![]() One way you could build a plot is to hand-generate an SVG file that is a set of specifications for lines and circles and text and whatnot that comprises a plot. ![]() You can imagine that there are many many steps to building that. Now, say we want to make a plot of some data. Steps take place, but because we use Pandas’s high-level functionality, those details are invisible to us, and glad we are of that. We can loop over the rows in the data frame with a for loop, check to see what the value of the insomnia column is with an if statement, put the value in the percent correct field into an appropriate array based on whether or not the subject suffers from insomnia, and then, given those arrays, sort them and pull out the middle value. There are elementary tasks that go into it if we were to code it up without using Pandas’s delicious functionality. We computed the median percent correct for those with and without insomnia. High-level and low-level plotting packages Īs a demonstration of what I mean by high-level and low-level plotting packages, let us first think about one of our tasks we did with Pandas with the facial matching data set. Plotting libraries, so we will use JavaScript-based plotting (as I do in my own work). Interactivity and portability (accomplished by rendering in browsers) are key features of modern Packages that use JavaScript for rendering are particularly well suited for interactivity in browsers. We will not discuss packages based on OpenGL. The landscape is divided into three main pods based on the low-level renderer of the graphics, JavaScript, Matplotlib, and OpenGL (though Matplotlib is higher-level than JavaScript and OpenGL). (It is three years old and definitely not complete, notably missing Panel, for example.) That landscape is depicted below, taken from this visualization of it by Nicolas ![]() In a talk at P圜on in 2017, Jake VanderPlas, who is one of the authors of one of them ( Altair), gave an overview of the Python visualization landscape. Let us start by looking at some of the many plotting packages available in Python. Now is the time in when we learn how to take data set and plot them. But I argue that what we want out of them most of the time is plots. We’ve learned how to use Pandas to handle the data sets and get what we want out of them. We have a couple nice data sets from the last few lessons, the data from the tongue strikes of frogs and facial matching data from people with sleep deprivation. Wrangling, EDA III, and Normal approximations Best Practices when using the Resnick High Performance Computing Center and other related topics Implementation of MLE for variate-covariate models Nonparametric inference with hacker stats Plug-in estimates and confidence intervals Bokeh’s grammar and our first plot with Bokeh.High-level and low-level plotting packages.Preparing computing resources for the course Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. Python backend system that decouples API from implementation unumpy provides a NumPy API. Manipulate JSON-like data with NumPy-like idioms. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics. ![]() Labeled, indexed multi-dimensional arrays for advanced analytics and visualization NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. 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.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. Nearly every scientist working in Python draws on the power of NumPy. ![]()
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