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Data scientists commonly use the following Python libraries:
1. **Pandas**: For data manipulation and analysis, providing data structures like DataFrames.
2. **NumPy**: For numerical operations, especially with large arrays and matrices.
3. **Matplotlib**: For creating static, interactive, and animated visualizations.
4. **Seaborn**: For statistical data visualization, built on top of Matplotlib.
5. **SciPy**: For scientific and technical computing, including optimization and integration.
6. **Scikit-learn**: For machine learning and data mining tasks, offering various algorithms and tools.
7. **TensorFlow**: For deep learning and neural networks.
8. **Keras**: A high-level neural networks API, running on top of TensorFlow.
9. **Statsmodels**: For statistical modeling and hypothesis testing.
10. **NLTK**: For natural language processing and text analysis.
These libraries help with data manipulation, visualization, and machine learning tasks.
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Python libraries commonly used by data scientists include **NumPy** for numerical computations, **Pandas** for data manipulation, **Matplotlib** and **Seaborn** for data visualization, **Scikit-learn** for machine learning, **TensorFlow** and **PyTorch** for deep learning, and **SciPy** for scientific computing.
read lessIn data science, Python libraries like Pandas (for data manipulation), NumPy (for numerical operations), and Matplotlib (for basic visualizations) are essential. Scikit-learn offers machine learning tools, while TensorFlow and PyTorch are used for deep learning. These tools streamline analysis, modeling, and visualization for data projects.
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