#Python

Machine Learning & Deep Learning	Scikit-learn - Machine learning algorithms TensorFlow - Deep learning and neural networks PyTorch - Deep learning, especially dynamic computation graphs Keras - High-level neural networks API XGBoost - Gradient boosting LightGBM - Efficient gradient boosting framework CatBoost - Gradient boosting with categorical features FastAI - Simplified deep learning with PyTorch backend Hugging Face Transformers - NLP with pre-trained models Optuna - Hyperparameter optimization
Data Analysis & Manipulation	Pandas - Data manipulation and analysis NumPy - Numerical computing Dask - Parallel and distributed data processing Polars - Fast DataFrames for big data Vaex - Lazy and memory-efficient DataFrames
Data Visualization	Matplotlib - 2D plotting Seaborn - Statistical plots Plotly - Interactive plots Bokeh - Web-based interactive visualization Altair - Declarative statistical visualization
Natural Language Processing (NLP)	NLTK - Traditional NLP tasks spaCy - Industrial-strength NLP Gensim - Topic modeling and word vectors TextBlob - Simple NLP operations Flair - Pre-trained embeddings and NLP
Data Engineering & ETL	Airflow - Workflow automation Luigi - Pipeline building Pyspark - Distributed data processing Petl - ETL operations Kedro - Reproducible data science pipelines
Mathematics & Scientific Computing	SciPy - Scientific computations SymPy - Symbolic mathematics Statsmodels - Statistical modeling NetworkX - Graph theory and networks CVXPY - Convex optimization
Testing and Experimentation	Pytest - Testing framework Unittest - Built-in test framework Hypothesis - Property-based testing Testcontainers - Integration testing with containers Tox - Automated testing across environments