<jupyter_start><jupyter_text>HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible.  Instead of specifying every step for each plot, HoloViews lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization. Examples of how HoloViews is used in Python scripts as well as in live [Jupyter Notebooks](http://jupyter.org) may be accessed directly from the [holoviews gallery](http://holoviews.org/gallery/index.html) webpage. Here is a quick example of HoloViews in action:<jupyter_code>import numpy as np
import holoviews as hv
hv.notebook_extension('matplotlib')
fractal = hv.Image(np.load('mandelbrot.npy'))

((fractal * hv.HLine(y=0)).hist() + fractal.sample(y=0))<jupyter_output><empty_output><jupyter_text>Fundamentally, a HoloViews object is just a thin wrapper around your data, with the data always being accessible in its native numerical format, but with the data displaying itself automatically whether alone or alongside or overlaid with other HoloViews objects as shown above. The actual rendering is done using a separate library like [matplotlib](http://matplotlib.org) or [bokeh](http://bokeh.pydata.org), but all of the HoloViews objects can be used without any plotting library available, so that you can easily create, save, load, and manipulate HoloViews objects from within your own programs for later analysis.  HoloViews objects support arbitrarily high dimensions, using continuous, discrete, or categorical indexes and values, with flat or hierarchical organizations, and sparse or dense data formats.  The objects can then be flexibly combined, selected, sliced, sorted, sampled, or animated, all by specifying what data you want to see rather than by writing plotting code. The goal is to put the plotting code into the background, as an implementation detail to be written once and reused often, letting you focus clearly on your data itself in daily work.  More detailed example Even extremely complex relationships between data elements can be expressed succinctly in HoloViews, allowing you to explore them with ease:<jupyter_code>%%opts Points [scaling_factor=50] Contours (color='w')
dots = np.linspace(-0.45, 0.45, 19)

layouts = {y: (fractal * hv.Points(fractal.sample([(i,y) for i in dots])) +
               fractal.sample(y=y) +
               hv.operation.threshold(fractal, level=np.percentile(fractal.sample(y=y)['z'], 90)) +
               hv.operation.contours(fractal, levels=[np.percentile(fractal.sample(y=y)['z'], 60)]))
            for y in np.linspace(-0.3, 0.3, 21)}

hv.HoloMap(layouts, kdims=['Y']).collate().cols(2)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08443177769861061, 'fraction_numerical': 0.008193801211257571, 'mean_word_length': 5.104347826086957, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '299686', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 814, 'n_tokens_neox': 767, 'n_words': 331, 'path': 'doc/Homepage.ipynb', 'repo_name': 'kgullikson88/holoviews', 'size': '2807'}