Python and SQL Bible

Python and SQL Bible

by Cuantum Technologies LLC
Released June 2024
Publisher(s): Packt Publishing
ISBN: 9781836206279

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Book description

Dive into comprehensive learning with Python and SQL Bible. This course covers everything from Python fundamentals to advanced SQL, empowering technical professionals with essential programming and data analysis skills.

Key Features

  • Comprehensive coverage of Python and SQL from basics to advanced techniques.
  • Equip yourself with essential programming and data analysis skills for the tech industry.
  • Learn through detailed explanations, interactive exercises, and real-world projects.

Book Description

Embark on a transformative journey with this course designed to equip you with robust Python and SQL skills. Starting with an introduction to Python, you'll delve into fundamental building blocks, control flow, functions, and object-oriented programming. As you progress, you'll master data structures, file I/O, exception handling, and the Python Standard Library, ensuring a solid foundation in Python.

The course then transitions to SQL, beginning with an introduction and covering basics, and proceeding to advanced querying techniques. You'll learn about database administration and how Python integrates seamlessly with SQL, enhancing your data manipulation capabilities. By combining Python with SQLAlchemy, you'll perform advanced database operations and execute complex data analysis tasks, preparing you for real-world challenges.

By the end of this course, you will have developed the expertise to utilize Python and SQL for scientific computing, data analysis, and database management. This comprehensive learning path ensures you can tackle diverse projects, from basic scripting to sophisticated data operations, making you a valuable asset in the tech industry. You'll also gain hands-on experience with real-world datasets, enhancing your problem-solving skills and boosting your confidence.

What you will learn

  • Understand and apply Python fundamentals.
  • Master control flow and object-oriented programming in Python.
  • Perform advanced SQL queries and database administration.
  • Integrate Python with SQL for enhanced data manipulation.
  • Conduct complex data analysis using Python and SQLAlchemy.
  • Manage files and handle exceptions in Python effectively.

Who this book is for

This course is ideal for a wide range of learners, including technical professionals, aspiring data scientists, software developers, and database administrators looking to enhance their skill set. It's perfect for beginners with little to no programming experience, as well as those with some background in coding who want to deepen their knowledge of Python and SQL. Additionally, it serves business analysts and IT professionals aiming to leverage data analysis and database management in their roles.

Table of contents

  1. Who we are
    1. Our Philosophy:
    2. Our Expertise:
  2. Introduction
  3. Chapter 1: Python: An Introduction
    1. 1.1 Brief History of Python
    2. 1.2 Benefits of Python
      1. 1.2.1 Readability and Simplicity
      2. 1.2.2 High-Level Language
      3. 1.2.3 Extensive Libraries
      4. 1.2.4 Cross-Platform Compatibility
      5. 1.2.5 Dynamically Typed
      6. 1.2.6 Support for Multiple Programming Paradigms
      7. 1.2.7 Strong Community and Widespread Adoption
      8. 1.2.8 Integration with Other Languages
      9. 1.2.9 Versatility
    3. 1.3 Python Applications
      1. 1.3.1 Web Development
      2. 1.3.2 Data Analysis and Data Visualization
      3. 1.3.3 Machine Learning and Artificial Intelligence
      4. 1.3.4 Game Development
      5. 1.3.5 Automation and Scripting
      6. 1.3.6 Cybersecurity
      7. 1.3.7 Internet of Things (IoT)
      8. 1.3.8 Robotics
      9. 1.3.9 Bioinformatics and Computational Biology
      10. 1.3.10 Education
    4. 1.4 Setting up the Python Environment and Writing Your First Python Program
      1. 1.4.1 Setting up Python Environment
      2. 1.4.2 Your First Python Program
    5. Chapter 1 Conclusion
  4. Chapter 2: Python Building Blocks
    1. 2.1 Python Syntax and Semantics
      1. 2.1.1 Python Syntax
      2. 2.1.2 Python Semantics
    2. 2.2 Variables and Data Types
      1. 2.2.1 Integers
      2. 2.2.2 Floating-Point Numbers
      3. 2.2.3 Strings
      4. 2.2.4 Booleans
      5. 2.2.5 Lists
      6. 2.2.6 Tuples
      7. 2.2.7 Dictionaries
      8. 2.2.8 Type Conversion
      9. 2.2.9 Dynamic Typing
      10. 2.2.10 Variable Scope
    3. 2.3 Basic Operators
      1. 2.3.1 Arithmetic Operators
      2. 2.3.1 Comparison Operators
      3. 2.3.2 Logical Operators
      4. 2.3.3 Assignment Operators
      5. 2.3.4 Bitwise Operators
      6. 2.3.5 Membership Operators
      7. 2.3.6 Identity Operators
      8. 2.3.6 Operator Precedence
    4. 2.4 Practice Exercises
    5. Chapter 2 Conclusion
  5. Chapter 3: Controlling the Flow
    1. 3.1 Control Structures in Python
      1. 3.1.1 Conditional Statements (if, elif, else)
      2. 3.1.2 Loop Structures (for, while)
    2. 3.2 Error and Exception Handling
      1. 3.2.1 Handling Exceptions with try and except
      2. 3.2.2 The else and finally Clauses
      3. 3.2.3 Raising Exceptions
      4. 3.2.4 The assert Statement
    3. 3.3 Understanding Iterables and Iterators
      1. 3.3.1 Iterators in Python
      2. 3.3.2 The for loop and Iterators
      3. 3.3.3 Iterators and Built-in Types
      4. 3.3.4 Python's itertools Module
      5. 3.3.5 Python Generators
    4. 3.4 Practice Exercises
      1. Exercise 1: Conditional Statements
      2. Exercise 2: Loops
      3. Exercise 3: Error and Exception Handling
      4. Exercise 4: Iterables and Iterators
    5. Chapter 3 Conclusion
  6. Chapter 4: Functions, Modules, and Packages
    1. 4.1 Function Definition and Call
      1. 4.1.1 Function Definition
      2. 4.1.2 Function Call
      3. 4.1.3 Function Parameters
      4. 4.1.4 Docstrings
      5. 4.1.5 Local and Global Variables
    2. 4.2 Scope of Variables
      1. 4.2.1 Global Scope
      2. 4.2.2 Local Scope
      3. 4.2.3 Nonlocal Scope
      4. 4.2.4 Built-In Scope
      5. 4.2.5 Best Practices for Variable Scope
    3. 4.3 Modules and Packages
      1. 4.3.1 Modules in Python
      2. 4.3.2 Packages in Python
      3. 4.3.3 Python's import system
    4. 4.4 Recursive Functions in Python
      1. 4.4.1 Understanding Recursion
      2. 4.4.2 Recursive Functions Must Have a Base Case
      3. 4.4.3 The Call Stack and Recursion
    5. 4.5 Practical Exercises
      1. Exercise 1: Writing and Calling a Function
      2. Exercise 2: Understanding Variable Scope
      3. Exercise 3: Importing and Using a Module
      4. Exercise 4: Recursive Function
      5. Exercise 5: Error Handling
    6. Chapter 4 Conclusion
  7. Chapter 5: Deep Dive into Data Structures
    1. 5.1 Advanced Concepts on Lists, Tuples, Sets, and Dictionaries
      1. 5.1.1 Advanced Concepts on Lists
      2. 5.1.2 Advanced Concepts on Tuples
      3. 5.1.3 Advanced Concepts on Sets
      4. 5.1.4 Advanced Concepts on Dictionaries
      5. 5.1.5 Combining Different Data Structures
      6. 5.1.6 Immutable vs Mutable Data Structures
      7. 5.1.7 Iterating over Data Structures
      8. 5.1.8 Other Built-in Functions for Data Structures
    2. 5.2 Implementing Data Structures (Stack, Queue, Linked List, etc.)
      1. 5.2.1 Stack
      2. 5.2.2 Queue
      3. 5.2.3 Linked Lists
      4. 5.2.4 Trees
    3. 5.3 Built-in Data Structure Functions and Methods
    4. 5.4 Python's Collections Module
    5. 5.5 Mutability and Immutability
    6. 5.6 Practical Exercises
      1. Exercise 1: Implementing a Stack
      2. Exercise 2: Implementing a Queue
      3. Exercise 3: Using List Comprehensions
      4. Exercise 4: Implementing a Linked List
    7. Chapter 5 Conclusion
  8. Chapter 6: Object-Oriented Programming in Python
    1. 6.1 Classes, Objects, and Inheritance
    2. 6.2 Polymorphism and Encapsulation
      1. 6.2.1 Polymorphism
      2. 6.2.2 Encapsulation
    3. 6.3 Python Special Functions
    4. 6.4 Abstract Base Classes (ABCs) in Python
      1. 6.4.1 ABCs with Built-in Types
    5. 6.5 Operator Overloading
    6. 6.6 Metaclasses in Python
    7. 6.7 Practical Exercises
      1. Exercise 6.7.1: Class Definition and Object Creation
      2. Exercise 6.7.2: Inheritance and Polymorphism
      3. Exercise 6.7.3: Encapsulation
    8. Chapter 6 Conclusion
  9. Chapter 7: File I/O and Resource Management
    1. 7.1 File Operations
      1. 7.1.1 Opening a file
      2. 7.1.2 Exception handling during file operations
      3. 7.1.3 The with statement for better resource management
      4. 7.1.4 Working with Binary Files
      5. 7.1.5 Serialization with pickle
      6. 7.1.6 Working with Binary Files
      7. 7.1.7 Serialization with pickle
      8. 7.1.8 Handling File Paths
      9. 7.1.9 The pathlib Module
    2. 7.2 Context Managers
    3. 7.3 Directories and Filesystems
    4. 7.4 Working with Binary Data: The pickle and json modules
    5. 7.5 Working with Network Connections: The socket Module
    6. 7.6 Memory Management in Python
      1. 7.6.1 Reference Counting
      2. 7.6.2 Garbage Collection
    7. 7.7 Practical Exercises
      1. Exercise 1
      2. Exercise 2
      3. Exercise 3
    8. Chapter 7 Conclusion
  10. Chapter 8: Exceptional Python
    1. 8.1 Error and Exception Handling
      1. 8.1.1 Else Clause
      2. 8.1.2 Finally Clause
      3. 8.1.3 Custom Exceptions
    2. 8.2 Defining and Raising Custom Exceptions
      1. 8.2.1 Defining Custom Exceptions
      2. 8.2.2 Adding More Functionality to Custom Exceptions
      3. 8.2.3 Raising Custom Exceptions
    3. 8.3 Good practices related to raising and handling exceptions
    4. 8.4 Logging in Python
    5. 8.5 Practical Exercises
      1. Exercise 1: Creating a custom exception
      2. Exercise 2: Adding exception handling
      3. Exercise 3: Logging
      4. Exercise 4: Advanced logging
    6. Chapter 8 Conclusion
  11. Chapter 9: Python Standard Library
    1. 9.1 Overview of Python Standard Library
      1. 9.1.1 Text Processing Services
      2. 9.1.2 Binary Data Services
      3. 9.1.3 Data Types
      4. 9.1.4 Mathematical Modules
      5. 9.1.5 File and Directory Access
      6. 9.1.6 Functional Programming Modules
      7. 9.1.7 Data Persistence
      8. 9.1.8 Data Compression and Archiving
      9. 9.1.9 File Formats
    2. 9.2 Exploring Some Key Libraries
      1. 9.2.1 numpy
      2. 9.2.2 pandas
      3. 9.2.3 matplotlib
      4. 9.2.4 requests
      5. 9.2.5 flask
      6. 9.2.6 scipy
      7. 9.2.7 scikit-learn
      8. 9.2.8 beautifulsoup4
      9. 9.2.9 sqlalchemy
      10. 9.2.10 pytorch and tensorflow
    3. 9.3 Choosing the Right Libraries
      1. 9.3.1 Suitability for Task
      2. 9.3.2 Maturity and Stability
      3. 9.3.3 Community and Support
      4. 9.3.4 Documentation and Ease of Use
      5. 9.3.5 Performance
      6. 9.3.6 Community Support
    4. 9.4 Practical Exercises
      1. Exercise 1: Exploring the Math Library
      2. Exercise 2: Data Manipulation with Pandas
      3. Exercise 3: File Operations with os and shutil Libraries
    5. Chapter 9 Conclusion
  12. Chapter 10: Python for Scientific Computing and Data Analysis
    1. 10.1 Introduction to NumPy, SciPy, and Matplotlib
      1. 10.1.1 Understanding NumPy Arrays
      2. 10.1.2 Efficient Mathematical Operations with NumPy
      3. 10.1.3 Linear Algebra with SciPy
      4. 10.1.4 Data Visualization with Matplotlib
    2. 10.2 Digging Deeper into NumPy
      1. 10.2.1 Array slicing and indexing
      2. 10.2.2 Array reshaping and resizing
    3. 10.3 Working with SciPy
      1. 10.3.1 Optimization with SciPy
      2. 10.3.2 Statistics with SciPy
    4. 10.4 Visualizing Data with Matplotlib
      1. 10.4.1 Basic Plotting with Matplotlib
      2. 10.4.2 Creating Subplots
      3. 10.4.3 Plotting with Pandas
    5. 10.5 Exploring Pandas for Data Analysis
      1. 10.5.1 Creating a DataFrame
      2. 10.5.2 Data Selection
      3. 10.5.3 Data Manipulation
      4. 10.5.4 Reading Data from Files
    6. 10.6 Introduction to Scikit-Learn
    7. 10.7 Introduction to Statsmodels
    8. 10.8 Introduction to TensorFlow and PyTorch
    9. 10.9 Practical Exercises
      1. Exercise 10.1
      2. Exercise 10.2
      3. Exercise 10.3
      4. Exercise 10.4
    10. Chapter 10: Conclusion
  13. Chapter 11: Testing in Python
    1. 11.1 Unit Testing with unittest
      1. 11.1.1 setUp and tearDown
      2. 11.1.2 Test Discovery
      3. 11.1.3 Testing for Exceptions
    2. 11.2 Mocking and Patching
      1. 11.2.1 Mock and Side Effects
      2. 11.2.2 PyTest
    3. 11.3 Test-Driven Development
    4. 11.4 Doctest
    5. 11.5 Practical Exercises
      1. Exercise 1: Unit Testing
      2. Exercise 2: Mocking and Patching
      3. Exercise 3: Test-Driven Development
    6. Chapter 11 Conclusion
  14. Chapter 12: Introduction to SQL
    1. 12.1 Brief History of SQL
    2. 12.2 SQL Syntax
      1. 12.2.1 Basic Query Structure
      2. 12.2.2 SQL Keywords
      3. 12.2.3 SQL Statements
      4. 12.2.4 SQL Expressions
    3. 12.3 SQL Data Types
      1. 12.3.1 Numeric Types
      2. 12.3.2 Date and Time Types
      3. 12.3.3 String Types
      4. 12.3.4 SQL Constraints
    4. 12.4 SQL Operations
      1. 12.4.1 Data Definition Language (DDL)
      2. 12.4.2 Data Manipulation Language (DML)
    5. 12.5 SQL Queries
      1. 12.5.1 Filtering with the WHERE clause
      2. 12.5.2 Sorting with the ORDER BY clause
      3. 12.5.3 Grouping with the GROUP BY clause
      4. 12.5.4 Joining Tables
    6. 12.6 Practical Exercises
      1. Exercise 1
      2. Exercise 2
      3. Exercise 3
      4. Exercise 4
      5. Exercise 5
      6. Exercise 6
      7. Exercise 7
    7. Chapter 12 Conclusion
  15. Chapter 13: SQL Basics
    1. 13.1 Creating Databases and Tables
    2. 13.2 Inserting Data into Tables
    3. 13.3 Selecting Data from Tables
    4. 13.4 Updating Data in Tables
    5. 13.5 Deleting Data from Tables
    6. 13.6 Filtering and Sorting Query Results
    7. 13.7 NULL Values
    8. 13.8 Practical Exercises
      1. Exercise 1: Creating Databases and Tables
      2. Exercise 2: Inserting Data
      3. Exercise 3: Updating and Deleting Data
      4. Exercise 4: Querying Data
      5. Exercise 5: Working with NULL
    9. Chapter 13 Conclusion
  16. Chapter 14: Deep Dive into SQL Queries
    1. 14.1 Advanced Select Queries
      1. 14.1.1 The DISTINCT Keyword
      2. 14.1.2 The ORDER BY Keyword
      3. 14.1.3 The WHERE Clause
      4. 14.1.4 The LIKE Operator
      5. 14.1.5 The IN Operator
      6. 14.1.6 The BETWEEN Operator
    2. 14.2 Joining Multiple Tables
      1. 14.2.1 LEFT JOIN and RIGHT JOIN
      2. 14.2.2 FULL OUTER JOIN
      3. 14.2.3 UNION and UNION ALL
      4. 14.2.4 Subqueries
    3. 14.3 Aggregate Functions
    4. 14.4 Practical Exercises
      1. Exercise 1 - Advanced Select Queries
      2. Exercise 2 - Joining Multiple Tables
      3. Exercise 3 - Aggregate Functions
    5. Chapter 14 Conclusion
  17. Chapter 15: Advanced SQL
    1. 15.1 Subqueries
      1. 15.1.1 Scalar Subquery
      2. 15.1.2 Correlated Subquery
      3. 15.1.3 Common Table Expressions (CTEs)
    2. 15.2 Stored Procedures
      1. 15.2.1 Different Types of Stored Procedures
    3. 15.3 Triggers
      1. 15.3.1 Additional Details
    4. 15.4 Practical Exercises
      1. Exercise 1: Working with Subqueries
      2. Exercise 2: Creating and Using Stored Procedures
      3. Exercise 3: Triggers
    5. Chapter 15 Conclusion
  18. Chapter 16: SQL for Database Administration
    1. 16.1 Creating, Altering, and Dropping Tables
      1. 16.1.1 Creating Tables
      2. 16.1.2 Altering Tables
      3. 16.1.3 Dropping Tables
    2. 16.2 Database Backups and Recovery
      1. 16.2.1 Database Backups
      2. 16.2.2 Database Recovery
      3. 16.2.3 Point-In-Time Recovery (PITR)
    3. 16.3 Security and Permission Management
      1. 16.3.1 User Management
      2. 16.3.2 Granting Permissions
      3. 16.3.3 Revoking Permissions
      4. 16.3.4 Deleting Users
    4. 16.4 Practical Exercises
      1. Exercise 1: Creating, Altering, and Dropping Tables
      2. Exercise 2: Database Backups and Recovery
      3. Exercise 3: Security and Permission Management
    5. Chapter 16 Conclusion
  19. Chapter 17: Python Meets SQL
    1. 17.1 Python's sqlite3 Module
      1. 17.1.1 Inserting Data
      2. 17.1.2 Fetching Data
    2. 17.2 Python with MySQL
    3. 17.3 Python with PostgreSQL
    4. 17.4 Performing CRUD Operations
      1. 17.4.1 Create Operation
      2. 17.4.2 Read Operation
      3. 17.4.3 Update Operation
      4. 17.4.4 Delete Operation
      5. 17.4.5 MySQL
      6. 17.4.6 PostgreSQL
    5. 17.5 Handling Transactions in Python
    6. 17.6 Handling SQL Errors and Exceptions in Python
    7. 17.7 Practical Exercises
      1. Exercise 17.7.1
      2. Exercise 17.7.2
      3. Exercise 17.7.3
      4. Exercise 17.7.4
      5. Exercise 17.7.5
      6. Exercise 17.7.6
    8. Chapter 17 Conclusion
  20. Chapter 18: Data Analysis with Python and SQL
    1. 18.1 Data Cleaning in Python and SQL
    2. 18.2 Data Transformation in Python and SQL
      1. 18.2.1 Data Transformation in SQL
      2. 18.2.2 Data Transformation in Python
    3. 18.3 Data Visualization in Python and SQL
      1. 18.3.1 Data Visualization in SQL
      2. 18.3.2 Data Visualization in Python
    4. 18.4 Statistical Analysis in Python and SQL
      1. 18.4.1 Statistical Analysis in SQL
      2. 18.4.2 Statistical Analysis in Python
    5. 18.5 Integrating Python and SQL for Data Analysis
      1. 18.5.1 Querying SQL Database from Python
      2. 18.5.2 Using pandas with SQL
      3. 18.5.3 Using SQLAlchemy for Database Abstraction
    6. 18.6 Practical Exercises
      1. Exercise 1: Data Cleaning
      2. Exercise 2: Data Transformation
      3. Exercise 3: Querying SQL Database from Python
    7. Chapter 18 Conclusion
  21. Chapter 19: Advanced Database Operations with SQLAlchemy
    1. 19.1 SQLAlchemy: SQL Toolkit and ORM
    2. 19.2 Connecting to Databases
    3. 19.3 Understanding SQLAlchemy ORM
    4. 19.4 CRUD Operations with SQLAlchemy ORM
      1. 19.4.1 Creating Records
      2. 19.4.2 Reading Records
      3. 19.4.3 Updating Records
      4. 19.4.4 Deleting Records
    5. 19.5 Managing Relationships with SQLAlchemy ORM
    6. 19.6 Querying with Joins in SQLAlchemy
    7. 19.7 Transactions in SQLAlchemy
    8. 19.8 Managing Relationships in SQLAlchemy
    9. 19.9 SQLAlchemy SQL Expression Language
    10. 19.10 Practical Exercise
      1. Exercise 19.1
    11. Chapter 19 Conclusion
  22. Appendix A: Python Interview Questions
  23. Appendix B: SQL Interview Questions
  24. Appendix C: Python Cheat Sheet
    1. Basic Python Syntax
    2. Data Structures
    3. List Comprehensions
  25. Appendix D: SQL Cheat Sheet
    1. SQL Syntax
    2. CRUD Operations
  26. References
  27. Conclusion
  28. Where to continue?
  29. Know more about us

Product information

  • Title: Python and SQL Bible
  • Author(s): Cuantum Technologies LLC
  • Release date: June 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781836206279