Table of Contents

 

Python Training Overview

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis

What are the Python Course Pre-requisites

There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. Also, basic concepts related to Programming and Database is beeficial but not mandatory.

Objectives of the Course

  • To understand the concepts and constructs of Python
  • To create own Python programs, know the machine learning algorithms in Python and work on a real-time project running on Python

Who should do the course

  • Big Data Professionals
  • IT Developers
  • Those who are showing interest to build their career in Python

Python Training Course Duration

  • 35 Days, Daily 1 Hours

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 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 Note Book, 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
  • Non sequence
  • 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 String object
  • String Formatting
  • String functions
  • String Immutability
  • Case studies

List Collection

  • What is List
  • 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, shallowCopy and DeepCopy
  • zip() in Python
  • How to unzip?
  • Python Arrays:
  • Case studies

Tuple Collection

  • What is 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 set
  • Difference between list and set
  • Iteration Over Sets
  • Accessing elements of 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 dictionary?
  • Difference between list, set and dictionary
  • How to create a dictionary?
  • PYTHON HASHING?
  • Accessing values of 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 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 – Pre defined, 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 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
  • Un installing Python packages

OOPs

  • Procedural v/s Object oriented programming
  • Principles of OOP – Encapsulation , Abstraction (Data Hiding)
  • Classes and Objects
  • How to define 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()