NumPy

numpy.org

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Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.

NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

First Image of a Black HoleHow NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole

Detection of Gravitational WavesIn 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy.

Sports AnalyticsCricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses.

Pose Estimation using deep learningDeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales.

An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

A streamplot made in matplotlib

A scatter-plot graph made in ggpy

A box-plot made in plotly

A streamgraph made in altair

A Voronoi diagram made in vispy.

▶ Video