Job Ready Python

Get ready to take on Python with a practical and job-focused guide  Job Ready Python offers readers a straightforward and elegant approach to learning Python that emphasizes hands-on and employable skills you can apply to real-world environments immediately.  Based on the renowned mthree Global Academy and Software Guild training program, this book will get you up to speed in the basics of Python, loops and data structures, object-oriented programming, and data processing. You’ll also get:  Thorough discussions of Extract, Transform, and Load (ETL) scripting in Python  Explorations of databases, including MySQL, and MongoDB—all commonly used database platforms in the field  Simple, step-by-step approaches to dealing with dates and times, CSV files, and JSON files  Ideal for Python newbies looking to make a transition to an exciting new career, Job Ready Python also belongs on the bookshelves of Python developers hoping to brush up on the fundamentals with an authoritative and practical new handbook.  

Table of Contents:
About the Authors v About the Technical Writer v About the Technical Editor v Acknowledgments vi Introduction xvii   Part I: Getting Started with Python 1 Lesson 1: Setting Up a Python Programming Environment 3 Python Overview 4 Using Replit Online 4 Getting Started with Jupyter Notebook 14 A Quick Look at Visual Studio Code 21 Using Python from the Command Line 24 Summary 26 Exercises 26 Lesson 2: Understanding Programming Basics 29 The Future of Computer Programming 30 Programming Languages 32 Data Types and Variables 37 Variables 40 Constants 44 Summary 46 Exercises 46 Lesson 3: Exploring Basic Python Syntax 49 Using with Single- Line Commands 51 Using Semicolons 52 Continuing with Backslash 54 Working with Case Structure 55 Adding Comments 56 Using the Input Function 57 Storing Input 59 Understanding Variable Types 61 Displaying Variable Values 62 Naming Variables 64 Summary 65 Exercises 65 Lesson 4: Working with Basic Python Data Types 69 Review of Data Types 70 Number Data Types 70 Identifying Data Types 72 Mathematical Operations 74 Pemdas 77 Common Math Functions 81 Math Library Functions 83 Using Numbers with User Input 86 Boolean Types and Boolean Operations 89 Logic Operations 92 Comparative Operators 95 Summary 96 Exercises 97 Lesson 5: Using Python Control Statements 101 Control Structures Review 101 Understanding Sequence Control Structure 102 Understanding Selection Statements 103 Understanding Conditional Statements 106 If- Else Statements 108 Working with Nested Conditions 109 Embedding Conditions 112 Summary 114 Exercises 114 Lesson 6: Pulling It All Together: Income Tax Calculator 117 Getting Started 118 Step 1: Gather Requirements 118 Step 2: Design the Program 120 Step 3: Create the Inputs 120 Step 4: Calculate the Taxable Income 122 Step 5: Calculate the Tax Rate 124 Step 6: Update the Application 133 Step 7: Address the UI 136 On Your Own 139 Summary 139 Part II: Loops and Data Structures 141 Lesson 7: Controlling Program Flow with Loops 143 Iterations Overview 144 The Anatomy of a Loop 144 The for Loop 145 The while Loop 146 for vs. while Loops 149 Strings and String Operations 151 Iterating through Strings 164 Summary 167 Exercises 167 Lesson 8: Understanding Basic Data Structures: Lists 173 Data Structure Overview—Part 1 174 Creating Lists 175 Determining List Length 179 Working with List Indexes 179 Negative Indexing in Lists 182 Slicing Lists 184 Adding Items to a List 189 Inserting List Items 190 Removing List Items 192 Concatenating Lists 196 List Comprehension 197 Sorting Lists 199 Copying Lists 200 Summary 202 Exercises 202 Lesson 9: Understanding Basic Data Structures: Tuples 205 Tuples and Tuple Operations 206 Tuple Index Values 209 Negative Indexing in Tuples 210 Slicing Tuples 212 Immutability 213 Concatenating Tuples 216 Searching Tuples 217 Summary 218 Exercises 219 Lesson 10: Diving Deeper into Data Structures: Dictionaries 223 Data Structure Overview— Part 2 224 Getting Started with Dictionaries 224 Generating a Dictionary 227 Retrieving Items from a Dictionary 230 Using the keys() Method 233 Using the items() Method 234 Reviewing the keys(), values(), and items() Methods 236 Using the get() Method 239 Using the pop() Method 241 Working with the in Operator 245 Updating a Dictionary 246 Duplicating a Dictionary 249 Clearing a Dictionary 254 Summary 255 Exercises 255 Lesson 11: Diving Deeper into Data Structures: Sets 259 Sets 260 Retrieving Items from a Set 261 Adding Items to a Set 262 Creating an Empty Set 262 Understanding Set Uniqueness 263 Searching Items in a Set 265 Calculating the Length of a Set 267 Deleting Items from a Set 268 Clearing a Set 270 Popping Items in a Set 272 Deleting a Set 273 Determining the Difference Between Sets 274 Intersecting Sets 277 Combining Sets 278 Summary 279 Exercises 279 Lesson 12: Pulling It All Together: Prompting for an Address 283 Step 1: Getting Started 284 Step 2: Accept User Input 285 Step 3: Display the Input Value 286 Step 4: Modify the Output 287 Step 5: Split a Text Value 288 Step 6: Display Only the House Number 290 Step 7: Display the Street Name 291 Step 8: Add the Period 292 Summary 293 Lesson 13: Organizing with Functions 295 Functions Overview 295 Defining Functions in Python 296 Function Syntax 300 Default Input Values 301 Parameter Syntax 303 Arbitrary Arguments 304 Keyword Arguments 306 Arbitrary Keyword Arguments 306 Summary 308 Exercises 309 Part III: Object- Oriented Programming in Python 311 Lesson 14: Incorporating Object- Oriented Programming 313 Object- Oriented Programming Overview 314 Defining Classes 314 Creating Objects 316 Working with Methods 319 Class Attributes 324 Summary 330 Exercises 330 Lesson 15: Including Inheritance 333 Understanding Inheritance 334 Creating a Parent Class 335 Creating a Child Class 335 Inheriting at Multiple Levels 338 Overriding Methods 340 Summary 343 Exercises 344 Lesson 16: Pulling It All Together: Building a Burger Shop 349 Requirements for Our Application 350 Plan the Code 350 Create the Classes 351 Create the Food Item Class 352 Create the Main File 357 Display the Output 364 Tie the Code Files Together 364 Summary 368 Part IV: Data Processing with Python 369 Lesson 17: Working with Dates and Times 371 Getting Started with Dates and Times 372 Getting the Current Date and Time 376 Splitting a Date String 377 Using datetime Attributes 379 Creating Custom datetime Objects 380 Compare datetime Values 381 Working with UTC Format 383 Applying Timestamps 384 Arithmetic and Dates 387 Calculating the Difference in Days 388 Using Date without Time 390 Using Time without Date 392 Summary 394 Exercises 394 Calculator 1: Time Duration 396 Calculator 2: Add or Subtract Time from a Date 397 Calculator 3: Age Calculator 397 Lesson 18: Processing Text Files 399 File Processing Overview 401 Introduction to File Input/Output 402 Processing Text Files 404 Opening a File 404 Reading Text from a File 406 Add Content to a File 412 Overwriting the Contents of a File 415 Creating a New File 417 Using the os Module 418 Deleting a File 419 Summary 421 Exercises 421 Lesson 19: Processing CSV Files 425 Reading CSV Files 426 Using the DictReader Class 430 Creating a Dataset List 432 Using writerow() 434 Appending Data 436 Writing Rows as Lists 439 Writing Rows from Dictionaries 440 Summary 444 Exercises 444 Lesson 20: Processing JSON Files 447 Processing JSON Files 448 Creating a JSON File with dump() 448 Converting to JSON with dumps() 449 Formatting JSON Data 450 Using json.loads() 452 Iterating through JSON Data 454 Reading and Writing JSON Data 457 Summary 460 Exercises 461 Part V: Data Analysis and Exception Handling 465 Lesson 21: Using Lambdas 467 Creating a Lambda Function 468 Working with Multiple Inputs 469 Placing Lambda Functions inside a Function 471 Using the map() Function 472 Combining Map and Lambda Functions 475 Using the filter() Function 477 Combining a Filter and a Lambda 479 Using the reduce() Function 480 Summary 486 Exercises 486 Lesson 22: Handling Exceptions 491 Built- In Exceptions 492 Working with try and except 493 Working with Multiple Excepts 495 Combining Exception Types 498 Using Multiple Operations in a try 500 Using the raise Keyword 501 Exploring the General Exception Classes 502 Adding finally 505 Summary 506 Exercises 506 Lesson 23: Pulling It All Together: Word Analysis in Python 511 Examine the Data 512 Read the Data 514 Tokenize the Dataset 517 Count the Words in Each Review 524 Summary 528 Lesson 24: Extracting, Transforming, and Loading with ETL Scripting 531 ETL Scripting in Python 532 Design and Implement Custom ETL Scripts 532 The extract Class 534 The transform Class 546 The load Class 569 Summary 582 Exercises 582 Lesson 25: Improving ETL Scripting 585 Converting to Static Methods for the extract Class 586 Converting to Static Methods for the transform Class 588 Summary 607 Exercises 608 Part VI: Appendices 611 Appendix A: Flowcharts 613 Flowchart Basics 613 Common Flowcharting Shapes 615 Appendix B: Creating Pseudocode 621 What Is Pseudocode? 621 Appendix C: Installing MySQL 623 MySQL Installation 623 Verify the Installation 628 The MySQL Notifier 630 Appendix D: Installing Vinyl DB 631 Database Structure 631 Create the Database 632 Appendix E: Installing MongoDB 637 Installing MongoDB Community Server 637 Running MongoDB 642 Appendix F: Importing to MongoDB 643 Index 645

About the Author :
HAYTHEM BALTI, PhD, is the associate dean at Wiley’s mthree academy. He has created courses used by thousands of Software Guild and mthree alumni to learn Go, Java, Python, and other development and data science skills. KIMBERLY A. WEISS is a veteran course developer, specializing in Computer Science courses since 2002. She was an assistant professor in Computer Science for over ten years before deciding to focus exclusively on course design. She has worked with multiple universities as well as corporate training settings to develop interactive instructional content appropriate for the target learners and course goals.