Top 10 Python libraries commonly used by data scientists

1. NumPy: A fundamental package for scientific computing with support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.

2. pandas: A powerful data manipulation and analysis library that provides data structures and functions for working with structured data.

3. matplotlib: A widely-used plotting library for creating a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and more.

4. scikit-learn: A comprehensive machine learning library that provides tools for data mining and data analysis, including algorithms for classification, regression, clustering, and more.

5. TensorFlow: An open-source machine learning framework developed by Google for building and training machine learning models, particularly for deep learning tasks.

6. Keras: A high-level neural networks API that is built on top of TensorFlow and provides an easy-to-use interface for building and training deep learning models.

7. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

8. SciPy: A library that builds on NumPy and provides a wide range of scientific and technical computing functions, including optimization, integration, interpolation, and more.

9. Statsmodels: A library that provides classes and functions for the estimation of many different statistical models, as well as conducting statistical tests and exploring data.

10. XGBoost: An optimized gradient boosting library that is widely used for supervised learning tasks, such as regression and classification.

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