# Python para Data Engineers

Python es un lenguaje bastante extenso, pero para un rol de Data Engineer, los conocimientos que se requieren saber normalmente están acotados a unos cuantos. Lo ideal es mantener el foco y concentrarse en las funciones y conocimientos específicos mínimos para un Data Engineer y crecer esta habilidad conforme se vaya requiriendo. De esta manera, podemos reducir el tiempo de aprendizaje del lenguaje.

Primero, separo la importancia de familiarizarse con Python y con su estructura y sintaxis en primera instancia, y después entrar a los conocimientos particulares necesarios para un Data Engineer. Además, dejo fuera de este artículo temas como Pandas o PySpark, a los que les dedicaré artículos por separado por su importancia.

### **Conceptos basicos**

* **Syntax and Data Structures:**
    
    Understanding Python syntax, variables, basic data types (integers, floats, strings, booleans), and fundamental data structures like lists, tuples, dictionaries, and sets.
    
* **Control Flow:**
    
    Proficiency in using conditional statements (if/elif/else), loops (for, while), and error handling with `try-except` blocks.
    
* **Functions:**
    
    Defining and calling functions, understanding arguments, return values, and scope.
    
* **File Handling:**
    
    Reading from and writing to various file formats, including CSV, JSON, and potentially more advanced formats like Parquet or Avro.
    
* **Object-Oriented Programming (OOP) Basics:**
    
    Grasping concepts like classes, objects, inheritance, and encapsulation for building modular and reusable code.
    

### **Conceptos y Librerias especificas de Data Engineering**

* **Pandas:**
    
    Essential for data manipulation and analysis, including reading and writing data, filtering, sorting, merging, and aggregating dataframes.
    
* **Numpy:**
    
    Important for numerical operations and working with arrays, especially when dealing with large datasets or mathematical computations.
    
* **Database Connectivity:**
    
    Utilizing libraries like `SQLAlchemy`, `psycopg2` (for PostgreSQL), or `pymysql` (for MySQL) to connect to and interact with databases.
    
* **API Interaction:**
    
    Using the `requests` library to interact with RESTful APIs for data extraction or integration.
    
* **Datetime Handling:**
    
    Working with `datetime` objects, managing timezones, and converting between different date and time formats.
    
* **Automation and Orchestration:**
    
    Understanding how Python can be used to automate tasks, potentially including basic scripting for cron jobs or interacting with orchestration tools like Apache Airflow.
    
* **Testing and Debugging:**
    
    Writing unit tests for code and effectively debugging Python scripts to identify and resolve issues.
    

### Cursos y temas en Youtube interesantes

* [Learn Python in Less than 10 Minutes for Beginners (Fast & Easy)](https://www.youtube.com/watch?v=fWjsdhR3z3c&pp=ygUNUHl0aG9uIGJhc2ljcw%3D%3D)
    
* %[https://www.youtube.com/watch?v=fWjsdhR3z3c&pp=ygUNUHl0aG9uIGJhc2ljcw%3D%3D] 
    
* [Python for Data Analysis: Getting Started](https://www.youtube.com/watch?v=2_6O39UdFi0&list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc)
    
* %[https://www.youtube.com/watch?v=2_6O39UdFi0&list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc] 
    
* [Python Full Course for Beginners \[2025\]](https://www.youtube.com/watch?v=K5KVEU3aaeQ)
    
* %[https://www.youtube.com/watch?v=K5KVEU3aaeQ] 
    

### Cursos

Gratuitos:

* [Coursera.org - Python and Pandas for Data Engineering](https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke)
    

Con costo:

* [Udemy.com - Python For Data Engineering with 500+ Coding Questions](https://www.udemy.com/course/python-for-data-engineering/)
    

### **Extras**

Realizar los ejercicios y challenges de Python en paginas como HackerRank para agilizar el proceso de aprendizaje:

* [HackerRank.com - Python](https://www.hackerrank.com/domains/python)
    

I’ll keep updating the article, sorry for the spanish-english mix.