Asabeneh/Python-for-Everyone

INTRODUCTION

Python for Everyon is a step by step guide to learn Python and programming in general. Python For Everyone is a guide for both beginners and advanced Python developers. Welcome to Python For Everyone.

Congratulations for deciding to learn Python. Python is eating the world. It has been the choice for developers to use python for different puprpose specifically to deal with data.

In this step by step guide, you will learn Pyton. Python is one of the most popular programming language. You use Python to develop web applicaton, to develop mobile apps, desktop applications, games and mostly Python is use for data Sciecne, machine learning and AI. Python has increased in popularity in recent years and has been the leading programming language for data science and machine language.

SET UP

First install Python. To write python code, we need to have a code editor. Lets install text or code editor. There are many open source code editors which can help you to write python code. These are the most commonly used ones:

• Jupter Note book
• vscode
• pycharm
• atom
• notepad++ or others.

Jupter Notebook is highly recommended. You can use jupter note book by installing anaconda. Anaconda is the most popular Python/R platform which allow you to use different development tools and making package installing so simple.

Environment Setup

Installing Python

To run a python script you need to install python. Let's download python. If your are a windows user. Click the button encircled in red.

If you are a macOS user. Click the button encircled in red.

To check if python is installed write the following command on your device terminal.

python --version

As you can see from the terminal, I am using python 3.7.5 version at the moment. If you mange to see the python version, well done. Python has been installed on your machine. Continue to the next section.

Python Shell

Python is an interpreted scripting language, so it doesn't need to be compiled. It means it executes the code line by line. Python comes with a Python Shell (Python Interactive Shell). It is used to execute a single python command and get the result.

Python Shell waits for the python code from the user. When you enter the code, it interprets the code and shows the result in the next line. Open your terminal or command prompt(cmd) and write:

python

The python interactive shell is opened and it is waiting for you to write python code. You will write your python script next to this symbol >>> and then click Enter. Lets write our very first script on the python scripting shell.

Well done, you wrote your first python script on python interactive shell. How do we close this shell ? To close the shell, next to this symbol >> write exit() command and press Enter.

Now, you know how to open the python interactive shell and how to exit from it.

Python will give you results if you write scripts that python understands, if not it returns errors. Let's make a deliberate mistake and see what python will return.

As you can see from the returned error, python is so clever that it knows the mistake we made and which was Syntax Error: invalid syntax. Using x as multiplication in python is a syntax error because (x) is not a valid syntax in python. Instead of (x) we use asterisk (*) for multiplication. The returned error clearly shows what to fix. The process of identifying and removing errors from a program is called debugging. Let's debug it by putting * in place of x.

Our bug was fixed, the code ran and we got a result we were expecting. As a programmer you will see such kind of errors on daily basis. It is good to know how to debug. To be good at debugging you should understand what kind of errors you are facing:SyntaxError, IndexError, ModuleNotFoundError, KeyError, ImportError etc. We will see more about different python error types in later sections.

Let's practice more how to use python interactive shell. Go to your terminal or command prompt and write the word python.

The python interactive shell is opened. Let's do some basic mathematic operations (addition, subtraction, multiplication, division, modulus, exponential). Lets do some maths first before we write any python code:

• 2 + 3 = 5
• 3 - 2 = 1
• 3 * 2 = 6
• 3 / 2 = 1.5
• 3 ^ 2 = 3 x 3 = 9

In python we have the following additional operations:

• 3 % 2 = 1 => which means finding the remainder
• 3 // 2 = 1 => which means removing the remainder

Lets change the above mathematical expressions to code. The python shell has been opened and lets write a comment at the very beginning of the shell. A comment is a part of the code which is not executed by python. So we can leave some text in our code to make our code more readable. Python does not run the comment part. A comment in python starts with hash(#) symbol. This is how you write a comment in python

 # comment starts with hash
 # this is a python comment, because it starts with a (#) symbol

Before we move on to the next section, lets practice more on the python interactive shell. Close the opened shell by writing exit() on the shell and open it again and let's practice how to write text on the python shell.

Installing Visual Studio Code

The python interactive shell is good to try and test small script codes but it won't be for a big project. In real work environment, developers use different code editors to write codes. In this 30 days of python programming challenge we will use visual studio code. Visual studio code is a very popular open source text editor. I am a fan of vscode and I would recommend to download visual studio code, but if you are in favor of other editors, feel free to follow with what you have.

If you installed visual studio code, let's see how to use it.

How to use visual studio code

Open the visual studio code by double clicking the visual studio icon. When you open it you will get this kind of interface. Try to interact with the labeled icons.

Create a folder named 30DaysOfPython on your desktop. Then open it using visual studio code.

After opening it you will see shortcuts for creating files and folders inside of 30DaysOfPython project's directory. As you can see below, I have created the very first file, helloworld.py. You can do the same.

After a long day of coding, you want to close your code editor, right? This is how you will close the opened project.

Congratulations, you have finished setting up the development environment. Let's start coding.

Basic Python

Python Syntax

A python script can be written in python interactive shell or in the code editor. A python file has an extension .py.

Python Indentation

An indentation is a white space in a text. Indentation in many languages is used to increase code readability, however python uses indentation to create block of codes. In other programming languages curly brackets are used to create blocks of codes instead of indentation. One of the common bugs when writing python code is wrong indentation.

Comments

Comments are very important to make the code more readable and to leave remarks in our code. Python doesn't run comment parts of our code. Any text starting with hash(#) in python is a comment.

Example: Single Line Comment

    # This is the first comment
    # This is the second comment
    # Python is eating the world

Example: Multiline Comment

Triple quote can be used for multiline comment if it is not assigned to a variable

"""This is multiline comment
multiline comment takes multiple lines.
python is eating the world
"""

Data types

In python there are several types of data types. Let's get started with the most common ones. Different data types will be covered in detail in other sections. For the time being let us just go through the different data types and get familiar with them. You do not have to have a clear understanding now.

Number

• Integer: Integer(negative, zero and positive) numbers Example: ... -3, -2, -1, 0, 1, 2, 3 ...
• Float: Decimal number Example ... -3.5, -2.25, -1.0, 0.0, 1.1, 2.2, 3.5 ...
• Complex Example 1 + j, 2 + 4j

String

A collection of one or more characters under a single or double quote. If a string is more than one sentence then we use a triple quote.

Example:

'Asabeneh'
'Finland'
'Python'
'I love teaching'
'I hope you are enjoying Python for Everyone'

Booleans

A boolean data type is either a True or False value. T and F should be always uppercase.

Example:

    True  #  Is the light on? If it is on, then the value is True
    False # Is the light on? If it is off, then the value is False

List

Python list is an ordered collection which allows to store different data type items. A list is similar to an array in JavaScript.

Example:

[0, 1, 2, 3, 4, 5]  # all are the same data types - a list of numbers
['Banana', 'Orange', 'Mango', 'Avocado'] # all the same data types - a list of strings (fruits!)
['Finland','Estonia', 'Sweden','Norway'] # all the same data types - a list of strings (countries!)
['Banana', 10, False, 9.81] # different data types in the list - string, integer, boolean and float

Dictionary

A python dictionary object is an unordered collection of data in a key:value pair format.

Example:

{'name':'Asabeneh', 'country':'Finland', age:250, 'is_married':True}

Tuple

A tuple is an ordered collection of different data types like list but tuples can not be modified once they are created. They are immutable.

Example:

('Asabeneh', 'Brook', 'Abraham', 'Lidiya')

Set

A set is a collection of data types similar to list and tuple. Unlike list and tuple, set is not an ordered collection of items. Like in mathematics, set in python stores only unique items.

In later sections, we will go in detail about each and every python data type.

Example:

{3.14, 9.81, 2.7} # order is not important in set

Checking Data types

To check the data type of certain data/variable we use the type function. In the following terminal you will see different python data types:

Python File

First open your project folder, 30DaysOfPython. If you don't have this folder, create a folder name called 30DaysOfPython. Inside this folder, create a file called helloworld.py. Now, let's do what we did on python interactive shell using visual studio code. The python interactive shell was printing without using print but on visual studio code to see our result we should use a built in function print(some data to print). See the examples below.

Example:

The file name is helloworld.py

# Day 1 - 30DaysOfPython Challenge

print(2 + 3)             # addition(+)
print(3 - 1)             # subtraction(-)
print(2 * 3)             # multiplication(*)
print(3 / 2)             # division(/)
print(3 ** 2)            # exponential(**)
print(3 % 2)             # modulus(%)
print(3 // 2)            # Floor division operator(//)

# Checking data types
print(type(10))          # Int
print(type(3.14))        # Float
print(type(1 + 3j))      # Complex number
print(type('Asabeneh'))  # String
print(type([1, 2, 3]))   # List
print(type({'name':'Asabeneh'})) # Dictionary
print(type({9.8, 3.14, 2.7}))    # Set
print(type((9.8, 3.14, 2.7)))    # Tuple

To run the python file check the image below. You can run the python file either by running the green button or by typing python helloworld.py in the terminal .

COMMENT

Comment is essential to make code readable to ourselves or for others. Commented part of our code is not interperated by Python interperator.

# This is a single line comment

Exerciese:

• Write your first python comment
• Write an other more python comment
• Write you third pyhon comment
• Write a comment which says I am exited to learn Python Programming.
• Write a comment which says It is recommended to add a comment at the beginning of a code

PRINTING USING PYTHON

# Printing in Python
print('Hello, World!')
print('Hello,', "World", "!")
print("Welcome to Python!")
print('Learn python in', 2019)
print('Welcome to Python for Everyone')
print('Welcome','to', 'Python','for','Everyone')

Hello, World!
Hello, World !
Welcome to Python!
Learn python in 2019
Welcome to Python for Everyone
Welcome to Python for Everyone

Exerciese:

1. Writ a python comment saying 'How to print using print () at beginning of your code'
2. Print your first name using print()
3. Print your last name using print ()
4. Print your full name using print ()
5. Print your country using print()
6. Print your city using print()
7. Print your age using print ()
8. Print the year using print()
9. Print your qualification or your job using Iprint()
10. Are you single or married ? Answer should be True or False. Print the answer using print()

VARIABLES

Variables are containers or a means to store data in computer memory. pneumonic varialbes recommend to use in many programming langauges.

Valid variable names

    firstname
    lastname
    age
    country
    city
    first_name
    last_name
    capital_city
    _if # if we want to use reservered word as a variable
    first_name
    year_2019
    year2019
    current_year_2019
    num1
    num2

Invalid varaible names

• first-name
• num-1
• 1num

In Pyton For Everyone, we will use standard python varible naming which has been adopted by many python developers. The example below is an example of standard naming of variables, underscore when the variable name is long.

# Variables in Python

first_name = "Asabeneh"
last_name = "Yetayeh"
country = "Helsinki"
age = 250
is_married = True
# Printing the values stored in the variables
print('First name:', first_name)
print('Last name: ', last_name)
print('Country: ', country)
print('Age: ', age)
print('Married: ', is_married)

First name: Asabeneh
Last name:  Yetayeh
Country:  Helsinki
Age:  250
Married:  True

Variable can also be declared in one line:

first_name, last_name, country, age, is_married = 'Asabeneh', 'Yetayeh', 'Helsink', 250, True
print(first_name, last_name, country, age, is_married)
print('First name:', first_name)
print('Last name: ', last_name)
print('Country: ', country)
print('Age: ', age)
print('Married: ', is_married)

Asabeneh Yetayeh Helsink 250 True
First name: Asabeneh
Last name:  Yetayeh
Country:  Helsink
Age:  250
Married:  True

Exerciese: Variables

1. Writ a python comment saying 'Variables in python'
2. Declare a first name variable and assign a value to it
3. Declare a last name variable and assign a value to it
4. Declare a full name variable and assign a value to it
5. Declare a country variable and assign a value to it
6. Declare a city variable and assign a value to it
7. Declare an age varialbel and assign a value to it
8. Declare a year varaible and assign a value to it
9. Declare a variable is_married and assign a value to it
10. Declare a variable is_true and assign a value to it
11. Declare a variable is_light_on and assign a value to it
12. Declare multiple variable on one line

Getting User Input

name = input('What is your name ?')
age = int(input('How old are you ?'))

if age < 18:
    print(name,' you are under age')
else:
    print(name, ', you are old enough')

What is your name ?Asabeneh
How old are you ?100
Asabeneh , you are old enough

PYTHON DATA TYPES

Different data types in python. There are different data type in python programming. To identify the data tpe we use the type method.

# Different python data types
# Let's declare different data tyeps

first_name = 'Asabeneh' # String
last_name = 'Yetayeh' # String
country = 'Finland' # String
city= 'Helsinki'
age = 250 # Number, it is not my real age, don't worry about it

print(type('Asabeneh'))
print(type(first_name))
print(type(10))
print(type(3.14))
print(type(1 + 1j))
print(type(True))
print(type([1, 2,3,4]))
print(type({"name":"Asabeneh","age":250, "is_married":250}))
print(type((1,2)))
print(type(zip([1,2],[3,4])))

<class 'str'>
<class 'str'>
<class 'int'>
<class 'float'>
<class 'complex'>
<class 'bool'>
<class 'list'>
<class 'dict'>
<class 'tuple'>
<class 'zip'>

Exerciese: Data Types

1. Writ a python comment saying 'Python Data types'
2. Declare a first name variable and assign a value to it
3. Declare a last name variable and assign a value to it
4. Declare an age varialbel and assign a value to it
5. Declare a full name variable and assign a value to it
6. Declare a country variable and assign a value to it
7. Declare a city variable and assign a value to it
8. Declare a variable is_married and assign a value to it
9. Declare a variable is_true and assign a value to it
10. Declare a variable is_light_on and assign a value to it
11. Check the data types of each variable using type()

Numbers

1. Integers
2. Floating Numbers
3. Complex Numbers

Numbers are python data types. Arthimetic Operators: +, -, *, /

# Arthimetic Operations in Python
# Integers

print('Additon: ', 1 + 2)
print('Substraction: ', 2 - 1)
print('Multiplication: ', 2 * 3)
print ('Division: ', 4 / 2) # Division in python gives floating number
print('Division: ', 6 / 2)
print('Division: ', 7 / 2)
print('Division without the remainder: ', 7 // 2) # gives without the floating number or without the remaining
print('Modulous: ', 3 % 2)
print ('Division without the remainder: ',7 // 3)
print('Exponentation: ', 3 ** 2)
# Floating numbers
print('Floating Number', 3.14)
# Complex numbers
print('Complex number: ', 1+1j)
print('Multiplying complext number: ',(1+1j) * (1-1j))


print('== Additon, Subtraction, Multipliation, Division, Modules ==')

num_one = 3
num_two = 4

total = num_one + num_two
diff = num_two - num_one
product = num_one * num_two
div = num_two / num_two
remainder = num_two % num_one


print('sum: ', total)
print('difference: ', diff)
print('product: ', product)
print('division: ', div)
print('remainder: ', remainder)

mass = 75
gravity = 9.81
# Calcualte the wieght of the object on planet earth
weight = mass * gravity

print(weight, 'N')

Additon:  3
Substraction:  1
Multiplication:  6
Division:  2.0
Division:  3.0
Division:  3.5
Division without the remainder:  3
Modulous:  1
Division without the remainder:  2
Exponentation:  9
Floating Number 3.14
Complex number:  (1+1j)
Multiplying complext number:  (2+0j)
== Additon, Subtraction, Multipliation, Division, Modules ==
sum:  7
difference:  1
product:  12
division:  1.0
remainder:  1
735.75 N

Exerciese: Numbers

1. Declare your age as integer variable
2. Declare your height as a float variable
3. Declare a complex number variable
4. Calcaulate an area of a triangle (area = 0.5 x b x h)
5. Calaculate the primeter of triangle (p = a + b + c)
6. Calaculate the of area rectangle (area = lenght x width)
7. Calaculate the primeter of rectangle (p = 2 x (length + width))
8. Calculate the area of a circle (area = 3.14 x r x r)
9. Calculate the circumference of a circle(c = 2 x pi x r)
10. Calculate the value of y (y = x2 + 6x + 9). Try to use different x value and figure out at what x value y is 0.

Strings

String is data type. Any data under single or double quot are a string. There are diferent string methods to deal with string data types. To check the length of a string use the len() method.

# Assigning variables to string value

first_name = "Asabeneh"
space = ' ' # an empty space string
last_name = "Yetayeh"
country = "Helsinki"
full_name = ' Asabeneh Yetayeh'

print(first_name)
print(len(first_name))
print(last_name)
print(len(last_name))
print(country)
print(space)  # You don't see the printed empty space
print(full_name) # there is indent because of the trailing space in the full name is string

Asabeneh
8
Yetayeh
7
Helsinki

 Asabeneh Yetayeh

String Concatination

Merging two or more strings together is called concatination

# String concatination
first_name = "Asabeneh"
space = ' ' # an empty space string
last_name = "Yetayeh"
country = "Finland"
city = 'Helsinki'
full_name = first_name  + space + last_name
git_repo= 'Python ' + ' for ' + ' Everyone'
person_info = 'I am ' + full_name + '. I live in ' + country + ', '+ city + '.'
print('Full name: ', full_name)
print('Person Information:', person_info)
print('Git repository: ', git_repo)

Full name:  Asabeneh Yetayeh
Person Information: I am Asabeneh Yetayeh. I live in Finland, Helsinki.
Git repository:  Python  for  Everyone

String transformations

• s.lower(): Change all letters to lowercase
• s.upper(): Change all letters to uppercase
• s.capitalize(): Change all letters to capitalcase
• s.title(): Change to titlecase
• s.swapcase(): Change all uppercase letters to lowercase, and vice versa

firstName = 'Asabeneh'
lastName = 'Yetayeh'
space = ' '
full_Name = first_name + space + last_name;

# Changing string to lower case
print('=== change to lower case ===')
print(first_name.lower())
print(last_name.lower())
print(full_Name.lower())

# Changing string to upper case
print('=== change to upper case ===')
print(first_name.upper())
print(lastName.upper())
print(full_Name.upper())


# Changing string to capitalize
print('=== change to capitalize ===')
print(first_name.capitalize())
print(lastName.capitalize())
print(full_Name.capitalize())

# Changing string to capitalize

print('=== change to title ===')
title = 'python for everyone'
sub_title = 'learning programming using python'
lang = 'python'
level = 'both begineer and advanced learners'
print(title.title())
print(sub_title.title())
print(lang.title())
print(level.title())

# Changing string to swapcase()
print('=== swapping cases ===')

first_name = 'ASABEEH'
last_name = 'yetayeh'
space = ' '
full_name = first_name + space + last_name;

print(first_name.swapcase())
print(last_name.swapcase())
print(full_name.swapcase())

=== change to lower case ===
asabeneh
yetayeh
asabeneh yetayeh
=== change to upper case ===
ASABENEH
YETAYEH
ASABENEH YETAYEH
=== change to capitalize ===
Asabeneh
Yetayeh
Asabeneh yetayeh
=== change to title ===
Python For Everyone
Learning Programming Using Python
Python
Both Begineer And Advanced Learners
=== swapping cases ===
asabeeh
YETAYEH
asabeeh YETAYEH

Spliting string

Empty space is the default paramter.

• s.split(): Change the string to a list containing the string
• s.split(' '):Change the string to a list containing the words of in the string
• s.split(','): split the words at the comma

# Spliting a stirng to list
first_name = 'Asabeneh'
last_name = 'Yetayeh'
full_name = 'Asabeneh Yetayeh'

programming_lang = 'python, R, matlab, and Java'


# Changing a string to list
print(first_name.split())
print(list(first_name))
print(full_name.split(' '))
print(programming_lang.split(','))


# To check the length of string
print(len(first_name))

# To check the data type of the string
print(type(first_name))

['Asabeneh']
['A', 's', 'a', 'b', 'e', 'n', 'e', 'h']
['Asabeneh', 'Yetayeh']
['python', ' R', ' matlab', ' and Java']
8
<class 'str'>

String Formatting

Exercises- Strings

1. Concatinate the string 'Python', 'For','Everyone' to a single string, 'Python for Everyone'
2. Concatinate the string 'Coding', 'For' , 'All' to a single string, 'Coding For All'
3. Declare a variable name company and assign it to an initial value "Coding For All".
4. Print company using print()
5. Print the length of the company string using len() method and print()
6. Change all the string to capital letters using upper() method
7. Change all the string to lowercase letters using lower() method
8. Use capitalize(),title(), swapcase() methods to format the value stored in the varaible name company
9. Cut(slice) out the first word of the company string
10. Check if the string contains a word Coding using the method index, find or other methods.
11. Replace the word coding in the string 'Coding For All' to Python using replace or other methods.
12. Change Python for Everyone to Python for All using the replace method or other methods
13. Split the string 'Coding For All' at the space using split() method
14. "Facebook, Google, Microsoft, Apple, IBM, Oracle, Amazon" split the string at the comma
15. What is character at index 10 in "Coding For All"
16. Create an acronym or an abbrivaton for the name 'Python For Everyone'
17. Create an acronym or an abbrivaton for the name 'Coding For All'
18. Use index to determine the position of the first occurrence of C in Coding For All
19. Use index to determine the position of the first occurrence of F in Coding For All
20. Use rfind to determine the position of the last occurrence of l in Coding For All People.
21. Use index or find to find the position of the first occurrence of the word because in the following sentence:'You cannot end a sentence with because because because is a conjunction'
22. Use rindex to find the position of the last occurrence of the word because in the following sentence:'You cannot end a sentence with because because because is a conjunction'
23. Slice out the phase because because because in the following sentence:'You cannot end a sentence with because because because is a conjunction'
24. Use search to find the position of the first occurrence of the word because in the following sentence:'You cannot end a sentence with because because because is a conjunction'
25. Slice out the phase because because because in the following sentence:'You cannot end a sentence with because because because is a conjunction'
26. Use trim() to remove if there is trailing whitespace at the beginning and the end of a string.E.g " Coding For All ".
27. Use startswith() method with the string Coding For All make the result true
28. Use endswith() method with the string Python for Everyone make the result true

Booleans

A boolean value is either True or False.

• Comparison operatiors: >, >=, <, <=, ==, !=
• Logical Operators or, and, !

Data Type Conversion

num_str = '10'
print(num_str)
print(type(num_str))

num_int = int(num)
print(num_int)
print(type(num_int))

num_float = float(num_str)
print(num_float)

e = 2.71

10
<class 'str'>
10
<class 'int'>
10.0

CONDITIONALS

# Comparing something give either a True or False
print('True == True: ', True == True)
print('True == False: ', True == False)
print('False == False:', False == False)
print('True and True: ', True and True)
print('True or False:', True or False)
print('a in an:', 'a' in 'an')
print('4 is 2 ** 2:', 4 is 2 **2)

print(3 > 2)
print(3 >= 2)
print(3 != 2)
print(3 < 2)
print(3 <= 2)
print(3==2)
print(3!='3')
print(3==3)

True == True:  True
True == False:  False
False == False: True
True and True:  True
True or False: True
a in an: True
4 is 2 ** 2: True
True
True
True
False
False
False
True
True

num = 10
if num > 5:
    print('Number is greater than 5')
# The is conditon does't get exected so we need else
if num < 5:
    print('Number is less than 5')

if num < 5:
    print('Number is less than 5')
else:
    print('Number is greater than 5')

num = 5
if num < 5:
    print('Number is less than 5')
elif num == 5:
    print('Number is 5')
else:
    print('Number is greater than 5')

Number is greater than 5
Number is greater than 5
Number is 5

Exercises: Conditional

1. Get user input using input(“Enter your age:”). If user is 18 or older , give feedback:You are old enough to drive but if not 18 give feedback to wait for the years he supposed to wait for.

   Enter your age: 30
   You are old enough to drive.
   

   Enter your age:15
   You are left with 3 years to drive.
   

2. Compare the values of myAge and yourAge using if … else. Based on the comparison log to console who is older (me or you). Use prompt(“Enter your age:”) to get the age as input.

   Enter your age: 30
   You are 5 years older than me.
   

3. If a is greater than b return a is greater than b else a is less than b. Output: sh let a = 4; let b = 3; 4 is greater than 3

4. Write a code which give grade students according to theirs scores:

   • 80-100, A
   • 70-89, B
   • 60-69, C
   • 50-59, D
   • 0 -49, F

5. Check if the season is Autumn, Winter, Spring or Summer. If the user input is:

   • September, October or November, the season is Autumn.
   • December, January or February, the season is Winter.
   • March, April or May, the season is Spring
   • June, July or August, the season is Summer

LOOPS

For loops:

print('=== Whole Numbers===')
for x in range(11):
    print(x)

print('=== Even Numbers===')
# the range(initial, stop, step)
for n in range(0, 11, 2):
    print(n)

print('=== Odd Numbers===')
# the range(initial, stop, step)
for n in range(1, 11, 2):
    print(n)

=== Whole Numbers===
0
1
2
3
4
5
6
7
8
9
10
=== Even Numbers===
0
2
4
6
8
10
=== Odd Numbers===
1
3
5
7
9

Continue and break in for loop

for x in range(10):
    if (x is 1):
        continue
    if (x > 5):
        break
    print(x)

0
2
3
4
5

While Loop

x = 0
while (x < 10):
    print(x)
    x += 1

0
1
2
3
4
5
6
7
8
9

Exercises

1. Iterate 0 to 10 using for loop, do the same using while and do while loop.
2. Iterate 10 to 0 using for loop, do the same using while and do while loop.
3. Write a loop that makes seven calls to print() to output the following triangle:

       #
       ##
       ###
       ####
       #####
       ######
       #######
   

4. Use nested loops to create the following:

    # # # # # # # #
    # # # # # # # #
    # # # # # # # #
    # # # # # # # #
    # # # # # # # #
    # # # # # # # #
    # # # # # # # #
    # # # # # # # #

1. Iterate the list, ['Python', 'Numpy','Pandas','Data Science', AI','ML'] using a for loop and print out the items.
2. Use for loop to iterate from 0 to 100 and print only even numbers
3. Use for loop to iterate from 0 to 100 and print only odd numbers
4. Use for loop to iterate from 0 to 100 and print and print the sum of all numbers.

    # The sum of all numbers is 5050.
   

5. Use for loop to iterate from 0 to 100 and print the sum of all evens and the sum of all odds.

   # The sum of all evens is 2550. And the sum of all odds is 2500.
   

Lists

Lists are like arrays in JavaScript. They store different elements unlike other varialbles. List has different methods to modify and manipulate the list. Some of the methods which are used to modiyf lists append, insert, extend.

Creating an empty list or list containing values

# Creating lists
lst = [] # empty list
print(list)
lst = list() # empty list
print(lst)
numbers = [1, 2, 3, 4, 5, 6] # creating list 1 to 6
print(numbers)
one_to_hunderd = list(range(11)) # creating list 1 to 10
even_numbers = list(range(0,51, 2)) # Create even number lists 0 to 50
print(one_to_hunderd)
print(even_numbers)

names = ['Asabeneh','Brook','Sami','David']
print(names)
it_companies = ['Google','Facebook','Nokia','Apple','Oracle','Amazon','IBM']
print(it_companies)
constants = [3.14, 9.81, 98.6, 100]
print(constants)

<class 'list'>
[]
[1, 2, 3, 4, 5, 6]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]
['Asabeneh', 'Brook', 'Sami', 'David']
['Google', 'Facebook', 'Nokia', 'Apple', 'Oracle', 'Amazon', 'IBM']
[3.14, 9.81, 98.6, 100]

for number in numbers:
    print(number)
    if (number % 2 == 0):
        print("is even")
    else:
        print("is odd")

print ("All done.")

1
is odd
2
is even
3
is odd
4
is even
5
is odd
6
is even
All done.

Appending value to lists

Using different methods to manipulate lists. Slicing using :, appending, extending and inserting:

# First lets create a list
x = [1, 2, 3, 4, 5, 6]
print(len(x)) # to check the length of the list

6

x.append(8)
x.append(9)
x

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

Inserting value among values in a list

Inserting a value at a certain index

x.insert(6,7) # the missing value in the list which is 7 is inserted
x

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

Use extend to two or more arrays together

x.extend([9,10])
x

[1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 10]

Slicing

x[:3]

[1, 2, 3]

x[3:]

[4, 5, 6, 7, 8, 9, 9, 10]

x[-2:]

[9, 10]

Concating lists (Merging)

a = [1, 2,3]
b = [4, 5,6]
c = a + b
print(c)

[1, 2, 3, 4, 5, 6]

List of lists

x = [1, 2,3]
y = [4, 5,6];
z = [x, y]
print(z)

[[1, 2, 3], [4, 5, 6]]

print(x[0])
print(z[0])
print(z[0][0])
print(z[1])
print(z[1][0])

1
[1, 2, 3]
1
[4, 5, 6]
4

z = [3, 2, 1]
z.sort()
z

[1, 2, 3]

z.sort(reverse=True)
z

[3, 2, 1]

Sort vs Sorted in list

nums_one = [3,1,4,2,5]
nums_sort =nums_one.sort() # mutate the original list and return None
print(nums_one)
print(nums_sort) # return None
nums_two = [3,1,4,2,5]
nums_sorted = sorted(nums_two) # Do not mutate the original list
print(nums_two)
print(nums_sorted)

[1, 2, 3, 4, 5]
None
[3, 1, 4, 2, 5]
[1, 2, 3, 4, 5]

Getting index of a list using enumerate

even_nums = [2,4,6,8,10]
for i, n in enumerate(even_nums):
    print('index:',i, n)

index: 0 2
index: 1 4
index: 2 6
index: 3 8
index: 4 10

for i, n in enumerate(range (10)):
    print('index:',i, n)

index: 0 0
index: 1 1
index: 2 2
index: 3 3
index: 4 4
index: 5 5
index: 6 6
index: 7 7
index: 8 8
index: 9 9

(name, age) = ['Asabeneh', 250]

print(name, age)

Asabeneh 250

List Comprehension

[i  for i in range (11)] # list of whole numbers

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

[i * i for i in range (11)] # List of squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

even_nums = [i for i in range(51) if i % 2 == 0] # List of even numbers
print(even_nums)

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]

odds = [i for i in range(51) if i % 2 != 0] # List of odd numbers
print(odds)

[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49]

numbers = [1, 2, 3,4,5]
[n * n for n in numbers]  # maping using list comprehension

[1, 4, 9, 16, 25]

names = ['Asabeneh','Brook','David','Martha']
[name.upper () for name in names] # Mapping using list comprehension

['ASABENEH', 'BROOK', 'DAVID', 'MARTHA']

numbers = [-4, -3, -2, -1, 0, 2, 4, 6]
[n for n in numbers if n > 0] # Filtering using list comprehension

[2, 4, 6]

Exercises

1. Declare an empty list;
2. Declare a list with more than 5 number of items
3. Find the length of your list
4. Get the first item, the middle item and the last item of the list
5. Declare a list called mixed_data_types,put different data types and in your array and the array size should be greater than 5
6. Declare a list variable name it_companies and assign initial values Facebook, Google, Microsoft, Apple, IBM, Oracle and Amazon.
7. Print the list using print()
8. Print the number of companies in the list
9. Print the first, middle and last company
10. Print out each company
11. Change companies to uppercase and print them out
12. Print the list like as a sentence: Facebook, Google, Microsoft, Apple, IBM,Oracle and Amazon are big IT companies.
13. Check if a certain company exists in the itcompanies list. If it exist return the company else return a company is _not found.
14. Filter out companies which have more than one 'o' without the filter method
15. Sort the array using sort() method
16. Reverse the array without method
17. Reverse the array using method
18. Slice out the first 3 companies from the list
19. Slice out the last 3 companies from the list
20. Slice out the middle IT company or companies from the list
21. Remove the first IT company from the array
22. Remove the middle IT company or companies from the list
23. Remove the last IT company from the list
24. Remove all IT companies

Tuples

Creating Tuples

#Tuples are just immutable lists. Use () instead of []
tp = tuple()
print(type (tp))
tp = ()
print(type(tp))
nums = (1, 2, 3)
print(nums)
len(nums)

<class 'tuple'>
<class 'tuple'>
(1, 2, 3)





3

for n in nums:
    print(n)

1
2
3

x = (1, 2, 3)
y = (4, 5, 6)
print(x[0])
y[0]

1





4

list_of_tuples = [x, y]
list_of_tuples

[(1, 2, 3), (4, 5, 6)]

(age, income) = "32,120000".split(',')
print(age)
print(income)

32
120000

numbers = [1, 2, 3,4,5]
[(n,n * n) for n in numbers]  # maping using list comprehension

[(1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]

names = ['Asabeneh','Brook','David','Martha']
[(name.upper (), len(name)) for name in names] # Mapping using list comprehension

[('ASABENEH', 8), ('BROOK', 5), ('DAVID', 5), ('MARTHA', 6)]

Dictionaries

# Creating empty dictionary

dic = dict()
dic = {}

# Another more dictionary
person = {
    'name':'Asabenh',
    'age':250,
    'country':'Finland',
    'city':'Helsinki',
    'job':'instructor and developer',
    'skills':['HTML','CSS','JavaScript','Python','Node'],
    'is_married':True
}
print(person)
print(len(person))

{'name': 'Asabenh', 'age': 250, 'country': 'Finland', 'city': 'Helsinki', 'job': 'instructor and developer', 'skills': ['HTML', 'CSS', 'JavaScript', 'Python', 'Node'], 'is_married': True}
7

print(person)

{'name': 'Asabenh', 'age': 250, 'country': 'Finland', 'city': 'Helsinki', 'job': 'instructor and developer', 'skills': ['HTML', 'CSS', 'JavaScript', 'Python', 'Node'], 'is_married': True}

print(person["name"])

Asabenh

print(person["age"])

250

print(person.get("is_married"))

True

for key in person:
        print(key + ": " + str(person[key]))

name: Asabenh
age: 250
country: Finland
city: Helsinki
job: instructor and developer
skills: ['HTML', 'CSS', 'JavaScript', 'Python', 'Node']
is_married: True

Dictionary Methods

keys, values, items

keys = person.keys()
values = person.values()
items = person.items()

print(keys)
print(values)
print(items)

dict_keys(['name', 'age', 'country', 'city', 'job', 'skills', 'is_married'])
dict_values(['Asabenh', 250, 'Finland', 'Helsinki', 'instructor and developer', ['HTML', 'CSS', 'JavaScript', 'Python', 'Node'], True])
dict_items([('name', 'Asabenh'), ('age', 250), ('country', 'Finland'), ('city', 'Helsinki'), ('job', 'instructor and developer'), ('skills', ['HTML', 'CSS', 'JavaScript', 'Python', 'Node']), ('is_married', True)])

for k, v in items:
    print(k,':', v)

name : Asabenh
age : 250
country : Finland
city : Helsinki
job : instructor and developer
skills : ['HTML', 'CSS', 'JavaScript', 'Python', 'Node']
is_married : True

numbers = [1, 2, 3,4,5]
[{n:n * n} for n in numbers]  # maping using list comprehension

[{1: 1}, {2: 4}, {3: 9}, {4: 16}, {5: 25}]

names = ['Asabeneh','Brook','David','Martha']
[{name:len(name)} for name in names] # Mapping using list comprehension

[{'Asabeneh': 8}, {'Brook': 5}, {'David': 5}, {'Martha': 6}]

Unpacking lists, tupples

Unpacking tuples

one, two, three = (1, 2,3)
print(one)
print(two)
print(three)

first_name, last_name, country = ('Asabeneh','Yetayeh','Finland')
print(first_name)
print(last_name)
print(country)

1
2
3
Asabeneh
Yetayeh
Finland

Unpacking lists

# First Example about unpacking list
names = ['Asabeneh','Eyob','David','Lidiya', 'Woyneshet','Yetayeh']
first_person, second_person, *rest = names
print(first_person)
print(second_person)
print(rest)

# Second Example about unpacking list
first, second, third,*rest, tenth = [1,2,3,4,5,6,7,8,9,10]
print(first)
print(second)
print(third)
print(rest)
print(tenth)

# Third Example about unpacking list
countries = ['Germany', 'France','Belgium','Sweden','Denmark','Finland','Norway','Iceland','Estonia']
gr, fr, bg, sw, *scandic, es = countries
print(gr)
print(fr)
print(bg)
print(sw)
print(scandic)
print(es)

Asabeneh
Eyob
['David', 'Lidiya', 'Woyneshet', 'Yetayeh']
1
2
3
[4, 5, 6, 7, 8, 9]
10
Germany
France
Belgium
Sweden
['Denmark', 'Finland', 'Norway', 'Iceland']
Estonia

SET

Creating a set

s = set()
s.add(1) # adding value to a set
s.add(2)
s.add(3)
s.add(4)
s.add(5)
print(s)
print(len(s))

nums = [1, 2,2,3,4,5,4,6,7,6]
numbers = set(nums)
print(numbers)
print(len(numbers))
for n in numbers:
    print(n)

{1, 2, 3, 4, 5}
5
{1, 2, 3, 4, 5, 6, 7}
7
1
2
3
4
5
6
7

FUNCTIONS

Function without parameter

def generate_full_name ():
    first_name = 'Asabeneh'
    last_name = 'Yetayeh'
    space = ' '
    full_name = first_name + space + last_name
    print(full_name)

generate_full_name () # calling a function

def add_two_numbers ():
    num_one = 2
    num_two = 3
    total = num_one + num_two
    print(total)
add_two_numbers() # call the functionK

# Function can also return values, if a function doen't return values the value of the function is None
# Lets rewrite the above functions using return
# From now on, we return value to a function instead of priting it

def generate_full_name ():
    first_name = 'Asabeneh'
    last_name = 'Yetayeh'
    space = ' '
    full_name = first_name + space + last_name
    return full_name
print(generate_full_name())

def add_two_numbers ():
    num_one = 2
    num_two = 3
    total = num_one + num_two
    return total
print(add_two_numbers())

Asabeneh Yetayeh
5
Asabeneh Yetayeh
5

Function with one parameter

def greetings (name):
    message = name + ', welcome to Python for Everyone!'
    return message

print(greetings('Asabeneh'))

def add_ten(num):
    ten = 10
    return num + ten

print(add_ten(90))


def square_number(x):
    return x * x

print(square_number(2))

def area_of_circle (r):
    PI = 3.14
    area = PI * r ** 2
    return area

print(area_of_circle(10))

Asabeneh, welcome to Python for Everyone!
100
4
314.0

Function with two parameter

def generate_full_name (first_name, last_name):
    space = ' '
    full_name = first_name + space + last_name
    return full_name
print('Full Name: ', generate_full_name('Asabeneh','Yetayeh'))

def sum_two_numbers (num_one, num_two):
    sum = num_one + num_two
    return sum
print('Sum of two numbers: ', sum_two_numbers(1, 9))

def calculate_age (current_year, birth_year):
    age = current_year - birth_year
    return age;

print('Age: ', calculate_age(2019, 1819))

def weight_of_object (mass, gravity):
    weight = str(mass * gravity)+ ' N' # the value has to be changed to string first
    return weight
print('Weight of an object in Newton: ', weight_of_object(100, 9.81))

Full Name:  Asabeneh Yetayeh
Sum of two numbers:  10
Age:  200
Weight of an object in Newton:  981.0 N

Function with arbitrary number of paramters

def sum_of_numbers(*args):
    s = 0;
    for i in args:
        s = s + i
    return s
print(sum_of_numbers(1,2,3))
print(sum_of_numbers(1,2,3, 4,5,6,7,8,9,10))

6
55

Function with default parameter

def greetings (name = 'Anonymous'):
    message = name + ', welcome to Python for Everyone!'
    return message

print(greetings())
print(greetings('Asabeneh'))

def generate_full_name (first_name = 'Asabeneh', last_name = 'Yetayeh'):
    space = ' '
    full_name = first_name + space + last_name
    return full_name



print(generate_full_name())
print(generate_full_name('David','Smith'))

def calculate_age (birth_year,current_year = 2019):
    age = current_year - birth_year
    return age;
print('Age: ', calculate_age(1819))


def weight_of_object (mass, gravity = 9.81):
    weight = str(mass * gravity)+ ' N' # the value has to be changed to string first
    return weight
print('Weight of an object in Newton: ', weight_of_object(100)) # 9.81 gravity at the surface of Earth
print('Weight of an object in Newton: ', weight_of_object(100, 1.62)) # gravit at surface of Moon

Anonymous, welcome to Python for Everyone!
Asabeneh, welcome to Python for Everyone!
Asabeneh Yetayeh
David Smith
Age:  200
Weight of an object in Newton:  981.0 N
Weight of an object in Newton:  162.0 N

Function with default parameter and aribtrary number of parameters

def generate_groups (team,*args):
    print(team)
    for i in args:
        print(i)
generate_groups('Team-1','Asabeneh','Brook','David','Eyob')

Team-1
Asabeneh
Brook
David
Eyob

Function as parameter of other function

#You can pass functions around as parameters
def square_number (n):
    return n * n
def do_something(f, x):
    return f(x)

print(do_something(square_number, 3))

9

Lamda Function

Lamda function is similary to annonymous function in JavaScript. When we do not like to reuse a function we can make a lambda function. See the example below.

#Lambda functions let you inline simple functions
print(do_something(lambda x: x * x * x, 3))

27

Exercises

1. Declare a function full_name and it print out your full name.

2. Declare a function full_name and now it takes firstName, lastName as a parameter and it returns your full - name.

3. Declare a function add_two_numbers and it takes two two parameters and it returns sum.

4. An area of a rectangle is calculated as follows: area = lenght x width. Write a function which calculates area_of_rectangle.

5. A perimeter of a rectangle is calculated as follows: perimeter= 2x(lenght + width). Write a function which calculates perimeter_of_rectangle.

6. A volume of a rectangular prism is calculated as follows: volume = lenght x width x height. Write a function which calculates volume_of_rect_prism.

7. Area of a circle is calculated as follows: area = π x r x r. Write a function which calculates area_of_circle

8. Circumference of a circle is calculated as follows: circumference = 2πr. Write a function which calculates _circum_of_circle

9. Density of a substance is calculated as follows:density= mass/volume. Write a function which calculates density.

10. Speed is calculated by dividing the total distance covered by a moving object divided by the total amount of time taken. Write a fucntion which calculates a speed of a moving object, speed.

11. Weight of a substance is calculated as follows: weight = mass x gravity. Write a function which calculates weight.

12. Temperature in oC can be converted to oF using this formula: oF = (oC x 9/5) + 32. Write a function which converst oC to oF convert_celcius_to_fahrenheit.

13. Body mass index(BMI) is calculated as follows: bmi = weight in Kg / (height x height) in m2. Write a function which calculates bmi. BMI is used to broadly define different weight groups in adults 20 years old or older.Check if a person is underweight, normal, overweight or obsese based the information given below.

    • The same groups apply to both men and women.
    • Underweight: BMI is less than 18.5
    • Normal weight: BMI is 18.5 to 24.9
    • Overweight: BMI is 25 to 29.9
    • Obese: BMI is 30 or more

14. Write a function called check-season, it takes a month parameter and returns the season:Autumn, Winter, Spring or Summer.

15. Linear equation is calculated as follows: ax + b = c. Write a function which calculates value of a linear equation, solve_linear_equation.

16. Quadratic equation is calculated as follows: ax2 + bx + c = 0. Write a function which calculates value or values of a quadratic equation, solve_quadratic_equation.

17. Declare a function name _print_list. It takes list as a parameter and it prints out each element of the list.

18. Declare a functin name swap_values. This function swaps value of x to y.

        swap_values(3, 4) # x => 4, y=>3
        swap_values(4, 5) # x = 5, y = 4
    

19. Declare a function name reverse_list. It takes array as a parameter and it returns the reverse of the array (dont’ use method).

        pirnt(reverseArray([1, 2, 3, 4, 5]))
        # [5, 4, 3, 2, 1]
        print(reverseArray(["A", "B", "C"]))
        # ["C", "B", "A"]
    

20. Declare a function name capitalize_list_items. It takes list as a parameter and it returns the capitalized list of the elements

21. Declare a function name _add_item. It takess an item parameter and it returns a list after adding the element

22. Declare a function name remove_tem. It takes an index parameter and it returns a list after removing an element

23. Declare a function name sum_of_numbers. It takes a number parameter and it adds all the numbers in that range.

24. Declare a function name sum_of_odds. It takes a number parameter and it adds all the odd numbers in that - range.

25. Declare a function name sum_of_even. It takes a number parameter and it adds all the even numbers in that - range.

26. Declare a function name evens_and_odds . It takes a positive integer as parameter and it counts number of evens and odds in the number.

        print(evens_and_odds(100))
        # The number of odds are 50.
        # The number of evens are 51.
    

27. Write a funcition which takes any number of arguments and return the sum of the arguments

    sum_all_numbers(1, 2, 3) // -> 6
    sum_all_numbers(1, 2, 3, 4) // -> 10
    

28. Writ a function which generates a random_user_id.

29. Write a function which generates a random_MAC_address

30. Declare a function name random_hexa_gen. When this function is called it generates a random hexadecimal number. The function return the hexadecimal number.

        print(random_hexa_gen());
       # '#ee33df'
    

31. Declare a function name user_id_gen. When this function is called it generates seven character id. The function return the id.

        print(user_id_gen());
        # 41XTDbE
    

32. Modify question number above . Declare a function name user_id_gen_by_user. It doesn’t take any parameter but it takes two inputs using input(). One of the input is the number of characters and the second input is the number of ids which are supposed to be generated.

    user_id_gen_by_user()
    "kcsy2
    SMFYb
    bWmeq
    ZXOYh
    2Rgxf
    "
     user_id_gen_by_user()
    "1GCSgPLMaBAVQZ26
    YD7eFwNQKNs7qXaT
    ycArC5yrRupyG00S
    UbGxOFI7UXSWAyKN
    dIV0SSUTgAdKwStr
    "
    

33. Write a function name rgb_color_gen and it generates rgb colors.

        print(rgb_color_gen())
        # rgb(125,244,255)
    

34. Write a function list_of_hexa_colors which return any number of hexadecimal colors in an array.

35. Write a function list_of_rgb_colors which return any number of RGB colors in an array.

36. Write a function convert_hexa_to_rgb which converts hexa color to rgb and it returns an rgb color.

37. Write a function *convert_rgb_to_hexa which converts rgb to hexa color and it returns an hexa color.

38. Write a function generate_colors which can generate any number of hexa or rgb colors.

          generate_colors('hexa', 3)
          # ['#a3e12f','#03ed55','#eb3d2b']
          generate_colors('hexa', 1)
          # '#b334ef'
    
          generate_colors('rgb', 3)
          # ['rgb(5, 55, 175','rgb(50, 105, 100','rgb(15, 26, 80']
          generate_colors('rgb', 1)
          # 'rgb(33,79, 176)'
    
    

39. Call your function _shuffle_list, it takes a list as a parameter and it returns a shuffled list

40. Call your function factorial, it takes a whole number as a parameter and it return a factorial of the number

41. Call your function is_empty, it takes a parameter and it checks if it is empty or not

42. Write a function called sum_of_list_items, it takes a list parameter and return the sum of all the items. Check if all the list items are number types. If not give return reasonable feedback.

43. Write a function called average, it takes an array parameter and returns the average of the items. Check if all the array items are number types. If not give return reasonable feedback.

44. Write a function called modify_list takes list as parameter and modifies the fifth element of the list and return the list. If the list length is less than five it return 'item not found'.

        print(modifyArray(["Avocado", "Tomato", "Potato","Mango", "Lemon","Carrot"]);
        # →["Avocado", "Tomato", "Potato","Mango", "LEMON", "Carrot"]
        print(modifyArray(["Google", "Facebook","Apple", "Amazon","Microsoft",  "IBM"]);
        # →["Google", "Facebook","Apple", "Amazon","MICROSOFT",  "IBM"]
        print(modifyArray(["Google", "Facebook","Apple", "Amazon"]);
        # →"Not Found"
    

45. Write a function called is_prime, which checks if a number is prime number.

46. Write a functions which checks if all items are unique in the array.

47. Write a function which checks if all the itmes of the array are the same data type.

48. JavaScript variable name does not support special characters or symbols execpt $ or _. Write a function *is_valid_variable which check if a variable is valid or invlaid variable.

49. Write a function which returns array of seven random numbers in a range of 0-9. All the numbers must be unique.

CLASSES

Classes and Objects

Python is an object oriented programming language. Everything in Python is an object, with its properties and methods. A number, string, list, dictionary,tuple, set etc. used in a program is an object of a corresponding built-in class. We create class to create an object. A Class is like an object constructor, or a "blueprint" for creating objects. We instantiate a class to create an object. The class defines attributes and the behavior of the object, while the object, on the other hand, represents the class.

We have been working with classes and objects right from the beginning of these challenge unknowingly. Every element in a Python program is an object of a class. Let's check if everything in python is class:

Last login: Tue Dec 10 09:35:28 on console
asabeneh@Asabeneh:~$ pyhton
-bash: pyhton: command not found
asabeneh@Asabeneh:~$ python
Python 3.7.5 (default, Nov  1 2019, 02:16:32)
[Clang 11.0.0 (clang-1100.0.33.8)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> num = 10
>>> type(num)
<class 'int'>
>>> string = 'string'
>>> type(string)
<class 'str'>
>>> boolean = True
>>> type(boolean)
<class 'bool'>
>>> lst = []
>>> type(lst)
<class 'list'>
>>> tpl = ()
>>> type(tpl)
<class 'tuple'>
>>> set1 = set()
>>> type(set1)
<class 'set'>
>>> dct = {}
>>> type(dct)
<class 'dict'>

Creating a Class

To create a class we need the key word class followed by colon. Class name should be CamelCase.

# syntax
class ClassName:
  code goes here

Example:

class Person:
  pass

<__main__.Person object at 0x10804e510>

Creating an Object

We can create an object by calling the class.

p = Person()
print(p)

Class Constructor

In the above examples, we have created an object from the Person class. However, Class without a constructor is not really useful in real applications. Let's use constructor function to make our class more useful. Like the constructor function in Java or JavaScript, python has also a builtin init() constructor function. The init constructor function has self parameter which is a reference to the current instance of the class Examples:

class Person:
      def __init__ (self, name):
          self.name =name

p = Person('Asabeneh')
print(p.name)
print(p)

# output
Asabeneh

Let's add more parameter to the constructor function.

class Person:
      def __init__(self, firstname, lastname, age, country, city):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city


p = Person('Asabeneh', 'Yetayeh', 250, 'Finland', 'Helsinki')
print(p.firstname)
print(p.lastname)
print(p.age)
print(p.country)
print(p.city)

# output
Asabeneh
Yetayeh
250
Finland
Helsinki

Object Methods

Objects can have methods. The methods are functions which are belongs to the object. Example:

class Person:
      def __init__(self, firstname, lastname, age, country, city):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city

      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} year old. He lives in {self.city}, {self.country}'


p = Person('Asabeneh', 'Yetayeh', 250, 'Finland', 'Helsinki')
print(p.person_info())

# output
Asabeneh Yetayeh is 250 year old. He lives in Helsinki, Finland

Object default methods

Sometimes, you may want to have a default values for you object methods. If we give a default values for the parameters in the constructor, we can avoid error when we call or instantiate our class without parameters. Let's see how it looks using example.

Example:

class Person:
      def __init__(self, firstname='Asabeneh', lastname='Yetayeh', age=250, country='Finland', city='Helsinki'):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city

      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} year old. He lives in {self.city}, {self.country}.'

p1 = Person()
print(p1.person_info())
p2 = Person('John', 'Doe', 30, 'Nomanland', 'Noman city')
print(p2.person_info())

# output
Asabeneh Yetayeh is 250 year old. He lives in Helsinki, Finland.
John Doe is 30 year old. He lives in Noman city, Nomanland.

Method to modify class default values

In the example below, the person class, all the constructor parameters have default values and in addition to that we have a skills default value which we can access it using method. Let's create add_skill method to add skill to the skills list.

class Person:
      def __init__(self, firstname='Asabeneh', lastname='Yetayeh', age=250, country='Finland', city='Helsinki'):
          self.firstname = firstname
          self.lastname = lastname
          self.age = age
          self.country = country
          self.city = city
          self.skills = []

      def person_info(self):
        return f'{self.firstname} {self.lastname} is {self.age} year old. He lives in {self.city}, {self.country}.'
      def add_skill(self, skill):
          self.skills.append(skill)

p1 = Person()
print(p1.person_info())
p1.add_skill('HTML')
p1.add_skill('CSS')
p1.add_skill('JavaScript')
p2 = Person('John', 'Doe', 30, 'Nomanland', 'Noman city')
print(p2.person_info())
print(p1.skills)
print(p2.skills)

# output
Asabeneh Yetayeh is 250 year old. He lives in Helsinki, Finland.
John Doe is 30 year old. He lives in Noman city, Nomanland.
['HTML', 'CSS', 'JavaScript']
[]

Inheritance

Using inheritance we can reuse parent class code. Inheritance allows us to define a class that inherits all the methods and properties from another class. The parent class or super or base class is the class which gives all the methods and properties. Child class is the class the inherits from another class. Let's see create a student class by inheriting from person class.

class Student(Person):
    pass


s1 = Student('Eyob', 'Yetayeh', 30, 'Finland', 'Helsinki')
s2 = Student('Lidiya', 'Teklemariam', 28, 'Finland', 'Espoo')
print(s1.person_info())
s1.add_skill('JavaScript')
s1.add_skill('React')
s1.add_skill('Python')
print(s1.skills)

print(s2.person_info())
s2.add_skill('Organizing')
s2.add_skill('Marketing')
s2.add_skill('Digital Marketing')
print(s2.skills)

output
Eyob Yetayeh is 30 year old. He lives in Helsinki, Finland.
['JavaScript', 'React', 'Python']
Lidiya Teklemariam is 28 year old. He lives in Espoo, Finland.
['Organizing', 'Marketing', 'Digital Marketing']

We didn't call the init() constructor in the child class. If we didn't call it we can access all the properties but if we call it once we access the parent properties by calling super.
We can write add a new method to the child or we can overwrite the parent class by creating the same method name in the child class. When we add the init() function, the child class will no longer inherit the parent's init() function.

Overriding parent method

class Student(Person):
    def __init__ (self, firstname='Asabeneh', lastname='Yetayeh',age=250, country='Finland', city='Helsinki', gender='male'):
        self.gender = gender
        super().__init__(firstname, lastname,age, country, city)
    def person_info(self):
        gender = 'He' if self.gender =='male' else 'She'
        return f'{self.firstname} {self.lastname} is {self.age} year old. {gender} lives in {self.city}, {self.country}.'

s1 = Student('Eyob', 'Yetayeh', 30, 'Finland', 'Helsinki','male')
s2 = Student('Lidiya', 'Teklemariam', 28, 'Finland', 'Espoo', 'female')
print(s1.person_info())
s1.add_skill('JavaScript')
s1.add_skill('React')
s1.add_skill('Python')
print(s1.skills)

print(s2.person_info())
s2.add_skill('Organizing')
s2.add_skill('Marketing')
s2.add_skill('Digital Marketing')
print(s2.skills)

Eyob Yetayeh is 30 year old. He lives in Helsinki, Finland.
['JavaScript', 'React', 'Python']
Lidiya Teklemariam is 28 year old. She lives in Espoo, Finland.
['Organizing', 'Marketing', 'Digital Marketing']

💻 Exercises:

1. Python has the module called statistics and we can use this module to do all the statistical caluculations. Hower to challlenge ourselves, let's try to develop a program which calculate measure of central tendency of a sample(mean, median, mode) and measure of variability(range, variance, standard deviation). In addition to those measures find the min, max, count, percentile, and frequency distribution of the sample. You can create a class called Statistics and create all the functions which do statistical calculations as method for the Statistics class. Check the output below.

ages = [31, 26, 34, 37, 27, 26, 32, 32, 26, 27, 27, 24, 32, 33, 27, 25, 26, 38, 37, 31, 34, 24, 33, 29, 26]

print('Count:', data.count()) # 25
print('Sum: ', data.sum()) # 744
print('Min: ', data.min()) # 24
print('Max: ', data.max()) # 38
print('Range: ', data.range() # 14
print('Mean: ', data.mean()) # 30
print('Median: ',data.median()) # 29
print('Mode: ', data.mode()) # {'mode': 26, 'count': 5}
print('Variance: ',data.var()) # 17.5
print('Standard Deviation: ', data.std()) # 4.2
print('Variance: ',data.var()) # 17.5
print('Frequency Distribution: ',data.freq_dist()) # [(20.0, 26), (16.0, 27), (12.0, 32), (8.0, 37), (8.0, 34), (8.0, 33), (8.0, 31), (8.0, 24), (4.0, 38), (4.0, 29), (4.0, 25)]

# you output should look like this
print(data.describe())
Count: 25
Sum:  744
Min:  24
Max:  38
Range:  14
Mean:  30
Median:  29
Mode:  (26, 5)
Variance:  17.5
Standard Deviation:  4.2
Frequency Distribution: [(20.0, 26), (16.0, 27), (12.0, 32), (8.0, 37), (8.0, 34), (8.0, 33), (8.0, 31), (8.0, 24), (4.0, 38), (4.0, 29), (4.0, 25)]

1. Create a class called PersonAccount. It has firstname, lastname, incomes, expenses properties and it has total_income, total_expense, account_info,add_income, add_expense and account_balance methods. Incomes is a set of incomes and its description and the same goes for expenses.

REGULAR EXPRESSONS

Regular Expression

A regular expression or RegEx is a small programming language that helps to find pattern in data. A RegEx can be used to check if some pattern exists in a different data type. To use RegEx in python first we should import the RegEx module which is re.

Import re module

After importing the module we can use it to detect or find patterns.

import re

re functions

To find a pattern we use different set of re functions that allows to search a string for match.

• re.match():searches only in the beginning of the first line of the string and return match object if found, else return none.
• re.search:Returns a Match object if there is a match anywhere in the string including or in multiline string.
• re.findall:Returns a list containing all matches
• re.split: Returns a list where the string has been split at each match
• re.sub: Replaces one or many matches with a string

Match

# syntac
re.match(substring, string, re.I)
# substring is a string or a pattern, string is the text we look for a pattern , re.I is case ignore

txt = 'I love to teach python or javaScript'
# It return an object with span, and match
match = re.match('I love to teach', txt, re.I)
print(match)  # <re.Match object; span=(0, 15), match='I love to teach'>
# We can get the starting and ending position of the match as tuple using span
span = match.span()
print(span)     # (0, 15)
# Lets find the start and stop position from the span
start, end = span
print(start, end)  # 0, 15
substring = txt[start:end]
print(substring)       # I love to teach

As you can see from the above example, the pattern we are looking for or the substring I love to teach is the beginning of the text. The match function only returns an object if the text starts with the pattern.

Search

# syntax
re.match(substring, string, re.I)
# substring is a pattern, string is the text we look for a pattern , re.I is case ignore flag

txt = '''Python is the most beautiful language that a human begin has ever created.
I recommend python for a first programming language'''

# It return an object with span, and match
match = re.search('first', txt, re.I)
print(match)  # <re.Match object; span=(100, 105), match='first'>
# We can get the starting and ending position of the match as tuple using span
span = match.span()
print(span)     # (100, 105)
# Lets find the start and stop position from the span
start, end = span
print(start, end)  # 100 105
substring = txt[start:end]
print(substring)       # first

As you can see search is much better than match because it can look for the pattern through out the text. Search return returns a match object right way a first match found. A much better re function is findall. This function check the pattern through the string and returns all the matches as a list.

Searching all matches using findall

findall() returns all the matches as a list

txt = '''Python is the most beautiful language that a human begin has ever created.
I recommend python for a first programming language'''

# It return a list
matches = re.findall('language', txt, re.I)
print(matches)  # ['language', 'language']

As you can see, the word language found two times in the string. Let's practice more

Let's look for the word both Python and python in the string

txt = '''Python is the most beautiful language that a human begin has ever created.
I recommend python for a first programming language'''

# It returns list
matches = re.findall('python', txt, re.I)
print(matches)  # ['Python', 'python']

Since we are using re.I both lowercase and uppercase are included but if we don't have the flag, we write our pattern differently. Let's see that

txt = '''Python is the most beautiful language that a human begin has ever created.
I recommend python for a first programming language'''

matches = re.findall('Python|python', txt)
print(matches)  # ['Python', 'python']

#
matches = re.findall('[Pp]ython', txt)
print(matches)  # ['Python', 'python']

Replacing a substring

txt = '''Python is the most beautiful language that a human begin has ever created.
I recommend python for a first programming language'''

match_replaced = re.sub('Python|python', 'JavaScript', txt, re.I)
print(match_replaced)  # JavaScript is the most beautiful language that a human begin has ever created.
# OR
match_replaced = re.sub('[Pp]ython', 'JavaScript', txt, re.I)
print(match_replaced)  # JavaScript is the most beautiful language that a human begin has ever created.

Let's add one more example, the following string is really hard to read unless we remove the % symbol. Replacing the % with a empty string will clean the text.

txt = '''%I a%m te%%a%%che%r% a%n%d %% I l%o%ve te%ach%ing.
T%he%re i%s n%o%th%ing as m%ore r%ewarding a%s e%duc%at%i%ng a%n%d e%m%p%ow%er%ing p%e%o%ple.
I fo%und te%a%ching m%ore i%n%t%er%%es%ting t%h%an any other %jobs.
D%o%es thi%s m%ot%iv%a%te %y%o%u to b%e a t%e%a%cher.'''

matches = re.sub('%', '', txt)
print(matches)  # ['Python', 'python']

I am teacher and  I love teaching.
There is nothing as more rewarding as educating and empowering people.
I found teaching more interesting than any other jobs.
Does this motivate you to be a teacher.

Spliting text using RegEx split

txt = '''I am teacher and  I love teaching.
There is nothing as more rewarding as educating and empowering people.
I found teaching more interesting than any other jobs.
Does this motivate you to be a teacher.'''
print(re.split('\n', txt))

['I am teacher and  I love teaching.', 'There is nothing as more rewarding as educating and empowering people.', 'I found teaching more interesting than any other jobs.', 'Does this motivate you to be a teacher.']

Writing RegEx pattern

To declare a string variable we use a single or double quote. To declare RegEx variable r''. The following pattern only identifies apple with lowercase, to make it case insensitive either we should rewrite our pattern or we should add a flag.

regex_pattern = r'apple'
txt = 'Apple and banana are fruits. An old cliche says an apple a day a doctor way has been replaced by a banana a day keeps the doctor far far away. '
matches = re.findall(regex_pattern, txt)
print(matches)  # ['apple']

# To make case insensitive adding flag '
matches = re.findall(regex_pattern, txt, re.I)
print(matches)  # ['Apple', 'apple']
# or we can use set of characters method
regex_pattern = r'[Aa]pple'  # this mean the first letter could be Apple or apple
matches = re.findall(regex_pattern, txt)
print(matches)  # ['Apple', 'apple']

• []: A set of characters
  • [a-c] means, a or b or c
  • [a-z] means, any letter a to z
  • [A-Z] means, any character A to Z
  • [0-3] means, 0 or 1 or 2 or 3
  • [0-9] means any number 0 to 9
  • [A-Za-z0-9] any character which is a to z, A to Z, 0 to 9
• \: uses to escape special characters
  • \d mean:match where the string contains digits (numbers from 0-9)
  • \D mean: match where the string does not contain digits
• . : any character except new line character(\n)
• ^: starts with
  • r'^substring' eg r'^love', a sentence which starts with a word love
  • r'[^abc] mean not a, not b, not c.
• $: ends with
  • r'substring$' eg r'love$', sentence ends with a word love
• *: zero or more times
  • r'[a]*' means a optional or it can be occur many times.
• +: one or more times
  • r'[a]+' mean at least once or more times
• ?: zero or one times
  • r'[a]?' mean zero times or once
• {3}: Exactly 3 characters
• {3,}: At least 3 character
• {3,8}: 3 to 8 characters
• |: Either or
  • r'apple|banana' mean either of an apple or a banana
• (): Capture and group

Let's use example to clarify the above meta characters

Square Bracket

Let's use square bracket to include lower and upper case

regex_pattern = r'[Aa]pple' # this square bracket mean either A or a
txt = 'Apple and banana are fruits. An old cliche says an apple a day a doctor way has been replaced by a banana a day keeps the doctor far far away. '
matches = re.findall(regex_pattern, txt)
print(matches)  # ['Apple', 'apple']

If we want to look for the banana, we write the pattern as follows:

regex_pattern = r'[Aa]pple|[Bb]anana' # this square bracket mean either A or a
txt = 'Apple and banana are fruits. An old cliche says an apple a day a doctor way has been replaced by a banana a day keeps the doctor far far away. '
matches = re.findall(regex_pattern, txt)
print(matches)  # ['Apple', 'banana', 'apple', 'banana']

Using the square bracket and or operator , we manage to extract Apple, apple, Banana and banana.

Escape character(\) in RegEx

regex_pattern = r'\d'  # d is a special character which means digits
txt = 'This regular expression example was made in December 6,  2019.'
matches = re.findall(regex_pattern, txt)
print(matches)  # ['6', '2', '0', '1', '9'], this is not what we want

regex_pattern = r'\d+'  # d is a special character which means digits, + mean one or more
txt = 'This regular expression example was made in December 6,  2019.'
matches = re.findall(regex_pattern, txt)
print(matches)  # ['6', '2019']

One or more times(+)

regex_pattern = r'\d+'  # d is a special character which means digits, + mean one or more times
txt = 'This regular expression example was made in December 6,  2019.'
matches = re.findall(regex_pattern, txt)
print(matches)  # ['6', '2019']

Period(.)

regex_pattern = r'[a].'  # this square bracket means a and . means any character except new line
txt = '''Apple and banana are fruits'''
matches = re.findall(regex_pattern, txt)
print(matches)  # ['an', 'an', 'an', 'a ', 'ar']

regex_pattern = r'[a].+'  # . any character, + any character one or more times
matches = re.findall(regex_pattern, txt)
print(matches)  # ['and banana are fruits']

Zero or more times(*)

Zero or many times. The pattern could may not occur or it can occur many times.

regex_pattern = r'[a].*'  # . any character, + any character one or more times
txt = '''Apple and banana are fruits'''
matches = re.findall(regex_pattern, txt)
print(matches)  # ['and banana are fruits']

Zero or one times(?)

Zero or one times. The pattern could may not occur or it may occur once.

txt = '''I am not sure if there is a convention how to write the word e-mail.
Some people write it email others may write it as Email or E-mail.'''
regex_pattern = r'[Ee]-?mail'  # ? means optional
matches = re.findall(regex_pattern, txt)
print(matches)  # ['e-mail', 'email', 'Email', 'E-mail']

Quantifier in RegEx

We can specify the length of the substring we look for in a text, using a curly bracket. Lets imagine, we are interested in substring that their length are 4 characters

txt = 'This regular expression example was made in December 6,  2019.'
regex_pattern = r'\d{4}'  # exactly four times
matches = re.findall(regex_pattern, txt)
print(matches)  # ['2019']

txt = 'This regular expression example was made in December 6,  2019.'
regex_pattern = r'\d{1, 4}'   # 1 to 4
matches = re.findall(regex_pattern, txt)
print(matches)  # ['6', '2019']

Cart ^

• Starts with

txt = 'This regular expression example was made in December 6,  2019.'
regex_pattern = r'^This'  # ^ means starts with
print(matches)  # ['This']

• Negation

txt = 'This regular expression example was made in December 6,  2019.'
regex_pattern = r'[^A-Za-z ]+'  # ^ in set character means negation, not A to Z, not a to z, no space
matches = re.findall(regex_pattern, txt)
print(matches)  # ['e-mail', 'email', 'Email', 'E-mail']

💻 Exercises:

1. What is the most frequent word in the following paragraph ?

    paragraph = 'I love teaching. If you do not love teaching what else can you love. I love Python if you do not love something which can give you all the capabilities to develop an application what else can you love.

    [(6, 'love'),
    (5, 'you'),
    (3, 'can'),
    (2, 'what'),
    (2, 'teaching'),
    (2, 'not'),
    (2, 'else'),
    (2, 'do'),
    (2, 'I'),
    (1, 'which'),
    (1, 'to'),
    (1, 'the'),
    (1, 'something'),
    (1, 'if'),
    (1, 'give'),
    (1, 'develop'),
    (1, 'capabilities'),
    (1, 'application'),
    (1, 'an'),
    (1, 'all'),
    (1, 'Python'),
    (1, 'If')]

2. The position of some particles on the horizontal x-axis -12, -4, -3 and -1 in the negative direction, 0 at origin, 4 and 8 in the positive direction. Extract these numbers and find the distance between the two furthest particles.

points = ['-1', '2', '-4', '-3', '-1', '0', '4', '8']
sorted_points =  [-4, -3, -1, -1, 0, 2, 4, 8]
distance = 12

3. Write a pattern which identify if a string is a valid python variable

   is_valid_variable('first_name') # True
   is_valid_variable('first-name') # False
   is_valid_variable('1first_name') # False
   is_valid_variable('firstname') # True
   

4. Clean the following text. After cleaning, count three most frequent words in the string.

   sentence = '''%I $am@% a %tea@cher%, &and& I lo%#ve %tea@ching%;. There $is nothing; &as& mo@re rewarding as educa@ting &and& @emp%o@wering peo@ple. ;I found tea@ching m%o@re interesting tha@n any other %jo@bs. %Do@es thi%s mo@tivate yo@u to be a tea@cher!?'''
   
   print(clean_text(sentence));
   I am a teacher and I love teaching There is nothing as more rewarding as educating and empowering people I found teaching more interesting than any other jobs Does this motivate you to be a teacher
   print(most_frequent_words(cleaned_text)) # [(3, 'I'), (2, 'teaching'), (2, 'teacher')]
   

MODULES

Importing Modules

import math
import re
import pandas as pd
import numpy as np
import urllib3
import lxml

print(math.sqrt(2))
print(math.pow(3,2))
print(math.pow(3,2) == 3 ** 2)
print(math.pi)
print(math.e)
# constantcs
pi = math.pi
e = math.e
t = math.tau # 2pi

print(pi)
print(e)
print(t)


A = np.random.normal(25, 5.0, 10)
print (A)

1.4142135623730951
9.0
True
3.141592653589793
2.718281828459045
3.141592653589793
2.718281828459045
6.283185307179586
[22.76977213 31.19118012 31.39357782 28.82041376 18.95246175 33.6695274
 22.31571545 21.39637955 28.40572587 24.38512071]

Python for Statistical Analysis

Statistics

Statistics is the discipline that studies the collection, organization, displaying, analysis, interpretation and presentation of data. Statistics is a branch of mathematics that is recommended to be a prerequisite for data science and machine learning. Statistics is a very broad field but we will focus in this section only on the most relevant part. After completing this challenge, you may go to web development, data analysis, machine learning and data science path. Whatever path you may follow, at some point in your career you will get data which you may work on. Having some statistical knowledge will help you to make decision based on data, data tells as they say.

Data

What is data? Data is any set of characters that is gathered and translated for some purpose, usually analysis. It can be any character, including text and numbers, pictures, sound, or video. If data is not put into context, it doesn't give any sense to a human or computer. To make sense from data we need to work on the data using different tools.

The work flow of data analysis, data science or machine learning starts from data. Data can be provided from some data source or it can be created. There are structured and and unstructure data.

Data can be found as small or big data format. Most of the data types we will get have been covered in the file handling section.

Statistics Module

The python statistics module provides functions for calculating mathematical statistics of numeric data. The module is not intended to be a competitor to third-party libraries such as NumPy, SciPy, or proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab. It is aimed at the level of graphing and scientific calculators.

NumPy

In the first section we defined python as a great general-purpose programming language on its own, but with the help of other popular libraries (numpy, scipy, matplotlib, pandas etc) it becomes a powerful environment for scientific computing.

Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with arrays.

So far, we have been using vscode but from now on I would recommend using Jupyter Notebook. To access jupter notebook let's install anaconda. If you are using anaconda most of the common packages are included and you don't have install packages if you installed anaconda.

asabeneh@Asabeneh:~/Desktop/30DaysOfPython$ pip install numpy

Importing NumPy

Jupyter notebook is available if your are in favor of jupyter notebook

    # How to import numpy
    import numpy as np
    # How to check the version of the numpy package
    print('numpy:', np.__version__)
    # Checking the available methods
    print(dir(np))

Creating numpy array using

Creating int numpy arrays

    # Creating python List
    python_list = [1,2,3,4,5]

    # Checking data types
    print('Type:', type (python_list)) # <class 'list'>
    # 
    print(python_list) # [1, 2, 3, 4, 5]

    two_dimensional_list = [[0,1,2], [3,4,5], [6,7,8]]

    print(two_dimensional_list)  # [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

    # Creating Numpy(Numerical Python) array from python list

    numpy_array_from_list = np.array(python_list) 
    print(type (numpy_array_from_list))   # <class 'numpy.ndarray'>
    print(numpy_array_from_list) # array([1, 2, 3, 4, 5])

Creating float numpy arrays

Creating a float numpy array from list with a float data type parameter

    # Python list
    python_list = [1,2,3,4,5]

    numy_array_from_list2 = np.array(python_list, dtype=float)
    print(numy_array_from_list2) # array([1., 2., 3., 4., 5.])

Creating boolean numpy arrays

Creating a boolean a numpy array from list

    numpy_bool_array = np.array([0, 1, -1, 0, 0], dtype=bool)
    print(numpy_bool_array) # array([False,  True,  True, False, False])

Creating multidimensional array using numpy

A numpy array may have one or multiple rors and columns

    two_dimensional_list = [[0,1,2], [3,4,5], [6,7,8]]
    numpy_two_dimensional_list = np.array(two_dimensional_list)
    print(type (numpy_two_dimensional_list))
    print(numpy_two_dimensional_list)

    <class 'numpy.ndarray'>
    [[0 1 2]
     [3 4 5]
     [6 7 8]]

Converting numpy array to list

# We can always convert an array back to a python list using tolist().
np_to_list = numpy_array_from_list.tolist()
print(type (np_to_list))
print('one dimensional array:', np_to_list)
print('two dimensional array: ', numpy_two_dimensional_list.tolist())

    <class 'list'>
    one dimensional array: [1, 2, 3, 4, 5]
    two dimensional array:  [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

Creating numpy array from tuple

# Numpy array from tuple
# Creating tuple in Python
python_tuple = (1,2,3,4,5)
print(type (python_tuple)) # <class 'tuple'>
print('python_tuple: ', python_tuple) # python_tuple:  (1, 2, 3, 4, 5)

numpy_array_from_tuple = np.array(python_tuple)
print(type (numpy_array_from_tuple)) # <class 'numpy.ndarray'>
print('numpy_array_from_tuple: ', numpy_array_from_tuple) # numpy_array_from_tuple:  [1 2 3 4 5]

Shape of numpy array

The shape method provide the shape of the array as a tuple. The first is the row and the second is the column. If the array is just one dimensional it returns the size of the array.

    nums = np.array([1, 2, 3, 4, 5])
    print(nums)
    print('shape of nums: ', nums.shape)
    print(numpy_two_dimensional_list)
    print('shape of numpy_two_dimensional_list: ', numpy_two_dimensional_list.shape)
    three_by_four_array = np.array([[0, 1, 2, 3],
        [4,5,6,7],
        [8,9,10, 11]])
    print(three_by_four_array.shape)

    [1 2 3 4 5]
    shape of nums:  (5,)
    [[0 1 2]
     [3 4 5]
     [6 7 8]]
    shape of numpy_two_dimensional_list:  (3, 3)
    (3, 4)

Data type of numpy array

Type of data types: str, int, float, complex, bool, list, None

int_lists = [-3, -2, -1, 0, 1, 2,3]
int_array = np.array(int_lists)
float_array = np.array(int_lists, dtype=float)

print(int_array)
print(int_array.dtype)
print(float_array)
print(float_array.dtype)

    [-3 -2 -1  0  1  2  3]
    int64
    [-3. -2. -1.  0.  1.  2.  3.]
    float64

Size of a numpy array

In numpy to know the number of items in a numpy array list we use size

numpy_array_from_list = np.array([1, 2, 3, 4, 5])
two_dimensional_list = np.array([[0, 1, 2],
                              [3, 4, 5],
                              [6, 7, 8]])

print('The size:', numpy_array_from_list.size) # 5
print('The size:', two_dimensional_list.size)  # 3

    The size: 5
    The size: 9

Mathematical Operation using numpy

Numpy array is not like exactly like python list. To do mathematical operation in pyhton list we have to loop through the items but numpy can allow to do any mathematical operation without looping. Mathematical Operation:

• Addition (+)
• Subtraction (-)
• Multiplication (*)
• Division (/)
• Modules (%)
• Floor Division(//)
• Exponential(**)

Addition

# Mathematical Operation
# Addition
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_plus_original = numpy_array_from_list  + 10
print(ten_plus_original)

    original array:  [1 2 3 4 5]
    [11 12 13 14 15]

Subtraction

# Subtraction
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_minus_original = numpy_array_from_list  - 10
print(ten_minus_original)

    original array:  [1 2 3 4 5]
    [-9 -8 -7 -6 -5]

Multiplication

# Multiplication
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_times_original = numpy_array_from_list * 10
print(ten_times_original)

    original array:  [1 2 3 4 5]
    [10 20 30 40 50]

Division

# Division
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_times_original = numpy_array_from_list / 10
print(ten_times_original)

    original array:  [1 2 3 4 5]
    [0.1 0.2 0.3 0.4 0.5]

Modulus

# Modulus; Finding the remainder
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_times_original = numpy_array_from_list % 3
print(ten_times_original)

    original array:  [1 2 3 4 5]
    [1 2 0 1 2]

Floor Division

# Floor division: the division result without the remainder
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_times_original = numpy_array_from_list // 10
print(ten_times_original)

Exponential

# Exponential is finding some number the power of another:
numpy_array_from_list = np.array([1, 2, 3, 4, 5])
print('original array: ', numpy_array_from_list)
ten_times_original = numpy_array_from_list  ** 2
print(ten_times_original)

    original array:  [1 2 3 4 5]
    [ 1  4  9 16 25]

Checking data types

#Int,  Float numbers
numpy_int_arr = np.array([1,2,3,4])
numpy_float_arr = np.array([1.1, 2.0,3.2])
numpy_bool_arr = np.array([-3, -2, 0, 1,2,3], dtype='bool')

print(numpy_int_arr.dtype)
print(numpy_float_arr.dtype)
print(numpy_bool_arr.dtype)

    int64
    float64
    bool

Converting types

We can convert the data types of numpy array

1. Int to Float

numpy_int_arr = np.array([1,2,3,4], dtype = 'float')
numpy_int_arr

array([1., 2., 3., 4.])

2. Float to Int

numpy_int_arr = np.array([1., 2., 3., 4.], dtype = 'int')
numpy_int_arr

    array([1, 2, 3, 4])

3. Int ot boolean

np.array([-3, -2, 0, 1,2,3], dtype='bool')

    array([ True,  True, False,  True,  True,  True])

4. Int to str

numpy_float_list.astype('int').astype('str')

    array(['1', '2', '3'], dtype='<U21')

Multi-dimensional Arrays

# 2 Dimension Array
two_dimension_array = np.array([(1,2,3),(4,5,6), (7,8,9)])
print(type (two_dimension_array))
print(two_dimension_array)
print('Shape: ', two_dimension_array.shape)
print('Size:', two_dimension_array.size)
print('Data type:', two_dimension_array.dtype)

    <class 'numpy.ndarray'>
    [[1 2 3]
     [4 5 6]
     [7 8 9]]
    Shape:  (3, 3)
    Size: 9
    Data type: int64

Getting items from a numpy array

# 2 Dimension Array
two_dimension_array = np.array([[1,2,3],[4,5,6], [7,8,9]])
first_row = two_dimension_array[0]
second_row = two_dimension_array[1]
third_row = two_dimension_array[2]
print('First row:', first_row)
print('Second row:', second_row)
print('Third row: ', third_row)

    First row: [1 2 3]
    Second row: [4 5 6]
    Third row:  [7 8 9]

first_column= two_dimension_array[:,0]
second_column = two_dimension_array[:,1]
third_column = two_dimension_array[:,2]
print('First column:', first_column)
print('Second column:', second_column)
print('Third column: ', third_column)
print(two_dimension_array)

    First column: [1 4 7]
    Second column: [2 5 8]
    Third column:  [3 6 9]
    [[1 2 3]
     [4 5 6]
     [7 8 9]]

Slicing Numpy array

Slicing in numpy is similar to slicing in python list

two_dimension_array = np.array([[1,2,3],[4,5,6], [7,8,9]])
first_two_rows_and_columns = two_dimension_array[0:2, 0:2]
print(first_two_rows_and_columns)

    [[1 2]
     [4 5]]

How to reverse the rows and the whole array?

two_dimension_array[::]

    array([[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]])

Reverse the row and column positions

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

    array([[9, 8, 7],
           [6, 5, 4],
           [3, 2, 1]])

How to represent missing values ?

    print(two_dimension_array)
    two_dimension_array[1,1] = 55
    two_dimension_array[1,2] =44
    print(two_dimension_array)

    [[1 2 3]
     [4 5 6]
     [7 8 9]]
    [[ 1  2  3]
     [ 4 55 44]
     [ 7  8  9]]

    # Numpy Zeroes
    # numpy.zeros(shape, dtype=float, order='C')
    numpy_zeroes = np.zeros((3,3),dtype=int,order='C')
    numpy_zeroes

    array([[0, 0, 0],
           [0, 0, 0],
           [0, 0, 0]])

# Numpy Zeroes
numpy_ones = np.ones((3,3),dtype=int,order='C')
print(numpy_ones)

    [[1 1 1]
     [1 1 1]
     [1 1 1]]

twoes = numpy_ones * 2

# Reshape
# numpy.reshape(), numpy.flatten()
first_shape  = np.array([(1,2,3), (4,5,6)])
print(first_shape)
reshaped = first_shape.reshape(3,2)
print(reshaped)

    [[1 2 3]
     [4 5 6]]
    [[1 2]
     [3 4]
     [5 6]]

flattened = reshaped.flatten()
flattened

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

    ## Horitzontal Stack
    np_list_one = np.array([1,2,3])
    np_list_two = np.array([4,5,6])

    print(np_list_one + np_list_two)

    print('Horizontal Append:', np.hstack((np_list_one, np_list_two)))

    [5 7 9]
    Horizontal Append: [1 2 3 4 5 6]

    ## Vertical Stack
    print('Vertical Append:', np.vstack((np_list_one, np_list_two)))

    Vertical Append: [[1 2 3]
     [4 5 6]]

Generating Random Numbers

    # Generate a random float  number
    random_float = np.random.random()
    random_float

    0.018929887384753874

    # Generate a random float  number
    random_floats = np.random.random(5)
    random_floats

    array([0.26392192, 0.35842215, 0.87908478, 0.41902195, 0.78926418])

    # Generating a random integers between 0 and 10

    random_int = np.random.randint(0, 11)
    random_int

    4

    # Generating a random integers between 2 and 11, and creating a one row array
    random_int = np.random.randint(2,10, size=4)
    random_int

    array([8, 8, 8, 2])

    # Generating a random integers between 0 and 10
    random_int = np.random.randint(2,10, size=(3,3))
    random_int

    array([[3, 5, 3],
           [7, 3, 6],
           [2, 3, 3]])

Generationg random numbers

    # np.random.normal(mu, sigma, size)
    normal_array = np.random.normal(79, 15, 80)
    normal_array

    array([ 89.49990595,  82.06056961, 107.21445842,  38.69307086,
            47.85259157,  93.07381061,  76.40724259,  78.55675184,
            72.17358173,  47.9888899 ,  65.10370622,  76.29696568,
            95.58234254,  68.14897213,  38.75862686, 122.5587927 ,
            67.0762565 ,  95.73990864,  81.97454563,  92.54264805,
            59.37035153,  77.76828101,  52.30752166,  64.43109931,
            62.63695351,  90.04616138,  75.70009094,  49.87586877,
            80.22002414,  68.56708848,  76.27791052,  67.24343975,
            81.86363935,  78.22703433, 102.85737041,  65.15700341,
            84.87033426,  76.7569997 ,  64.61321853,  67.37244562,
            74.4068773 ,  58.65119655,  71.66488727,  53.42458179,
            70.26872028,  60.96588544,  83.56129414,  72.14255326,
            81.00787609,  71.81264853,  72.64168853,  86.56608717,
            94.94667321,  82.32676973,  70.5165446 ,  85.43061003,
            72.45526212,  87.34681775,  87.69911217, 103.02831489,
            75.28598596,  67.17806893,  92.41274447, 101.06662611,
            87.70013935,  70.73980645,  46.40368207,  50.17947092,
            61.75618542,  90.26191397,  78.63968639,  70.84550744,
            88.91826581, 103.91474733,  66.3064638 ,  79.49726264,
            70.81087439,  83.90130623,  87.58555972,  59.95462521])

Numpy and Statistics

import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.hist(normal_array, color="grey", bins=50)

    (array([2., 0., 0., 0., 1., 2., 2., 0., 2., 0., 0., 1., 2., 2., 1., 4., 3.,
            4., 2., 7., 2., 2., 5., 4., 2., 4., 3., 2., 1., 5., 3., 0., 3., 2.,
            1., 0., 0., 1., 3., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),
     array([ 38.69307086,  40.37038529,  42.04769973,  43.72501417,
             45.4023286 ,  47.07964304,  48.75695748,  50.43427191,
             52.11158635,  53.78890079,  55.46621523,  57.14352966,
             58.8208441 ,  60.49815854,  62.17547297,  63.85278741,
             65.53010185,  67.20741628,  68.88473072,  70.56204516,
             72.23935959,  73.91667403,  75.59398847,  77.27130291,
             78.94861734,  80.62593178,  82.30324622,  83.98056065,
             85.65787509,  87.33518953,  89.01250396,  90.6898184 ,
             92.36713284,  94.04444727,  95.72176171,  97.39907615,
             99.07639058, 100.75370502, 102.43101946, 104.1083339 ,
            105.78564833, 107.46296277, 109.14027721, 110.81759164,
            112.49490608, 114.17222052, 115.84953495, 117.52684939,
            119.20416383, 120.88147826, 122.5587927 ]),
     <a list of 50 Patch objects>)

Matrix in numpy

four_by_four_matrix = np.matrix(np.ones((4,4), dtype=float))

four_by_four_matrix

matrix([[1., 1., 1., 1.],
            [1., 1., 1., 1.],
            [1., 1., 1., 1.],
            [1., 1., 1., 1.]])

np.asarray(four_by_four_matrix)[2] = 2
four_by_four_matrix

matrix([[1., 1., 1., 1.],
            [1., 1., 1., 1.],
            [2., 2., 2., 2.],
            [1., 1., 1., 1.]])

Numpy numpy.arange()

What is Arrange?

Sometimes, you want to create values that are evenly spaced within a defined interval. For instance, you want to create values from 1 to 10; you can use numpy.arange() function

# creating list using range(starting, stop, step)
lst = range(0, 11, 2)
lst

range(0, 11, 2)

for l in lst:
    print(l)

    2
    4
    6
    8
    10

# Similar to range arange numpy.arange(start, stop, step)
whole_numbers = np.arange(0, 20, 1)
whole_numbers

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
           17, 18, 19])

natural_numbers = np.arange(1, 20, 1)
natural_numbers

odd_numbers = np.arange(1, 20, 2)
odd_numbers

    array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19])

even_numbers = np.arange(2, 20, 2)
even_numbers

    array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])

Creating sequence of numbers using linspace

# numpy.linspace()
# numpy.logspace() in Python with Example
# For instance, it can be used to create 10 values from 1 to 5 evenly spaced.
np.linspace(1.0, 5.0, num=10)

    array([1.        , 1.44444444, 1.88888889, 2.33333333, 2.77777778,
           3.22222222, 3.66666667, 4.11111111, 4.55555556, 5.        ])

# not to include the last value in the interval
np.linspace(1.0, 5.0, num=5, endpoint=False)

array([1. , 1.8, 2.6, 3.4, 4.2])

# LogSpace
# LogSpace returns even spaced numbers on a log scale. Logspace has the same parameters as np.linspace.

# Syntax:

# numpy.logspace(start, stop, num, endpoint)

np.logspace(2, 4.0, num=4)

array([  100.        ,   464.15888336,  2154.43469003, 10000.        ])

# to check the size of an array
x = np.array([1,2,3], dtype=np.complex128)

x

    array([1.+0.j, 2.+0.j, 3.+0.j])

x.itemsize

16

# indexing and Slicing NumPy Arrays in Python
np_list = np.array([(1,2,3), (4,5,6)])
np_list

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

print('First row: ', np_list[0])
print('Second row: ', np_list[1])

    First row:  [1 2 3]
    Second row:  [4 5 6]

print('First column: ', np_list[:,0])
print('Second column: ', np_list[:,1])
print('Third column: ', np_list[:,2])

    First column:  [1 4]
    Second column:  [2 5]
    Third column:  [3 6]

NumPy Statistical Functions with Example

NumPy has quite useful statistical functions for finding minimum, maximum, mean, median, percentile,standard deviation and variance, etc from the given elements in the array. The functions are explained as follows − Statistical function Numpy is equipped with the robust statistical function as listed below

• Numpy Functions
  • Min np.min()
  • Max np.max()
  • Mean np.mean()
  • Median np.median()
  • Varience
  • Percentile
  • Standard deviation np.std()

np_normal_dis = np.random.normal(5, 0.5, 100)
np_normal_dis
## min, max, mean, median, sd
print('min: ', two_dimension_array.min())
print('max: ', two_dimension_array.max())
print('mean: ',two_dimension_array.mean())
# print('median: ', two_dimension_array.median())
print('sd: ', two_dimension_array.std())

min:  1
max:  55
mean:  14.777777777777779
sd:  18.913709183069525

min:  1
max:  55
mean:  14.777777777777779
sd:  18.913709183069525

print(two_dimension_array)
print('Column with minimum: ', np.amin(two_dimension_array,axis=0))
print('Column with maximum: ', np.amax(two_dimension_array,axis=0))
print('=== Row ==')
print('Row with minimum: ', np.amin(two_dimension_array,axis=1))
print('Row with maximum: ', np.amax(two_dimension_array,axis=1))

[[ 1  2  3]
 [ 4 55 44]
 [ 7  8  9]]
Column with minimum:  [1 2 3]
Column with maximum:  [ 7 55 44]
=== Row ==
Row with minimum:  [1 4 7]
Row with maximum:  [ 3 55  9]

How to create repeating sequences?

a = [1,2,3]

# Repeat whole of 'a' two times
print('Tile:   ', np.tile(a, 2))

# Repeat each element of 'a' two times
print('Repeat: ', np.repeat(a, 2))

Tile:    [1 2 3 1 2 3]
Repeat:  [1 1 2 2 3 3]

How to generate random numbers?

# One random number between [0,1)
one_random_num = np.random.random()
one_random_in = np.random
print(one_random_num)

0.6149403282678213

0.4763968133790438

0.4763968133790438

# Random numbers between [0,1) of shape 2,3
r = np.random.random(size=[2,3])
print(r)

[[0.13031737 0.4429537  0.1129527 ]
 [0.76811539 0.88256594 0.6754075 ]]

print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10))

['u' 'o' 'o' 'i' 'e' 'e' 'u' 'o' 'u' 'a']

['i' 'u' 'e' 'o' 'a' 'i' 'e' 'u' 'o' 'i']

['iueoaieuoi']

## Random numbers between [0, 1] of shape 2, 2
rand = np.random.rand(2,2)
rand

array([[0.97992598, 0.79642484],
       [0.65263629, 0.55763145]])

rand2 = np.random.randn(2,2)
rand2

array([[ 1.65593322, -0.52326621],
       [ 0.39071179, -2.03649407]])

# Random integers between [0, 10) of shape 2,5
rand_int = np.random.randint(0, 10, size=[5,3])
rand_int

array([[0, 7, 5],
       [4, 1, 4],
       [3, 5, 3],
       [4, 3, 8],
       [4, 6, 7]])

from scipy import stats
np_normal_dis = np.random.normal(5, 0.5, 1000) # mean, standard deviation, number of samples
np_normal_dis
## min, max, mean, median, sd
print('min: ', np.min(np_normal_dis))
print('max: ', np.max(np_normal_dis))
print('mean: ', np.mean(np_normal_dis))
print('median: ', np.median(np_normal_dis))
print('mode: ', stats.mode(np_normal_dis))
print('sd: ', np.std(np_normal_dis))

    min:  3.557811005458804
    max:  6.876317743643499
    mean:  5.035832048106663
    median:  5.020161980441937
    mode:  ModeResult(mode=array([3.55781101]), count=array([1]))
    sd:  0.489682424165213

plt.hist(np_normal_dis, color="grey", bins=21)
plt.show()

# numpy.dot(): Dot Product in Python using Numpy
# Dot Product
# Numpy is powerful library for matrices computation. For instance, you can compute the dot product with np.dot

# Syntax

# numpy.dot(x, y, out=None)

Linear Algebra

1. Dot Product

## Linear algebra
### Dot product: product of two arrays
f = np.array([1,2,3])
g = np.array([4,5,3])
### 1*4+2*5 + 3*6
np.dot(f, g)  # 23

NumPy Matrix Multiplication with np.matmul()

### Matmul: matruc product of two arrays
h = [[1,2],[3,4]]
i = [[5,6],[7,8]]
### 1*5+2*7 = 19
np.matmul(h, i)

    array([[19, 22],
           [43, 50]])

## Determinant 2*2 matrix
### 5*8-7*6np.linalg.det(i)

np.linalg.det(i)

-1.999999999999999

Z = np.zeros((8,8))
Z[1::2,::2] = 1
Z[::2,1::2] = 1

Z

array([[0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.],
       [0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0.]])

new_list = [ x + 2 for x in range(0, 11)]

new_list

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

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

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

np_arr = np.array(range(0, 11))
np_arr + 2

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

We use linear equation for quatities which have linear relationship. Let's see the example below:

temp = np.array([1,2,3,4,5])
pressure = temp * 2 + 5
pressure

array([ 7, 9, 11, 13, 15])

plt.plot(temp,pressure)
plt.xlabel('Temperature in oC')
plt.ylabel('Pressure in atm')
plt.title('Temperature vs Pressure')
plt.xticks(np.arange(0, 6, step=0.5))
plt.show()

To draw the Gaussian normal distribution using numpy. As you can see below, the numpy can generate random numbers. To create random sample, we need the mean(mu), sigma(standard deviation), mumber of data points.

mu = 28
sigma = 15
samples = 100000

x = np.random.normal(mu, sigma, samples)
ax = sns.distplot(x);
ax.set(xlabel="x", ylabel='y')
plt.show()

Summery

To summarise, the main differences with python lists are:

1. Arrays support vectorised operations, while lists don’t.
2. Once an array is created, you cannot change its size. You will have to create a new array or overwrite the existing one.
3. Every array has one and only one dtype. All items in it should be of that dtype.
4. An equivalent numpy array occupies much less space than a python list of lists.
5. numpy arrays support boolean indexing.

💻 Exercises:

1. Repeat all the examples

Pandas

Pandas is an open source,high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames Pandas provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation and imputation.

pip install conda
conda install pandas

Pandas data structure is based on Series and DataFrames A series is a column and a DataFrame is a multidimensional table made up of collection of series. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list. Let's see an example of a series:

Names pandas Series

Countries Series

Cities Series

As you can see, pandas series is just one column data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.

Let's see, an example of a pandas data frame:

Data from is a collection of rows and columns. Look at the table below it has many columns than the above

Next, we will see how to import pandas and how to create Series and DataFrames using pandas

Importing pandas

import pandas as pd # importing pandas as pd
import numpy  as np # importing numpy as np

Creating Pandas Series with default index

nums = [1, 2, 3, 4,5]
s = pd.Series(nums)
s

0    1
1    2
2    3
3    4
4    5
dtype: int64

Creating Pandas Series with custom index

nums = [1, 2, 3, 4, 5]
s = pd.Series(nums, index=[1, 2, 3, 4, 5])
s

1    1
2    2
3    3
4    4
5    5
dtype: int64

fruits = ['Orange','Banana','Mangao']
fruits = pd.Series(fruits, index=[1, 2, 3])
fruits

1    Orange
2    Banana
3    Mangao
dtype: object

Creating Pandas Series from a dictionary

dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}

s = pd.Series(dct)
s

name       Asabeneh
country     Finland
city       Helsinki
dtype: object

Creating a constant pandas series

s = pd.Series(10, index = [1, 2,3])
s

1    10
2    10
3    10
dtype: int64

Creating a pandas series using linspace

s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
s

0     5.000000
1     6.666667
2     8.333333
3    10.000000
4    11.666667
5    13.333333
6    15.000000
7    16.666667
8    18.333333
9    20.000000
dtype: float64

DataFrames

Pandas data frames can be created in different ways.

Creating DataFrames from list of lists

data = [
    ['Asabeneh', 'Finland', 'Helsink'], 
    ['David', 'UK', 'London'],
    ['John', 'Sweden', 'Stockholm']
]
df = pd.DataFrame(data, columns=['Names','Country','City'])
df

Creating DataFrame using Dictionary

data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[
    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}
df = pd.DataFrame(data)
df

Creating DataFrams from list of dictionaries

data = [
    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},
    {'Name': 'David', 'Country': 'UK', 'City': 'London'},
    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]
df = pd.DataFrame(data)
df

Reading CSV File using pandas

import pandas as pd

df = pd.read_csv('./data/weight-height.csv')

Data Exploration

Let's read only the first 5 rows using head()

df.head() # give five rows we can increase the number of rows by passing argument to the head() method

As you can see the csv file has three rows:Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod.

df.shape # as you can see 10000 rows and three columns

(10000, 3)

Let's get all the columns using columns.

df.columns

Index(['Gender', 'Height', 'Weight'], dtype='object')

Let's read only the last 5 rows using tail()

df.tail() # tails give the last five rows, we can increase the rows by passing argument to tail method

Now, lets get specif colums using the column key

heights = df['Height'] # this is now a a series

heights

0       73.847017
1       68.781904
2       74.110105
3       71.730978
4       69.881796
          ...    
9995    66.172652
9996    67.067155
9997    63.867992
9998    69.034243
9999    61.944246
Name: Height, Length: 10000, dtype: float64

weights = df['Weight'] # this is now a series

weights

0       241.893563
1       162.310473
2       212.740856
3       220.042470
4       206.349801
           ...    
9995    136.777454
9996    170.867906
9997    128.475319
9998    163.852461
9999    113.649103
Name: Weight, Length: 10000, dtype: float64

len(heights) == len(weights)

True

heights.describe() # give statisical information about height data

count    10000.000000
mean        66.367560
std          3.847528
min         54.263133
25%         63.505620
50%         66.318070
75%         69.174262
max         78.998742
Name: Height, dtype: float64

weights.describe()

count    10000.000000
mean       161.440357
std         32.108439
min         64.700127
25%        135.818051
50%        161.212928
75%        187.169525
max        269.989699
Name: Weight, dtype: float64

df.describe()  # describe can also give statistical information from a datafrom

Modifying DataFrame

Modifying a DataFrame * We can create a new DataFrame * We can create a new column and add to DataFrame, * we can remove an existing column from DataFrame, * we can modify an existing column from DataFrame, * we can change the data type of column values from DataFrame

Create a DataFrame

All the time, first we import the necessary packages. Now, lets import pandas and numpy two best friends ever.

import pandas as pd
import numpy as np
data = [
    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
    {"Name": "David", "Country":"UK","City":"London"},
    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
df = pd.DataFrame(data)
df

Adding column in DataFrame is like adding a key in dictionary.

First let's use the previous example to create a DataFrame. After we create the DataFrame, we will start modifying the columns and column values.

Adding new column

Let's add a weight column in the DataFrame

weights = [74, 78, 69]
df['Weight'] = weights
df

Let's add a height column in the DataFrame

heights = [173, 175, 169]
df['Height'] =heights
df

As you can see from the above DataFrame, now we new added columns, the Weight and Height. Let's add one additional column by called BMI(Body Mass Index) by calculating their BMI using thier mass and height. BMI is mass divided by height square meter(Weight/Height * Height).

As you can see, the hieght is in centimeter, so we shoud change the height to meter. So, let's modify the height row

Modifying column values

df['Height'] = df['Height'] * 0.01
df

# Using function makes our code clean but you can just calculate the bmi without function
def calculate_bmi ():
    weights = df['Weight']
    heights = df['Height']
    bmi = []
    for w,h in zip(weights, heights):
        b = w/(h*h)
        bmi.append(b)
    return bmi
    
bmi = calculate_bmi()

df['BMI'] = bmi
df

Formating DataFrame column

The BMI of the above DataFrame has is float with many significant digits after decimal. Let's make it to have only one significant digit after point.

df['BMI'] = round(df['BMI'], 1)
df

The information in the DataFrame seems not yet complete, let's add birth year and current year columns.

birth_year = ['1769', '1985', '1990']
current_year = pd.Series(2019, index=[0, 1,2])
df['Birth Year'] = birth_year
df['Current Year'] = current_year
df

Checking data types of Column values

df.Weight.dtype

dtype('int64')

df['Birth Year'].dtype # it give string object , we should change this to number

dtype('O')

df['Birth Year'] = df['Birth Year'].astype('int')
df['Birth Year'].dtype # let's check the data type now

dtype('int64')

df['Current Year'] = df['Current Year'].astype('int')
df['Current Year'].dtype

dtype('int64')

Now, the column values of birth year and current year are integers. We can calculate the age.

ages = df['Current Year'] - df['Birth Year']
ages

0    250
1     34
2     29
dtype: int64

df['Ages'] = ages
df

The person in the first row lives 250 years. It is unlikely for someone to live 250 years. Either it is a typo or the data is cooked. So lets fill that data with average of the columns without including outlier.

mean = (34 + 29)/ 2

mean = (34 + 29)/ 2
mean

31.5

Boolean Indexing

df[df['Ages'] > 120]

df[df['Ages'] < 120]

df['Ages']  = df[df['Ages'] > 120]
        
        

Exercises:

1. Read the hacker_ness.csv file from data directory
2. Get the first five rows
3. Get the last five rows
4. Get the title column as pandas series
5. Count the number of rows and columns

• Filter the titles which contain python
• Filter the titles which contain JavaScript
• Explore the data and make sense of the data

Challenges Coming ...

Write python comment Declare different varaibles Check different data tyypes Concatinate strings Write some code that creates a list of integers, loops through each element of the list, and only prints out even numbers!

list_nums = range(100);
print(list(list_nums))

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]

def sum_of_evens_and_sum_of_odds (n):
    numbers = range(n)
    evens_sum = 0;
    odds_sum = 0;
    for n in numbers:
        if n % 2 == 0 :
            evens_sum = evens_sum + n
        else:
            odds_sum = odds_sum + n;
    return [evens_sum, odds_sum]

sum_of_evens_and_sum_of_odds(101)

[2550, 2500]