Python Course Content:

Core Python

Introduction to Languages

• What is Language?

• Types of languages

• Introduction to Translators

  • Compiler

  • Interpreter

• What is Scripting Language?

• Types of Script

• Programming Languages v/s Scripting Languages

• Difference between Scripting and Programming languages

• What is a programming paradigm?

• Procedural programming paradigm

• Object-Oriented Programming paradigm

Introduction to Python

• What is Python?

• WHY PYTHON?

• History

• Features – Dynamic, Interpreted, Object-oriented, Embeddable, Extensible, Large standard libraries, Free and Open source

• Why Python is General Language?

• Limitations of Python

• What is PSF?

• Python implementations

• Python applications

• Python versions

• PYTHON IN REALTIME INDUSTRY

• Difference between Python 2.x and 3.x

• Difference between Python 3.7 and 3.8

• Software Development Architectures

Python Software’s

• Python Distributions

• Download &Python Installation Process in Windows, Unix, Linux, and Mac

• Online Python IDLE

• Python Real-time IDEs like Spyder, Jupyter NoteBook, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc

Python Language Fundamentals

• Python Implementation Alternatives/Flavors

• Keywords

• Identifiers

• Constants / Literals

• Data types

• Python VS JAVA

• Python Syntax

Different Modes of Python

• Interactive Mode

• Scripting Mode

• Programming Elements

• Structure of Python program

• First Python Application

• Comments in Python

• Python file extensions

• Setting Path in Windows

• Edit and Run python program without IDE

• Edit and Run python program using IDEs

• INSIDE PYTHON

• Programmers View of Interpreter

• Inside INTERPRETER

• What is Byte Code in PYTHON?

• Python Debugger

Python Variables

• bytes Data Type

• byte array

• String Formatting in Python

• Math, Random, Secrets Modules

• Introduction

• Initialization of variables

• Local variables

• Global variables

• ‘global’ keyword

• Input and Output operations

• Data conversion functions – int(), float(), complex(), str(), chr(), ord()

Operators

• Arithmetic Operators

• Comparison Operators

• Python Assignment Operators

• Logical Operators

• Bitwise Operators

• Shift operators

• Membership Operators

• Identity Operators

• Ternary Operator

• Operator precedence

• Difference between “is” vs “==”

Input & Output Operators

• Print

• Input

• Command-line arguments

Control Statements

• Conditional control statements

• If

• If-else

• If-elif-else

• Nested-if

• Loop control statements

• for

• while

• Nested loops

• Branching statements

• Break

• Continue

• Pass

• Return

• Case studies

Data Structures or Collections

• Introduction

• Importance of Data structures

• Applications of Data structures

• Types of Collections

• Sequence

• Strings, List, Tuple, range

• Nonsequence

• Set, Frozen set, Dictionary

• Strings

• What is string

• Representation of Strings

• Processing elements using indexing

• Processing elements using Iterators

• Manipulation of String using Indexing and Slicing

• String operators

• Methods of the String object

• String Formatting

• String functions

• String Immutability

• Case studies

List Collection

• What is List

• The need of List collection

• Different ways of creating List

• List comprehension

• List indices

• Processing elements of List through Indexing and Slicing

• List object methods

• List is Mutable

• Mutable and Immutable elements of List

• Nested Lists

• List_of_lists

• Hardcopy, shallow copy and DeepCopy

• zip() in Python

• How to unzip?

• Python Arrays:

• Case studies

Tuple Collection

• What is a tuple?

• Different ways of creating Tuple

• Method of Tuple object

• Tuple is Immutable

• Mutable and Immutable elements of Tuple

• Process tuple through Indexing and Slicing

• List v/s Tuple

• Case studies

Set Collection

• What is set?

• Different ways of creating a set

• Difference between list and set

• Iteration Over Sets

• Accessing elements of the set

• Python Set Methods

• Python Set Operations

• Union of sets

• functions and methods of set

• Python Frozen set

• Difference between set and frozenset ?

• Case study

Dictionary Collection

• What is a dictionary?

• Difference between list, set, and dictionary

• How to create a dictionary?

• PYTHON HASHING?

• Accessing values of the dictionary

• Python Dictionary Methods

• Copying dictionary

• Updating Dictionary

• Reading keys from Dictionary

• Reading values from Dictionary

• Reading items from Dictionary

• Delete Keys from the dictionary

• Sorting the Dictionary

• Python Dictionary Functions and methods

• Dictionary comprehension

Functions

• What is Function?

• Advantages of functions

• Syntax and Writing function

• Calling or Invoking the function

• Classification of Functions

  • No arguments and No return values

  • With arguments and No return values

  • With arguments and With return values

  • No arguments and With return values

  • Recursion

• Python argument type functions :

  • Default argument functions

  • Required(Positional) arguments function

  • Keyword arguments function

  • Variable arguments functions

• ‘pass’ keyword in functions

• Lambda functions/Anonymous functions

  • map()

  • filter()

  • reduce()

• Nested functions

• Non-local variables, global variables

• Closures

• Decorators

• Generators

• Iterators

• Monkey patching

Advanced Python

Python Modules

• Importance of modular programming

• What is module

• Types of Modules – Predefined, User-defined.

• User-defined modules creation

• Functions based modules

• Class-based modules

• Connecting modules

• Import module

• From … import

• Module alias / Renaming module

• Built-In properties of the module

Packages

• Organizing python project into packages

• Types of packages – pre-defined, user-defined.

• Package v/s Folder

• py file

• Importing package

• PIP

• Introduction to PIP

• Installing PIP

• Installing Python packages

• Uninstalling Python packages

OOPs

• Procedural v/s Object-oriented programming

• Principles of OOP – Encapsulation, Abstraction (Data Hiding)

• Classes and Objects

• How to define a class in python

• Types of variables – instance variables, class variables.

• Types of methods – instance methods, class method, static method

• ​

• Object initialization

• ‘self’ reference variable

• ‘cls’ reference variable

• Access modifiers – private(__) , protected(_), public

• AT property class

• Property() object

• Creating object properties using setaltr, getaltr functions

• Encapsulation(Data Binding)

• What is polymorphism?

• Overriding

1. i) Method overriding

2. ii) Constructor overriding

• Overloading

1. i) Method Overloading

2. ii) Constructor Overloading

iii)  Operator Overloading

• Class re-usability

• Composition

• Aggregation

• Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance

• Constructors in inheritance

• Object class

• super()

• Runtime polymorphism

• Method overriding

• Method resolution order(MRO)

• Method overriding in Multiple inheritance and Hybrid Inheritance

• Duck typing

• Concrete Methods in Abstract Base Classes

• Difference between Abstraction & Encapsulation

• Inner classes

• Introduction

• Writing inner class

• Accessing class level members of inner class

• Accessing object level members of inner class

• Local inner classes

• Complex inner classes

• Case studies

Exception Handling & Types of Errors

• What is Exception?

• Why exception handling?

• Syntax error v/s Runtime error

• Exception codes – AttributeError, ValueError, IndexError, TypeError…

  • Handling exception – try except block

  • Try with multi except

  • Handling multiple exceptions with single except block

• Finally block

  • Try-except-finally

  • Try with finally

  • Case study of finally block

• Raise keyword

  • Custom exceptions / User defined exceptions

  • Need to Custom exceptions

• Case studies

Regular expressions

• Understanding regular expressions

• String v/s Regular expression string

• “re” module functions

• Match()

• Search()

• Split()

• Findall()

• Compile()

• Sub()

• Subn()

• Expressions using operators and symbols

• Simple character matches

• Special characters

• Character classes

• Mobile number extraction

• Mail extraction

• Different Mail ID patterns

• Data extraction

• Password extraction

• URL extraction

• Vehicle number extraction

• Case study

File &Directory handling

• Introduction to files

• Opening file

• File modes

• Reading data from file

• Writing data into file

• Appending data into file

• Line count in File

• CSV module

• Creating CSV file

• Reading from CSV file

• Writing into CSV file

• Object serialization – pickle module

• XML parsing

• JSON parsing

Python Logging

• Logging Levels

• implement Logging

• Configure Log File in over writing Mode

• Timestamp in the Log Messages

• Python Program Exceptions to the Log File

• Requirement of Our Own Customized Logger

• Features of Customized Logger

Date & Time module

• How to use Date & Date Time class

• How to use Time Delta object

• Formatting Date and Time

• Calendar module

• Text calendar

• HTML calendar

OS module

• Shell script commands

• Various OS operations in Python

• Python file system shell methods

• Creating files and directories

• Removing files and directories

• Shutdown and Restart system

• Renaming files and directories

• Executing system commands

Multi-threading & Multi Processing

• Introduction

• Multi tasking v/s Multi threading

• Threading module

• Creating thread – inheriting Thread class , Using callable object

• Life cycle of thread

• Single threaded application

• Multi threaded application

• Can we call run() directly?

• Need to start() method

• Sleep()

• Join()

• Synchronization – Lock class – acquire(), release() functions

• Case studies

Garbage collection

• Introduction

• Importance of Manual garbage collection

• Self reference objects garbage collection

• ‘gc’ module

• Collect() method

• Threshold function

• Case studies

Python Data Base Communications(PDBC)

• Introduction to DBMS applications

• File system v/s DBMS

• Communicating with MySQL

• Python – MySQL connector

• connector module

• connect() method

• Oracle Database

• Install cx_Oracle

• Cursor Object methods

• execute() method

• executeMany() method

• fetchone()

• fetchmany()

• fetchall()

• Static queries v/s Dynamic queries

• Transaction management

• Case studies

Python – Network Programming

• What is Sockets?

• What is Socket Programming?

• The socket Module

• Server Socket Methods

• Connecting to a server

• A simple server-client program

• Server

• Client

Tkinter & Turtle

• Introduction to GUI programming

• Tkinter module

• Tk class

• Components / Widgets

• Label , Entry , Button , Combo, Radio

• Types of Layouts

• Handling events

• Widgets properties

• Case studies

Data analytics modules

• Numpy

• Introduction

• Scipy

• Introduction

• Arrays

• Datatypes

• Matrices

• N dimension arrays

• Indexing and Slicing

• Pandas

• Introduction

• Data Frames

• Merge , Join, Concat

• MatPlotLib introduction

• Drawing plots

• Introduction to Machine learning

• Types of Machine Learning?

• Introduction to Data science

DJANGO

• Introduction to PYTHON Django

• What is Web framework?

• Why Frameworks?

• Define MVT Design Pattern

• Difference between MVC and MVT

PANDAS

Pandas – Introduction

Pandas – Environment Setup

Pandas – Introduction to Data Structures

• Dimension & Description

• Series

• DataFrame

• Data Type of Columns

• Panel

Pandas — Series

• Series

• Create an Empty Series

• Create a Series f

• rom ndarray

• rom dict

• rom Scalar

• Accessing Data from Series with Position

• Retrieve Data Using Label (Index)

Pandas – DataFrame

• DataFrame

• Create DataFrame

• Create an Empty DataFrame

• Create a DataFrame from Lists

• Create a DataFrame from Dict of ndarrays / Lists

• Create a DataFrame from List of Dicts

• Create a DataFrame from Dict of Series

• Column Selection

• Column Addition

• Column Deletion

• Row Selection, Addition, and Deletion

Pandas – Panel

• Panel()

• Create Panel

• Selecting the Data from Panel

Pandas – Basic Functionality

• DataFrame Basic Functionality

Pandas – Descriptive Statistics

• Functions & Description

• Summarizing Data

Pandas – Function Application

• Table-wise Function Application

• Row or Column Wise Function Application

• Element Wise Function Application

Pandas – Reindexing

• Reindex to Align with Other Objects

• Filling while ReIndexing

• Limits on Filling while Reindexing

• Renaming

Pandas – Iteration

• Iterating a DataFrame

• iteritems()

• iterrows()

• itertuples()

Pandas – Sorting

• By Label

• Sorting Algorithm

Pandas – Working with Text Data

Pandas – Options and Customization

• get_option(param)

• set_option(param,value)

• reset_option(param)

• describe_option(param)

• option_context()

Pandas – Indexing and Selecting Data

• .loc()

• .iloc()

• .ix()

• Use of Notations

Pandas – Statistical Functions

• Percent_change

• Covariance

• Correlation

• Data Ranking

Pandas – Window Functions

• .rolling() Function

• .expanding() Function

• .ewm() Function

Pandas – Aggregations

• Applying Aggregations on DataFrame

Pandas – Missing Data

• Cleaning / Filling Missing Data

• Replace NaN with a Scalar Value

• Fill NA Forward and Backward

• Drop Missing Values

• Replace Missing (or) Generic Values

Pandas – GroupBy

• Split Data into Groups

• View Groups

• Iterating through Groups

• Select a Group

• Aggregations

• Transformations

• Filtration

Pandas – Merging/Joining

• Merge Using ‘how’ Argument

Pandas – Concatenation

• Concatenating Objects

• Time Series

Pandas – Date Functionality

Pandas – Timedelta

Pandas – Categorical Data

• Object Creation

Pandas – Visualization

• Bar Plot

• Histograms

• Box Plots

• Area Plot

• Scatter Plot

• Pie Chart

Pandas – IO Tools

• csv

Pandas – Sparse Data

Pandas – Caveats & Gotchas

Pandas – Comparison with SQL

NUMPY

 NUMPY − INTRODUCTION

NUMPY − ENVIRONMENT

NUMPY − NDARRAY OBJECT

NUMPY − DATA TYPES

• Data Type Objects (dtype)

NUMPY − ARRAY ATTRIBUTES

• shape

• ndim

• itemsize

• flags

NUMPY − ARRAY CREATION ROUTINES

• empty

• zeros

• ones

NUMPY − ARRAY FROM EXISTING DATA

• asarray

• frombuffer

• fromiter

NUMPY − ARRAY FROM NUMERICAL RANGES

• arange

• linspace

• logspace

NUMPY − INDEXING & SLICING

NUMPY − ADVANCED INDEXING

• Integer Indexing

• Boolean Array Indexing

NUMPY − BROADCASTING

NUMPY − ITERATING OVER ARRAY

• Iteration

• Order

• Modifying Array Values

• External Loop

• Broadcasting Iteration

NUMPY – ARRAY MANIPULATION

• reshape

• ndarray.flat

• ndarray.flatten

• ravel

• transpose

• ndarray.T

• swapaxes

• rollaxis

• broadcast

• broadcast_to

• expand_dims

• squeeze

• concatenate

• stack

• hstack and numpy.vstack

• split

• hsplit and numpy.vsplit

• resize

• append

• insert

• delete

• unique

NUMPY – BINARY OPERATORS

• bitwise_and

• bitwise_or

• invert()

• left_shift

• right_shift

NUMPY − STRING FUNCTIONS

NUMPY − MATHEMATICAL FUNCTIONS

• Trigonometric Functions

• Functions for Rounding

NUMPY − ARITHMETIC OPERATIONS

• reciprocal()

• power()

• mod()

NUMPY − STATISTICAL FUNCTIONS

• amin() and numpy.amax()

• ptp()

• percentile()

• median()

• mean()

• average()

• Standard Deviation

• Variance

NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

• sort()

• argsort()

• lexsort()

• argmax() and numpy.argmin()

• nonzero()

• where()

• extract()

NUMPY − BYTE SWAPPING

• ndarray.byteswap()

NUMPY − COPIES & VIEWS

• No Copy

• View or Shallow Copy

• Deep Copy

NUMPY − MATRIX LIBRARY

• empty()

• matlib.zeros()

• matlib.ones()

• matlib.eye()

• matlib.identity()

• matlib.rand()

NUMPY − LINEAR ALGEBRA

• dot()

• vdot()

• inner()

• matmul()

• Determinant

• linalg.solve()

NUMPY − MATPLOTLIB

• Sine Wave Plot

• subplot()

• bar()

NUMPY – HISTOGRAM USING MATPLOTLIB

• histogram()

• plt()

NUMPY − I/O WITH NUMPY

• save()

• savetxt()