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python-data-engineer

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Learn Python language for beginners in Data Engineering and Data Analytics

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# Python Data Engineer Learning Repository

Welcome to the **Python Data Engineer** learning repository! This repo contains a structured, practical set of Jupyter notebooks and example projects for learning core Python concepts with a focus on data engineering.

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## Topics Covered

### 01. [Python Introduction](https://github.com/gkdevops/python-data-engineer/blob/main/01-Python-Introduction.ipynb)
- **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

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### 02. [Python Conditions](https://github.com/gkdevops/python-data-engineer/blob/main/02-Python-Conditions.ipynb)
- **Overview:** Mastering conditional statements for decision making.
- **Key Concepts:** `if`, `elif`, `else`, comparison and logical operators, nested conditions.

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### 03. [Python Loops](https://github.com/gkdevops/python-data-engineer/blob/main/03-Python-Loops.ipynb)
- **Overview:** Using loops to automate repetitive tasks.
- **Key Concepts:** `for` and `while` loops, loop control (`break`, `continue`, `pass`), iterating collections.

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### 04. [Python Functions](https://github.com/gkdevops/python-data-engineer/blob/main/04-Python-Functions.ipynb)
- **Overview:** Writing reusable blocks of code with functions.
- **Key Concepts:** Defining and calling functions, parameters, return values, scope, lambda functions.

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### 05. [Python Operators](https://github.com/gkdevops/python-data-engineer/blob/main/05-Python-Operators.ipynb)
- **Overview:** Using operators to manipulate data.
- **Key Concepts:** Arithmetic, assignment, comparison, logical, bitwise, and membership operators.

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### 06. [Python Collections](https://github.com/gkdevops/python-data-engineer/blob/main/06-Python-Collection.ipynb)
- **Overview:** Mastering data structures for efficient storage and retrieval.
- **Key Concepts:** Lists, tuples, sets, dictionaries, and real-world examples.

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### 07. [Python Modules & Packages](https://github.com/gkdevops/python-data-engineer/blob/main/07-Python-Modules%20%26%20Packages.ipynb)
- **Overview:** Organizing and reusing code with modules and packages.
- **Key Concepts:**
  - Difference between modules, packages, and libraries
  - Importing and using built-in and external libraries (e.g., Pandas, NumPy, Matplotlib, Requests)
  - Creating custom modules and packages

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### 08. [Files, CSV & JSON](https://github.com/gkdevops/python-data-engineer/tree/main/files)
- **Overview:** File handling (text & CSV) and JSON management for configuration and data exchange. (JSON content previously listed separately has been merged into this section.)
- **Key Concepts:**
  - Reading and writing text and CSV files using built-in modules and pandas
  - Using `os` and `shutil` for file and directory operations
  - Reading, writing, parsing, and serializing JSON with Python’s `json` module
  - Data extraction and ingestion from files and JSON API responses
  - Error handling and path management

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### 09. [Python OOPs](https://github.com/gkdevops/python-data-engineer/tree/main/09-Python-OOPs)
- **Overview:** Object-oriented programming in Python.
- **Key Concepts:**
  - Defining classes and creating objects
  - Constructors (`__init__`)

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### 10. [Python Randoms](https://github.com/gkdevops/python-data-engineer/tree/main/10-Python-Random)
- **Overview:** Working with randomness, generating random numbers and data for testing and simulations.
- **Key Concepts:** `random` module, `faker` library, random sampling and anonymization.

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### 11. [Code Blocks & Utilities](https://github.com/gkdevops/python-data-engineer/tree/main/11-Python-Blocks)
- **Overview:** Reusable scripts and code blocks for modular data engineering workflows.
- **Key Concepts:** Encapsulating logic in functions and scripts, templates for batch processing.

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### 12. [Logging & Monitoring](https://github.com/gkdevops/python-data-engineer/tree/main/12-Python-Logging)
- **Overview:** Logging and monitoring data engineering processes.
- **Key Concepts:** Python’s `logging` module, log formats, levels, handlers, and best practices.

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### 13. [Python Interview Qs](https://github.com/gkdevops/python-data-engineer/tree/main/13-Python-Interview-Qs)
- **Overview:** Common Python interview questions and concise answers, focusing on practical explanations.

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### 14. [Project: Streamlit Examples](https://github.com/gkdevops/python-data-engineer/tree/main/14-Project-Streamlit)
- **Overview:** Example Streamlit apps and instructions to run them.
- **Key Concepts:** Installing Streamlit, building and running simple apps, basic visualization with Plotly.

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### 15. [Capstone Projects](https://github.com/gkdevops/python-data-engineer/tree/main/15-Capstone-Projects)
- **Overview:** End-to-end projects to apply learned concepts.
- **Contents:** Project ideas, example implementations, and deployment notes.

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## 📎 How to Use This Repo

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 sample scripts, data, and projects.  
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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## 🔗 Explore More

- [View All Files and Folders](https://github.com/gkdevops/python-data-engineer/tree/main/)
- [Issues / Suggestions](https://github.com/gkdevops/python-data-engineer/issues)

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python-data-engineer

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