دانلود کتاب Python For Beginners. 2 Books in 1: A Completed Guide to Master the Basics of Python Language Programming and Data Science. Learn Coding Fast with Examples and Tips

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Python For Beginners\nPYTHON PROGRAMMING\nIntroduction\n The Parts You Should Know about the Python Code\n Getting That Environment Set Up\nChapter 1. Basic Background of Python\n What Is Python?\n Why Python?\n Installing Python\n Using a Text Editor\n Using an IDE\n Your First Program\n Code Comments and Your Program\nChapter 2. Data Types in Python\n Strings\n Numeric Data Type\n Booleans\n List\n Variables\n User-Input Values\nChapter 3. Operators - The Types and Their Uses\n The Types\n The Operator Precedence\n The Logical Operators\nChapter 4. Loops and Functions\n LOOPS\n Nested if Statements in Python\n For Loop in Python\n Range() Function in Python\n Using for Loop with Else\n While Loop in Python\n Using While Loop with Else\n Python’s Break and Continue\n Continue Statement in Python\n Pass Statement in Python\n Functions in Python\n Calling a Function in Python\n Docstring\n Python Function Return Statement\n Random Function in Python\n Iterators\n Manually Iterating Through Items in Python\n Explaining the Loop\n Creating Custom Iterator in Python\n Infinite Iterators\n Closure Function in Python\n Projects - Implementing Simple Calculator in Python\nChapter 5. Exception Handling\n What Is Exception Handling?\n Handling the Zero Division Error Exception\n Using Try-Except Blocks\n Reading an Exception Error Trace Back\n Using Exceptions to Prevent Crashes\n The Else Block\n Failing Silently\n Handling the File Not Found Exception Error\n Checking If File Exists\n Try and Except\n Creating a New File\nChapter 6. Variable Scope and Lifetime in Python Functions\n Function Types\n Keywords Arguments in Python\n Arbitrary Arguments\n Recursion in Python\n Python Anonymous Function\n Python’s Global, Local and Nonlocal\n Creating a Local Variable in Python\n Python’s Global and Local Variable\n Python’s Nonlocal Variables\n Global Keyword in Python\n Creating Global Variables across Python Modules\n Python Modules\n Module Import\n Import Statement in Python\n Importing All Names\n Module Search Path in Python\n Reloading a Module\n Dir() built-in Python function\n Python Package\n Number Conversion\n Type Conversion\n Mathematics in Python\n Random Function in Python\n Lists in Python\n Nested Lists\n Accessing Elements from a List\nChapter 7. Modules\n How to Create a Module?\n Import Statement\n Locate a Module\n Syntax of PYTHONPATH\nChapter 8. Working with Files\n Reading from a File\n File Pointer\n File Access Modes\n Writing to a File\n Practice Exercise\n Summary\nChapter 9. Object-Oriented Programming\n Classes and Objects\nChapter 10. Real-World Examples of Python\n Data Science\n Machine Learning\n Applications in Web Development\n Automation\n Things We Can Do in Python\n Comment\n Reading and Writing\n Files\n Integers\n Triple Quotes\n Variables\n The Scope of a Variable\n Modifying Values\n The Assignment Operator\nChapter 11. Getting Started; Python Tips and Tricks\n Web Scraping\nChapter 12. Common Programming Challenges\n Debugging\n Working Smart\n User Experience\n Estimates\n Constant Updates\n Problems Communicating\n Security Concerns\n Relying on Foreign Code\n Lack of Planning\n Finally\nConclusion\nIntroduction\n Effectiveness of Libraries for Python\n There Is Always Someone Available to Help in the Python Community\nChapter 1: What Is Data Science?\n The Importance of Data Science\n How Is Data Science Used?\n The Lifecycle of Data Science\n The Components of Data Science\nChapter 2: Basics of Python\n Python IDEs\n Getting Started with Python\n Data Types\n Functions and Modules\n Object-Oriented Programming\n Class Inheritance\n Regular Expressions\n Match and Search Functions\n Exception Handling\n File Handling\nChapter 3: The Best Python Libraries for Data Science\n Core Libraries and Statistics\n Visualization\n Machine Learning Libraries\n Deep Learning\nChapter 4: Data Science and Applications\n Banking and Finance\n Health and Medicine\n Oil and Gas\n The Internet\n Travel and Tourism\nChapter 5: The Lifecycle of Data Science\n The Discovery Phase\n The Data Preparation Phase\n The Model Planning Phase\n The Operationalize Phase\n The Communicate Results Phase\nChapter 6: Probability, Statistics, and Data Types\n Real-Life Probability Examples\n Statistics\n Data Types\n The Importance of Data Types\n Statistical Methods\n Descriptive Statistics\nChapter 7: Most Common Data Science Problems\n Management Expects the World\n Misunderstanding How Data Works\n Taking the Blame for Bad News\n Communication as a Solution\nChapter 8: Comparison of Python with Other Languages\n Python versus Java comparison\n Python versus C#\n Python versus JavaScript\n Python versus Perl\n Python versus Tcl\n Python versus Smalltalk\n Python versus C++\n Python versus Common Lisp and Scheme\n Python versus Node.js\n Coding Everything in JavaScript\n Python versus PHP\nChapter 9: Data Cleaning and Preparation\n What Is Data Preparation?\n Why Do I Need Data Preparation?\n What Are the Steps for Data Preparation?\n Handling the Missing Data\nChapter 10: Data Visualization\n Data Visualization to the End-User\n Matplotlib\n Visualization Using Pandas\n The Objective of Visualization\n The Simplest Method to Complex Visualization of Data\n Overview of Plotly\n Heat Maps\nConclusion