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UNIT4 is a Dutch holding company that owns and develops several software products; one such is Agresso Business World (ABW). ABW helps businesses organize and manage information around their operations, and has been sold in more than 100 countries around the world. ABW is an ERP solution customizable for its customers.

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Lavante [SIM] allows you to collect detailed information on all of your suppliers& Customers, including detailed information on each contact. The supplier interface allows a supplier to log-in and manage 100% of their information. The SIM system streamlines on-boarding process and dynamically cleans vendor master file to ensure supplier on-boarding and up-to-date supplier list. The first vendor management tool to dramatically reduce the cost and risk of on boarding and managing suppliers.

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A data structure is a specialized format for organizing and storing data. General data structure types include the array, the file, the record, the table, the tree, and so on

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Advanced Python & Django Course Content

A Python Q&A

  • Why Do People Use Python?
  • Software Quality
  • Developer Productivity
  • Is Python a “Scripting Language”?
  • OK, but What’s the Downside?
  • Who Uses Python Today?
  • What Can I Do with Python?
  • Systems Programming
  • GUIs
  • Internet Scripting
  • Component Integration
  • Database Programming
  • Rapid Prototyping
  • Numeric and Scientific Programming
  • And More: Gaming, Images, Data Mining, Robots, Excel...
  • How Is Python Developed and Supported?
  • Open Source Tradeoffs
  • What Are Python’s Technical Strengths?
  • It’s Object-Oriented and Functional
  • It’s Free
  • It’s Portable
  • It’s Powerful
  • It’s Mixable
  • It’s Relatively Easy to Use
  • It’s Relatively Easy to Learn
  • It’s Named After Monty Python
  • How Does Python Stack Up to Language X?
  • Module Packages

  • Package Import Basics
  • Packages and Search Path Settings
  • Package Files
  • Package Import Example
  • from Versus import with Packages
  • Why Use Package Imports?
  • A Tale of Three Systems
  • Package Relative Imports
  • Changes in Python .X
  • Relative Import Basics
  • Why Relative Imports?
  • The Scope of Relative Imports
  • Module Lookup Rules Summary
  • Relative Imports in Action
  • Pitfalls of Package-Relative Imports: Mixed Use
  • Python . Namespace Packages
  • Namespace Package Semantics
  • Impacts on Regular Packages: Optional
  • Namespace Packages in Action
  • Namespace Package Nesting
  • Files Still Have Precedence over Directories
  • Advanced Module Topics

  • Module Design Concepts
  • Data Hiding in Modules
  • Minimizing from * Damage: _X and __all__
  • Enabling Future Language Features: __future__
  • Mixed Usage Modes: __name__ and __main__
  • Unit Tests with __name__
  • Example: Dual Mode Code
  • Currency Symbols: Unicode in Action
  • Docstrings: Module Documentation at Work
  • Changing the Module Search Path
  • The as Extension for import and from
  • Example: Modules Are Objects
  • Importing Modules by Name String
  • Running Code Strings
  • Direct Calls: Two Options
  • Example: Transitive Module Reloads
  • A Recursive Reloader
  • Alternative Codings
  • Module Gotchas
  • Module Name Clashes: Package and Package-Relative Imports
  • Statement Order Matters in Top-Level Code
  • from Copies Names but Doesn’t Link
  • from * Can Obscure the Meaning of Variables
  • reload May Not Impact from Imports
  • reload, from, and Interactive Testing
  • Recursive from Imports May Not Work
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Test Your Knowledge: Part V Exercises
  • Classes and OOP

  • Why Use Classes?
  • OOP from , Feet
  • Attribute Inheritance Search
  • Classes and Instances
  • Method Calls
  • Coding Class Trees
  • Operator Overloading
  • OOP Is About Code Reuse
  • Class Coding Basics

  • Classes Generate Multiple Instance Objects
  • Class Objects Provide Default Behavior
  • Instance Objects Are Concrete Items
  • A First Example
  • Classes Are Customized by Inheritance
  • A Second Example
  • Classes Are Attributes in Modules
  • Classes Can Intercept Python Operators
  • A Third Example
  • Why Use Operator Overloading?
  • The World’s Simplest Python Class
  • Records Revisited: Classes Versus Dictionaries
  • A More Realistic Example

  • Step : Making Instances
  • Coding Constructors
  • Testing As You Go
  • Using Code Two Ways
  • Step : Adding Behavior Methods
  • Coding Methods
  • Step : Operator Overloading
  • Providing Print Displays
  • Step : Customizing Behavior by Subclassing
  • Coding Subclasses
  • Augmenting Methods: The Bad Way
  • Augmenting Methods: The Good Way
  • Polymorphism in Action
  • Inherit, Customize, and Extend
  • OOP: The Big Idea
  • Step : Customizing Constructors, Too
  • OOP Is Simpler Than You May Think
  • Other Ways to Combine Classes
  • Step : Using Introspection Tools
  • Special Class Attributes
  • A Generic Display Tool
  • Instance Versus Class Attributes
  • Name Considerations in Tool Classes
  • Our Classes’ Final Form
  • Step (Final): Storing Objects in a Database
  • Pickles and Shelves
  • Storing Objects on a Shelve Database
  • Exploring Shelves Interactively
  • Updating Objects on a Shelve
  • Future Directions
  • Class Coding Details

  • The class Statement
  • General Form
  • Example
  • Methods
  • Method Example
  • Calling Superclass Constructors
  • Other Method Call Possibilities
  • Inheritance
  • Attribute Tree Construction
  • Specializing Inherited Methods
  • Class Interface Technique
  • Abstract Superclasses
  • Namespaces: The Conclusion
  • Simple Names: Global Unless Assigned
  • Attribute Names: Object Namespaces
  • The “Zen” of Namespaces: Assignments Classify Names
  • Nested Classes: The LEGB Scopes Rule Revisited
  • Namespace Dictionaries: Review
  • Namespace Links: A Tree Climber
  • Documentation Strings Revisited
  • Classes Versus Modules
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Operator Overloading

  • The Basics
  • Constructors and Expressions: __init__ and __sub__
  • Common Operator Overloading Methods
  • Indexing and Slicing: __getitem__ and __setitem__
  • Intercepting Slices
  • Slicing and Indexing in Python .X
  • But .X’s __index__ Is Not Indexing!
  • Index Iteration: __getitem__
  • Iterable Objects: __iter__ and __next__
  • User-Defined Iterables
  • Multiple Iterators on One Object
  • Coding Alternative: __iter__ plus yield
  • Membership: __contains__, __iter__, and __getitem__
  • Attribute Access: __getattr__ and __setattr__
  • Attribute Reference
  • Attribute Assignment and Deletion
  • Other Attribute Management Tools
  • Emulating Privacy for Instance Attributes: Part
  • String Representation: __repr__ and __str__
  • Why Two Display Methods?
  • Display Usage Notes
  • Right-Side and In-Place Uses: __radd__ and __iadd__
  • Right-Side Addition
  • In-Place Addition
  • Call Expressions: __call__
  • Function Interfaces and Callback-Based Code
  • Comparisons: __lt__, __gt__, and Others
  • The __cmp__ Method in Python .X
  • xxii | Table of Contents
  • Boolean Tests: __bool__ and __len__
  • Boolean Methods in Python .X
  • Object Destruction: __del__
  • Destructor Usage Notes
  • Designing with Classes

  • Python and OOP
  • Polymorphism Means Interfaces, Not Call Signatures
  • OOP and Inheritance: “Is-a” Relationships
  • OOP and Composition: “Has-a” Relationships
  • Stream Processors Revisited
  • OOP and Delegation: “Wrapper” Proxy Objects
  • Pseudoprivate Class Attributes
  • Name Mangling Overview
  • Why Use Pseudoprivate Attributes?
  • Methods Are Objects: Bound or Unbound
  • Unbound Methods Are Functions in .X
  • Bound Methods and Other Callable Objects
  • Classes Are Objects: Generic Object Factories
  • Why Factories?
  • Multiple Inheritance: “Mix-in” Classes
  • Coding Mix-in Display Classes
  • Other Design-Related Topics
  • Advanced Class Topics

  • Extending Built-in Types
  • Extending Types by Embedding
  • Extending Types by Subclassing
  • The “New Style” Class Model
  • Just How New Is New-Style?
  • New-Style Class Changes
  • Attribute Fetch for Built-ins Skips Instances
  • Type Model Changes
  • All Classes Derive from “object”
  • Diamond Inheritance Change
  • More on the MRO: Method Resolution Order
  • Example: Mapping Attributes to Inheritance Sources
  • New-Style Class Extensions
  • Slots: Attribute Declarations
  • Properties: Attribute Accessors
  • __getattribute__ and Descriptors: Attribute Tools
  • Other Class Changes and Extensions
  • Static and Class Methods
  • Why the Special Methods?
  • Static Methods in .X and .X
  • Static Method Alternatives
  • Using Static and Class Methods
  • Counting Instances with Static Methods
  • Counting Instances with Class Methods
  • Decorators and Metaclasses: Part
  • Function Decorator Basics
  • A First Look at User-Defined Function Decorators
  • A First Look at Class Decorators and Metaclasses
  • For More Details
  • The super Built-in Function: For Better or Worse?
  • The Great super Debate
  • Traditional Superclass Call Form: Portable, General
  • Basic super Usage and Its Tradeoffs
  • The super Upsides: Tree Changes and Dispatch
  • Runtime Class Changes and super
  • Cooperative Multiple Inheritance Method Dispatch
  • The super Summary
  • Class Gotchas
  • Changing Class Attributes Can Have Side Effects
  • Changing Mutable Class Attributes Can Have Side Effects, Too
  • Multiple Inheritance: Order Matters
  • Scopes in Methods and Classes
  • Miscellaneous Class Gotchas
  • KISS Revisited: “Overwrapping-itis”
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Test Your Knowledge: Part VI Exercises
  • Part VII. Exceptions and Tools
  • Exception Basics

  • Why Use Exceptions?
  • Exception Roles
  • Exceptions: The Short Story
  • Default Exception Handler
  • Catching Exceptions
  • Raising Exceptions
  • User-Defined Exceptions
  • Termination Actions
  • Exception Coding Details

  • The try/except/else Statement
  • How try Statements Work
  • try Statement Clauses
  • The try else Clause
  • Example: Default Behavior
  • Example: Catching Built-in Exceptions
  • The try/finally Statement
  • Example: Coding Termination Actions with try/finally
  • Unified try/except/finally
  • Unified try Statement Syntax
  • Combining finally and except by Nesting
  • Unified try Example
  • The raise Statement
  • Raising Exceptions
  • Scopes and try except Variables
  • Propagating Exceptions with raise
  • Python .X Exception Chaining: raise from
  • The assert Statement
  • Example: Trapping Constraints (but Not Errors!)
  • with/as Context Managers
  • Basic Usage
  • The Context Management Protocol
  • Multiple Context Managers in ., ., and Later
  • How Python Runs

  • Introducing the Python Interpreter
  • Program Execution
  • The Programmer’s View
  • Python’s View
  • Execution Model Variations
  • Python Implementation Alternatives
  • Execution Optimization Tools
  • Frozen Binaries
  • Types and Operations

  • The Python Conceptual Hierarchy
  • Why Use Built-in Types?
  • Python’s Core Data Types
  • Numbers
  • Strings
  • Sequence Operations
  • Immutability
  • Type-Specific Methods
  • Getting Help
  • Other Ways to Code Strings
  • Unicode Strings
  • Pattern Matching
  • Lists
  • Sequence Operations
  • Type-Specific Operations
  • Bounds Checking
  • Nesting
  • Comprehensions
  • Dictionaries
  • Mapping Operations
  • Nesting Revisited
  • Missing Keys: if Tests
  • Sorting Keys: for Loops
  • Iteration and Optimization
  • Tuples
  • Why Tuples?
  • Files
  • Binary Bytes Files
  • Unicode Text Files
  • Other File-Like Tools
  • Other Core Types
  • How to Break Your Code’s Flexibility
  • User-Defined Classes
  • And Everything Else
  • Numbers in Action
  • Variables and Basic Expressions
  • Numeric Display Formats
  • Comparisons: Normal and Chained
  • Division: Classic, Floor, and True
  • Integer Precision
  • Complex Numbers
  • Hex, Octal, Binary: Literals and Conversions
  • Bitwise Operations
  • Other Built-in Numeric Tools
  • Other Numeric Types
  • Decimal Type
  • Fraction Type
  • Sets
  • Booleans
  • Numeric Extensions
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers

    Unicode and Byte Strings

  • String Changes in .X
  • String Basics
  • Character Encoding Schemes
  • How Python Stores Strings in Memory
  • Python’s String Types
  • Text and Binary Files
  • Coding Basic Strings
  • Python .X String Literals
  • Python .X String Literals
  • String Type Conversions
  • Coding Unicode Strings
  • Coding ASCII Text
  • Coding Non-ASCII Text
  • Encoding and Decoding Non-ASCII text
  • Other Encoding Schemes
  • Byte String Literals: Encoded Text
  • Converting Encodings
  • Coding Unicode Strings in Python .X
  • Source File Character Set Encoding Declarations
  • Using .X bytes Objects
  • Method Calls
  • Sequence Operations
  • Other Ways to Make bytes Objects
  • Mixing String Types
  • Using .X/.+ bytearray Objects
  • bytearrays in Action
  • Python .X String Types Summary
  • Using Text and Binary Files
  • Text File Basics
  • Text and Binary Modes in .X and .X
  • Type and Content Mismatches in .X
  • Using Unicode Files
  • Reading and Writing Unicode in .X
  • Handling the BOM in .X
  • Unicode Files in .X
  • Unicode Filenames and Streams
  • Other String Tool Changes in .X
  • The re Pattern-Matching Module
  • The struct Binary Data Module
  • The pickle Object Serialization Module
  • XML Parsing Tools
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Table of Contents | xxvii
  • Assignments, Expressions, and Prints

  • Assignment Statements
  • Assignment Statement Forms
  • Sequence Assignments
  • Extended Sequence Unpacking in Python .X
  • Multiple-Target Assignments
  • Augmented Assignments
  • Variable Name Rules
  • Expression Statements
  • Expression Statements and In-Place Changes
  • Print Operations
  • The Python .X print Function
  • The Python .X print Statement
  • Print Stream Redirection
  • Version-Neutral Printing
  • if Tests and Syntax Rules

  • if Statements
  • General Format
  • Basic Examples
  • Multiway Branching
  • Python Syntax Revisited
  • Block Delimiters: Indentation Rules
  • Statement Delimiters: Lines and Continuations
  • A Few Special Cases
  • Truth Values and Boolean Tests
  • The if/else Ternary Expression
  • while and for Loops

  • while Loops
  • General Format
  • Examples
  • break, continue, pass, and the Loop else
  • General Loop Format
  • pass
  • continue
  • break
  • Loop else
  • for Loops
  • General Format
  • Examples
  • Loop Coding Techniques
  • Counter Loops: range
  • Sequence Scans: while and range Versus for
  • Sequence Shufflers: range and len
  • Nonexhaustive Traversals: range Versus Slices
  • Changing Lists: range Versus Comprehensions
  • Parallel Traversals: zip and map
  • Generating Both Offsets and Items: enumerate
  • Iterations and Comprehensions

  • Iterations: A First Look
  • The Iteration Protocol: File Iterators
  • Manual Iteration: iter and next
  • Other Built-in Type Iterables
  • List Comprehensions: A First Detailed Look
  • List Comprehension Basics
  • Using List Comprehensions on Files
  • Extended List Comprehension Syntax
  • Other Iteration Contexts
  • New Iterables in Python .X
  • Impacts on .X Code: Pros and Cons
  • The range Iterable
  • The map, zip, and filter Iterables
  • Multiple Versus Single Pass Iterators
  • Dictionary View Iterables
  • Other Iteration Topics
  • Function Basics

  • Why Use Functions?
  • Coding Functions
  • def Statements
  • def Executes at Runtime
  • A First Example: Definitions and Calls
  • Definition
  • Calls
  • Polymorphism in Python
  • A Second Example: Intersecting Sequences
  • Definition
  • Calls
  • Polymorphism Revisited
  • Local Variables
  • Modules: The Big Picture

  • Why Use Modules?
  • Python Program Architecture
  • How to Structure a Program
  • Imports and Attributes
  • Standard Library Modules
  • How Imports Work
  • . Find It
  • . Compile It (Maybe)
  • . Run It
  • Byte Code Files: __pycache__ in Python .+
  • Byte Code File Models in Action
  • The Module Search Path
  • Configuring the Search Path
  • Search Path Variations
  • The sys.path List
  • Module File Selection
  • Module Coding Basics

  • Module Creation
  • Module Filenames
  • Other Kinds of Modules
  • Module Usage
  • The import Statement
  • The from Statement
  • The from * Statement
  • Imports Happen Only Once
  • import and from Are Assignments
  • import and from Equivalence
  • Potential Pitfalls of the from Statement
  • Module Namespaces
  • Files Generate Namespaces
  • Namespace Dictionaries: __dict__
  • Attribute Name Qualification
  • Imports Versus Scopes
  • Namespace Nesting
  • Reloading Modules
  • reload Basics
  • reload Example
  • How You Run

  • The Interactive Prompt
  • Starting an Interactive Session
  • The System Path
  • New Windows Options in .: PATH, Launcher
  • Where to Run: Code Directories
  • What Not to Type: Prompts and Comments
  • Running Code Interactively
  • Why the Interactive Prompt?
  • Usage Notes: The Interactive Prompt
  • System Command Lines and Files
  • A First Script
  • Running Files with Command Lines
  • Command-Line Usage Variations
  • Usage Notes: Command Lines and Files
  • Unix-Style Executable Scripts: #!
  • Unix Script Basics
  • The Unix env Lookup Trick
  • The Python . Windows Launcher: #! Comes to Windows
  • Clicking File Icons
  • Icon-Click Basics
  • Clicking Icons on Windows
  • The input Trick on Windows
  • Other Icon-Click Limitations
  • Module Imports and Reloads
  • Import and Reload Basics
  • The Grander Module Story: Attributes
  • Usage Notes: import and reload
  • Using exec to Run Module Files
  • The IDLE User Interface
  • IDLE Startup Details
  • IDLE Basic Usage
  • IDLE Usability Features
  • Advanced IDLE Tools
  • Usage Notes: IDLE
  • Other IDEs
  • Other Launch Options
  • Embedding Calls
  • Frozen Binary Executables
  • Text Editor Launch Options
  • Still Other Launch Options
  • Future Possibilities?
  • Which Option Should I Use?
  • Managed Attributes

  • Why Manage Attributes?
  • Inserting Code to Run on Attribute Access
  • Properties
  • The Basics
  • A First Example
  • Computed Attributes
  • Coding Properties with Decorators
  • Descriptors
  • The Basics
  • A First Example
  • Computed Attributes
  • Using State Information in Descriptors
  • How Properties and Descriptors Relate
  • __getattr__ and __getattribute__
  • The Basics
  • A First Example
  • Computed Attributes
  • __getattr__ and __getattribute__ Compared
  • Management Techniques Compared
  • Intercepting Built-in Operation Attributes
  • Example: Attribute Validations
  • Using Properties to Validate
  • Using Descriptors to Validate
  • Using __getattr__ to Validate
  • Using __getattribute__ to Validate
  • String Fundamentals

  • This Chapter’s Scope
  • Unicode: The Short Story
  • String Basics
  • String Literals
  • Single- and Double-Quoted Strings Are the Same
  • Escape Sequences Represent Special Characters
  • Raw Strings Suppress Escapes
  • Triple Quotes Code Multiline Block Strings
  • Strings in Action
  • Basic Operations
  • Indexing and Slicing
  • String Conversion Tools
  • Changing Strings I
  • String Methods
  • Method Call Syntax
  • Methods of Strings
  • String Method Examples: Changing Strings II
  • String Method Examples: Parsing Text
  • Other Common String Methods in Action
  • The Original string Module’s Functions (Gone in .X)
  • String Formatting Expressions
  • Formatting Expression Basics
  • Advanced Formatting Expression Syntax
  • Advanced Formatting Expression Examples
  • Dictionary-Based Formatting Expressions
  • String Formatting Method Calls
  • Formatting Method Basics
  • Adding Keys, Attributes, and Offsets
  • Advanced Formatting Method Syntax
  • Advanced Formatting Method Examples
  • Comparison to the % Formatting Expression
  • Why the Format Method?
  • General Type Categories
  • Types Share Operation Sets by Categories
  • Mutable Types Can Be Changed in Place
  • Lists and Dictionaries

  • Lists
  • Lists in Action
  • Basic List Operations
  • List Iteration and Comprehensions
  • Indexing, Slicing, and Matrixes
  • Changing Lists in Place
  • Dictionaries
  • Dictionaries in Action
  • Basic Dictionary Operations
  • Dictionaries in Place
  • More Dictionary Methods
  • Example: Movie Database
  • Dictionary Usage Notes
  • Other Ways to Make Dictionaries
  • Dictionary Changes in Python .X and
  • Tuples, Files, and Everything

  • Tuples
  • Tuples in Action
  • Why Lists and Tuples?
  • Records Revisited: Named Tuples
  • Files
  • Opening Files
  • Using Files
  • Files in Action
  • Text and Binary Files: The Short Story
  • Storing Python Objects in Files: Conversions
  • Storing Native Python Objects: pickle
  • Storing Python Objects in JSON Format
  • Storing Packed Binary Data: struct
  • File Context Managers
  • Other File Tools
  • Core Types Review and Summary
  • Object Flexibility
  • References Versus Copies
  • Comparisons, Equality, and Truth
  • The Meaning of True and False in Python
  • Python’s Type Hierarchies
  • Type Objects
  • Other Types in Python
  • Built-in Type Gotchas
  • Assignment Creates References, Not Copies
  • Repetition Adds One Level Deep
  • Beware of Cyclic Data Structures
  • Immutable Types Can’t Be Changed in Place
  • Handling Errors by Testing Inputs
  • Handling Errors with try Statements
  • Nesting Code Three Levels Deep


  • Lists
  • Python Scope Basics
  • Scope Details
  • Name Resolution: The LEGB Rule
  • Scope Example
  • The Built-in Scope
  • The global Statement
  • Program Design: Minimize Global Variables
  • Program Design: Minimize Cross-File Changes
  • Other Ways to Access Globals
  • Scopes and Nested Functions
  • Nested Scope Details
  • Nested Scope Examples
  • Factory Functions: Closures
  • Retaining Enclosing Scope State with Defaults
  • The nonlocal Statement in .X
  • nonlocal Basics
  • nonlocal in Action
  • Why nonlocal? State Retention Options
  • State with nonlocal: .X only
  • State with Globals: A Single Copy Only
  • State with Classes: Explicit Attributes (Preview)
  • State with Function Attributes: .X and .X
  • Arguments

  • Lists
  • Argument-Passing Basics
  • Arguments and Shared References
  • Avoiding Mutable Argument Changes
  • Simulating Output Parameters and Multiple Results
  • Special Argument-Matching Modes
  • Argument Matching Basics
  • Argument Matching Syntax
  • The Gritty Details
  • Keyword and Default Examples
  • Arbitrary Arguments Examples
  • Python .X Keyword-Only Arguments
  • The min Wakeup Call!
  • Full Credit
  • Bonus Points
  • The Punch Line...
  • Generalized Set Functions
  • Emulating the Python .X print Function
  • Using Keyword-Only Arguments
  • Advanced Function Topics

  • Function Design Concepts
  • Recursive Functions
  • Summation with Recursion
  • Coding Alternatives
  • Loop Statements Versus Recursion
  • Handling Arbitrary Structures
  • Function Objects: Attributes and Annotations
  • Indirect Function Calls: “First Class” Objects
  • Function Introspection
  • Function Attributes
  • Function Annotations in .X
  • Anonymous Functions: lambda
  • lambda Basics
  • Why Use lambda?
  • How (Not) to Obfuscate Your Python Code
  • Scopes: lambdas Can Be Nested Too
  • Functional Programming Tools
  • Mapping Functions over Iterables: map
  • Selecting Items in Iterables: filter
  • Combining Items in Iterables: reduce
  • Comprehensions and Generations

  • List Comprehensions and Functional Tools
  • List Comprehensions Versus map
  • Adding Tests and Nested Loops: filter
  • Example: List Comprehensions and Matrixes
  • Don’t Abuse List Comprehensions: KISS
  • Generator Functions and Expressions
  • Generator Functions: yield Versus return
  • Generator Expressions: Iterables Meet Comprehensions
  • Generator Functions Versus Generator Expressions
  • Generators Are Single-Iteration Objects
  • Generation in Built-in Types, Tools, and Classes
  • Example: Generating Scrambled Sequences
  • Don’t Abuse Generators: EIBTI
  • Example: Emulating zip and map with Iteration Tools
  • Comprehension Syntax Summary
  • Scopes and Comprehension Variables
  • Comprehending Set and Dictionary Comprehensions
  • Extended Comprehension Syntax for Sets and Dictionaries
  • The Benchmarking Interlude

  • Timing Iteration Alternatives
  • Timing Module: Homegrown
  • Timing Script
  • Timing Results
  • Timing Module Alternatives
  • Other Suggestions
  • Timing Iterations and Pythons with timeit
  • Basic timeit Usage
  • Benchmark Module and Script: timeit
  • Benchmark Script Results
  • More Fun with Benchmarks
  • Other Benchmarking Topics: pystones
  • Function Gotchas
  • Local Names Are Detected Statically
  • Defaults and Mutable Objects
  • Functions Without returns
  • Miscellaneous Function Gotchas
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Test Your Knowledge: Part IV Exercises
  • Part V. Modules and Packages
  • Decorators

  • What’s a Decorator?
  • Managing Calls and Instances
  • Managing Functions and Classes
  • Using and Defining Decorators
  • Why Decorators?
  • The Basics
  • Function Decorators
  • Class Decorators
  • Decorator Nesting
  • Decorator Arguments
  • Decorators Manage Functions and Classes, Too
  • Coding Function Decorators
  • Tracing Calls
  • Decorator State Retention Options
  • Class Blunders I: Decorating Methods
  • Timing Calls
  • Adding Decorator Arguments
  • Coding Class Decorators
  • Singleton Classes
  • Tracing Object Interfaces
  • Class Blunders II: Retaining Multiple Instances
  • Decorators Versus Manager Functions
  • Why Decorators? (Revisited)
  • Managing Functions and Classes Directly
  • Example: “Private” and “Public” Attributes
  • Implementing Private Attributes
  • Implementation Details I
  • Generalizing for Public Declarations, Too
  • Implementation Details II
  • Open Issues
  • Python Isn’t About Control
  • Example: Validating Function Arguments
  • The Goal
  • A Basic Range-Testing Decorator for Positional Arguments
  • Generalizing for Keywords and Defaults, Too
  • Implementation Details
  • Open Issues
  • Decorator Arguments Versus Function Annotations
  • Other Applications: Type Testing (If You Insist!)
  • Metaclasses

  • To Metaclass or Not to Metaclass
  • Increasing Levels of “Magic”
  • A Language of Hooks
  • The Downside of “Helper” Functions
  • Metaclasses Versus Class Decorators: Round
  • The Metaclass Model
  • Classes Are Instances of type
  • Metaclasses Are Subclasses of Type
  • Class Statement Protocol
  • Declaring Metaclasses
  • Declaration in .X
  • Metaclass Dispatch in Both .X and .X
  • Coding Metaclasses
  • A Basic Metaclass
  • Customizing Construction and Initialization
  • Other Metaclass Coding Techniques
  • Inheritance and Instance
  • Metaclass Versus Superclass
  • Inheritance: The Full Story
  • Metaclass Methods
  • Metaclass Methods Versus Class Methods
  • Operator Overloading in Metaclass Methods
  • Example: Adding Methods to Classes
  • Manual Augmentation
  • Metaclass-Based Augmentation
  • Metaclasses Versus Class Decorators: Round
  • Example: Applying Decorators to Methods
  • Tracing with Decoration Manually
  • Tracing with Metaclasses and Decorators
  • Applying Any Decorator to Methods
  • Metaclasses Versus Class Decorators: Round (and Last)
  • The Dynamic Typing Interlude

  • The Case of the Missing Declaration Statements
  • Variables, Objects, and References
  • Types Live with Objects, Not Variables
  • Objects Are Garbage-Collected
  • Shared References
  • Shared References and In-Place Changes
  • Shared References and Equality
  • Dynamic Typing Is Everywhere
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Exception Objects

  • Exceptions: Back to the Future
  • String Exceptions Are Right Out!
  • Class-Based Exceptions
  • Coding Exceptions Classes
  • Why Exception Hierarchies?
  • Built-in Exception Classes
  • Built-in Exception Categories
  • Default Printing and State
  • Custom Print Displays
  • Custom Data and Behavior
  • Providing Exception Details
  • Providing Exception Methods
  • Designing with Exceptions

  • Nesting Exception Handlers
  • Example: Control-Flow Nesting
  • Example: Syntactic Nesting
  • Exception Idioms
  • Breaking Out of Multiple Nested Loops: “go to”
  • Exceptions Aren’t Always Errors
  • Functions Can Signal Conditions with raise
  • Closing Files and Server Connections
  • Debugging with Outer try Statements
  • Running In-Process Tests
  • More on sys.exc_info
  • Displaying Errors and Tracebacks
  • Exception Design Tips and Gotchas
  • What Should Be Wrapped
  • Catching Too Much: Avoid Empty except and Exception
  • Catching Too Little: Use Class-Based Categories
  • Core Language Summary
  • The Python Toolset
  • Development Tools for Larger Projects
  • Chapter Summary
  • Test Your Knowledge: Quiz
  • Test Your Knowledge: Answers
  • Test Your Knowledge: Part VII Exercises
  • Django Course


  • Audience
  • Prerequisites
  • Basics

  • History of Django
  • Design Philosophies
  • Advantages of Django
  • Overview

  • MVC Pattern
  • DJANGO MVC - MVT Pattern
  • Environment

  • Installing Python
  • Installing Django
  • Database Setup
  • Web Server
  • Creating a Project

  • Create a Project
  • The Project Structure
  • Setting Up Your Project
  • Sending E-mails

  • Sending a Simple E-mail
  • Sending Multiple Mails with send_mass_mail
  • Sending Multiple Mails with send_mass_mail
  • Sending E-mail with Attachment
  • Generic Views

  • Static Pages
  • List and Display Data from DB
  • Form Processing

  • Using Form in a View
  • Using Our Own Form Validation
  • File Uploading
  • Uploading an Image
  • Apps Life Cycle

  • Create an Application
  • Get the Project to Know About Your Application
  • Admin Interface

  • Starting the Admin Interface
  • Creating Views
  • Simple View
  • Template System

  • The Render Function
  • Django Template Language (DTL)
  • Filters
  • Tags
  • Tags

  • Creating a Model
  • Manipulating Data (CRUD)
  • Linking Models
  • Comments

  • Dreamreal Model
  • hello view
  • hello.html template
  • Real Time Projects

  • E-Commerce domain applications
  • Front-End
  • Back-End
  • HTML
  • CSS
  • SQLite
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Python Training Benefits for


  • Easy to learn, and it can be used as a stepping stone into other programming languages and frameworks.
  • Python is widely used, including by a number of big companies IBM, HP, Infosys, Accenture, Flipkart, Amazon and many more
  • Job Openings are Plenty to get Placed Immediatly after course
  • Since a lot of big companies rely on the language, you can make good money as a Python developer.

Working Professionals

  • More Job opportunities in Python Technologies compared with others.
  • Employers are looking for fully stacked programmers and Python will help you get there
  • For people planning to become software developers, learning this type of programming in one area will help you adapt easily in other environments.
  • Great Career Prospect
  • Python make it one of the best languages to learn. Today, several Python developers across the globe are developing a variety of software packages for application in numerous industries


python jobs in bangalore for freshers and experienced employees in BANGALORE, INDIA


    Full Stack Developer

    Desired Skills and Experience:

    • Min 3+ Yrs as Python Developer
    • Skills: Python, Djnago , HTML Mandatory
    • Qual: B.E/B.Tech/MCA/M.Tech/M.S
    • Exp working Supply Chain domain is an advantage
    • Candidate will be working to build and expand our Software as a service web application.
    • Design and develop scalable supply chain applications using Python, Django, HTML5 and Javascript among other technologies
    • Create technical documentation for software you develop.
    • Work with teams in multiple continents in building a world class product.
    • Troubleshoot and help with product support.


    Role and Responsibilities

    • As a Python Developer, you will create and expand our in-house tools that adapts to quickly evolving business requirements.
    • - Ability to work in a rapid environment and convert high-level design into high quality python code.
    • You can expect:

    • - Heady highs and head-down lows.
    • - To always listen to data.
    • - Your ideas to be taken seriously and valued.
    • - To be working with bleeding edge tech.
  • Tech Lead - Python/Django (3-7 yrs) Bangalore (Online/Mobile/Web)


    Role and Responsibilities

    • • Collaborating within and across cross-functional, agile teams to deliver software that achieves business objectives
    • • Coordination of efforts between the Engineering, Quality Assurance, Infrastructure, and Product Management.
    • • Experience with NoSQL like MongoDB, Cassandra or Jackrabbit. • Knowledge of Kafka or any message queue systems is a desirable
    • • Mentoring more junior developers and peers • Experience with SQL, RDBMS, Data Modeling.
    • • Strong hands on experience with SOAP and/or RESTful APIs. • Sound knowledge in using caching systems such as Aerospike, Redis.
    • • Excellent communication skills • Be creative and curious about software solutions and emerging technologies. • eCommerce search experience will be preffered
    • • A consultative approach to evangelizing concepts and teaching others to fish Skills Required • Expertise in Python development with Django framework.


    ROLE DESCRIPTION: Design, develop, and configure software systems to meet market and/or client requirements either end-to-end from analysis, design, implementation, quality assurance (including testing), to delivery and maintenance of the software product or system or for a specific phase of the lifecycle. Apply knowledge of technologies, applications, methodologies, processes and tools to support a client, project or entity.

    • Python with Unix/Linux (willing to work on cloud domain )
    • Location - Bangalore
    • Exp - Min 3yrs
    • JD:Mandatory
    • Python on Linux/Unix platform
    • Shell scripting / Unix
    • Optional: Virtualization
    • Domain: Infrastructure management

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Do you provide Placements?

Yes, We do provide Placements Support. We have Dedicated placement Officer taking care of the Students placement. Over and above we have tie-ups with so many IT Companies where the prospective HRs and Employers contact us for placements & internships. you are updated on various job opportunities for python developers in Bangalore and Chennai and depending upon your interest in those your resumes are shared and the process is taken ahead.

Do We Get To Work On Live Projects?

The entire python training has been built around Real Time Implemenation, You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio, GitHub repository and Showcase to Recruiters in Interviews & Get Placed.

Will i Get Technical Support Even after Completion of Course?

Yes, You can Ask any technical Doubts/Question to Trainer and get Clarify, Even you can Reattend Classes for the topics you want Revision. While Pursing, You should complete your course sincerely by doing Assignments Regularly given by trainer.

What all certifications you provide?

We provide up to 5 industry recognized certifications which are:
  • PCEP – Certified Entry-Level Python Programmer
  • PCAP – Certified Associate in Python Programming
  • PCPP – Certified Professional in Python Programming
  • PCPP-2 – Certified Professional in Python Programming 2
  • CEPP – Certified Expert in Python Programming

Who are the Trainers?

Our Trainers are chosen not only for their knowledge and expertise but also for their real-world experience in the field they teach. We Will Help you Get Resume Ready and Provided Interviews Question and Answers. our trainers are employees in
  • IBM
  • Accenture
  • Adobe
  • Honeywell
  • Many more Top IT Companies

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