![]() ![]() Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. When it comes time to run the server, we tell Bokeh to serve the bokehapp directory and it will automatically search for and run the main.py script. Python backend system that decouples API from implementation unumpy provides a NumPy API. There are three main parts: data, scripts, and main.py, under one parentbokehapp directory. 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 Import packages import numpy as np import random from bokeh.io import outputfile, show from otting import figure Create an array xarray np.array ( 10,20,30,40,50,60) yarray np.array ( 50,60,70,80,90,100) Create a line plot plot figure () plot. NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. We are going to cover: How to create an interactive Bokeh figure with five data points Integrating a free cloud database with 3,000 data points ( Easybase. Our project will feature UI widgets (sliders, menus) that, when changed, update the displayed data. 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. For our example, we are going to create an interactive explorer for movie data. 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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