Python libraries used for data science¶

Python has a rich ecosystem of libraries for data science. Here are some of the most commonly used ones:

NumPy: A library for numerical computing with Python. It provides powerful tools for working with arrays and matrices, and is a foundation for many other data science libraries.

Pandas: A library for data manipulation and analysis. It provides powerful data structures, such as dataframes, and tools for working with structured data.

Matplotlib: A library for data visualization. It provides tools for creating a wide range of plots and charts, from basic line plots to advanced 3D visualizations.

Seaborn: A library for statistical data visualization. It is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative visualizations.

Scikit-learn: A library for machine learning. It provides a wide range of tools for data preprocessing, feature selection, model selection, and evaluation, as well as many popular machine learning algorithms.

TensorFlow: A library for machine learning and deep learning. It provides tools for creating and training neural networks, as well as pre-built models for common tasks.

Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, Theano, or CNTK. It is designed to enable fast experimentation with deep neural networks.

PyTorch: A library for machine learning and deep learning. It provides a dynamic computation graph, making it easy to experiment with different model architectures.

Statsmodels: A library for statistical modeling. It provides a wide range of statistical models, from linear regression to time series analysis.

NetworkX: A library for network analysis. It provides tools for working with graphs and networks, and is commonly used in social network analysis and bioinformatics.