Top Python Libraries for Data Science – 2025 Edition

Want to build real-world data science projects faster and smarter? Here’s your essential Python stack – organized by category:

🧮 Core Libraries
→ NumPy – Numerical operations
→ Pandas – Data manipulation & analysis

📊 Data Visualization
→ Matplotlib – Static plots
→ Seaborn – Statistical visualizations
→ Plotly – Interactive dashboards

🤖 Machine Learning
→ Scikit-learn – ML algorithms
→ XGBoost, LightGBM, CatBoost – Gradient boosting

⚙️ AutoML
→ PyCaret – Low-code ML
→ Auto-sklearn, H2O, TPOT – Automated model building
→ Optuna, FLAML – Hyperparameter tuning

🧠 Deep Learning
→ TensorFlow, Keras – Scalable deep learning
→ PyTorch, Lightning, FastAI – Flexible, production-ready DL

🗣️ Natural Language Processing (NLP)
→ spaCy, NLTK, Gensim – Text processing
→ Hugging Face Transformers – Pretrained LLMs (BERT, GPT)

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