Here's a list of 50+ Python libraries for data science👇

1. NumPy - "Handles arrays and math operations efficiently."
2. pandas - "Data manipulation made easy with data frames."
3. Matplotlib - "Plots and charts for data visualization."
4. Seaborn - "Creates attractive statistical plots."
5. SciPy - "Scientific and technical computing toolkit."
6. scikit-learn - "Machine learning at your fingertips."
7. TensorFlow - "For deep learning and neural networks."
8. Keras - "High-level deep learning API."
9. PyTorch - "Deep learning framework for researchers."
10. Statsmodels - "Statistical models and tests."
11. NLTK - "Natural language processing toolkit."
12. Gensim - "Topic modeling and document similarity."
13. XGBoost - "Gradient boosting for better predictions."
14. LightGBM - "Efficient gradient boosting framework."
15. CatBoost - "Optimized gradient boosting for categories."
16. NetworkX - "Build and analyze networks and graphs."
17. Beautiful Soup - "HTML and XML parsing made simple."
18. Requests - "Effortless HTTP requests."
19. SQLAlchemy - "Relational database interactions."
20. Pandas Profiling - "Generate data reports quickly."
21. Featuretools - "Automated feature engineering."
22. H2O - "Open-source machine learning platform."
23. Yellowbrick - "Visualize machine learning results."
24. Plotly - "Interactive and shareable plots."
25. Dash - "Build web apps for data visualization."
26. Flask - "Lightweight web app framework."
27. Streamlit - "Create apps with minimal code."
28. Bokeh - "Interactive web-based visualization."
29. GeoPandas - "Geospatial data analysis made easy."
30. Altair - "Declarative statistical visualization."
31. Prophet - "Time series forecasting with ease."
32. Feature-engine - "Feature engineering for ML."
33. Dask - "Parallel computing for big data."
34. Vaex - "Efficient dataframes for big data."
35. Optuna - "Automated hyperparameter tuning."
36. imbalanced-learn - "Handling imbalanced datasets."
37. Eli5 - "Interpret machine learning models."
38. SHAP - "Explainability for ML models."
39. scikit-image - "Image processing in Python."
40. TextBlob - "Text processing and sentiment analysis."
41. Polars - "Fast DataFrame library."
42. Cufflinks - "Combines Plotly with pandas."
43. TA-Lib - "Technical analysis for financial data."
44. OpenCV - "Computer vision and image processing."
45. Pymc3 - "Probabilistic programming for Bayesian analysis."
46. Scrapy - "Web scraping toolkit."
47. PySpark - "Apache Spark for big data processing."
48. PyArrow - "Columnar data format for analytics."
49. OptimalFlow - "AutoML for data scientists."
50. Pycaret - "Automated machine learning toolkit."

These libraries cover a wide range of data science tasks, from data manipulation and visualisation to machine learning and deep learning, making them essential tools for any data scientist or Python programmer.

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