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What are the Python libraries that are used by data scientists?

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Data scientists commonly use the following Python libraries: 1. *NumPy*: For numerical computations and data manipulation. 2. *pandas*: For data manipulation, analysis, and visualization. 3. *Matplotlib* and *Seaborn*: For data visualization. 4. *Scikit-learn*: For machine learning algorithms...
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Data scientists commonly use the following Python libraries: 1. *NumPy*: For numerical computations and data manipulation. 2. *pandas*: For data manipulation, analysis, and visualization. 3. *Matplotlib* and *Seaborn*: For data visualization. 4. *Scikit-learn*: For machine learning algorithms and modeling. 5. *TensorFlow* and *Keras*: For deep learning and neural networks. 6. *PyTorch*: For deep learning and neural networks. 7. *Statsmodels*: For statistical modeling and analysis. 8. *Scipy*: For scientific computing and signal processing. 9. *Plotly*: For interactive data visualization. 10. *Bokeh*: For interactive data visualization. 11. *Pandas-datareader*: For accessing and manipulating financial and economic data. 12. *Beautiful Soup* and *Scrapy*: For web scraping and data extraction. 13. *NLTK* and *spaCy*: For natural language processing and text analysis. 14. *OpenCV*: For computer vision and image processing. 15. *XGBoost* and *LightGBM*: For gradient boosting and machine learning. These libraries provide efficient data analysis, machine learning, and visualization capabilities, making Python a popular choice for data science tasks. read less
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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...
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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 less
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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...
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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. Here is my number Call 073-1485-0321. read less
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pandas, numpy, matplotlib, seaborn, sklearn
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In 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,...
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In 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. read less
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