linspace Method:

import numpy as np

myArray =np.linspace(0, 100, 10)
print(myArray)

Output:

[ 0. 11.11111111 22.22222222 33.33333333 44.44444444

55.55555556 66.66666667 77.77777778 88.88888889 100. ]

Create Squere Matrix Array Using NumPy eye Method:

import numpy as np

myArray =np.eye(3)
print(myArray)

Output:

[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]

Create Random Values Of Array Using NumPy Random Method:

import numpy as np

myArray1 =np.random.rand(4)
myArray2 =np.random.randn(2, 3)
myArray3 =np.random.randint(0, 100, 10)

print(myArray1)
print(myArray2)
print(myArray3)

Output:

[0.70705863 0.26303514 0.90838782 0.10973079]
[[ 0.7811629 -1.62630961 1.53149854]
[-1.62526936 1.53977785 1.09274884]]
[65 7 99 13 13 76 28 5 17 53]

Find Minimum And Maximum Values From NumPy Array Using min, max Method:

import numpy as np

myArray =np.array([10,20,30,40,50 
            ,60,70,80,90])
print(myArray.min())
print(myArray.max())

Output:

10
90

Find Minimum And Maximum Values Index From NumPy Array Using argmin, argmax Method:

import numpy as np

myArray =np.array([45,34,67,82 
            ,12,53,89,40])
print(myArray.argmin())
print(myArray.argmax())

Output:

Find Type Of Values Using NumPy Array dtype Method:

import numpy as np

myArray1 =np.array([1,2,3,4, 
            5,6,7,8,9])
myArray2 =np.array([1.4,5.7, 
            45.2,9.5,23.6])
myArray3 =np.array(['aew','eer',
         'ere','rdt','ewe'])
myArray4 =np.array([[3,54,5,45],
        [2,4,5,56],[54,3,23,78]])
myArray5 =np.array([True,False,False,
         True,False,True, True])
 
print(myArray1.dtype)
print(myArray2.dtype)
print(myArray3.dtype)
print(myArray4.dtype)

Output:

int64
float64
<U3
int64

bool

Find Shape And Reshape The Array Using NumPy shape, reshape Method:

import numpy as np

myArray1 =np.array([[1,2,3,4], 
     [5,6,7,8], [9,10,11,12]])
#3x4=12 and also 6x2=12 
myArray2 =myArray1.reshape(6,2)
print(myArray1.shape)
print(myArray2)

Output:

(3, 4)
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]
[11 12]]

Find Value Using Indexing And Slicing The Array:

import numpy as np

myArray =np.array([10,20,30,40,
        50,60,70,80,90])
print(myArray[5])
print(myArray[:4])
print(myArray[3:])
print(myArray[3:6])

myArray[:] =10
print(myArray)

Output:

60
[10 20 30 40]
[40 50 60 70 80 90]
[40 50 60]
[10 10 10 10 10 10 10 10 10]

Perform Conditions From A Array:

import numpy as np

myArray =np.array([10,20,30,40,
        50,60,70,80,90])

print(myArray[myArray>=50])
print(myArray[myArray<=50])
print(myArray>=50)
print(myArray<=50)
print(60 in myArray)
print(46 in myArray)

Output:

[50 60 70 80 90]
[10 20 30 40 50]

[False False False False True True True True True]
[ True True True True True False False False False]
True
False

Perform Basic Mathematical Operations In NumPy Array:

import numpy as np

myArray =np.array([10,20,30,40,
        50,60,70,80,90])

print(myArray+10)
print(myArray-5)
print(myArray*2)
print(myArray/2)
print(myArray%3)

Output:

[ 20 30 40 50 60 70 80 90 100]
[ 5 15 25 35 45 55 65 75 85]
[ 20 40 60 80 100 120 140 160 180]
[ 5. 10. 15. 20. 25. 30. 35. 40. 45.]
[1 2 0 1 2 0 1 2 0]

Perform Advanced Mathematical Operations In NumPy Array:

import numpy as np

myArray =np.array([10,20,30,40,
        50,60,70,80,90])

print(np.sin(myArray))
print(np.tan(myArray))
print(np.cos(myArray))
print(np.sqrt(myArray))
print(np.log(myArray))

Output:

[-0.54402111 0.91294525 -0.98803162 0.74511316 -0.26237485 -0.30481062
0.77389068 -0.99388865 0.89399666]
[ 0.64836083 2.23716094 -6.4053312 -1.11721493 -0.27190061 0.32004039
1.22195992 9.00365495 -1.99520041]
[-0.83907153 0.40808206 0.15425145 -0.66693806 0.96496603 -0.95241298
0.6333192 -0.11038724 -0.44807362]
[3.16227766 4.47213595 5.47722558 6.32455532 7.07106781 7.74596669
8.36660027 8.94427191 9.48683298]
[2.30258509 2.99573227 3.40119738 3.68887945 3.91202301 4.09434456
4.24849524 4.38202663 4.49980967

Perform Sum Operation Using NumPy sum Method:

import numpy as np

myArray1 =np.array([10,20,30,40,
        50,60,70,80,90])

myArray2 =np.array([[1,2,3],
                    [4,5,6],
                    [7,8,9]])

print(myArray1.sum())
# sum of column
print(myArray2.sum(axis=0))
# sum of row
print(myArray2.sum(axis=1))

Output:

450
[12 15 18]
[ 6 15 24]

To Be Continue...

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