数据可视化是目前最牛逼的行业之一！这些必学库你会几个呢？

推荐

数据可视化的库有挺多的，这里推荐几个比较常用的：

• Matplotlib
•  
• Plotly
•  
• Seaborn
•  
• Ggplot
•  
• bokeh
•  
• Pyechart
•  
• Pygal

Plotly

Plotly 文档地址：

• https://plot.ly/python/#financial-charts

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使用方式：

Plotly 有 online 和 offline 两种方式，这里只介绍 offline 的。

这是 Plotly 官方教程的一部分

import plotly.plotly as py
import numpy as np
data = [dict(
 visible=False,line=dict(color='#00CED1',width=6),# 配置线宽和颜色
 name=' = ' + str(step),x=np.arange(0,10,0.01),# x 轴参数
 y=np.sin(step * np.arange(0,0.01))) for step in np.arange(0,5,0.1)] # y 轴参数
data[10]['visible'] = True
py.iplot(data,filename='Single Sine Wave')

只要将最后一行中的

py.iplot

替换为下面代码

py.offline.plot

便可以运行。

漏斗图

这个图代码太长了，就不 po 出来了。

Basic Box Plot

import plotly.plotly
import plotly.graph_objs as go
import numpy as np
y0 = np.random.randn(50)-1
y1 = np.random.randn(50)+1
trace0 = go.Box(
 y=y0
)
trace1 = go.Box(
 y=y1
)
data = [trace0,trace1]
plotly.offline.plot(data)

Wind Rose Chart

import plotly.graph_objs as go
trace1 = go.Barpolar(
 r=[77.5,72.5,70.0,45.0,22.5,42.5,40.0,62.5],text=['north','N-E','East','S-E','South','S-W','West','N-W'],name='11-14 m/s',marker=dict(
 color='rgb(106,81,163)'
 )
)
trace2 = go.Barpolar(
 r=[57.49999999999999,50.0,35.0,20.0,37.5,55.00000000000001],# 鼠标浮动标签文字描述
 name='8-11 m/s',marker=dict(
 color='rgb(158,154,200)'
 )
)
trace3 = go.Barpolar(
 r=[40.0,30.0,7.5,32.5,40.0],name='5-8 m/s',marker=dict(
 color='rgb(203,201,226)'
 )
)
trace4 = go.Barpolar(
 r=[20.0,15.0,2.5,12.5,22.5],name='< 5 m/s',marker=dict(
 color='rgb(242,240,247)'
 )
)
data = [trace1,trace2,trace3,trace4]
layout = go.Layout(
 title='Wind Speed distribution in Laurel,NE',font=dict(
 size=16
 ),legend=dict(
 font=dict(
 size=16
 )
 ),radialaxis=dict(
 ticksuffix='%'
 ),orientation=270
)
fig = go.figure(data=data,layout=layout)
plotly.offline.plot(fig,filename='polar-area-chart')

Basic Ternary Plot with Markers

篇幅有点长，这里就不 po 代码了。

bokeh

这里展示一下常用的图表和比较抢眼的图表，详细的文档可查看。

条形图

这配色看着还挺舒服的，比 pyecharts 条形图的配色好看一点。

from bokeh.io import show,output_file
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
output_file("colormapped_bars.html")# 配置输出文件名
fruits = ['Apples','魅族','OPPO','VIVO','小米','华为'] # 数据
counts = [5,3,4,2,6] # 数据
source = ColumnDataSource(data=dict(fruits=fruits,counts=counts,color=Spectral6))
p = figure(x_range=fruits,y_range=(0,9),plot_height=250,title="Fruit Counts",toolbar_location=None,tools="")# 条形图配置项
p.vbar(x='fruits',top='counts',width=0.9,color='color',legend="fruits",source=source)
p.xgrid.grid_line_color = None # 配置网格线颜色
p.legend.orientation = "horizontal" # 图表方向为水平方向
p.legend.location = "top_center"
show(p) # 展示图表

年度条形图

可以对比不同时间点的量。

from bokeh.io import show,output_file
from bokeh.models import ColumnDataSource,FactorRange
from bokeh.plotting import figure
output_file("bars.html") # 输出文件名
fruits = ['Apple','华为'] # 参数
years = ['2015','2016','2017'] # 参数
data = {'fruits': fruits,'2015': [2,1,4],'2016': [5,6],'2017': [3,3]}
x = [(fruit,year) for fruit in fruits for year in years]
counts = sum(zip(data['2015'],data['2016'],data['2017']),()) 
source = ColumnDataSource(data=dict(x=x,counts=counts))
p = figure(x_range=FactorRange(*x),title="Fruit Counts by Year",tools="")
p.vbar(x='x',source=source)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
show(p)

饼图

from collections import Counter
from math import pi
import pandas as pd
from bokeh.io import output_file,show
from bokeh.palettes import Category20c
from bokeh.plotting import figure
from bokeh.transform import cumsum
output_file("pie.html")
x = Counter({
 '中国': 157,'美国': 93,'日本': 89,'巴西': 63,'德国': 44,'印度': 42,'意大利': 40,'澳大利亚': 35,'法国': 31,'西班牙': 29
})
data = pd.DataFrame.from_dict(dict(x),orient='index').reset_index().rename(index=str,columns={0:'value','index':'country'})
data['angle'] = data['value']/sum(x.values()) * 2*pi
data['color'] = Category20c[len(x)]
p = figure(plot_height=350,title="Pie Chart",tools="hover",tooltips="@country: @value")
p.wedge(x=0,y=1,radius=0.4,start_angle=cumsum('angle',include_zero=True),end_angle=cumsum('angle'),line_color="white",fill_color='color',legend='country',source=data)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p) 

条形图

年度水果进出口

from bokeh.io import output_file,show
from bokeh.models import ColumnDataSource
from bokeh.palettes import GnBu3,OrRd3
from bokeh.plotting import figure
output_file("stacked_split.html")
fruits = ['Apples','Pears','Nectarines','Plums','Grapes','Strawberries']
years = ["2015","2016","2017"]
exports = {'fruits': fruits,3]}
imports = {'fruits': fruits,'2015': [-1,-1,-3,-2,-1],'2016': [-2,-2],'2017': [-1,-2]}
p = figure(y_range=fruits,x_range=(-16,16),title="Fruit import/export,by year",toolbar_location=None)
p.hbar_stack(years,y='fruits',height=0.9,color=GnBu3,source=ColumnDataSource(exports),legend=["%s exports" % x for x in years])
p.hbar_stack(years,color=OrRd3,source=ColumnDataSource(imports),legend=["%s imports" % x for x in years])
p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.legend.location = "top_left"
p.axis.minor_tick_line_color = None
p.outline_line_color = None
show(p)

散点图

from bokeh.plotting import figure,output_file,show
output_file("line.html")
p = figure(plot_width=400,plot_height=400)
p.circle([1,5],[6,7,size=20,color="navy",alpha=0.5)
show(p)

六边形图

这两天，马蜂窝刚被发现数据造假，这不，与马蜂窝应应景。

import numpy as np
from bokeh.io import output_file,show
from bokeh.plotting import figure
from bokeh.util.hex import axial_to_cartesian
output_file("hex_coords.html")
q = np.array([0,1])
r = np.array([0,0])
p = figure(plot_width=400,plot_height=400,toolbar_location=None) # 
p.grid.visible = False # 配置网格是否可见
p.hex_tile(q,r,size=1,fill_color=["firebrick"] * 3 + ["navy"] * 4,alpha=0.5)
x,y = axial_to_cartesian(q,"pointytop")
p.text(x,y,text=["(%d,%d)" % (q,r) for (q,r) in zip(q,r)],text_baseline="middle",text_align="center")
show(p)

环比条形图

这个实现挺厉害的，看了一眼就吸引了我。我在代码中都做了一些注释，希望对你理解有帮助。注：圆心为正中央，即直角坐标系中标签为（0，0）的地方。

from collections import OrderedDict
from math import log,sqrt
import numpy as np
import pandas as pd
from six.moves import cStringIO as StringIO
from bokeh.plotting import figure,show,output_file
antibiotics = """
bacteria,penicillin,streptomycin,neomycin,gram
结核分枝杆菌,800,negative
沙门氏菌,0.8,0.09,negative
变形杆菌,0.1,negative
肺炎克雷伯氏菌,850,1.2,negative
布鲁氏菌,0.02,negative
铜绿假单胞菌,0.4,negative
大肠杆菌,100,negative
产气杆菌,870,1.6,negative
白色葡萄球菌,0.007,0.001,positive
溶血性链球菌,14,positive
草绿色链球菌,0.005,40,positive
肺炎双球菌,11,positive
"""
drug_color = OrderedDict([# 配置中间标签名称与颜色
 ("盘尼西林","#0d3362"),("链霉素","#c64737"),("新霉素","black"),])
gram_color = {
 "positive": "#aeaeb8","negative": "#e69584",}
# 读取数据
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
# 整体配置
p = figure(plot_width=width,plot_height=height,title="",x_axis_type=None,y_axis_type=None,x_range=(-420,420),y_range=(-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 #计算角度
colors = [gram_color[gram] for gram in df.gram] # 配置颜色
p.annular_wedge(
 0,inner_radius,outer_radius,-big_angle + angles,angles,color=colors,)
# small wedges
p.annular_wedge(0,rad(df.penicillin),-big_angle + angles + 5 * small_angle,-big_angle + angles + 6 * small_angle,color=drug_color['盘尼西林'])
p.annular_wedge(0,rad(df.streptomycin),-big_angle + angles + 3 * small_angle,-big_angle + angles + 4 * small_angle,color=drug_color['链霉素'])
p.annular_wedge(0,rad(df.neomycin),-big_angle + angles + 1 * small_angle,-big_angle + angles + 2 * small_angle,color=drug_color['新霉素'])
# 绘制大圆和标签
labels = np.power(10.0,np.arange(-3,4))
radii = a * np.sqrt(np.log(labels * 1E4)) + b
p.circle(0,radius=radii,fill_color=None,line_color="white")
p.text(0,radii[:-1],[str(r) for r in labels[:-1]],text_font_size="8pt",text_align="center",text_baseline="middle")
# 半径
p.annular_wedge(0,inner_radius - 10,outer_radius + 10,color="black")
# 细菌标签
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
# 绘制各个细菌的名字
p.text(xr,yr,df.bacteria,angle=label_angle,text_font_size="9pt",text_baseline="middle")
# 绘制圆形，其中数字分别为 x 轴与 y 轴标签
p.circle([-40,-40],[-370,-390],color=list(gram_color.values()),radius=5)
# 绘制文字
p.text([-30,-30],text=["Gram-" + gr for gr in gram_color.keys()],text_font_size="7pt",text_align="left",text_baseline="middle")
# 绘制矩形，中间标签部分。其中 -40，-40，-40 为三个矩形的 x 轴坐标。18，0，-18 为三个矩形的 y 轴坐标
p.rect([-40,-40,[18,-18],width=30,height=13,color=list(drug_color.values()))
# 配置中间标签文字、文字大小、文字对齐方式
p.text([-15,-15,-15],text=list(drug_color),text_baseline="middle")
output_file("burtin.html",title="burtin.py example")
show(p)

元素周期表

元素周期表，这个实现好牛逼啊，距离初三刚开始学化学已经很遥远了，想当年我还是化学课代表呢！由于基本用不到化学了，这里就不实现了。

真实状态

Pyecharts

pyecharts 也是一个比较常用的数据可视化库，用得也是比较多的了，是百度 Echarts 库的 Python 支持。这里也展示一下常用的图表。

文档地址为：

• http://pyecharts.org/#/zh-cn/prepare?id=%E5%AE%89%E8%A3%85-pyecharts

条形图

from pyecharts import Bar
bar = Bar("我的第一个图表","这里是副标题")
bar.add("服装",["衬衫","羊毛衫","雪纺衫","裤子","高跟鞋","袜子"],[5,20,36,75,90])
# bar.print_echarts_options() # 该行只为了打印配置项，方便调试时使用
bar.render() # 生成本地 HTML 文件

散点图

from pyecharts import Polar
import random
data_1 = [(10,random.randint(1,100)) for i in range(300)]
data_2 = [(11,100)) for i in range(300)]
polar = Polar("极坐标系-散点图示例",width=1200,height=600)
polar.add("",data_1,type='scatter')
polar.add("",data_2,type='scatter')
polar.render()

饼图

import random
from pyecharts import Pie
attr = ['A','B','C','D','E','F']
pie = Pie("饼图示例",width=1000,height=600)
pie.add(
 "",attr,[random.randint(0,100) for _ in range(6)],radius=[50,55],center=[25,50],is_random=True,)
pie.add(
 "",[random.randint(20,radius=[0,45],rosetype="area",center=[65,rosetype="radius",)
pie.render()

词云

这个是我在前面的文章中用到的图片实例，这里就不 po 具体数据了。

from pyecharts import WordCloud
name = ['Sam S Club'] # 词条
value = [10000] # 权重
wordcloud = WordCloud(width=1300,height=620)
wordcloud.add("",name,value,word_size_range=[20,100])
wordcloud.render()

树图

这个是我在前面的文章中用到的图片实例，这里就不 po 具体数据了。

from pyecharts import TreeMap
data = [ # 键值对数据结构
 {
 value: 1212,# 数值
 # 子节点
 children: [
 {
 # 子节点数值
 value: 2323,# 子节点名
 name: 'description of this node',children: [...],},{
 value: 4545,name: 'description of this node',children: [
 {
 value: 5656,children: [...]
 },...
 ]
 }
 ]
 },...
 ]
treemap = TreeMap(title,height=600) # 设置标题与宽高
treemap.add("深圳",data,is_label_show=True,label_pos='inside',label_text_size=19)
treemap.render()

地图

from pyecharts import Map
value = [155,66,78,33,80,190,53,49.6]
attr = [
 "福建","山东","北京","上海","甘肃","新疆","河南","广西","西藏"
 ]
map = Map("Map 结合 VisualMap 示例",height=600)
map.add(
 "",maptype="china",is_visualmap=True,visual_text_color="#000",)
map.render()

3D 散点图

from pyecharts import Scatter3D
import random
data = [
 [random.randint(0,100),random.randint(0,100)] for _ in range(80)
]
range_color = [
 '#313695','#4575b4','#74add1','#abd9e9','#e0f3f8','#ffffbf','#fee090','#fdae61','#f46d43','#d73027','#a50026']
scatter3D = Scatter3D("3D 散点图示例",height=600) # 配置宽高
scatter3D.add("",visual_range_color=range_color) # 设置颜色等
scatter3D.render() # 渲染

后记

大概介绍就是这样了，三个库的功能都挺强大的，bokeh 的中文资料会少一点，如果阅读英文有点难度，还是建议使用 pyecharts 就好。总体也不是很难，按照文档来修改数据都能够直接上手使用。主要是多练习。