Python: 3 books in 1: Python basics for Beginners + Python Automation Techniques And Web Scraping + Python For Data Science And Machine Learning

PYTHONPython Basics for Beginners
PYTHONPython Basics for Beginners
Introduction
Chapter One: A Brief History of Python
Chapter One: A Brief History of Python
    Python’s First Release
    Python 3.0
    The Future
    How to Install Python
    Installing Python Windows 10
    Installing Python on Mac OS
    Installing Python on Linux
    Troubleshooting Installation Problems
Chapter Two: Python Datatypes
Chapter Two: Python Datatypes
    Python Variables
    Naming Variables
    Python Datatypes
    Strings
    String Concatenation
    Whitespaces
    Stripping Whitespace
    Syntax Errors
    Numbers
    The Zen of Python
Chapter Three: Python Lists
Chapter Three: Python Lists
    Defining Lists
    Accessing Elements
    List Modification
    Remove() Method
    List Organization
    Sorted() Function
    List Length
    Looping Through Lists
    Indentation
    Post-Loop Unnecessary Indentation
    Numerical Lists
    List Slicing
    Copying Lists
Chapter Four: Python Tuples
Chapter Four: Python Tuples
    Length
    Changing Elements
    Creating Loop
    Overwriting a Tuple
    Code Styling
Chapter Five: Python Conditionals
Chapter Five: Python Conditionals
    Numerical Testing
    Testing Multiple Conditions
    The if Statement
    if-else Statements
    The if-elif-else Chain
    Multiple elif Statements
    Omitting else Block
    Multiple Conditions
    If Statement & Lists
    Special Items
Chapter Six: Python Dictionaries
Chapter Six: Python Dictionaries
    A Dictionary
    Accessing Values
    Empty Dictionary
    Modification
    Key-Pair Removal
    Looping a Dictionary
    Nesting
    Dictionary & Lists
Chapter Seven: User Input & While Loops
Chapter Seven: User Input & While Loops
    Input() Function
    While Loops
    Quit Button
    Flag
    Break Statement
    Continue Statement
    While Loops, Lists & Dictionaries
    Removing Instances
    Filling Up Dictionaries
Chapter Eight: Python Functions
Chapter Eight: Python Functions
    Arguments & Parameters
    Default Values
Chapter Nine: Classes
Chapter Nine: Classes
    Creating Eagle Class
Conclusion
References
PYTHONPython Automation Techniques and Web Scraping
PYTHONPython Automation Techniques and Web Scraping
Introduction
Chapter One: An Introduction to Python
Chapter One: An Introduction to Python
    Running Python
    Choosing the Right Version
    Learning While Having Fun
    Choosing to Code
    Getting Started
    Using Python to Create Files
Chapter Two: Data Types and Variables
Chapter Two: Data Types and Variables
    Choosing the Right Identifier
    Python Keywords
    Creating and Assigning Values to Variables
    Using Quotes
    Using Whitespace Characters
Chapter Three: Data Structures
Chapter Three: Data Structures
    Items in Sequences
    Tuples
    List
    Stacks and Queues
    Dictionaries
Chapter Four: Working with Strings
Chapter Four: Working with Strings
    Splitting Strings
    Concatenation and Joining Strings
    Editing Strings
    How to Match Patterns
Chapter Five: Conditional Statements
Chapter Five: Conditional Statements
    Comparing Variables
    How to Control the Process
    Using the Conditional Code
    Loops
    For
    Understanding the Jargon
Chapter Six: How to Use Files
Chapter Six: How to Use Files
    How to Open Files
    Modes and Buffers
    Reading and Writing
    Closing Files
Chapter Seven: Working with Functions
Chapter Seven: Working with Functions
    Function Definitions
    Defining Parameters
    Documenting Your Function
    Working with Scope
    Manipulating Dictionaries and Lists
    Abstraction
Chapter Eight: Web Scraping Using Python
Chapter Eight: Web Scraping Using Python
    Understanding Web Scraping
    Scraping a Job Website
Chapter Nine: Tasks to Automate Using Python
Chapter Nine: Tasks to Automate Using Python
    Automating Tasks Using Python
Chapter Ten: Cleaning Data Using Python
Chapter Ten: Cleaning Data Using Python
    Dropping Columns in a Data Frame
    Changing the Index of a Data Frame
    Clearing Fields in the Data Set
    Combining NumPy and Str Methods to Clean Column Data
    Cleaning the Entire Data Set Using the applymap()
    Renaming Columns and Skipping Rows
    Python Data Cleaning: Recap and Resources
    Manipulating Data Using Python
    Exploring the Data Set
Chapter Eleven: Mistakes Made by Programmers
Chapter Eleven: Mistakes Made by Programmers
    Using Defaults as Function Arguments
    Incorrect Use of Class Variables
    Specifying Incorrect Parameters
    Misusing Scope of Pythons
    Modifying and Iterating Lists
    Creating Circular Modules
    Forgetting to Understand the Changes Between Versions
    Incorrect Use of Del Method
Chapter Twelve: Solutions
Chapter Twelve: Solutions
    Concatenate two strings
    Sum of Two Numbers
    Even and Odd Numbers
    Fibonacci Series
    Palindrome
    Access Elements in a List
    Slice a List
    Delete Elements in a List
    Access Elements in a Tuple
    Change a Tuple
    Create a String
Conclusion
References
PYTHONPython for Data Science and Machine Learning
PYTHONPython for Data Science and Machine Learning
Introduction
Part One: An Introduction to Data Science and Machine Learning
Part One: An Introduction to Data Science and Machine Learning
    What Is Data Science?
    How Important Is Data Science?
    Data Science Limitations
    What Is Machine Learning?
    How Important Is Machine Learning?
    Machine Learning Limitations
    Data Science vs. Machine Learning
Part Two: Introducing NumPy
Part Two: Introducing NumPy
    What Is NumPy Library?
    How to Create a NumPy Array
    Shaping and Reshaping a NumPy array
    Index and Slice a NumPy Array
    Stack and Concatenate NumPy Arrays
    Broadcasting in NumPy Arrays
    NumPy Ufuncs
    Doing Math with NumPy Arrays
    NumPy Arrays and Images
Part Three: Data Manipulation with Pandas
Part Three: Data Manipulation with Pandas
    Question One: How Do I Create a Pandas DataFrame?
    Question Two – How Do I Select a Column or Index from a DataFrame?
    Question Three: How Do I Add a Row, Column, or Index to a DataFrame?
    Question Four: How Do I Delete Indices, Rows, or Columns From a Data Frame?
    Question Five: How Do I Rename the Columns or Index of a DataFrame?
    Question Six: How Do I Format the DataFrame Data?
    Question Seven: How Do I Create an Empty DataFrame?
    Question Eight: When I Import Data, Will Pandas Recognize Dates?
    Question Nine: When Should a DataFrame Be Reshaped? Why and How?
    Question Ten: How Do I Iterate Over a DataFrame?
    Question Eleven: How Do I Write a DataFrame to a File?
Part Four: Data Visualization with Matplotlib and Seaborn
Part Four: Data Visualization with Matplotlib and Seaborn
    Using Matplotlib to Generate Histograms
    Using Matplotlib to Generate Scatter Plots
    Using Matplotlib to Generate Bar Charts
    Using Matplotlib to Generate Pie Charts
    Visualizing Data with Seaborn
    Using Seaborn to Generate Histograms
    Using Seaborn to Generate Scatter Plots
    Using Seaborn to Generate Heatmaps
    Using Seaborn to Generate Pairs Plot
Part Five: An In-Depth Guide to Machine Learning
Part Five: An In-Depth Guide to Machine Learning
    Machine Learning Past to Present
    Common Machine Learning Algorithms
    Gaussian Naive Bayes classifier
    K-Nearest Neighbors
    Support Vector Machine Learning Algorithm
    Fitting Support Vector Machines
    Linear Regression Machine Learning Algorithm
    Logistic Regression Machine Learning Algorithm
    A Logistic Regression Model
    Decision Tree Machine Learning Algorithm
    Random Forest Machine Learning Algorithm
    Artificial Neural Networks Machine Learning Algorithm
    Machine Learning Steps
    Evaluating a Machine Learning Model
    Model Evaluation Metrics
    Regression Metrics
    Implementing Machine Learning Algorithms with Python
    Advantages and Disadvantages of Machine Learning
Conclusion
References