TOP PYTHON LIBRARIES FOR DATA SCIENCE

Staples (Data Handling & Visualization)

NumPy → Fast n-dimensional arrays, linear algebra

Pandas → DataFrames, joins, groupby, time series

Matplotlib → Core plotting (static & animated)

Seaborn → Statistical visualization (built on Matplotlib)

Plotly → Interactive charts & dashboards

Machine Learning

Scikit-learn → Classical ML algorithms & pipelines

LightGBM → Fast, efficient gradient boosting

XGBoost → High-performance boosted trees

CatBoost → Handles categorical features easily

Statsmodels → Regression & statistical analysis

RAPIDS (cuDF, cuML) → GPU-accelerated data science

Optuna → Hyperparameter optimization

AutoML (Low-Code ML)

PyCaret → End-to-end ML with minimal code

H2O AutoML → Scalable ML & deployment

TPOT → Genetic programming for pipelines

Auto-sklearn → Bayesian optimization for ML

FLAML → Lightweight, efficient AutoML

Deep Learning

TensorFlow → Scalable DL ecosystem

PyTorch → Flexible research-to-production DL

fastai → High-level API for fast results

Keras → Beginner-friendly DL API

PyTorch Lightning → Structured DL training

Natural Language Processing (NLP)

NLTK → Classic NLP toolkit

spaCy → Industrial-strength NLP

Gensim → Topic modeling & similarity

Hugging Face Transformers → Pretrained SOTA models

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