Bokeh，一個(gè)超強(qiáng)交互式Python可視化庫(kù)！
今天這篇推文，給大家介紹一下Python中常用且可靈活交互使用的的可視化繪制包- Bokeh，由于網(wǎng)上關(guān)于該包較多及官方介紹也較為詳細(xì)，這里就在不再過(guò)多介紹，我們直接放出幾副精美的可視化作品供大家欣賞：
在jupyter notebook 中顯示
在繪制可視化作品之前需輸入：
output_notebook()
即可在jupyter notebook 中交互顯示可視化結(jié)果。
Bokeh 可視化作品欣賞
bar_colormapped
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
output_file("bar_colormapped.html")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts))
p = figure(x_range=fruits, plot_height=350, toolbar_location=None, title="Fruit Counts")
p.vbar(x='fruits', top='counts', width=0.9, source=source, legend_field="fruits",
line_color='white', fill_color=factor_cmap('fruits', palette=Spectral6, factors=fruits))
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.y_range.end = 9
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
show(p)
hexbin
import numpy as np
from bokeh.io import output_file, show
from bokeh.models import HoverTool
from bokeh.plotting import figure
n = 500
x = 2 + 2*np.random.standard_normal(n)
y = 2 + 2*np.random.standard_normal(n)
p = figure(title="Hexbin for 500 points", match_aspect=True,
tools="wheel_zoom,reset", background_fill_color='#440154')
p.grid.visible = False
r, bins = p.hexbin(x, y, size=0.5, hover_color="pink", hover_alpha=0.8)
p.circle(x, y, color="white", size=1)
p.add_tools(HoverTool(
tooltips=[("count", "@c"), ("(q,r)", "(@q, @r)")],
mode="mouse", point_policy="follow_mouse", renderers=[r]
))
output_file("hexbin.html")
show(p)
boxplot
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
# generate some synthetic time series for six different categories
cats = list("abcdef")
yy = np.random.randn(2000)
g = np.random.choice(cats, 2000)
for i, l in enumerate(cats):
yy[g == l] += i // 2
df = pd.DataFrame(dict(score=yy, group=g))
# find the quartiles and IQR for each category
groups = df.groupby('group')
q1 = groups.quantile(q=0.25)
q2 = groups.quantile(q=0.5)
q3 = groups.quantile(q=0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
lower = q1 - 1.5*iqr
# find the outliers for each category
def outliers(group):
cat = group.name
return group[(group.score > upper.loc[cat]['score']) | (group.score < lower.loc[cat]['score'])]['score']
out = groups.apply(outliers).dropna()
# prepare outlier data for plotting, we need coordinates for every outlier.
if not out.empty:
outx = []
outy = []
for keys in out.index:
outx.append(keys[0])
outy.append(out.loc[keys[0]].loc[keys[1]])
p = figure(tools="", background_fill_color="#efefef", x_range=cats, toolbar_location=None)
# if no outliers, shrink lengths of stems to be no longer than the minimums or maximums
qmin = groups.quantile(q=0.00)
qmax = groups.quantile(q=1.00)
upper.score = [min([x,y]) for (x,y) in zip(list(qmax.loc[:,'score']),upper.score)]
lower.score = [max([x,y]) for (x,y) in zip(list(qmin.loc[:,'score']),lower.score)]
# stems
p.segment(cats, upper.score, cats, q3.score, line_color="black")
p.segment(cats, lower.score, cats, q1.score, line_color="black")
# boxes
p.vbar(cats, 0.7, q2.score, q3.score, fill_color="#E08E79", line_color="black")
p.vbar(cats, 0.7, q1.score, q2.score, fill_color="#3B8686", line_color="black")
# whiskers (almost-0 height rects simpler than segments)
p.rect(cats, lower.score, 0.2, 0.01, line_color="black")
p.rect(cats, upper.score, 0.2, 0.01, line_color="black")
# outliers
if not out.empty:
p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = "white"
p.grid.grid_line_width = 2
p.xaxis.major_label_text_font_size="16px"
output_file("boxplot.html", title="boxplot.py example")
show(p)
burtin
from collections import OrderedDict
from io import StringIO
from math import log, sqrt
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
antibiotics = """
bacteria, penicillin, streptomycin, neomycin, gram
Mycobacterium tuberculosis, 800, 5, 2, negative
Salmonella schottmuelleri, 10, 0.8, 0.09, negative
Proteus vulgaris, 3, 0.1, 0.1, negative
Klebsiella pneumoniae, 850, 1.2, 1, negative
Brucella abortus, 1, 2, 0.02, negative
Pseudomonas aeruginosa, 850, 2, 0.4, negative
Escherichia coli, 100, 0.4, 0.1, negative
Salmonella (Eberthella) typhosa, 1, 0.4, 0.008, negative
Aerobacter aerogenes, 870, 1, 1.6, negative
Brucella antracis, 0.001, 0.01, 0.007, positive
Streptococcus fecalis, 1, 1, 0.1, positive
Staphylococcus aureus, 0.03, 0.03, 0.001, positive
Staphylococcus albus, 0.007, 0.1, 0.001, positive
Streptococcus hemolyticus, 0.001, 14, 10, positive
Streptococcus viridans, 0.005, 10, 40, positive
Diplococcus pneumoniae, 0.005, 11, 10, positive
"""
drug_color = OrderedDict([
("Penicillin", "#0d3362"),
("Streptomycin", "#c64737"),
("Neomycin", "black" ),
])
gram_color = OrderedDict([
("negative", "#e69584"),
("positive", "#aeaeb8"),
])
df = pd.read_csv(StringIO(antibiotics),
skiprows=1,
skipinitialspace=True,
engine='python')
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10
minr = sqrt(log(.001 * 1E4))
maxr = sqrt(log(1000 * 1E4))
a = (outer_radius - inner_radius) / (minr - maxr)
b = inner_radius - a * maxr
def rad(mic):
return a * np.sqrt(np.log(mic * 1E4)) + b
big_angle = 2.0 * np.pi / (len(df) + 1)
small_angle = big_angle / 7
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=(-420, 420), y_range=(-420, 420),
min_border=0, outline_line_color="black",
background_fill_color="#f0e1d2")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges
angles = np.pi/2 - big_angle/2 - df.index.to_series()*big_angle
colors = [gram_color[gram] for gram in df.gram]
p.annular_wedge(
0, 0, inner_radius, outer_radius, -big_angle+angles, angles, color=colors,
)
# small wedges
p.annular_wedge(0, 0, inner_radius, rad(df.penicillin),
-big_angle+angles+5*small_angle, -big_angle+angles+6*small_angle,
color=drug_color['Penicillin'])
p.annular_wedge(0, 0, inner_radius, rad(df.streptomycin),
-big_angle+angles+3*small_angle, -big_angle+angles+4*small_angle,
color=drug_color['Streptomycin'])
p.annular_wedge(0, 0, inner_radius, rad(df.neomycin),
-big_angle+angles+1*small_angle, -big_angle+angles+2*small_angle,
color=drug_color['Neomycin'])
# circular axes and lables
labels = np.power(10.0, np.arange(-3, 4))
radii = a * np.sqrt(np.log(labels * 1E4)) + b
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0, radii[:-1], [str(r) for r in labels[:-1]],
text_font_size="11px", text_align="center", text_baseline="middle")
# radial axes
p.annular_wedge(0, 0, inner_radius-10, outer_radius+10,
-big_angle+angles, -big_angle+angles, color="black")
# bacteria labels
xr = radii[0]*np.cos(np.array(-big_angle/2 + angles))
yr = radii[0]*np.sin(np.array(-big_angle/2 + angles))
label_angle=np.array(-big_angle/2+angles)
label_angle[label_angle < -np.pi/2] += np.pi # easier to read labels on the left side
p.text(xr, yr, df.bacteria, angle=label_angle,
text_font_size="12px", text_align="center", text_baseline="middle")
# OK, these hand drawn legends are pretty clunky, will be improved in future release
p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)
p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()],
text_font_size="9px", text_align="left", text_baseline="middle")
p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13,
color=list(drug_color.values()))
p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color),
text_font_size="12px", text_align="left", text_baseline="middle")
output_file("burtin.html", title="burtin.py example")
show(p)
其他可視化作品我們直接放出結(jié)果，繪制代碼省略，大家可自行去官網(wǎng)搜索哈：
periodic
markers
以上所有的可視化作品都是可以交互操作的哦，除此之外，Bokeh 還提供大量的可視化APP應(yīng)用，具體內(nèi)容，感興趣的小伙伴可自行搜索哈~~
總結(jié)
這一期我們分享了Python-Bokeh庫(kù)繪制的可視化作品，體驗(yàn)了Python用于繪制交互式可視化作品放入方便性，還是那句話，適合自己的才是最好的，不要糾結(jié)所使用的工具哈，讓我們一起探索數(shù)據(jù)可視化的魅力吧~~
參考來(lái)源：https://docs.bokeh.org/en/latest/docs/gallery.html
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