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 pd
import 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", sort=False))
    }
).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 flowers
from holoviews.operation import gridmatrix

ds = 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, 254, 265, 266, 267, 120, 111])
pypy=np.array([12, 33, 47, 15, 126, 121, 144, 233, 254, 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)))/2
norm = 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)))/2
lognorm = 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, airports

hv.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 hv
from holoviews import dim

from  bokeh.sampledata.autompg import autompg
hv.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

[1]Python-HoloViews库官网: https://holoviews.org/。

[2]Python-HoloViews样例: https://holoviews.org/gallery/index.html。

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库的资料请关注脚本之家其它相关文章！