Download Python Overview

Python Overview
Installation, Data types, Control flow structures,
Loops, Functions, Strings, OOP, Data Structures,
Tuples, Destructuring Assignments
Python Overview
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Table of Contents

Python Overview

Installing Python

Python IDEs

Data types

Control flow structures

Loops

Data Structures and comprehensions
 Lists, sets, dictionaries and tuples

Destructuring assignments
Table of Contents

Functions

Lambda functions

Modules

Object-oriented programming
 Creating classes
 Properties, instance and class methods
 Inheritance

Best Practices

The Zen of Python
Python Overview
What is Python? What can be used for?
Python Overview
 Python is a widely used general-purpose, high-
level programming language
 Its design philosophy emphasizes code
readability
 Its syntax allows programmers to express
concepts in fewer lines of code
 The language provides constructs intended to
enable clear programs on both a small and large
scale
Python Overview

Python supports multiple programming
paradigms, including
 Object-oriented
 Imperative and functional programming

It features a dynamic type system and automatic
memory management

Python is widely used for:
 Web applications development: Django, Pyramid
 Games with PyGame
 Automations scripts, Sikuli
 And more
Installing Python
On MAC, Linux and Windows
Installing Python

On MAC
 Homebrew can be used

$ brew install python
On Linux
 Part of Linux is built with Python, so it comes outof-the-box

On Windows
 Download the installer from https://www.python.org/
 Add the installation path to System Variables $PATH
Installing Python on
Windows and MAC
Live Demo
Running Python in REPL

Open the CMD/Terminal and run:

You can run python code:
 Print the numbers from 0 to 4
for i in range(5):
print(i)
 Sum the numbers from 5 to 9
sum = 0
for i in range(5, 10):
sum += i
print(sum)
$ python
Running Python

Open the CMD/Terminal and run:

You can run python code:
 Print the numbers from 0 to 4
for i in range(5):
print(i)
 Sum the numbers from 5 to 9
sum = 0
for i in range(5, 10):
sum += i
print(sum)
Significant whitespace
matters a lot
$ python
Significant Whitespace

Significant whitespace is a way to write code in
python
 This is actually the indentation
 Significant whitespace creates blocks in
python code
 It is the equivalent of curly brackets ({}) in
other languages
 Good practices say "Use four spaces for indent"
Python IDEs
Sublime Text, PyCharm, REPL
Python IDEs

Python is quite popular and there are my IDEs:
 PyCharm
 Commercial and Community editions
 Ready-to-use
 Sublime Text
 Free, open-source
 Needs plugins and some setup to work properly
 LiClipse
 Free, based on Eclipse
 Ready-to-use
Python IDEs
Live Demo
Data types in Python
int, float, etc
Data types in Python
 Python supports all the primary
data types:
 int – integer numbers
 int(intAsString) parses a string to int
 float – floating-point numbers
 float(floatAsString) parses a string to float
 None – like null
 bool – Boolean values (True and False)
 str – string values (sequence of characters)
Data Types
Live Demo
Control Flow Structures
If-elif-else
Control Flow Structures
 Python supports conditionals:
if conditionOne:
# run code if conditionOne is True
elif conditionTwo:
# run code if conditionOne is False
#
but conditionTwo is True
else
# run code if conditionOne and
#
conditionTwo are False
 The conditions are True-like and False-like
 ""(empty string), 0, None are evaluated to False
 Non-empty strings, any number or object are
evaluated to True
Conditional Statements
Live Demo
Loops
for and while
Loops
 There are two types of loops in Python
 for loop
for i in range(5):
# run code with values of i: 0, 1, 2, 3 and 4
names = ['Doncho', 'Asya', 'Evlogi', 'Nikolay', 'Ivaylo']
for i in range(len(names)):
print('Hi! I am %s!'%names[i]);
 while loop
number = 1024
binNumber = '';
while number >= 1:
binNumber += '%i'%(number%2)
number /= 2
print(binNumber[::-1])
Loops
Live Demo
Data Structures
Lists, Dictionaries, Sets
Data Structures in Python

Python has three primary data structures
 List
 Keep a collection of objects
 Objects can be accessed and changed by index
 Objects can be appended/removed dynamically
 Dictionary
 Keep a collection of key-value pairs
 Values are accessed by key
 The key can be of any type
 Sets
 Keep a collection of unique objects
Lists in Python
Collections of objects
Lists in Python

Lists are created using square brackets
('[' and ']'):
numbers = [1, 2, 3, 4, 5, 6, 7]
names = ["Doncho", "Niki", "Ivo", "Evlogi"]
 Print a list of items:
 Object by object
for name in names:
print(name)
 Or by index:
for index in len(names):
print(names[index])
 Add new object to the list:
names.append("Asya")
Lists in Python
Live Demo
List Comprehensions
 List comprehensions are a easier way
to create
lists with values
 They use the square brackets ('[' and ']') and
an expression inside
even = [number for number in numbers if not number % 2]
longerNames = [name for name in names if len(name) > 6]
kNames = [name for name in names if name.startswith("K")]
[eat(food) for food in foods if food is not "banana"]
List Comprehensions
Live Demo
Sets in Python
Collections of unique values
Sets in Python
 Sets are created like lists,
but using curly
brackets instead of square
 They contain unique values
 i.e. each value can be contained only once
 __eq__(other) methods is called for each object
names = {"Doncho", "Nikolay"}
names.add("Ivaylo")
names.add("Evlogi")
names.add("Doncho")
print(names)
# the result is {'Nikolay', 'Ivaylo', 'Evlogi', 'Doncho'}
# 'Doncho' is contained only once
Sets in Python
Live Demo
Set Comprehensions
 Set comprehensions are exactly like list
comprehensions
 But contain only unique values
sentence = "Hello! My name is Doncho Minkov and …"
parts = re.split("[,!. ]", sentence)
words = {word.lower() for word in parts if word is not ""}
Set Comprehensions
Live Demo
Dictionaries in Python
Key-value pairs
Dictionaries in Python

Dictionary is a collection of key-value pairs
 The key can be of any type
 For custom types, the methods __hash__() and
__eq__(other) must be overloaded
 Values can be of any type
 Even other dictionaries or lists
musicPreferences = {
"Rock": ["Peter", "Georgi"],
"Pop Folk": ["Maria", "Teodor"],
"Blues and Jazz": ["Alex", "Todor"],
"Pop": ["Nikolay", "Ivan", "Elena"]
}
Dictionaries in Python
Live Demo
Dictionary Comprehensions
 Dictionary
comprehensions follow pretty much
the same structure as list comprehensions
 Just use curly brackets {} instead of square []
 Provide the pair in the format key: value
names = {trainer.name for trainer in trainers}
trainersSkills = { name: [] for name in names}
{trainersSkills[trainer.name].append(trainer.skill)
for trainer in trainers}
Dictionary Comprehensions
Live Demo
Tuples
Tuples in Python
 A tuple is a sequence of immutable objects
 Much like lists, but their values cannot be
changed
 Use parenthesis instead of square brackets
languages = ("Python", "JavaScript", "Swift")
for lang in languages:
print(lang)
print("Number of languages: {0}".format(len(languages))
Tuples
Live Demo
Operations with Tuples

The following operations are supported for tuples:
 Get the length of the tuple:
len((-1, 5, 11)) # returns 3
 Concatenate tuples:
(1, 2) + (7, 8, 9) # returns (1, 2, 7, 8, 9)
 Multiply by number:
(1,)*5 # returns (1, 1, 1, 1, 1)
 Check for value:
3 in (1, 2, 3) # returns True
Operations with Tuples
Live Demo
Destructuring Assignments
Destructuring Assignments
 Destructuring assignments (bind) allow
easier
extracting of values
 With tuples
x, y = 1, 5 # equivalent to x = 1 and y = 5
x, y = y, x # swap the values
 With arrays
numbers = [1, 2]
[one, two] = numbers
numbers = list(range(1, 10))
[first, *middle, last] = numbers
Destructuring Assignments
Live Demo
Functions in Python
Separate the code in smaller and reusable pieces
Functions in Python
 Functions in Python:
 Are defined using the keyword "def"
 Have an identifier
 A list of parameters
 A return type
def toBin(number):
bin = ''
while number >= 1:
bin += '%i'%(number%2)
number /= 2
return bin[::-1]
Functions in Python
Live Demo
Anonymous Functions
 Python allows
the creation of anonymous
functions (lambda functions)
 Using the keyword lambda and a special syntax:
printMsg = lambda msg: print('Message: {0}'.format(msg))
 Same as:
def printMsg2(msg):
print('Message: {0}'.format(msg))
 Mostly used for data structures manipulation
evenNumbers = filter(lambda n: not n%2, numbers)
Lambda Functions
Live Demo
Modules in Python
How to separate the code in different modules
Modules in Python

Python applications are separated into modules
 These module represent just a list of functions
and/or classes

To use all functions from a module numeralSystems:
import numeralSystems
print(numeralSystems.toBin(1023))
print(numeralSystems.toHex(1023))
 A single function:
from numeralSystems import toBin as bin
 Or some functions
from numeralSystems import toBin as bin, toHex as hex
Modules in Python
Live Demo
Object-oriented Programming
With Python
Object-oriented Programming
with Python

Python is an object-oriented language
 Support for classes, inheritance, method
overloading, etc…

To create a class:
 Use the keyword class
 Give the class a name
 Define __init__ method and other methods
 Attributes (fields) are defined inside
the contructor (__init__) method
class Presentation:
def __init__(self, title, duration):
self.title = title
self.duration = duration
Creating Classes
with Python
Live Demo
Properties in Python

Python has a concept for providing properties for
class attributes
 Properties are used for data hiding and validation
of the input values
class Presentation:
def __init__(self, title, duration):
self.title = title
self.duration = duration
@property
def duration(self):
return self._duration
@duration.setter
def duration(self, value):
if(0 < duration and duration <= 4):
self._duration = value
Classes with Properties
Live Demo
Instance methods
 Instance methods are methods that are used
on a concrete instance of the class
 To create an instance method, provide
self as
first parameter to the method:
class Trainer(Person):
# …
def deliver(self, presentation):
print("{0} is delivering presentation about {1}"
.format(self.name, presentation.title))
Instance methods
 Instance methods are methods that are used
on a concrete instance of the class
 To create an instance method, provide
self as
first parameter to the method:
class Trainer(Person):
# …
def deliver(self, presentation):
print("{0} is delivering presentation about {1}"
.format(self.name, presentation.title))
self is like this
for other languages
Instance Methods
Live Demo
Class Methods
 Class
methods are shared between all
instances of the class, and other objects
 They are used on the class, instead of on
concrete object
 Defined as regular functions
 But inside the class
 Without self as parameter
class Person:
# …
def validate_person_age(age):
return 0 < age and age < 150
Class Methods
Live Demo
Class Inheritance in Python
Extend classes
Class Inheritance in Python

Class inheritance is the process of creating a class
that extends the state and behavior of other class
 Functions and attributes are inherited
 New functions and attributes can be added
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
class Trainer(Person):
def __init__(self, name, age, speciality):
super().__init__(name, age)
self.speciality = speciality
def deliver(self, presentation):
print("{0} is delivering presentation about {1}"
.format(self.name, presentation.title))
Class Inheritance in Python

Class inheritance is the process of creating a class
that extends the state and behavior of other class
 Functions and attributes are inherited
 New functions and attributes can be added
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
Trainer inherits Person
class Trainer(Person):
def __init__(self, name, age, speciality):
super().__init__(name, age)
self.speciality = speciality
def deliver(self, presentation):
print("{0} is delivering presentation about {1}"
.format(self.name, presentation.title))
Class Inheritance in Python

Class inheritance is the process of creating a class
that extends the state and behavior of other class
 Functions and attributes are inherited
 New functions and attributes can be added
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
class Trainer(Person):
def __init__(self, name, age, speciality):
super().__init__(name, age)
super() calls
self.speciality = speciality
the parent
def deliver(self, presentation):
print("{0} is delivering presentation about {1}"
.format(self.name, presentation.title))
Class Inheritance in Python
Live Demo
Best Practices
How to write readable Python code?
Best Practices

Use 4-space indentation, and no tabs

Wrap lines so that they don’t exceed 79 characters

Use blank lines to separate functions and classes

Use docstrings

Use spaces around operators and after commas

Name your classes and functions consistently
 PascalCase for classes
 lower_case_with_underscores for functions and
methods
The Zen of Python
Python Overview
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