Introduction

This repository contains various types of files which will help you in your Python course if you are following the book Python Programming: Problem Solving, Packages and Libraries The book has 20 chapters in the print/ Kindle format and another 5 chapters which can be downloaded from the McGraw Hill web site. These chapters are also available on GitGub here

The repository has the following folders:-

1. Online-chapters-21-25
2. book-chapters-html
3. PPT-notes-as-pdf

The first folder contains the last 5 chapters of my book in pdf format. You can download these chapters. The chapters are complete in all respects.

The other 2 folders (at serial number 2 and 3) contain help guides for each of the 20 chapters in the book in html, pptx (converted to pdf) format. However the formats may not render accurately on GitHub. So you need to either download or clone the repository to your local machine and then view them with appropriate viewer application. For example you may download the pptx files in pdf and then view them with a pdf viewer. Similarly you can view the html files in your favourite web browser. The book is available in print/ kindle here.

I can be contacted on 999.anuraggupta@gmail.com

Given below are the topics contained in the book

Table of Contents

Chapter 1 Python Basics—I
1.1 Introduction
1.2 Basic Concepts
1.2.1 History and Introduction 1.2.2 A Very Brief Note on Versions Python 2.x and 3.x 2
1.2.3 Features
1.2.4 Compiler versus Interpreter 1.2.5 Open Source versus Propriety (Also free vs. paid)
1.2.6 Portable
1.3 Basic Concepts
1.3.1 Dynamic Typing or Dynamic Binding
1.3.2 Automatic Memory Management
1.3.3 Command Line versus Script Mode
1.3.4 IPython, Anaconda and Conda
1.3.5 Jupyter Notebook
1.3.6 Relation of Anaconda to Spyder IDE and Jupyter Notebook
1.3.7 Difference between Jupyter Notebook and Spyder
1.3.8 Using Python Interactive Mode as a Calculator
1.4 Basic Concepts
1.4.1 Using Simple Print Statement in Python 2.x (which is print() function in 3.x)
1.4.2 Executing Python from a “file” i.e. a “script” Rather than Command Line 1.4.3 Single Line Comments and Multi-line Comments
1.5 Some Aspects of Python Statements and Indentation
1.5.1 Explicit Line Continuation 1.5.2 Implicit Multiline Statement 1.5.3 Multiple Statements in Single Line Using Semicolons
1.5.4 Python Indentation
1.6 Python Keywords, Identifiers and Variables
1.6.1 Python Keywords and Identifiers
1.6.2 Variables, Literals, References and Built-in Types
1.6.3 Variable
1.6.4 Assignment
1.6.5 Reference in Python 1.6.6 Shared Reference 1.6.7 Variable vs. Identifier

Chapter 2 Python Basics—II
2.1 Introduction
2.2 Data Types in Python
2.2.1 Numbers (Integers, Floating Points, Complex and Bool)
2.2.2 Complex (Complex Numbers)
2.2.3 Bool
2.2.4 Sequence and Other Containers (Non-sequenced Containers)
2.2.5 None
2.3 Mutable versus Immutable
2.4 Type Casting (also called Type Conversion) in Python
2.4.1 Explicit versus Implicit Type Casting
2.4.2 Implicit Type Conversion in Boolean Context
2.5 Input to a Python Program
2.6 Modular Programming and Python Modules
2.6.1 Accessing the Attributes and Methods of a Module
2.6.2 Function Defined Inside Modules are Called Methods of the Module
2.7 Strings (Basics)
2.7.1 Using String Function len(str) on a “Literal String”
2.7.2 Applying a Function and a Method to a Variable, which Refers to a
String
2.7.3 Python Strings are “immutable”
2.7.4 A String as a Sequence of Character in the Memory
2.7.5 The ‘+’ Operator Can Concatenate Two Strings
2.7.6 The str(object) Function Converts Objects to Strings
2.7.7 Single Quotes Within Double Quotes
2.7.8 Indexing of Strings
2.7.9 Slicing of Strings
2.8 Binary Literals in Python
2.9 The Zen of Python on Jupyter
Chapter 3 Operators in Python
3.1 Introduction
3.2 Assignment (and reassignment)
3.3 Overview of Operators
3.3.1 Some Common Arithmetic Operators
3.3.2 Order of Precedence of Operators
3.3.3 Comparison
3.3.4 Logical Operators
3.3.5 Bitwise Operators 5 3.3.6 Boolean Operators versus Bit-wise Operators
3.3.7 Special Operators
3.3.8 Identity Operator
3.3.9 Membership Operator
Chapter 4 Functions—Part I
4.1 Introduction
4.2 Need for Functions
4.3 Basics of Functions
4.3.1 Built-in Functions
4.3.2 Functions in Modules
4.3.3 Calling a Function (As Opposed to Defining or Writing a Function)
4.3.4 Some Important Built-in Functions in Python
4.3.5 Some Important Functions in Modules in Python
4.4 Defining your own functions and function syntax
4.4.1 Syntax for Writing/Defining Your Function
4.3.7 Scope namespace and Lifetime of Variables
Chapter 5 Functions—Part II
5.1 Introduction 5.2 Passing Variables in a Function Call
5.3 Function Arguments
5.3.1 Providing Default Arguments or Parameter Values 5.3.2 Passing of Arguments by Position
5.3.3 Keyword Arguments
5.3.4 Using “Default-values” and “Keyword-arguments” Together
5.3.5 Using Variable Number of Arguments in a Function Call by Using the Syntax with * in Function Definition
5.3.6 Using ** kwarg in Function Definition to Pass a Key Worded, Variable-length of Arguments
5.4 Additional Note on Modules in Python
5.4.1 How Python Interpreter Searches for Modules
5.4.2 Using if __name__ == “__main__”:
5.5 Recursion 5.5.1 Recursive Function to Find Factorial of a Number
5.5.2 Recursive Function to Find a Number is Even or Not (not very efficient) 5.5.3 Recursive Function to find ab
5.5.4 Recursive Function to Generate Fibonacci Numbers 1 5.5.5 Recursion versus Iteration (Advantage/Disadvantage of Recursion)
5.5.6 Recursion Example: Tower of Hanoi
5.5.7 Memoizing Example: Fibonacci Series 5.6 Some Special Functions
5.6.1 zip Function
5.6.2 Using zip to unzip
5.6.3 Lambda Functions
5.6.4 map() Function
5.6.5 filter() Function
5.6.6 Generator Functions

Chapter 6 Flow Control
6.1 Introduction 6.2 Using ‘if’
6.2.1 If…elif…else statement
6.2.2 if…else Statement (Without elif)
6.2.3 Omitting the Else Clause (i.e., Using an ‘if’ Without an ‘else’)
6.2.4 Nested if... else Statements 6.2.5 How to Avoid nested if (Good Programming Technique) 6.3 while Loop 6.3.1 Basics of while Loop
6.3.2 break, continue and pass statements 6.3.3 while With else
6.3.4 Python does not have “do- - until”
6.3.5 pass Statement
6.3.6 Infinite loop
6.4 for Loop
6.4.1 Basics of “for” loop
6.4.2 Nested for loops
6.4.3 When to Use ‘while’ and When to Use ‘for’ Loop
6.5 range Function 6.5.1 The Function range([start], stop[, step])
6.5.2 Using range() Function in Loop 6.5.3 Using “in” Operator versus Using range() Function in “for” Loop 6.6 Common Errors in Flow Control 6.7 Iterable Chapter 7 Strings
7.1 Introduction 7.2 Creating, Initializing and Accessing Elements of a String 7.2.1 Creating Strings
7.2.2 String Indexing 7.2.3 Special Character and Escape Sequence 7.3 Traversing a String
7.3.1 Traversing a String using ‘for’ Loop
7.3.2 Traversing a String using while Loop 7.3.3 Traversing a String using ‘for’ Loop with the range() Function
7.4 String Operations
7.4.1 The plus, that is, ‘ + ’ operator
7.4.2 The multiplication operator *
7.4.3 Operatorsin and not in
7.4.4 String slicing
7.4.5 String Extended Slicing
7.4.6 Comparison of Strings using Relational Operators
7.5 Difference between Functions, Methods and Attributes
7.6 String Functions versus String Methods 7.6.1 str.capitalize() where str is a String
7.6.2 str.count(sub[, start[, stop]]) 7.6.3 Checking for a Palindrome
7.7 A Short Note on String Module
Chapter 8 Lists
8.1 Introduction
8.2 Some Basic Concepts of Lists
8.2.1 Concept of Containers(or collections), Sequence and Mapping
8.2.2 Mutability 8.2.3 Sequences in Python
8.2.4 Some Common Operations on Sequences 8.3 Creating, Traversing and Slicing Lists
8.3.1 Creating list 8.3.2 List Comprehension (Comprehension means construction)
8.3.3 Traversing the list (ie iterating over items of a list)
8.3.4 Ways of Adding to a List 8.3.5 list slicing
8.3.6 myList[start: end] or myList[m:n]
8.3.7 myList[m:n:s] or mylist[start:end:step]
8.3.8 Index and Slice Assignment
8.3.9 Difference between Assigning a List to Another List and Copying a List
8.3.10 Concept of Aliasing
8.4 List Functions and Methods
8.4.1 len
8.4.2 sort 8.4.3 Searching, Adding, Removing, Reversing, and Others
8.4.4 index() Method
8.4.5 List method: pop 8.4.6 List in Boolean Context 8.5 Nested Lists and Using Them as Matrix
8.5.1 Sample Script (To Initialize Items in a Matrix)
8.5.2 Sample Script (Get Items in Diagonals of a Matrix)
8.5.3 Sample Script (Add all Numbers which are even in a Matrix)
8.5.4 Sample Script (Upper/Lower Triangle Matrix)
Chapter 9 Dictionaries
9.1 Introduction
9.2 Basics of Dictionary—1
9.2.1 Properties of a Dictionary
9.2.2 Concept of Hashable
9.2.3 Mutability of Dictionary
9.2.4 Creating, Initializing, Accessing Elements
9.3 Basic Concepts—2 9.3.1 Dictionary Comprehension
9.3.2 Checking for Presence/Absence of a Key in a Dictionary
9.3.3 Traversing a Dictionary 9.3.4 Duplicate Keys are Not Allowed (But duplicate values are allowed)
9.3.5 Keys Must be Immutable (Must be “Hashable” Objects)
9.4 Dictionary Functions and Methods 9.4.1 sorted(d) 9.4.2 del d[key]
9.4.3 len(d) 9.4.4 cmp(d1, d2) (Where d1 and d2 are two dictionaries being compared) 9.5 Dictionary Methods 9.5.1 d.clear() 9.5.2 d.get(some_key [, default_value])
9.5.3 d. has_key()
9.5.4 d.keys() 9.5.5 d.values() 9.5.6 d.items() 9.5.7 d1.update(d2) (where d1 and d2 are dictionaries) 9.6 Dictionary View Objects Chapter 10 Tuples
10.1 Introduction 10.2 Some Basic Concepts Regarding Tuples 10.2.1 Immutability Concept 10.2.2 Creating, initializing and Accessing Elements 10.2.3 Accessing Items in a Tuple and Creating New Tuples from Existing 10.2.4 Short Note on Creating a Tuple Slice 10.2.5 Creating a Tuple from User Input (Using + Operator) 10.2.6 Immutability versus Reassignment 10.2.7 Other Features of Tuples 10.3 Some Additional Topics
10.3.1 Operations
10.3.2 Some Common Tuple Functions 10.3.3 Swapping Tuples 10.3.4 Unpacking Tuples Chapter 11 Regular Expression
11.1 Introduction 11.2 Basic Concepts of Regular Expressions
11.2.1 Raw Strings in Python 11.2.2 The Concept of RE 11.3 Special Characters, Groups of Characters and Anchors
11.3.1 Special Characters
11.3.2 Matching Group of Characters 11.3.3 Start of String and End of String Anchors
11.4 Understanding Re Module 11.5 The Match object and match(), search() Methods 11.5.1 The match() and search() Methods 11.5.2 Difference Between match() and search() Methods/Functions 11.6 Some Important Methods of the re module 11.6.1 re.findall(pattern, string)
11.6.2 re.finditer(pattern, string) 11.6.3 re.sub(pattern, repl, string, count=0, flags=0)
11.6.4 A Special Note on Methods of re module and Methods of Pattern Object 11.7 Some Methods/Attributes of the Match Object
11.7.1 Using group() Method of Match Object 11.7.2 Using start() and end() Methods of Match Object 11.7.3 The span() Method of Match Object
11.7.4 Greedy versus Non-greedy Matching 11.8 Some Common Scripts using RE Chapter 12 Some Additional Advanced Topics
12.1 Introduction 2 12.2 Concepts of Shared Reference and Docstrings 2 12.2.1 Shared Reference and In-place Change (Relating to Function Calls) 2 12.2.2 Shared Reference, Equality and Sameness 12.2.3 Docstring (In Function Definition) 2 12.3 Concepts Related to Python Module, name attribute and Virtual Environment
12.3.1 How Python Interpreter Searches for Modules (Relating to import statements)
12.3.2 Problems That May Arise in Importing Modules
12.3.3 Importing Only Some of the Attributes 12.3.4 Using Import *
12.3.5 Attributes with Leading Underscore and Import*
12.3.6 Using __file__ Attribute to Find Location of an Imported Module 12.3.7 Using reload() 12.3.8 Understanding the Use of the Command Line
12.3.9 Running a Python Script from the Command Line 12.3.10 __name__ attribute
12.3.11 Getting the ‘dependency’ Tree of a Module
12.3.12 Virtual Environment
12.4 Getting Help in Python and Some “Common Errors”
12.4.1 Getting Help Using dir()
12.4.2 Using help()
12.4.3 Some Common Errors (Part 1)
12.4.4 Some Common Errors in Python (Part 2)
12.5 Using Jupyter Notebook in Interactive Mode
12.5.1 Installation 12.5.2 Using Widgets of ipywidgets
12.5.3 Getting Details of a Widget
12.5.4 The UI and Event Handler of a Widget
12.6 Linting in Python
12.6.1 Linting Packages Available
12.6.2 pycodestyle
12.6.3 Using pycodestyle
12.6.4 Using pylint
12.6.5 Pylint and Static Code Analysis in Spyder
Chapter 13 Object-Oriented Programming with Python 13.1 Introduction
13.2 Basic Concept of Object-Oriented Programming
13.2.1 Concept of Class, Object and Abstraction
13.2.2 Concept of Data Abstraction
13.2.3 Concept of Data Encapsulation through Class and Object
13.2.4 Concept of Object
13.2.5 Concept of Inheritance 3 13.2.6 Concept of Polymorphism
13.2.7 Concept of Operator Overloading
13.2.8 Short Note on Function Overloading
13.2.9 Creating a Simple Class and Simple Objects
13.3 OOP Concepts Related Specifically to Python 3 13.3.1 A Class which has an __init__() Method
13.3.2 A Class which has Attributes, __init__() and also default Values for __init__()
13.3.3 A Class which has Attributes as well as Member Functions or Class Methods
13.3.4 Concept of Instance Methods (or Methods Applicable to Objects), Static Methods and Class Methods
13.3.5 Function Decorator @staticmethod
13.3.6 Data Hiding, Mangling, Pseudo-private Member Variables in a Class
13.3.7 Static versus Dynamic Binding
13.4 Some Common “Built in” Attributes and Methods of a Python Modules and Classes
13.4.1__name__ 13.4.2 __module__
13.4.3__dict__ 13.4.4 __doc__
13.4.5__bases__ 13.4.6__del__()
13.4.7 some_object.__str__()
13.5 Common Mistakes while Creating Objects
Chapter 14 Inheritance and Namespace 14.1 Introduction 14.2 Basics of Inheritance in Python
14.2.1 Introduction to Subclassing (Inheritance)
14.2.2 Types of Inheritance
14.3 Single Inheritance
14.3.1 A Simple Inheritance of all Functionalities of the Base Class or Parent Class
14.3.2 Inheritance, where the Derived Class has an init() Method of its Own
14.3.3 Both the Derived Class and the Parent Class have their Own __init__() Methods
14.3.4 Use of super() to Call Methods other than init() of Base Class also 14.3.5 Calling the __init__() Methods of the Parent Class by Using the Name of the Parent Class
14.3.6 Abstract Methods
14.4 Multiple Inheritance
14.4.1 Potential Problem in Multiple Inheritance
14.4.2 Python in-built Class Attribute __mro__
14.4.3 Abstract Methods and Abstract Class
14.4.4 Creating Custom Containers
14.5 Concept of Namespace 14.5.1 locals() and globals()
14.5.2 Namespace Dictionary __dict__
Chapter 15 File Operations in Python
15.1 Introduction
15.2 Basics of file Operations in Python
15.2.1 Basics of a File (Text versus Binary File) 3 15.2.2 Opening and Closing Files
15.2.3 The file_name Argument in open() Function
15.2.4 Using open() Function to Create a New File
15.2.5 File Object, Access Modes 3 15.3 Reading and Writing a File
15.3.1 The read() Method: (and the tell() method)
15.3.2 seek(offset[,from_what]) method
15.3.3 readline([size]) and readlines([sizehint])
15.3.4 Writing to Files Using write() Method
15.3.5 writelines(aList) Method of File Object
15.4 Some More Advanced Concepts in File Operations
15.4.1 Using the “with” Statement to Close File Automatically
15.4.2 Python IO Stream Object
15.4.3 Some Scripts in Python to Demonstrate Concepts of File Usage
15.4.4 Pickling and Unpickling
15.4.5 Pickle Module and Steps in Unpickling
15.5 Some Useful Methods of the OS Module
15.6 Writing Small Scripts for Inserting Data in a File 3 Chapter 16 Python Exceptions
16.1 Introduction
16.2 Basic Concepts of Exceptions in Python
16.2.1 Errors versus Exceptions
16.2.2 The raise Statement
16.2.3 The try-except-else Block of Code in Python
16.2.4 The try-except-else-finally Block with Multiple Except and Except with No Exception Type
16.2.5 Using try-except Block to Read a File
16.3 User-defined Exceptions
16.4 Built-in Exceptions
16.4.1 An except Clause may Name Multiple Exceptions as a
Parenthesized Tuple
Chapter 17 Linear List Manipulation, Stacks and Queues
17.1 Introduction
17.2 Basics of Data Structures and Lists in Python
17.2.1 Data Structures
17.2.2 Implementing list in Memory
17.2.3 List Operation: Traversal
17.2.4 Short Note on Insertion
17.2.5 Inserting Element in a Sorted List
17.2.6 Inserting an Element in a Sorted List using Bisect Module
17.2.7 insort(sequence, item) Method
17.3 Some Algorithms for Insertion, Deletion, Searching and Sorting of Lists
17.3.1 Inserting an Item in a Sorted List Manually 17.3.2 Deleting an Item whose ‘index’ is Given from a List (sorted or
unsorted) 17.3.3 Linear search
17.3.4 Binary search
17.3.5 Binary Search (Using Recursion)
17.3.6 Selection sorting
17.3.7 Bubble sort 17.3.8 Insertion sort
17.4 Stack and Queues Using Lists
17.4.1 Stacks
17.4.2 Implement Stack Using a Class
17.4.3 Queue
17.4.4 Queue Operations
17.4.5 Implementing a Queue using Front and Rear Variable
17.5 Some Common List Methods
17.5.1 list.append(x) (equivalent to push in Python)
17.5.2 my_list.extend(seq) 17.5.3 my_list.insert(i, x)
17.5.4 my_list.remove(x) 17.5.5 my_list.pop([i])
17.5.6 del list[idx]
17.5.7 my_list.index(x)
17.5.8 my_list.count(x)
17.5.9 my_list.sort(cmp=None, key=None, reverse=False)
17.5.10list.reverse() Chapter 18 NumPy, SciPy
18.1 Introduction
18.2 Basics of NumPy and SciPy
18.2.1 N-dimensional Array in NumPy
18.2.2 Some NumPy Methods and Properties
18.2.3 SciPy Basics
18.2.4 Broadcasting in NumPy Array Operations
18.2.5 Array Indexing in NumPy
18.2.6 Infinity, Negative Infinity, Zero, Negative Zero and Some other Constants in NumPy
18.2.7 np.linspace
18.2.8 Understanding np.meshgrid()
18.2.9 Using NumPy, SciPy for Getting Some Basic Information about a Matrix 18.3 Using NumPy for Various Operations
18.3.1 Solving Linear System of Equations
18.3.2 Multiplying a Matrix by a Vector
18.3.3 Multiplication of a Diagonal Matrix to a Vector (Scaling)
18.3.4 Matrix Multiplication as a Reflection
18.3.5 Matrix Multiplication as Rotation
18.3.6 Eigenvalues and Eigenvectors
18.3.7 Eigen Decomposition
18.3.8 Singular Value Decomposition
Chapter 19 SymPy
19.1 Introduction
19.2 Basics of SymPy 1 19.2.1 The “symbols()” Function
19.2.2 Importing Symbols from Module sympy.abc
19.2.3 SymPy Online Shell
19.2.4 Equality Testing in SymPy using “==”
19.2.5 Numeric Types in SymPy
19.2.6 Using Operators on Combination of SymPy Objects and Python Objects
19.3 Basics of SymPy 2
19.3.1 Substitution in a SymPy expression
19.3.2 Convert Python Strings to SymPy Expression and Evaluating it
(Functions sympify() and evalf())
19.3.3 Singleton Class in SymPy
19.3.4 Functions in SymPy
19.3.5 Lambda Class in SymPy
19.4 Sets in SymPy
19.4.1 FiniteSet
19.4.2 Interval
19.4.3 EmptySet
19.4.4 Intersection
19.4.5 Union
19.4.6 ConditionSet
19.4.7 Complement
19.4.8 ImageSet (along with imageset function)
19.4.9 Set Operations in SymPy
19.5 Matrices
19.6 The Equality Class and Eq 19.7 The Solvers Module of SymPy
19.7.1 solveset()
19.8 The linsolve() Method
19.9 Calculus with SymPy
19.9.1 Differentiation
19.9.2 Integration
19.9.3 Finding limits (Lim)
19.9.4 Ordinary Differential Equations (ODE)
19.9.5 Solving ODE for an Undamped and Damped Harmonic Oscillator
Chapter 20 Pandas: Open Source Data Analysis and Manipulation Tool
20.1 Introduction
20.2 Basics of pandas
20.2.1 Series
20.2.2 DataFrame
20.2.3 Creating a DataFrame from List or from List of Lists
20.2.4 Using the Key: Value Pair of a Dictionary to Create a DataFrame Object
20.2.5 Panel
20.3 Using pandas for Working on Files in Various Formats
20.3.1 Using Pandas to Open csv Files
20.3.2 Using Pandas to Read html Files
20.3.3 Reading/Writing to JSON files

Appendix 1 Downloading and Installing Python

Appendix 2 Command Line, IDLE, Python Docs and Python Manual

Appendix 3 Anaconda and Jupyter Notebook Basics