Python Libraries

Learn the Python libraries that matter in real projects.

Explore beginner-friendly Python library tutorials and exercises for data analysis, visualization, machine learning, web scraping, scientific computing, and NLP.

11 core libraries
Tutorials clear concept guides
Exercises practice after learning

Start with fundamentals like NumPy and Pandas, then move into visualization, machine learning, deep learning, and natural language processing at your own pace.

NumPy

A fast and powerful Python library for numerical computing, arrays, and efficient mathematical operations.

Tutorial Exercise

Pandas

Use Pandas to clean, organize, and analyze CSV, Excel, and SQL data with ease.

Tutorial Exercise

Matplotlib

Create line charts, bar graphs, and 2D plots with Python's standard library for data visualization.

Tutorial Exercise

Seaborn

Build clear and attractive statistical visualizations with Seaborn, a high-level library built on Matplotlib.

Tutorial Exercise

Scikit-learn

Learn and build machine learning models for regression, classification, and clustering with Scikit-learn.

Tutorial Exercise

TensorFlow

Explore TensorFlow, Google's open-source platform for deep learning, neural networks, and AI model development.

Tutorial Exercise

Keras

Build deep learning models more easily with Keras, a high-level API that works smoothly with TensorFlow.

Tutorial Exercise

PyTorch

Work with PyTorch, a favorite among AI researchers and developers for dynamic deep learning workflows.

Tutorial Exercise

Beautiful Soup

Scrape and extract data from HTML and XML documents with Beautiful Soup, one of the easiest web scraping tools.

Tutorial Exercise

SciPy

Solve scientific and technical computing problems with SciPy, a powerful library built on top of NumPy.

Tutorial Exercise

NLTK

Analyze and process human language data with NLTK, a widely used library for natural language processing.

Tutorial Exercise