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PYTHON the Future Technology

Most Popular Uses of Python

40%MachineLearning

40%Web

Development

55%Data

Science

4th Most Used 85% of the TimeProgramming Languagein 2021, 44.1% of developersworked with it

Python was used as a primaryprogramming Language for thedevlopment

There are now 8.2 million developers in the world who code using Python and thatPopulation is now larger than those who build in Java, who number 7.6 million

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An average python developer

in India draws ₹779,449/- PA

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PYTHON COURSE CURRICULUM

COURSE DURATION5 WEEKS

SESSION HOURS60 HOURS

REALTIME PRACTICE

CASE STUDIES + MOCK TESTS

KEY HIGHLIGHTS

ä Personalized attention and guidance to accelerate the learning

ä Subject matter experts as trainers

ä Industry ready course roaster designed by architects and subject matter experts

ä Proven content, creating gateway to world of Job opportunities

ä Not just learning, Learn by doing methodology ä Access to Instant recorded live Class videos shared

ä Guest lectures from leading MNC Companies

ä Practical & detailed approach, everyone can understand and perform to excel

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ä Special guidance to students to win jobs with highest packages

ä Partnered and serving 100+ clients right from the inception of the firm

TRAINING HIGHLIGHTS

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Working with Data – Variables

Python - Programming from Absolute Beginning - Introduc�on to Computer Programs

Avoiding conflicts with namespaces

Programming Paradigms

Understanding Func�ons

Introduc�on to Programming Languages - Transla�ng code into something that the computer understands - Syntax and the building blocks of a programming language

Types of Applica�ons Standalone applica�ons , Client-Server Applica�ons, Web applica�ons Mobile Applica�ons ,Distributed applica�ons, Cloud-based applica�ons

So�ware Projects and How We Organize Our Code Working with so�ware projects , Working with packages to share code

Sequence – The Basic Building Block of a Computer Program

Composite type

Controlling the execu�on path ,

Declaring and ini�alizing variables , Primi�ve data types

Selec�on with the if and switch statement Itera�on with the for loop , Itera�on with the while loop Itera�ng over sequences using for each

Deciding what goes into a func�on , Wri�ng a func�on Returning values from a func�on ,Func�on arguments, Func�ons in ac�on , Local and global variables

When Things Go Wrong – Bugs and Excep�ons

Program Control Structures

Understanding so�ware bugs , types of so�ware bugs Finding bugs using a debugger

Unit tes�ng, Integra�on tes�ng , Other types of tests

So�ware releases

Deployment Automa�on

Programming Tools and Methodologies

Understanding structured programming , object-oriented programming , func�onal programming - logic programming

Understanding version control systems

Understanding so�ware deployment Understanding so�ware deployment

Code maintenance

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Do Mul�ple Comparisons with in

Forma�ng, Oldstyle: % , New styles: {} and format() ,

Data: Types, Values, Variables, and Names

Why Python? , Why Not Python?

Prac�cal Version Controlling with Git, So�ware defect management using JIRA Understanding Agile project Management using Scrum

Get Length with len() , Split with strip()

Assigning to Mul�ple Names, Reassigning a Name

Get a Character with [] , Get a Substring with a Slice

Comment with #, Con�nue Lines with \

Floats, Math Func�ons

Python Data are objects , Types , Mutability, Literal Values

Choose with if

Crea�ng with Quotes

Agile development

Defining code quality , Wri�ng code with readability in mind

Introduc�on to Python

Crea�ng with str(), Escape with \ Combine by Using + , Duplicate with +

Wri�ng code with efficiency in mind

Installing Python , Running Python , Moment of Zen

Compare with if, elif and else, What is True

Mysteries , Li�le Programs

Copying, Choose Good Variable Names

A Bigger Program , Python in the Real world

Precedence, Bases , Type Conversions, How Big is int?

Search and Select , Case , Alignment

Code Quality

Integers, Literal Integers, Integer Opera�ons ,Integers and Variables

Variables , Assignment, Variables are Names, Not Places

Numbers

A Taste of Python

Boolean

Text Strings

Newest Style: f-string , More String Things

Waterfall development , Spiral model So�ware deployment process methodologies

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Create with tuple()

Repeat with while

Cancel with break, Skip Ahead with con�nue, Check break Use with else Generate Number Sequences with range() , Other Iterators

Cancel with break, Skip Ahead with con�nue

Tuples and Lists

Create with Commas and ()

Loop with while and for

Check break Use with else ,Iterate with for and in

Tuples

Combine Tuples by Using +

Compare Tuples Iterate with for and in Modify a Tuple

Lists Create with [] , Create or Convert with list() Create from String with split() Get an Item by [ offset ] , Get Items with a Slice

Add an Item by offset with insert()

Change an item by [ offset ], Change Items with a Slice Duplicate All items with *, Combine Lists by Using extend() or +

Delete an Item by Value with remove() Get an Item by Offset and Delete It with pop()

Add an item to the End with append(),

Duplicate Items with *

Delete an Item by Offset with del,

Delete All items with clear(), Find an Item's Offset by Value with index() Test for a Value with in.,

Convert with dict()

Copy everything with deepcopy()

Create with {} Create with dict()

Get Length with len() Reorder Items with sort() or sorted() Convert a List to a String with join()

Create a List with a Comprehension- Tuples vs Lists

Assign with =, Copy with copy(), list() or a Slice

Count Occurrences of a Value with count()

Dic�onariesDic�onaries and Sets

Iterate Mul�ple Sequences with zip() Compare Lists(), Iterate with for and in

Add or Change an Item by [ key ]

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Copy Everything with deepcopy()

Get Length with len()

Iterate with for and in

Set Comprehensions

Define a Func�on with def

Dic�onary Comprehensions

Posi�onal arguments

Specify Default Parameter Values

Delete an Item with remove()

Iterate with for and in

Delete an Item by Key with del

Combine Dic�onaries with {**a, **b}

Delete All Items with clear()

Copy with copy() Assign with =

Get All Key-Value Pairs with items()

Combine Dic�onaries with update()

Get an Item by Key and Delete it with pop()

Get All Keys with keys()

Compare Dic�onaries

Convert with set()

Sets

Get Length with len ()

Get All Values with values()

Create with set()

Add an Item with add()

Combina�ons and Operators

Create an Immutable Set with frozenset()

Func�ons

Call a Func�on with Parentheses Arguments and Parameters None is useful

Keyword Arguments

Explode/Gather Posi�onal Arguments with * Explode/Gather Keyword Arguments with **

Inner Func�ons Closures

Func�ons are First-Class Ci�zens Docstrings

Anonymous Func�ons: lambda Generators

Namespaces and Scope Uses of _ and __ in Names Recursion- Async Func�ons - Excep�ons

Keyword-only Arguments Mutable and Immutable Arguments

Decorators

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Specifying a�ributes and behaviors

Inheritance - Case Study

Objects in Python

Introducing object-oriented

Modules and packages

Hiding details and crea�ng the public interface

Objects and classes

Object-Oriented Design

Organizing module content

Composi�on

Who can access my data? - Third-party Libraries - Case Study

Crea�ng python classes Objects in python

Object-Oriented Design What are Objects?

Dataclasses

Inheritance

A�ribute Access In Self Defense

Name Mangling for privacy

Proper�es for A�ribute Access

Inherit from a Parent Class, Override a Method

Class and Object A�ributes

Class Methods Instance Methods

Simple Objects

Add a Method, Get Help from your Parent with super() Mul�ple Inheritance , Mixins

Methods, Ini�aliza�on A�ributes

Sta�c Methods Duck Typing Magic Methods

Direct Access Ge�ers and Se�ers

Aggrega�on and Composi�on

Method Types

When to Use Objects or Something else

Define a Class with class,

Named Tuples

A�rs

Proper�es for Computed Values

Objects Oriented Python Object Oriented Design

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INSTAGRAM

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Python built in func�ons

Mul�ple Inheritance

Abstract base classes

Case Study

When to use Object-Oriented Programming

Manager objects Case Study

When Objects are Alike When Objects are Alike

When to Use Object-Oriented Programming

Case Study

Adding behaviors to class data with proper�es

Python Data Structures

Basic Inheritance

Polymorphism

Python Data Structures

Treat objects as objects

Empty Objects Tuples and named Tuples

Excep�ons

Data classes , Dic�onaries

Raising excep�ons

Lists, Sets, Extending built-in func�ons Case Study

Python Object-Oriented Shortcuts Python Object-Oriented Shortcuts

Case Study

Strings and Serializa�on

Case Study

Func�ons are objects too

Strings

The Iterator Pa�ern

Design pa�erns in brief

Filesystem paths

An alterna�ve to method overloading

Strings and Serializa�on

The Iterator Pa�ern

Generators - Corou�nes - Case Study

Regular expressions

Serializing objects

Iterators - Comprehensions

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Count Items with Counter()

Iterate over Code Structures with itertools Print Nicely with pprint() -Get Random

Order by Key with OrderedDict() - Deque

More Ba�eries: Get Other Python Code -

Virtual Environments

Python Design Pa�erns The decorator Pa�ern

The State Pa�ern The Singleton Pa�ern

The observer Pa�ern

The adapter Pa�ern The facade Pa�ern

The Strategy Pa�ern

The flyweight Pa�ern The command Pa�ern

The template Pa�ern

Tes�ng with pytest Why test? - Unit tes�ng

Imita�ng expensive objects

The abstract factory Pa�ern

How much tes�ng is enough- Case Study

Concurrency

Import a Module

Packages

Threads

Modules and import Statement

Tes�ng Object-Oriented Programs

Import a Module with Another Name

Rela�ve and Absolute Imports

Concurrency

Modules, Packages, and Goodies

Mul�processing

The Module Search Path

Import Only What You want from a Module

Goodies in the Python Standard Library Handle Missing Keys with setdefault() and defaultdict()

Tes�ng Object-Oriented Programs

The composite Pa�ern

Futures Aysnc IO - Case Study

Namespace Packages , Modules Vs Objects

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Test plans

PyTest for Python Tes�ng

Introducing automa�c tests and test suites

Introducing test-driven development and unit tests Test-driven development, Test units

Integra�on tests, Func�onal tests

The tes�ng pyramid, The tes�ng trophy Tes�ng distribu�ons and coverage

Test Doubles

Understanding acceptance tests and doubles

Introducing test doubles

Checking behaviors with spies

Managing dependencies with dependency injec�on Using dependency injec�on frameworks

Building applica�ons, the TDD way

So�ware Tes�ng and Test-Driven Development

Ge�ng Started with So�ware Tes�ng - Introducing so�ware tes�ng and quality control

Using dummy objects

Test-Driven Development (TDD)

Scaling the Test Suite Scaling tests

Mul�ple test cases, Organizing tests

Using mocks

Understanding the tes�ng pyramid and trophy

Replacing components with stubs

Moving e2e to func�onal

Understanding integra�on and func�onal tests

Preven�ng regressions

Working with mul�ple suites

Replacing dependencies with fakes

Carrying out performance tests Compile suite , Commit tests, Smoke tests

Star�ng projects with TDD

Enabling con�nuous integra�on , Performance tes�ng

Wri�ng PyTest fixtures , Using fixtures for dependency injec�on Managing temporary data with tmp_path Tes�ng I/O with capsys, Running subsets of the test suites

Running tests with PyTest

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R

Configuring the test suite

Using Behavior-driven development Wri�ng acceptance tests Wri�ng first test, Defining a feature file

Genera�ng fixtures

Declaring the scenario, Running the scenario test

Performing ac�ons with the When step Assessing condi�ons with the Then step Embracing specifica�ons by example

PyTest Essen�al Plugins

Further setup with the And step

PyTest Essen�al Plugins Using pytest-cov for coverage repor�ng Coverage as a service Using pytest-benchmark for benchmarking

Dynamic and Parametric Tests and Fixtures

Genera�ng tests with parametric tests

Find Exact Beginning Match with match()

Comparing benchmark runs

Running tests in parallel with pytest-xdist

Split at Matches with split() , Replace at Matches with sub()

Playing with data (text and binary)

Convert Bytes/String with binascii() - BitOperators

Text Strings: Unicode

Other Binary Data Tools

Tes�ng mul�ple python versions with Tox

Pa�erns: Special Characters, Pa�erns: Using specifiers

Introducing Tox

Normaliza�on

UTF-8, Encode

Bytes and bytearray

Using flaky to rerun unstable tests

Managing Test Environments with Tox

Python 3 Unicode Strings

Text Strings: Regular Expressions

Convert Binary Data with struct

Using pytest-testmon to rerun tests on code changes

Find FirstMatch with search() , Find All Matches with findall()

Pa�erns: Specifying match() Output

Binary Data

Using environments for more that Python Versions

Decode, HTML En��es

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Leap Year, The date�me module , Using the �me module

File Opera�ons

Copy with copy()

Files and Directories

All the Conversions, Alterna�ve Modules Read and Write Dates and �mes

File Input and Output Create or Open with open(), Write a Text File with print() Write a Text File with write() , Read a Text File with read(), readline(), or readlines()

Close Files Automa�cally by using with, Change Posi�on with seek()

Memory Mapping

Calendars and Clocks

Write a Binary File with read() ,Read a Binary File with read()

Check existence with exists() Check Type with isfile()

Changing Name with rename()

Change permissions with chmod(), Change Ownership with chown()

Directory Opera�ons

Changing current directory with chdir() List Matching Files with glob()

Processes and Concurrency Program and Processes Create a Process with subprocess Create a Process with mul�processing Kill a Process with terminate Get System Info with os

Create with mkdir(), Delete with rmdir()

Pathnames, BytesIO and StringIO

Link with link() or symlink()

Get Process Info with psu�l

Command Automa�on Invoke Other Command Helpers Concurrence Queues Processes

Delete a File with remove()

List contents with listdir(),

Twisted

Threads - Concurrent.futures

Asyncio, Redis - Beyond Queues

Green Threads and gevent

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Know How Closures interact with Variable Scope

Ini�alize Parent Classes with super

Accept Func�ons Instead of Classes for Simple Interfaces

Use Node and Docstrings to Specify Dynamic Default Arguments

Define Func�on Decorators with functools.wraps

Compose Mul�ple Generators with yield from

Compose Classes Instead of Nes�ng Many Levels of Built-in Types

Provide Op�onal Behavior with Keywork Arguments

Comprehensions and Generators

Avoid Repeated Work in Comprehensions by Using Assignment Expressions Avoid More Than Two Control Subexpressions in Comprehensions

Be Defensive When Itera�ng Over Arguments

Avoid Causing State Transi�ons in Generators with throw

Classes and Interfaces

Avoid Injec�ng Data into Generators with send

Enforce Clarity with Keyword-Only and Posi�onal-Only Arguments

Consider Generator Expressions for Large List Comprehensions

Consider Generators Instead of Returning Lists

Consider itertools for Working with Iterators and Generators

Use Comprehensions Instead of map and filter

Reduce Visual Noise with Posi�onal Arguments

Use @classmethod Polymorphism to Construct Objects Generically

Be Cau�ous when relying on dict inser�on Ordering

Prefer default dict Over setdefault to Handle Missing Items in Internal State

Prefer enumerate over range

Know How to Construct Key-Dependent Default Values with __missing__

Func�ons

Lists and Dic�onaries

Prefer Raising excep�ons to Returning None

Differences between bytes and str

Pythonic Thinking

Wri�ng helper func�ons instead of complex expressions

Prevent Repe��on with Assignment Expressions

Mul�ple Assignment Unpacking Over Indexing

Sort by Complex Criteria Using the key parameter

Prefer get Over in and KeyError to Handle Missing Dic�onary Keys

Effec�ve and Performant Python

Never Unpack more than three variables when func�ons return mul�ple values

Using zip to process Iterators in Parallel

Follow PEP 8 Style Guide

Avoid Else blocks a�er for & while loops

Know How to Slice Sequences

Interpolated F-strings over C-style Format strings and str.format

Avoid Striding and Slicing in a Single Expression, Prefer Catch-All unpacking over slicing

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Use subprocess to Manage Child Processes

Prefer Public A�ributes Over Private Ones

Register Class Existence with __init_subclass__

Consider Composing Func�onality with Mix-in Classes

Use Plain A�ributes Instead of Se�er and Ge�er Methods Consider @property Instead of Refactoring A�ributes

Validate Subclasses with __init_subclass__

Inherit from collec�ons.abc for Custom Container Types

Annotate Class A�ributes with __set_name__

Concurrency and Parallelism

Use Descriptors for Reusable @property Methods Use __geta�r__, __geta�ribute__, and __seta�r__ for Lazy A�ributes

Meta classes and A�ributes

Prefer Class Decorators Over Metaclasses for Composable Class Extensions

Know How to Recognize When Concurrency Is Necessary

Understand How Using Queue for Concurrency Requires Refactoring

-When Threads Are Necessary for Concurrency

Avoid Crea�ng New Thread Instances for On-demand Fan-out

Mix Threads and Corou�nes to Ease the Transi�on to asyncio

Take Advantage of Each Block in try/except/else/finally

Use Lock to Prevent Data Races in Threads

Consider contextlib and with Statements for Reusable try

Achieve Highly Concurrent I/O with Corou�nes

Use Threads for Blocking I/O, Avoid for Parallelism

Make pickle Reliable with copyreg

Consider ThreadPoolExecutor -

Know How to Port Threaded I/O to asyncio

Use Queue to Coordinate Work Between Threads

Use date�me Instead of �me for Local Clocks

Use decimal When Precision Is Paramount Profile Before Op�mizing

Avoid Blocking the asyncio Event Loop to Maximize Responsiveness Consider concurrent.futures for True Parallelism

Prefer deque for Producer& Consumer Queues for Producer–Consumer Queues Consider Searching Sorted Sequences with bisect

Robustness and Performance

Know How to Use heapq for Priority Queues Consider memoryview and bytearray for Zero-Copy Interac�ons with bytes

Tes�ng and Debugging

Encapsulate Dependencies to Facilitate Mocking and Tes�ng Consider Interac�ve Debugging with pdb, Use tracemalloc to Understand Memory Usage

Isolate Tests from Each Other with setUp, tearDown, setUpModule, and tearDownModule Use Mocks to Test Code with Complex Dependencies

Use repr Strings for Debugging Output, Verify Related Behaviors in TestCase Subclasses

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How to Be a Highly Performant Programmer

Profiling to Find Bo�lenecks

Visualizing cProfile Output with SnakeViz

Use Virtual Environments for Isolated and Reproducible Dependencies Write Docstrings for Every Func�on, Class, and Module Use Packages to Organize Modules and Provide Stable APIs

Know How to Break Circular Dependencies

The Fundamental Computer System

Memory Units

dealized Compu�ng Versus the Python Virtual Machine So Why Use Python?

Good Working Prac�ces

Define a Root Excep�on to Insulate Callers from APIs

Understanding Performant Python

Calcula�ng the Full Julia Set

Consider Module-Scoped Code to Configure Deployment Environments

Communica�ons Layers

Simple Timing Using the Unix �me Command

Introducing the Julia Set

Using the dis Module to Examine CPython Bytecode

Know Where to Find Community-Built Modules

Using the cProfile Module

Profiling Efficiently

Consider warnings to Refactor and Migrate Usage

Collabora�on

Compu�ng Units

Pu�ng the Fundamental Elements Together

Consider Sta�c Analysis via typing to Obviate Bugs

Simple Approaches to Timing—print and a Decorator

Using line_profiler for Line-by-Line Measurements Using memory_profiler to Diagnose Memory Usage

Different Complexity

Unit Tes�ng During Op�miza�on to Maintain Correctness

Bytecode: Under the Hood

Strategies to Profile Your Code Successfully

Introspec�ng an Exis�ng Process with PySpy

Different Approaches,

No-op @profile Decorator

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Es�ma�ng Pi Using Processes and Threads

Using Python Objects

How Does async/await Work?

Serial Crawler Gevent, Tornado Aioh�p

Asynchronous I/O

Shared CPU–I/O Workload Serial, Batched Results Full Async

The mul�processing Module

Introduc�on to Asynchronous Programming

An Overview of the mul�processing Module

Es�ma�ng Pi Using the Monte Carlo Method

Lessons from the Field

Serial Solu�on

File Locking

Naive Pool Solu�on

Replacing mul�processing with Joblib

Finding Prime Numbers

Using Redis as a Flag

Queues of Work

Verifying Primes Using Interprocess Communica�on

A Less Naive Pool Solu�on Using Manager.Value as a Flag

Using RawValue as a Flag

Using mmap as a Flag Redux

Using Less RAM,

Using numpy Random Numbers in Parallel Systems

Locking a Value Clusters and Job Queues

Sharing numpy Data with mul�processingSynchronizing File and Variable Access

Using mmap as a Flag

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