Welcome to the Python Data Engineer learning repository! This repo contains a structured, practical set of Jupyter notebooks for learning core Python concepts, especially with a focus on data engineering. Each topic is covered with hands-on examples and explanations, and links are provided to the code for easy reference.

Note: This summary is based on the top-level files; for a full list of all tutorials and scripts, check the GitHub repository contents.

• Overview: Introduction to Python, variables, data types, and basic operations.
• Key Concepts:
  • Printing and string manipulation
  • Variable assignment and naming
  • Numeric, string, and boolean data types
  • Type conversion, built-in functions, and string methods
  • List basics and common list operations

• Overview: Mastering conditional statements for decision making.
• Key Concepts:
  • if, elif, else statements
  • Comparison and logical operators
  • Nested conditions and practical examples

• Overview: Using loops to automate repetitive tasks.
• Key Concepts:
  • for and while loops
  • Loop control (break, continue, pass)
  • Looping through lists, strings, and dictionaries

• Overview: Writing reusable blocks of code with functions.
• Key Concepts:
  • Defining and calling functions
  • Parameters, return values, and scope
  • Lambda functions and higher-order functions

• Overview: Using operators to manipulate data.
• Key Concepts:
  • Arithmetic, assignment, comparison, logical, bitwise, and membership operators
  • Precedence and associativity

• Overview: Mastering data structures for efficient storage and retrieval.
• Key Concepts:
  • Lists, tuples, sets, dictionaries
  • When and how to use each collection
  • Real-world data engineering examples using collections

• Overview: Organizing and reusing code with modules and packages.
• Key Concepts:
  • The difference between modules, packages, and libraries (with LEGO analogies)
  • Importing and using built-in and external libraries (e.g., Pandas, NumPy, Matplotlib, Requests, Scikit-learn)
  • Creating custom modules and packages

• Overview: CSV File handling and manipulation for data storage and retrieval.
• Key Concepts:
  • Reading and writing text and CSV files
  • Using csv file with Pandas library
  • File and directory operations using os and shutil
  • Handling file paths and exceptions
  • Data extraction and ingestion from files

• Overview: Managing JSON data formats for configuration and data exchange.
• Key Concepts:
  • Reading and writing JSON files with Python’s json module
  • Parsing and serializing complex JSON structures
  • Real-world use cases: configuration files, API responses
  • Data transformation between JSON and Python objects

• Overview: Working with randomness, generating random numbers and data for testing and simulations.
• Key Concepts:
  • Using Python’s random module for numbers, choices, and shuffling
  • Generating random data for data engineering tasks
  • Introduction to the faker library for synthetic data creation
  • Practical examples: random sampling, data anonymization

• Overview: Code blocks and reusable scripts for modular data engineering workflows.
• Key Concepts:
  • Encapsulating logic in code blocks (functions, scripts)
  • Organizing reusable code for ETL pipelines
  • Example templates for batch processing and automation

• Overview: Logging and monitoring data engineering processes.
• Key Concepts:
  • Using Python’s logging module for event tracking
  • Setting up log formats, levels, and handlers
  • Best practices for error handling and process monitoring
  • Writing logs to files and integrating with external tools

1. Browse Notebooks: Start with the Jupyter notebooks in the main directory for a structured learning path.
2. Explore Directories: Check out the additional folders for more scripts and data.
3. Try the Code: Run the notebooks locally or in an online Jupyter environment.
4. Contribute: Pull requests to add new topics or improve examples are welcome!

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• Issues / Suggestions