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Nearly every scientist working in Python draws on the power of NumPy.
NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
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NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.
|Array Library||Capabilities & Application areas|
|Dask||Distributed arrays and advanced parallelism for analytics, enabling performance at scale.|
|CuPy||NumPy-compatible array library for GPU-accelerated computing with Python.|
|JAX||Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.|
|Xarray||Labeled, indexed multi-dimensional arrays for advanced analytics and visualization|
|Sparse||NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.|
|PyTorch||Deep learning framework that accelerates the path from research prototyping to production deployment.|
|TensorFlow||An end-to-end platform for machine learning to easily build and deploy ML powered applications.|
|MXNet||Deep learning framework suited for flexible research prototyping and production.|
|Arrow||A cross-language development platform for columnar in-memory data and analytics.|
|xtensor||Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.|
|XND||Develop libraries for array computing, recreating NumPy's foundational concepts.|
|uarray||Python backend system that decouples API from implementation; unumpy provides a NumPy API.|
|tensorly||Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.|
NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:
NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing. Privilege Set of 3 Black/Natural Metal/Wood Accent Tables is another AI package, providing blueprints and templates for deep learning.
NumPy is an essential component in the burgeoning inktastic I Wear Orange for My Gigi Multiple Sclerosis Awareness, which includes Matplotlib, Seaborn, Plotly, Nothing Is Impastable Spaghetti Pasta Tank Top, Bokeh, Holoviz, Vispy, Napari, and Step By Step, to name a few.
NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.