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，由于網上關于該包較多及官方介紹也較為詳細，這里就在不再過多介紹，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果。
Bokeh 可視化作品欣賞
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_cmapoutput_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)
bar_colormapped
import?numpy?as?npfrom?bokeh.io?import?output_file,?show
from?bokeh.models?import?HoverTool
from?bokeh.plotting?import?figuren?=?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?=?Falser,?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)
hexbin
import?numpy?as?np
import?pandas?as?pdfrom?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.namereturn?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)
boxplot
from?collections?import?OrderedDict
from?io?import?StringIO
from?math?import?log,?sqrtimport?numpy?as?np
import?pandas?as?pdfrom?bokeh.plotting?import?figure,?output_file,?showantibiotics?=?"""
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?-?10minr?=?sqrt(log(.001?*?1E4))
maxr?=?sqrt(log(1000?*?1E4))
a?=?(outer_radius?-?inner_radius)?/?(minr?-?maxr)
b?=?inner_radius?-?a?*?maxrdef?rad(mic):return?a?*?np.sqrt(np.log(mic?*?1E4))?+?bbig_angle?=?2.0?*?np.pi?/?(len(df)?+?1)
small_angle?=?big_angle?/?7p?=?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)
burtin
其他可視化作品我們直接放出結果，繪制代碼省略，大家可自行去官網搜索哈：
periodic
markers plots
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應用，具體內容，感興趣的小伙伴可自行搜索哈~~
總結
這一期我們分享了Python-Bokeh庫繪制的可視化作品，體驗了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，不要糾結所使用的工具哈，讓我們一起探索數據可視化的魅力吧~~
參考來源：https://docs.bokeh.org/en/latest/docs/gallery.html
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總結
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