There are several commonly used Python libraries for data manipulation, analysis, and visualization. Here are some of the most popular ones:

1. NumPy: A fundamental library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
2. Pandas: A powerful library for data manipulation and analysis. It provides data structures like DataFrames and Series, which allow you to easily handle and analyze structured data. Pandas is widely used for tasks such as data cleaning, filtering, grouping, and merging.
3. Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. It provides a wide variety of plots, including line plots, scatter plots, bar plots, histograms, and more. Matplotlib is highly customizable and can be used for generating publication-quality figures.
4. Seaborn: A statistical data visualization library that is built on top of Matplotlib. Seaborn provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations such as heatmaps, violin plots, and joint plots.
5. SciPy: A library that builds on top of NumPy and provides a collection of scientific computing tools. It includes modules for optimization, interpolation, integration, linear algebra, signal and image processing, and more. SciPy is widely used in scientific and engineering applications.
6. Scikit-learn: A machine learning library that provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. Scikit-learn also includes utilities for data preprocessing, model evaluation, and model selection. It is known for its simple and consistent API.
7. TensorFlow: An open-source library for machine learning and deep learning. TensorFlow allows you to build and train neural networks using both high-level and low-level APIs. It provides various tools and resources for tasks such as image recognition, natural language processing, and time series analysis.
8. Keras: A high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. Keras simplifies the process of building and training deep learning models by providing a user-friendly interface and a rich set of pre-built neural network layers.
9. PyTorch: A machine learning library that provides a dynamic computational graph framework. PyTorch allows you to build and train neural networks using a more imperative and flexible programming style. It has gained popularity for its ease of use and its support for dynamic models.

These are just a few of the many Python libraries available for data-related tasks. The choice of libraries depends on your specific requirements and the nature of the data analysis or machine learning problem you are working on.