Welcome to NumPy New Course:

In this course we will learn NumPy from the absolute basics to the advanced level after reading out all the tutorials you will have a full understanding of how NumPy is used in different fields like Artificial Intelligence, Data Science, Machine Learning, or some other related field.

let's get started.

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NumPy

NumPy stands for 'Numerical Python'

def: "Numpy is the core library for scientific computing in python"

Numpy library is used for different types of purposes that could be:

Used in Data Science
Used in Machine Learning
Automation
Deep Learning etc.

The main focus of NumPy in the above fields is providing mathematics functions with just one or two lines of code and the rest is done even you don't have a deep understanding of mathematics.

Let's Start to study NumPy in details

How to install NumPy In your system:

If you want to install NumPy in your system write the following code in your command prompt. pip install numpy

But I will prefer to download Anaconda in your system that will automatically install NumPy and some other libraries of python in your system

Install Anaconda:

If you don't know how to install Anaconda Click Here to watch the tutorial.

Once you have downloaded Anaconda open Jupyter Notebook in any of your browsers and start code with us.

Once everything is done you need to import NumPy in your notebook as follow import numpy

But if you want to call NumPy with the short name then write the following code import numpy as np

you will use the short name for NumPy as np whenever you call NumPy

Before starting to work with NumPy methods and functions lets understand different types of arrays of metrics used by NumPy:

NumPy uses different types of array as follow:

Numpy Array Types

There are three types of NumPy arrays:

One dimensional NumPy Array.
Two-dimensional NumPy Array.
Three-dimensional NumPy Array.

1. One Dimensional:

In one dimensional there is only one row but having different columns

i-e: array = [1,2,3,4]

in this case, we have only one row and 4 columns

2. Two Dimensional:

In two dimensional there is multiple rows and multiple columns

i-e: array = [[1,2,3],

[4,5,6]]

in this case, we have two rows and three columns

3. Three Dimensional

In three dimensional many arrays are having many axes

But in this course, we will only focus on 1D and 2D arrays while you don't need to play with 3D or 4D arrays.

Creating Arrays:

Create 1D Array:

If you want to create a 1D array with one row and 4 columns in NumPy use the following code np.array([1,2,3,4])

Create 2D Array:

If you want to create a 2D array with 2 rows and 3 columns use the following code a = np.array([[1,2,3],[4,5,6],[7,8,9]])

Create an array of Zeros:

If you want to create an array with 3 rows and 4 columns while all the elements are zeros use the following: zeros = np.zeros((3,4))

Use Arrange Method to create an array:

if you want to create an array that is evenly spaced through arrange method use the following code arr = np.arange(10,100,10)

The above code will show you numbers in a list starting from 10 up to 90 because the last one is always skipping if you want to print up to 100 use 110 instead of 100

Create an array with random numbers

if you want to create an array with some random numbers use the following code where the (2,2) show's rows and columns in your array random_arr = np.random.random((2,2))

Create an empty array

If you want to create an empty array use the following code empty = np.empty((4,5))

Data Types

NumPy array uses multiple types of data as some of them are as follow:

1.

integers

NumPy array used integers values i-e 1,2,,3,4,55,33,22,33

2. floating numbers

NumPy array used floating-point numbers which have the decimal point in between

i-e 33.22, 55.33, 66.777, 0.1233

3. boolean type true or false

boolean types are two states either True or False

4. python fixed string

string are characters stored in the NumPy array as like array['nomi', 'abcd']

Inspecting NumPy Array

Array Shape

If you want to check the shape of your array and what type of dimension your array exist you can check by array name and then shape function as follow: a.shape

Length of Array

If you want to check the length of your array and how many rows and columns your array exist you can do as follow: len(a)

Number of Array Dimensions

If you want to find the type of your array that is it 2d or 1d you can do it by the following method. a.ndim

find Number of Array elements

if you want to find several array elements in your array you can use the following code: a.size

Find Name of data type

if you want to find what type of data your array exist you can check that as follow: a.dtype

Asking For Help

if you want get information about all the NumPy tools you can do that just as follow: np.info(a) give your array name inside parenthesis and you will know all the information about your array

Array Mathematics

Arithmetic Operations

We can do arithmetic operations on our array such as:

Subtraction
Addition
Multiplication
Division
Exponentiation
Square root
Print sines of an array
Element-wise cosine
Element-wise natural logarithm

Let's Apply these all arithmetic operations in our NumPy array one by one.

Subtraction

If you want to subtract one array or matric from another array you can do that in two ways

first is just write as follows if you have two arrays. array1 - array2 The second method is np.subtract(array1 - array2)

Addition

The addition of two arrays is also the same as subtraction as follow: array1 + array2 the second method is np.add(array1 , array2)

Multiplication

if you want to do multiplication if two matrics follow the same method as you already know a * b or

np.multiply(a,b)

Division

also, the division will be the same as: a/b or np.divide(a,b)

Exponentiation

if you want to take the exponent of your array use as follow: np.exp(a) This will take the exponent of each element in your array as like:

a = np.array([[3,4,5],[5,6,7]])
np.exp(a)
    
output:
    array([[  20.08553692,   54.59815003,  148.4131591 ],
    [ 148.4131591 ,  403.42879349, 1096.63315843]])

Square root

Also in the same way you can take the square root of each element in your array as like: np.sqrt(array)

Print sines and cosine of an array

also, you can print the sines and cosines of an array as:

np.sin(a) 
 np.cos(a)

it will take the sin and cosine of each element in your array

Natural logarithm

if you want to take the log of each element in your array do as follow: np.log(a)

Comparison

if you want to compare two arrays you can do as follow:

 a == b 
 output: array([[False, True, True], [False, False, False]]

it will give you the result in bool type if two elements at the same index are equal to each other it will show you True otherwise False also if you want to compare your array elements with a specific element then you can do as follows a > 2 for best practice create an array then do a comparison as above

Array-wise comparison

if you want to compare two arrays you can do that as follow by writing np.array_equal and then put your two arrays between parenthesis. np.array_equal(array1, array2)

Aggregate Functions

Array-wise sum

if you want to print the sum of your array you can do as follow: array.sum() it will find the sum of all elements in your array

Find Minimum value in the array

if you want to find minimum value in your array you can do as follow: array_name.min()

Find Maximum value in the array

if you want to find maximum value in your array you can do that as follow: array_name.max()

Mean

if you want to find the mean or average of an array of elements then you can do that as follow: array_name.mean()

Sort an array

if you want to sort an array in ascending order or descending order you can do that as follow array_name.sort()

Subsetting, Slicing, Indexing

Select the element any index

if you want to select an element from your array through indexing you can do that as write your array name and then inside large brackets specify the index as follow: a[2] in this case, we are collecting elements from the third index as we know that in python index always starts from zero.

Select the element from any row at any column

if you want to collect a specific element from the array at any row or column just specify the column and row index as follow array_name[1,2] in this case, we are collecting an element from the second row and third column

Slicing

you also can dod slicing as follow: array_name[:1] this will select all items at row 0

Reversed an array

you can also be doing reverse indexing for example if you want to get the last element in your array you can get that by -1 or if second last then -2 as like below: array_name[-1] this will give you the last element in your array

Array Manipulation

Transposing Array

if you want to take the transpose of an array transpose means converting rows into columns or columns into rows you can get the transpose as follows: np.transpose(array_name) this will change rows into columns and columns into rows

Append items to an array

you can also add or remove items in your array if you want to add or append an item you can do that as follow: np.append(a,b)

Insert items in an array

you can also insert items into your array as follow: np.insert(array_name, 1,5) this will add 1, and 5, into your array

Delete items from an array

also, you can delete items from your array through indexing as follow: np.delete(array_name,[1])

till now you have gain everything about NumPY which is necessary for data science and machine learning projects if you want to know more about NumPy you can read the NumPy documentation here

Thanks and good luck from Nomi

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