Learn Python Programming from Scratch | Python from Zero to Hero

Python: Zero to Hero in 3 Hours

In this course you will learn about Python from scratch. In this course you will learn about Python basics, Python functions, Variables and much more!

0:00:00 Introduction
0:10:53 Math Operations
0:17:51 Variables
0:20:26 Booleans / Conditions
0:27:55 If Statements
0:31:10 Introduction to Lists
0:33:24 For Loops
0:41:35 While Loops
0:47:30 If / Elif / Else
0:56:40 Functions
1:11:15 Is
1:27:13 Cool Function Tricks
1:34:27 File Reading / Writing
1:42:33 Objects and Classes
1:56:00 Comments / Docstrings
2:03:55 Lists in Detail
2:08:58 Dictionaries
2:17:02 Strings in Detail
2:27:47 Tuples
2:30:35 Sets
2:34:33 Errors / Try / Except
2:38:35 User Input
2:41:18 List Comprehension
2:49:13 ASCII / Ord / Chr
2:53:50 Modules / Pip / Packages
2:57:52 Python Scripts / Files
3:04:11 Local Python
3:07:38 Conclusion

Notebook available here: https://colab.research.google.com/drive/1YNJx2CMObRQqQvkDDMxIEISZfGs7npwU?usp=sharing 

Learning Python: From Zero to Hero

First of all, what is Python? According to its creator, Guido van Rossum, Python is a:

“high-level programming language, and its core design philosophy is all about code readability and a syntax which allows programmers to express concepts in a few lines of code.”

For me, the first reason to learn Python was that it is, in fact, a beautiful programming language. It was really natural to code in it and express my thoughts.

Another reason was that we can use coding in Python in multiple ways: data science, web development, and machine learning all shine here. Quora, Pinterest and Spotify all use Python for their backend web development. So let’s learn a bit about it.

The Basics

1. Variables

You can think about variables as words that store a value. Simple as that.

In Python, it is really easy to define a variable and set a value to it. Imagine you want to store number 1 in a variable called “one.” Let’s do it:

one = 1

How simple was that? You just assigned the value 1 to the variable “one.”

two = 2
some_number = 10000

And you can assign any other value to whatever other variables you want. As you see in the table above, the variable “two” stores the integer 2, and “some_number” stores 10,000.

Besides integers, we can also use booleans (True / False), strings, float, and so many other data types.

# booleans
true_boolean = True
false_boolean = False

# string
my_name = "Leandro Tk"

# float
book_price = 15.80

2. Control Flow: conditional statements

If” uses an expression to evaluate whether a statement is True or False. If it is True, it executes what is inside the “if” statement. For example:

if True:
  print("Hello Python If")

if 2 > 1:
  print("2 is greater than 1")

2 is greater than 1, so the “print” code is executed.

The “else” statement will be executed if the “if” expression is false.

if 1 > 2:
  print("1 is greater than 2")
  print("1 is not greater than 2")

1 is not greater than 2, so the code inside the “else” statement will be executed.

You can also use an “elif” statement:

if 1 > 2:
  print("1 is greater than 2")
elif 2 > 1:
  print("1 is not greater than 2")
  print("1 is equal to 2")

3. Looping / Iterator

In Python, we can iterate in different forms. I’ll talk about two: while and for.

While Looping: while the statement is True, the code inside the block will be executed. So, this code will print the number from 1 to 10.

num = 1

while num <= 10:
    num += 1

The while loop needs a “loop condition.” If it stays True, it continues iterating. In this example, when num is 11 the loop condition equals False.

Another basic bit of code to better understand it:

loop_condition = True

while loop_condition:
    print("Loop Condition keeps: %s" %(loop_condition))
    loop_condition = False

The loop condition is True so it keeps iterating — until we set it to False.

For Looping: you apply the variable “num” to the block, and the “for” statement will iterate it for you. This code will print the same as while code: from 1 to 10.

for i in range(1, 11):

See? It is so simple. The range starts with 1 and goes until the 11th element (10 is the 10th element).

List: Collection | Array | Data Structure

Imagine you want to store the integer 1 in a variable. But maybe now you want to store 2. And 3, 4, 5 …

Do I have another way to store all the integers that I want, but not in millions of variables? You guessed it — there is indeed another way to store them.

List is a collection that can be used to store a list of values (like these integers that you want). So let’s use it:

my_integers = [1, 2, 3, 4, 5]

It is really simple. We created an array and stored it on my_integer.

But maybe you are asking: “How can I get a value from this array?”

Great question. List has a concept called index. The first element gets the index 0 (zero). The second gets 1, and so on. You get the idea.

To make it clearer, we can represent the array and each element with its index. I can draw it:


Using the Python syntax, it’s also simple to understand:

my_integers = [5, 7, 1, 3, 4]
print(my_integers[0]) # 5
print(my_integers[1]) # 7
print(my_integers[4]) # 4

Imagine that you don’t want to store integers. You just want to store strings, like a list of your relatives’ names. Mine would look something like this:

relatives_names = [

print(relatives_names[4]) # Kaio

It works the same way as integers. Nice.

We just learned how Lists indices work. But I still need to show you how we can add an element to the List data structure (an item to a list).

The most common method to add a new value to a List is append. Let’s see how it works:

bookshelf = []
bookshelf.append("The Effective Engineer")
bookshelf.append("The 4 Hour Work Week")
print(bookshelf[0]) # The Effective Engineer
print(bookshelf[1]) # The 4 Hour Work Week

append is super simple. You just need to apply the element (eg. “The Effective Engineer”) as the append parameter.

Well, enough about Lists. Let’s talk about another data structure.

Dictionary: Key-Value Data Structure

Now we know that Lists are indexed with integer numbers. But what if we don’t want to use integer numbers as indices? Some data structures that we can use are numeric, string, or other types of indices.

Let’s learn about the Dictionary data structure. Dictionary is a collection of key-value pairs. Here’s what it looks like:

dictionary_example = {
  "key1": "value1",
  "key2": "value2",
  "key3": "value3"

The key is the index pointing to the value. How do we access the Dictionary value? You guessed it — using the key. Let’s try it:

dictionary_tk = {
  "name": "Leandro",
  "nickname": "Tk",
  "nationality": "Brazilian"

print("My name is %s" %(dictionary_tk["name"])) # My name is Leandro
print("But you can call me %s" %(dictionary_tk["nickname"])) # But you can call me Tk
print("And by the way I'm %s" %(dictionary_tk["nationality"])) # And by the way I'm Brazilian

I created a Dictionary about me. My name, nickname, and nationality. Those attributes are the Dictionary keys.

As we learned how to access the List using index, we also use indices (keys in the Dictionary context) to access the value stored in the Dictionary.

In the example, I printed a phrase about me using all the values stored in the Dictionary. Pretty simple, right?

Another cool thing about Dictionary is that we can use anything as the value. In the Dictionary I created, I want to add the key “age” and my real integer age in it:

dictionary_tk = {
  "name": "Leandro",
  "nickname": "Tk",
  "nationality": "Brazilian",
  "age": 24

print("My name is %s" %(dictionary_tk["name"])) # My name is Leandro
print("But you can call me %s" %(dictionary_tk["nickname"])) # But you can call me Tk
print("And by the way I'm %i and %s" %(dictionary_tk["age"], dictionary_tk["nationality"])) # And by the way I'm Brazilian

Here we have a key (age) value (24) pair using string as the key and integer as the value.

As we did with Lists, let’s learn how to add elements to a Dictionary. The key pointing to a value is a big part of what Dictionary is. This is also true when we are talking about adding elements to it:

dictionary_tk = {
  "name": "Leandro",
  "nickname": "Tk",
  "nationality": "Brazilian"

dictionary_tk['age'] = 24

print(dictionary_tk) # {'nationality': 'Brazilian', 'age': 24, 'nickname': 'Tk', 'name': 'Leandro'}

We just need to assign a value to a Dictionary key. Nothing complicated here, right?

Iteration: Looping Through Data Structures

As we learned in the Python Basics, the List iteration is very simple. We Python developers commonly use For looping. Let’s do it:

bookshelf = [
  "The Effective Engineer",
  "The 4-hour Workweek",
  "Zero to One",
  "Lean Startup",

for book in bookshelf:

So for each book in the bookshelf, we (can do everything with it) print it. Pretty simple and intuitive. That’s Python.

For a hash data structure, we can also use the for loop, but we apply the key :

dictionary = { "some_key": "some_value" }

for key in dictionary:
    print("%s --> %s" %(key, dictionary[key]))
# some_key --> some_value

This is an example how to use it. For each key in the dictionary , we print the key and its corresponding value.

Another way to do it is to use the iteritems method.

dictionary = { "some_key": "some_value" }

for key, value in dictionary.items():
    print("%s --> %s" %(key, value))

# some_key --> some_value

We did name the two parameters as key and value, but it is not necessary. We can name them anything. Let’s see it:

dictionary_tk = {
  "name": "Leandro",
  "nickname": "Tk",
  "nationality": "Brazilian",
  "age": 24

for attribute, value in dictionary_tk.items():
    print("My %s is %s" %(attribute, value))
# My name is Leandro
# My nickname is Tk
# My nationality is Brazilian
# My age is 24

We can see we used attribute as a parameter for the Dictionary key, and it works properly. Great!

Classes & Objects

A little bit of theory:

Objects are a representation of real world objects like cars, dogs, or bikes. The objects share two main characteristics: data and behavior.

Cars have data, like number of wheels, number of doors, and seating capacity They also exhibit behavior: they can accelerate, stop, show how much fuel is left, and so many other things.

We identify data as attributes and behavior as methods in object-oriented programming. Again:

Data → Attributes and Behavior → Methods

And a Class is the blueprint from which individual objects are created. In the real world, we often find many objects with the same type. Like cars. All the same make and model (and all have an engine, wheels, doors, and so on). Each car was built from the same set of blueprints and has the same components.

Python Object-Oriented Programming mode: ON

Python, as an Object-Oriented programming language, has these concepts: class and object.

A class is a blueprint, a model for its objects.

So again, a class it is just a model, or a way to define attributes and behavior (as we talked about in the theory section). As an example, a vehicle class has its own attributes that define what objects are vehicles. The number of wheels, type of tank, seating capacity, and maximum velocity are all attributes of a vehicle.

With this in mind, let’s look at Python syntax for classes:

class Vehicle:

We define classes with a class statement — and that’s it. Easy, isn’t it?

Objects are instances of a class. We create an instance by naming the class.

car = Vehicle()
print(car) # <__main__.Vehicle instance at 0x7fb1de6c2638>

Here car is an object (or instance) of the class Vehicle.

Remember that our vehicle class has four attributes: number of wheels, type of tank, seating capacity, and maximum velocity. We set all these attributes when creating a vehicle object. So here, we define our class to receive data when it initiates it:

class Vehicle:
    def __init__(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
        self.number_of_wheels = number_of_wheels
        self.type_of_tank = type_of_tank
        self.seating_capacity = seating_capacity
        self.maximum_velocity = maximum_velocity

We use the init method. We call it a constructor method. So when we create the vehicle object, we can define these attributes. Imagine that we love the Tesla Model S, and we want to create this kind of object. It has four wheels, runs on electric energy, has space for five seats, and the maximum velocity is 250km/hour (155 mph). Let’s create this object:

tesla_model_s = Vehicle(4, 'electric', 5, 250)

Four wheels + electric “tank type” + five seats + 250km/hour maximum speed.

All attributes are set. But how can we access these attributes’ values? We send a message to the object asking about them. We call it a method. It’s the object’s behavior. Let’s implement it:

class Vehicle:
    def __init__(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
        self.number_of_wheels = number_of_wheels
        self.type_of_tank = type_of_tank
        self.seating_capacity = seating_capacity
        self.maximum_velocity = maximum_velocity

    def number_of_wheels(self):
        return self.number_of_wheels

    def set_number_of_wheels(self, number):
        self.number_of_wheels = number

This is an implementation of two methods: number_of_wheels and set_number_of_wheels. We call it getter & setter. Because the first gets the attribute value, and the second sets a new value for the attribute.

In Python, we can do that using @property (decorators) to define getters and setters. Let’s see it with code:

class Vehicle:
    def __init__(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
        self.number_of_wheels = number_of_wheels
        self.type_of_tank = type_of_tank
        self.seating_capacity = seating_capacity
        self.maximum_velocity = maximum_velocity
    def number_of_wheels(self):
        return self.__number_of_wheels
    def number_of_wheels(self, number):
        self.__number_of_wheels = number

And we can use these methods as attributes:

tesla_model_s = Vehicle(4, 'electric', 5, 250)
print(tesla_model_s.number_of_wheels) # 4
tesla_model_s.number_of_wheels = 2 # setting number of wheels to 2
print(tesla_model_s.number_of_wheels) # 2

This is slightly different than defining methods. The methods work as attributes. For example, when we set the new number of wheels, we don’t apply two as a parameter, but set the value 2 to number_of_wheels. This is one way to write pythonic getter and setter code.

But we can also use methods for other things, like the “make_noise” method. Let’s see it:

class Vehicle:
    def __init__(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
        self.number_of_wheels = number_of_wheels
        self.type_of_tank = type_of_tank
        self.seating_capacity = seating_capacity
        self.maximum_velocity = maximum_velocity

    def make_noise(self):

When we call this method, it just returns a string VRRRRUUUUM.

tesla_model_s = Vehicle(4, 'electric', 5, 250)
tesla_model_s.make_noise() # VRUUUUUUUM

Encapsulation: Hiding Information

Encapsulation is a mechanism that restricts direct access to objects’ data and methods. But at the same time, it facilitates operation on that data (objects’ methods).

“Encapsulation can be used to hide data members and members function. Under this definition, encapsulation means that the internal representation of an object is generally hidden from view outside of the object’s definition.” — Wikipedia

All internal representation of an object is hidden from the outside. Only the object can interact with its internal data.

First, we need to understand how public and non-public instance variables and methods work.

Public Instance Variables

For a Python class, we can initialize a public instance variable within our constructor method. Let’s see this:

Within the constructor method:

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

Here we apply the first_name value as an argument to the public instance variable.

tk = Person('TK')
print(tk.first_name) # => TK

Within the class:

class Person:
    first_name = 'TK'

Here, we do not need to apply the first_name as an argument, and all instance objects will have a class attribute initialized with TK.

tk = Person()
print(tk.first_name) # => TK

Cool. We have now learned that we can use public instance variables and class attributes. Another interesting thing about the public part is that we can manage the variable value. What do I mean by that? Our object can manage its variable value: Get and Set variable values.

Keeping the Person class in mind, we want to set another value to its first_name variable:

tk = Person('TK')
tk.first_name = 'Kaio'
print(tk.first_name) # => Kaio

There we go. We just set another value (kaio) to the first_name instance variable and it updated the value. Simple as that. Since it’s a public variable, we can do that.

Non-public Instance Variable

We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work). — PEP 8

As the public instance variable , we can define the non-public instance variable both within the constructor method or within the class. The syntax difference is: for non-public instance variables , use an underscore (_) before the variable name.

“‘Private’ instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member)” — Python Software Foundation

Here’s an example:

class Person:
    def __init__(self, first_name, email):
        self.first_name = first_name
        self._email = email

Did you see the email variable? This is how we define a non-public variable :

tk = Person('TK', 'tk@mail.com')
print(tk._email) # tk@mail.com

We can access and update it. Non-public variables are just a convention and should be treated as a non-public part of the API.

So we use a method that allows us to do it inside our class definition. Let’s implement two methods (email and update_email) to understand it:

class Person:
    def __init__(self, first_name, email):
        self.first_name = first_name
        self._email = email

    def update_email(self, new_email):
        self._email = new_email

    def email(self):
        return self._email

Now we can update and access non-public variables using those methods. Let’s see:

tk = Person('TK', 'tk@mail.com')
print(tk.email()) # => tk@mail.com
# tk._email = 'new_tk@mail.com' -- treat as a non-public part of the class API
print(tk.email()) # => tk@mail.com
print(tk.email()) # => new_tk@mail.com
  1. We initiated a new object with first_name TK and email tk@mail.com
  2. Printed the email by accessing the non-public variable with a method
  3. Tried to set a new email out of our class
  4. We need to treat non-public variable as non-public part of the API
  5. Updated the non-public variable with our instance method
  6. Success! We can update it inside our class with the helper method

Public Method

With public methods, we can also use them out of our class:

class Person:
    def __init__(self, first_name, age):
        self.first_name = first_name
        self._age = age

    def show_age(self):
        return self._age

Let’s test it:

tk = Person('TK', 25)
print(tk.show_age()) # => 25

Great — we can use it without any problem.

Non-public Method

But with non-public methods we aren’t able to do it. Let’s implement the same Person class, but now with a show_age non-public method using an underscore (_).

class Person:
    def __init__(self, first_name, age):
        self.first_name = first_name
        self._age = age

    def _show_age(self):
        return self._age

And now, we’ll try to call this non-public method with our object:

tk = Person('TK', 25)
print(tk._show_age()) # => 25

We can access and update it. Non-public methods are just a convention and should be treated as a non-public part of the API.

Here’s an example for how we can use it:

class Person:
    def __init__(self, first_name, age):
        self.first_name = first_name
        self._age = age

    def show_age(self):
        return self._get_age()

    def _get_age(self):
        return self._age

tk = Person('TK', 25)
print(tk.show_age()) # => 25

Here we have a _get_age non-public method and a show_age public method. The show_age can be used by our object (out of our class) and the _get_age only used inside our class definition (inside show_age method). But again: as a matter of convention.

Encapsulation Summary

With encapsulation we can ensure that the internal representation of the object is hidden from the outside.

Inheritance: behaviors and characteristics

Certain objects have some things in common: their behavior and characteristics.

For example, I inherited some characteristics and behaviors from my father. I inherited his eyes and hair as characteristics, and his impatience and introversion as behaviors.

In object-oriented programming, classes can inherit common characteristics (data) and behavior (methods) from another class.

Let’s see another example and implement it in Python.

Imagine a car. Number of wheels, seating capacity and maximum velocity are all attributes of a car. We can say that an ElectricCar class inherits these same attributes from the regular Car class.

class Car:
    def __init__(self, number_of_wheels, seating_capacity, maximum_velocity):
        self.number_of_wheels = number_of_wheels
        self.seating_capacity = seating_capacity
        self.maximum_velocity = maximum_velocity

Our Car class implemented:

my_car = Car(4, 5, 250)

Once initiated, we can use all instance variables created. Nice.

In Python, we apply a parent class to the child class as a parameter. An ElectricCar class can inherit from our Car class.

class ElectricCar(Car):
    def __init__(self, number_of_wheels, seating_capacity, maximum_velocity):
        Car.__init__(self, number_of_wheels, seating_capacity, maximum_velocity)

Simple as that. We don’t need to implement any other method, because this class already has it (inherited from Car class). Let’s prove it:

my_electric_car = ElectricCar(4, 5, 250)
print(my_electric_car.number_of_wheels) # => 4
print(my_electric_car.seating_capacity) # => 5
print(my_electric_car.maximum_velocity) # => 250


That’s it!

We learned a lot of things about Python basics:

  • How Python variables work
  • How Python conditional statements work
  • How Python looping (while & for) works
  • How to use Lists: Collection | Array
  • Dictionary Key-Value Collection
  • How we can iterate through these data structures
  • Objects and Classes
  • Attributes as objects’ data
  • Methods as objects’ behavior
  • Using Python getters and setters & property decorator
  • Encapsulation: hiding information
  • Inheritance: behaviors and characteristics

Congrats! You completed this dense piece of content about Python.

Original article source at https://www.freecodecamp.org

#python #programming #developer 

What is GEEK

Buddha Community

Learn Python Programming from Scratch | Python from Zero to Hero
Ray  Patel

Ray Patel


Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services


When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Zakary  Goyette

Zakary Goyette


Learning Python: The Prompt, Then Read Template

In my last article I discussed how to use the Input-Process-Output template as a general guide to how programs should be structured in Python. To review, most Python programs will consist of three steps — getting input into the program, processing the input in some way, and outputting the results of the processing.

In this article, I’m going to focus on one part of that step — getting input into a program — by prompting the user to enter some data and then reading the data into the program. This is a mostly straightforward process except for some data conversions that have to occur when you are inputting numbers.

Defining a Prompt

Let’s start by defining the word prompt. A prompt is a message to the user of your program telling them what they are supposed to be entering into the program. A prompt needs to be descriptive but doesn’t have to be overly detailed.

For example, if your program needs the user to enter their name, you can use a prompt like this:

Enter your name:

However, you may need to be more specific if you want the user to enter their first name and their last name separately. The more appropriate prompt in this case might really be two prompts:

Enter your first name:
Enter your last name:

#learn-to-code #learn-python-programming #python #learning-python #python-programming

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Biju Augustian

Biju Augustian


Learn Python Programming

Learn Python Programming

Learn Python Programming and increase your python programming skills with Coder Kovid.

Python is the highest growing programming language in this era. You can use Python to do everything like, web development, software development, cognitive development, machine learning, artificial intelligence, etc. You should learn python programming and increase your skills of programming.

In this course of learn python programming you don’t need any prior programming knowledge. Every beginner can start with.

Basic knowledge
No prior knowledge needed to learn this course
What will you learn
Write Basic Syntax of Python Programming
Create Basic Real World Application
Program in a fluent manner
Get Familiar in Programming Environment

#Learn Python Programming # Learn Python #Python Programming # Programming

Biju Augustian

Biju Augustian


Learn Programming With Python In 100 Steps

We love Programming. Our aim with this course is to create a love for Programming.

Python is one of the most popular programming languages. Python offers both object oriented and structural programming features.

We take an hands-on approach using a combination of Python Shell and PyCharm as an IDE to illustrate more than 150 Python Coding Exercises, Puzzles and Code Examples.

In more than 150 Steps, we explore the most important Python Programming Language Features

Basics of Python Programming - Expressions, Variables and Printing Output
Python Operators - Python Assignment Operator, Relational and Logical Operators, Short Circuit Operators
Python Conditionals and If Statement
Methods - Parameters, Arguments and Return Values
An Overview Of Python Platform
Object Oriented Programming - Class, Object, State and Behavior
Basics of OOPS - Encapsulation, Inheritance and Abstract Class.
Basics about Python Data Types
Basics about Python Built in Modules
Conditionals with Python - If Else Statement, Nested If Else
Loops - For Loop, While Loop in Python, Break and Continue
Immutablity of Python Basic Types
Python Data Structures - List, Set, Dictionary and Tuples
Introduction to Variable Arguments
Basics of Designing a Class - Class, Object, State and Behavior. Deciding State and Constructors.
Introduction to Exception Handling - Your Thought Process during Exception Handling. try, except, else and finally. Exception Hierarchy. Throwing an Exception. Creating and Throwing a Custom Exception

To read more:

#Programming with Python #Learn Programming #Learn Programming with Python #Python