Python Training

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

• 60 Days, Daily 1.30 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()

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