Python可视化库之HoloViews的使用教程

⽬录

Python-HoloViews库介绍Python-HoloViews库样例介绍密度图+箱线图散点图+横线图IrisSplom⾯积图直⽅图系列RouteChord⼩提琴图总结参考资料

最近⼀直在整理统计图表的绘制⽅法，发现Python中除了经典Seaborn库外，还有⼀些优秀的可交互的第三⽅库也能实现⼀些常见的统计图表绘制，⽽且其还拥有Matplotlib、Seaborn等库所不具备的交互效果。

当然，同时也能绘制出版级别的图表要求，此外，⼀些在使⽤Matplotlib需⾃定义函数才能绘制的图表在⼀些第三⽅库中都集成了，这也⼤⼤缩短了绘图时间。

今天我就详细介绍⼀个优秀的第三⽅库-HoloViews，内容主要如下：

Python-HoloViews库介绍

Python-HoloViews库样例介绍

Python-HoloViews库介绍

Python-HoloViews库作为⼀个开源的可视化库，其⽬的是使数据分析结果和可视化完美衔接，其默认的绘图主题和配⾊以及较少的绘图代码量，可以使你专注于数据分析本⾝，同时其统计绘图功能也⾮常优秀。更多关于HoloViews库的介绍，可参考：Python-HoloViews库官⽹[1]

Python-HoloViews库样例介绍

这⼀部分⼩编重点放在⼀些统计图表上，其绘制结果不仅可以在⽹页上交互，同时其默认的绘图结果也完全满⾜出版界别的要求，主要内容如下(以下图表都是可交互的)：

密度图+箱线图

import pandas as pdimport holoviews as hv

from bokeh.sampledata import autompg

hv.extension('bokeh')

df = autompg.autompg_clean

bw = hv.BoxWhisker(df, kdims=[\"origin\"], vdims=[\"mpg\"])dist = hv.NdOverlay(

{origin: hv.Distribution(group, kdims=[\"mpg\"]) for origin, group in df.groupby(\"origin\")})bw + dist

密度图+箱线图

散点图+横线图

scatter = hv.Scatter(df, kdims=[\"origin\"], vdims=[\"mpg\"]).opts(jitter=0.3)yticks = [(i + 0.25, origin) for i, origin in enumerate(df[\"origin\"].unique())]spikes = hv.NdOverlay( {

origin: hv.Spikes(group[\"mpg\"]).opts(position=i)

for i, (origin, group) in enumerate(df.groupby(\"origin\ }

).opts(hv.opts.Spikes(spike_length=0.5, yticks=yticks, show_legend=False, alpha=0.3))scatter + spikes

散点图+横线图

Iris Splom

from bokeh.sampledata.iris import flowersfrom holoviews.operation import gridmatrixds = hv.Dataset(flowers)

grouped_by_species = ds.groupby('species', container_type=hv.NdOverlay)grid = gridmatrix(grouped_by_species, diagonal_type=hv.Scatter)

grid.opts(opts.Scatter(tools=['hover', 'box_select'], bgcolor='#efe8e2', fill_alpha=0.2, size=4))

Iris Splom

⾯积图

# create some example data

python=np.array([2, 3, 7, 5, 26, 221, 44, 233, 2, 265, 266, 267, 120, 111])

pypy=np.array([12, 33, 47, 15, 126, 121, 144, 233, 2, 225, 226, 267, 110, 130])jython=np.array([22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160])dims = dict(kdims='time', vdims='memory')

python = hv.Area(python, label='python', **dims)pypy = hv.Area(pypy, label='pypy', **dims)jython = hv.Area(jython, label='jython', **dims)

opts.defaults(opts.Area(fill_alpha=0.5))overlay = (python * pypy * jython)

overlay.relabel(\"Area Chart\") + hv.Area.stack(overlay).relabel(\"Stacked Area Chart\")

⾯积图

直⽅图系列

def get_overlay(hist, x, pdf, cdf, label): pdf = hv.Curve((x, pdf), label='PDF') cdf = hv.Curve((x, cdf), label='CDF')

return (hv.Histogram(hist, vdims='P(r)') * pdf * cdf).relabel(label)np.seterr(divide='ignore', invalid='ignore')label = \"Normal Distribution (µ=0, σ=0.5)\"mu, sigma = 0, 0.5

measured = np.random.normal(mu, sigma, 1000)

hist = np.histogram(measured, density=True, bins=50)

x = np.linspace(-2, 2, 1000)

pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2norm = get_overlay(hist, x, pdf, cdf, label)

label = \"Log Normal Distribution (µ=0, σ=0.5)\"mu, sigma = 0, 0.5

measured = np.random.lognormal(mu, sigma, 1000)hist = np.histogram(measured, density=True, bins=50)

x = np.linspace(0, 8.0, 1000)

pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2lognorm = get_overlay(hist, x, pdf, cdf, label)

label = \"Gamma Distribution (k=1, θ=2)\"k, theta = 1.0, 2.0

measured = np.random.gamma(k, theta, 1000)

hist = np.histogram(measured, density=True, bins=50)

x = np.linspace(0, 20.0, 1000)

pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)gamma = get_overlay(hist, x, pdf, cdf, label)

label = \"Beta Distribution (α=2, β=2)\"alpha, beta = 2.0, 2.0

measured = np.random.beta(alpha, beta, 1000)

hist = np.histogram(measured, density=True, bins=50)

x = np.linspace(0, 1, 1000)

pdf = x**(alpha-1) * (1-x)**(beta-1) / scipy.special.beta(alpha, beta)cdf = scipy.special.btdtr(alpha, beta, x)beta = get_overlay(hist, x, pdf, cdf, label)

label = \"Weibull Distribution (λ=1, k=1.25)\"lam, k = 1, 1.25

measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)hist = np.histogram(measured, density=True, bins=50)x = np.linspace(0, 8, 1000)

pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)cdf = 1 - np.exp(-(x/lam)**k)

weibull = get_overlay(hist, x, pdf, cdf, label)

直⽅图系列

Route Chord

import holoviews as hv

from holoviews import opts, dim

from bokeh.sampledata.airport_routes import routes, airportshv.extension('bokeh')

# Count the routes between Airports

route_counts = routes.groupby(['SourceID', 'DestinationID']).Stops.count().reset_index()nodes = hv.Dataset(airports, 'AirportID', 'City')

chord = hv.Chord((route_counts, nodes), ['SourceID', 'DestinationID'], ['Stops'])

# Select the 20 busiest airports

busiest = list(routes.groupby('SourceID').count().sort_values('Stops').iloc[-20:].index.values)busiest_airports = chord.select(AirportID=busiest, selection_mode='nodes')busiest_airports.opts(

opts.Chord(cmap='Category20', edge_color=dim('SourceID').str(),

height=800, labels='City', node_color=dim('AirportID').str(), width=800))

Route Chord

⼩提琴图

import holoviews as hvfrom holoviews import dim

from bokeh.sampledata.autompg import autompghv.extension('bokeh')

violin = hv.Violin(autompg, ('yr', 'Year'), ('mpg', 'Miles per Gallon')).redim.range(mpg=(8, 45))violin.opts(height=500, width=900, violin_fill_color=dim('Year').str(), cmap='Set1')

⼩提琴图

更多样例可查看：Python-HoloViews样例[2]

总结

今天的推⽂，⼩编主要介绍了Python可视化库HoloViews，着重介绍了其中统计图表部分，这个库也会在⼩编整理的资料中出现，对于⼀些常见且使⽤Matplotlib较难绘制的图表较为友好，感兴趣的⼩伙伴可以学习下哦~~

参考资料

以上就是Python可视化库之HoloViews的使⽤教程的详细内容，更多关于Python HoloViews库的资料请关注其它相关⽂章！