Python and SQL are two different languages with distinct purposes. While both can handle data manipulation and analysis, there are certain tasks that are more suitable for Python than SQL. Here are some examples of things you can do in Python that are not typically done in SQL:

1. Complex Data Transformations: Python provides extensive libraries like NumPy, Pandas, and SciPy, which offer powerful tools for data manipulation and transformation. You can perform complex calculations, merge datasets, reshape data, apply custom functions, and handle missing values, among other tasks. While SQL can handle basic data transformations, it is not as flexible or feature-rich as Python.
2. Machine Learning and Artificial Intelligence: Python has become the de facto language for machine learning and AI applications. Libraries such as scikit-learn, TensorFlow, and PyTorch provide comprehensive functionalities for building and training machine learning models. You can preprocess data, train models, perform feature engineering, and make predictions using Python. SQL, on the other hand, is primarily designed for database management and querying, and does not have built-in capabilities for machine learning.
3. Web Scraping and Automation: Python offers libraries like BeautifulSoup and Selenium, which enable web scraping and browser automation. You can extract data from websites, interact with web pages, submit forms, and perform various tasks programmatically. SQL, being a database language, does not have built-in features for web scraping or automation.
4. File Manipulation: Python provides extensive functionality for working with files and directories. You can read and write files, manipulate file paths, create directories, and perform various file system operations. SQL is not designed for file manipulation tasks.
5. GUI Development: Python offers frameworks like Tkinter, PyQt, and PySide for developing graphical user interfaces (GUI). You can create windows, buttons, menus, and other GUI components, and build interactive applications. SQL does not have native support for GUI development.
6. Text Processing and Natural Language Processing (NLP): Python has many libraries for text processing and NLP tasks. You can perform tasks such as text cleaning, tokenization, stemming, lemmatization, and sentiment analysis using libraries like NLTK, spaCy, and TextBlob. SQL lacks the comprehensive text processing capabilities of Python.
7. System-level Programming: Python allows you to interact with the operating system and perform system-level tasks. You can execute shell commands, manage processes, handle system signals, and perform other low-level operations. SQL is primarily focused on database operations and does not provide such system-level programming features.

These are just a few examples of tasks that are more suited for Python than SQL. However, it's important to note that Python and SQL can often be used together to complement each other's strengths.