Python has such amazing data science libraries.

NumPy is great for dealing with the input of any shape. Most probably anything can be thought of as a number, vector, matrix, or tensor. We can transform these, and feed them directly to ML and analytics libraries.

Pandas are great for dealing with tabular data, which happens to be the majority of datasets. Even if something doesn't fit nicely into a table, like a video, we can still store links to them in a Pandas data frame.?

Matplotlib has powerful yet beautiful visualizations because of the graphs and plots that it produces, it’s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.?

Scikit-Learn?is an amazing machine-learning library. It has an abundance of algorithms that are extremely easy to use, even if you don't know the underlying math behind them.

PyBrain is an open-source machine learning library for Python that provides algorithms for reinforcement learning, unsupervised learning, and supervised learning. It's built on top of the NumPy library and offers a flexible and easy-to-use API for training neural networks, creating and running simulations, and developing complex machine-learning models.

Python has a rich and diverse ecosystem of libraries that cater to various needs and applications in the fields of data science, machine learning, web development, computer vision, natural language processing, and many more. Some of the most popular libraries include NumPy, Pandas, Matplotlib, TensorFlow, PyTorch, scikit-learn, and OpenCV. Each library has its own strengths, weaknesses, and specific use cases, making it important to choose the right library for a particular task. The open-source nature of these libraries also allows for collaboration, contributions, and customization, making it easier for developers and researchers to advance the state of the art in their fields.