Python3:pyecharts数据可视化插件详解编程语言

Python3:pyecharts数据可视化插件

一、简介

pyecharts 是一个用于生成 Echarts 图表的类库。 Echarts 是百度开源的一个数据可视化 JS 库。主要用于数据可视化。

二、安装

pip install pyecharts

在线安装不成功,采用离线插件whl安装:

(1)下载:pyecharts-0.1.9.4-py2.py3-none-any.whl

(2)然后进入到所咋的文件夹,执行安装命令:

D:/whl>pip install pyecharts-0.1.9.4-py2.py3-none-any.whl

三、示例

1,代码:

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, 10, 75, 90]) 
bar.show_config() 
bar.render()

2,效果:

Python3:pyecharts数据可视化插件详解编程语言

3,分析:

  Tip:可以按右边的下载按钮将图片下载到本地;

  • add():主要方法,用于添加图表的数据和设置各种配置项

  • show_config():打印输出图表的所有配置项

  • render():默认将会在根目录下生成一个 render.html 的文件,支持 path 参数,设置文件保存位置,如 render(r”e:my_first_chart.html”),文件用浏览器打开。

  默认的编码类型为 UTF-8,在 Python3 中是没什么问题的,Python3 对中文的支持好很多。但是在 Python2 中,编码的处理是个很头疼的问题,暂时没能找到完美的解决方法,目前只能通过文本编辑器自己进行二次编码,我用的是 Visual Studio Code,先通过 Gbk 编码重新打开,然后再用 UTF-8 重新保存,这样用浏览器打开的话就不会出现中文乱码问题了。

  基本上所有的图表类型都是这样绘制的:

  1. chart_name = Type() 初始化具体类型图表。

  2. add() 添加数据及配置项。

  3. render() 生成 .html 文件。

四、图表类型

1,Bar(柱状图/条形图)

1.1 示例1:

from pyecharts import Bar 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[20, 40, 60, 80, 100, 120] 
v2 =[10, 20, 30, 40, 120, 80] 
bar =Bar("标记线和标记点示例") 
bar.add("商家A", attr, v1, mark_point=["average"]) 
bar.add("商家B", attr, v2, mark_line=["min", "max"]) 
bar.render()

 效果:

Python3:pyecharts数据可视化插件详解编程语言

1.2 示例2:

from pyecharts import Bar 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[20, 40, 60, 80, 100, 120] 
v2 =[10, 20, 30, 40, 120, 80] 
bar =Bar("x 轴和 y 轴交换") 
bar.add("商家A", attr, v1) 
bar.add("商家B", attr, v2, is_convert=True) 
bar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言 

2,EffectScatter(带有涟漪特效动画的散点图)

#示例2:EffectScatter(带有涟漪特效动画的散点图) 
from pyecharts import EffectScatter 
v1 =[10, 20, 30, 40, 50, 60] 
v2 =[25, 20, 15, 10, 60, 33] 
es =EffectScatter("动态散点图示例") 
es.add("effectScatter", v1, v2) 
es.render() 
es =EffectScatter("动态散点图各种图形示例") 
es.add("", [10], [10], symbol_size=20, effect_scale=3.5, effect_period=3, symbol="pin") 
es.add("", [20], [20], symbol_size=12, effect_scale=4.5, effect_period=4,symbol="rect") 
es.add("", [30], [30], symbol_size=30, effect_scale=5.5, effect_period=5,symbol="roundRect") 
es.add("", [40], [40], symbol_size=10, effect_scale=6.5, effect_brushtype='fill',symbol="diamond") 
es.add("", [50], [50], symbol_size=16, effect_scale=5.5, effect_period=3,symbol="arrow") 
es.add("", [60], [60], symbol_size=6, effect_scale=2.5, effect_period=3,symbol="triangle") 
es.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

3,Funnel(漏斗图)

#示例四:Funnel(漏斗图) 
from pyecharts import Funnel 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
value =[20, 40, 60, 80, 100, 120] 
funnel =Funnel("漏斗图示例") 
funnel.add("商品", attr, value, is_label_show=True, label_pos="inside", label_text_color="#fff") 
funnel.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

4,Gauge(仪表盘)

#示例五: Gauge(仪表盘) 
from pyecharts import Gauge  
gauge =Gauge("仪表盘示例") 
gauge.add("业务指标", "完成率", 66.66) 
gauge.show_config() 
gauge.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

5,Geo(地理坐标系)

5.1 示例1:

#示例5: Geo(地理坐标系) 
#5.1 
from pyecharts import Geo  
data=[("海门",9),("鄂尔多斯",12),("招远",12),("舟山",12),("齐齐哈尔",14),("盐城",15),("赤峰",16),("青岛",18),("乳山",18),("金昌",19),("泉州",21),("莱西",21), ("日照",21),("胶南",22),("南通",23),("拉萨",24),("云浮",24),("梅州",25)] 
geo=Geo("全国主要城市空气质量","data from pm2.5",title_color="#fff",title_pos="center",width=1200,height=600,background_color='#404a59') 
attr,value=geo.cast(data) 
geo.add("",attr,value,visual_range=[0,200],visual_text_color="#fff",symbol_size=15,is_visualmap=True) 
geo.show_config() 
geo.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

5.2 示例2:

#6.2 
from pyecharts import Geo  
data =[("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)] 
geo =Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') 
attr, value =geo.cast(data) 
geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) 
geo.show_config() 
geo.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6, Graph(关系图)

6.1 示例1:

#示例6:Graph(关系图) 
#6.1 
from pyecharts import Graph 
nodes =[{"name": "结点1", "symbolSize": 10}, {"name": "结点2", "symbolSize": 20}, {"name": "结点3", "symbolSize": 30}, {"name": "结点4", "symbolSize": 40}, {"name": "结点5", "symbolSize": 50}, {"name": "结点6", "symbolSize": 40}, {"name": "结点7", "symbolSize": 30}, {"name": "结点8", "symbolSize": 20}] 
links =[] 
for i in nodes:  
    for j in nodes:  
        links.append({"source": i.get('name'), "target": j.get('name')}) 
        graph =Graph("关系图-环形布局示例") 
        graph.add("", nodes, links, is_label_show=True, repulsion=8000, layout='circular', label_text_color=None) 
        graph.show_config() 
        graph.render()

效果:

 Python3:pyecharts数据可视化插件详解编程语言

6.2 示例2:

#6.2 
from pyecharts import Graph 
import json  
with open("..jsonweibo.json","r",encoding="utf-8") as f: 
    j =json.load(f) 
nodes, links,categories,cont,mid,userl=jgraph=Graph("微博转发关系图", width=1200, height=600) 
graph.add("",nodes,links,categories,label_pos="right", repulsion=50, is_legend_show=False, line_curve=0.2, label_text_color=None) 
graph.show_config() 
graph.render()

效果:待测试

7,Line(折线/面积图)

7.1 示例1:

#示例7:Line(折线/面积图) 
from pyecharts import Line 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[5, 20, 36, 10, 10, 100] 
v2 =[55, 60, 16, 20, 15, 80] 
line =Line("折线图示例") 
line.add("商家A", attr, v1, mark_point=["average"]) 
line.add("商家B", attr, v2, is_smooth=True, mark_line=["max", "average"]) 
line.show_config() 
line.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

7.2 示例2:

#7.2 
from pyecharts import Line 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[5, 20, 36, 10, 10, 100] 
v2 =[55, 60, 16, 20, 15, 80] 
line =Line("折线图-阶梯图示例") 
line.add("商家A", attr, v1, is_step=True, is_label_show=True) 
line.show_config() 
line.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

7.3 示例3:

#7.3 
from pyecharts import Line 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[5, 20, 36, 10, 10, 100] 
v2 =[55, 60, 16, 20, 15, 80] 
line =Line("折线图-面积图示例") 
line.add("商家A", attr, v1, is_fill=True, line_opacity=0.2, area_opacity=0.4, symbol=None) 
line.add("商家B", attr, v2, is_fill=True, area_color='#000', area_opacity=0.3, is_smooth=True) 
line.show_config() 
line.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

8,Liquid(水球图)

8.1 示例1:

#8.1 
from pyecharts import Liquid 
liquid =Liquid("水球图示例") 
liquid.add("Liquid", [0.6]) 
liquid.show_config() 
liquid.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

8.2 示例2:

#8.2 
from pyecharts import Liquid 
liquid =Liquid("水球图示例") 
liquid.add("Liquid", [0.6, 0.5, 0.4, 0.3], is_liquid_outline_show=False) 
liquid.show_config() 
liquid.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

8.3 示例3:

#8.3 
from pyecharts import Liquid 
liquid =Liquid("水球图示例") 
liquid.add("Liquid", [0.6, 0.5, 0.4, 0.3], is_liquid_animation=False, shape='diamond') 
liquid.show_config() 
liquid.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

9,Map(地图)

9.1 示例1:

#9.1 
from pyecharts import Map 
value =[155, 10, 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", width=1200, height=600) 
map.add("", attr, value, maptype='china', is_visualmap=True, visual_text_color='#000') 
map.show_config() 
map.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

9.2 示例2:

#9.2 
from pyecharts import Map 
value =[20, 190, 253, 77, 65] 
attr =['汕头市', '汕尾市', '揭阳市', '阳江市', '肇庆市'] 
map=Map("广东地图示例", width=1200, height=600) 
map.add("", attr, value, maptype='广东', is_visualmap=True, visual_text_color='#000') 
map.show_config() 
map.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

10,Parallel(平行坐标系)

#示例十一:Parallel(平行坐标系) 
from pyecharts import Parallel 
c_schema =[ {"dim": 0, "name": "data"}, {"dim": 1, "name": "AQI"}, {"dim": 2, "name": "PM2.5"}, {"dim": 3, "name": "PM10"}, {"dim": 4, "name": "CO"}, {"dim": 5, "name": "NO2"}, {"dim": 6, "name": "CO2"}, {"dim": 7, "name": "等级", "type": "category", "data": ['', '', '轻度污染', '中度污染', '重度污染', '严重污染']}] 
data =[ [1, 91, 45, 125, 0.82, 34, 23, ""], [2, 65, 27, 78, 0.86, 45, 29, ""], [3, 83, 60, 84, 1.09, 73, 27, ""], [4, 109, 81, 121, 1.28, 68, 51, "轻度污染"], [5, 106, 77, 114, 1.07, 55, 51, "轻度污染"], [6, 109, 81, 121, 1.28, 68, 51, "轻度污染"], [7, 106, 77, 114, 1.07, 55, 51, "轻度污染"], [8, 89, 65, 78, 0.86, 51, 26, ""], [9, 53, 33, 47, 0.64, 50, 17, ""], [10, 80, 55, 80, 1.01, 75, 24, ""], [11, 117, 81, 124, 1.03, 45, 24, "轻度污染"], [12, 99, 71, 142, 1.1, 62, 42, ""], [13, 95, 69, 130, 1.28, 74, 50, ""], [14, 116, 87, 131, 1.47, 84, 40, "轻度污染"]] 
parallel =Parallel("平行坐标系-用户自定义指示器") 
parallel.config(c_schema=c_schema) 
parallel.add("parallel", data) 
parallel.show_config() 
parallel.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

11, Pie(饼图)

11.1 示例1:

#11.1 
from pyecharts import Pie 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[11, 12, 13, 10, 10, 10] 
pie =Pie("饼图示例") 
pie.add("", attr, v1, is_label_show=True) 
pie.show_config() 
pie.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

11.2 示例2:

#11.2 
from pyecharts import Pie 
attr =["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] 
v1 =[11, 12, 13, 10, 10, 10] 
v2 =[19, 21, 32, 20, 20, 33] 
pie =Pie("饼图-玫瑰图示例", title_pos='center', width=900) 
pie.add("商品A", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius') 
pie.add("商品B", attr, v2, center=[75, 50], is_random=True, radius=[30, 75], rosetype='area', is_legend_show=False, is_label_show=True) 
pie.show_config()  
pie.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

12,Polar(极坐标系)

12.1 示例1:

#12.1 
from pyecharts import Polar 
radius =['周一', '周二', '周三', '周四', '周五', '周六', '周日'] 
polar =Polar("极坐标系-堆叠柱状图示例", width=1200, height=600) 
polar.add("A", [1, 2, 3, 4, 3, 5, 1], radius_data=radius, type='barRadius', is_stack=True) 
polar.add("B", [2, 4, 6, 1, 2, 3, 1], radius_data=radius, type='barRadius', is_stack=True) 
polar.add("C", [1, 2, 3, 4, 1, 2, 5], radius_data=radius, type='barRadius', is_stack=True) 
polar.show_config() 
polar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

12.2 示例2:

#12.2 
from pyecharts import Polar 
radius =['周一', '周二', '周三', '周四', '周五', '周六', '周日'] 
polar =Polar("极坐标系-堆叠柱状图示例", width=1200, height=600) 
polar.add("", [1, 2, 3, 4, 3, 5, 1], radius_data=radius, type='barAngle', is_stack=True) 
polar.add("", [2, 4, 6, 1, 2, 3, 1], radius_data=radius, type='barAngle', is_stack=True) 
polar.add("", [1, 2, 3, 4, 1, 2, 5], radius_data=radius, type='barAngle', is_stack=True) 
polar.show_config() 
polar.render()

效果:

 Python3:pyecharts数据可视化插件详解编程语言

13,Radar(雷达图)

13.1 示例1:

#13.1 
from pyecharts import Radar 
schema =[ ("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)] 
v1 =[[4300, 10000, 28000, 35000, 50000, 19000]] 
v2 =[[5000, 14000, 28000, 31000, 42000, 21000]] 
radar =Radar() 
radar.config(schema) 
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True) 
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False) 
radar.show_config() 
radar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

13.2 示例2:

#13.2 
from pyecharts import Radar 
value_bj =[[55, 9, 56, 0.46, 18, 6, 1], [25, 11, 21, 0.65, 34, 9, 2], [56, 7, 63, 0.3, 14, 5, 3], [33, 7, 29, 0.33, 16, 6, 4]] 
value_sh =[[91, 45, 125, 0.82, 34, 23, 1], [65, 27, 78, 0.86, 45, 29, 2], [83, 60, 84, 1.09, 73, 27, 3], [109, 81, 121, 1.28, 68, 51, 4]] 
c_schema=[{"name": "AQI", "max": 300, "min": 5}, {"name": "PM2.5", "max": 250, "min": 20}, {"name": "PM10", "max": 300, "min": 5}, {"name": "CO", "max": 5}, {"name": "NO2", "max": 200}, {"name": "SO2", "max": 100}] 
radar =Radar() 
radar.config(c_schema=c_schema, shape='circle') 
radar.add("北京", value_bj, item_color="#f9713c", symbol=None) 
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None) 
radar.show_config() 
radar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

14,Scatter(散点图)

14.1 示例1:

#14.1 
from pyecharts import Scatter 
v1 =[10, 20, 30, 40, 50, 60] 
v2 =[10, 20, 30, 40, 50, 60] 
scatter =Scatter("散点图示例") 
scatter.add("A", v1, v2) 
scatter.add("B", v1[::-1], v2) 
scatter.show_config() 
scatter.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

 14.2 示例2:散点打印Pyecharts字体(先准备一张png图片)

from pyecharts import Scatter 
scatter =Scatter("散点图示例") 
v1, v2 =scatter.draw("pyecharts-0.png") 
scatter.add("pyecharts", v1, v2, is_random=True) 
scatter.show_config() 
scatter.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

15, WordCloud(词云图)

 15.1 示例1:

#15.1 
from pyecharts import WordCloud 
name =['Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World', 'Charter Communications', 'Chick Fil A', 'Planet Fitness', 'Pitch Perfect', 'Express', 'Home', 'Johnny Depp', 'Lena Dunham', 'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham', 'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break'] 
value =[10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265] 
wordcloud =WordCloud(width=1300, height=620) 
wordcloud.add("", name, value, word_size_range=[20, 100]) 
wordcloud.show_config() 
wordcloud.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

15.2 示例2:

#15.2  
from pyecharts import WordCloud 
name =['Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World', 'Charter Communications', 'Chick Fil A', 'Planet Fitness', 'Pitch Perfect', 'Express', 'Home', 'Johnny Depp', 'Lena Dunham', 'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham', 'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break'] 
value =[10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265] 
wordcloud =WordCloud(width=1300, height=620) 
wordcloud.add("", name, value, word_size_range=[30, 100], shape='diamond') 
wordcloud.show_config() 
wordcloud.render() 

效果:

Python3:pyecharts数据可视化插件详解编程语言

五、用户自定义

用户还可以自定义结合 Line/Bar 图表

需使用 get_series() 和 custom() 方法

get_series():获取图表的 series 数据;

custom(series):追加自定义图表类型;

series -> dict:追加图表类型的 series 数据;

先用 get_series() 获取数据,再使用 custom() 将图表结合在一起;

示例代码:

#示例:自定义图标类型 
from pyecharts import Bar, Line 
attr =['A', 'B', 'C', 'D', 'E', 'F'] 
v1 =[10, 20, 30, 40, 50, 60] 
v2 =[15, 25, 35, 45, 55, 65] 
v3 =[38, 28, 58, 48, 78, 68] 
bar =Bar("Line - Bar 示例") 
bar.add("bar", attr, v1) 
line =Line() 
line.add("line", v2, v3) 
bar.custom(line.get_series()) 
bar.show_config() 
bar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

六、示例

6.1,用极坐标系画出一个爱心,代码:

#示例:用极坐标系画出一个爱心 
import math  
from pyecharts import Polar 
data =[] 
for i in range(101): 
    theta =i /100*360 
    r =5*(1+math.sin(theta /180*math.pi))  
    data.append([r, theta]) 
    hour =[i for i in range(1, 25)] 
    polar =Polar("极坐标系示例", width=1200, height=600) 
    polar.add("Love", data, angle_data=hour, boundary_gap=False,start_angle=0) 
    polar.show_config() 
    polar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.2 用极坐标系画出一朵小花,代码:

import math 
from pyecharts import Polar 
data =[] 
for i in range(361):  
    t =i /180*math.pi  
    r =math.sin(2*t) *math.cos(2*t)  
    data.append([r, i]) 
    polar =Polar("极坐标系示例", width=1200, height=600) 
    polar.add("Flower", data, start_angle=0, symbol=None, axis_range=[0, None]) 
    polar.show_config() 
    polar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.3 涂上颜色的小红花,代码:

#示例:涂上颜色的小红花 
import math  
from pyecharts import Polar 
data =[] 
for i in range(361): 
    t =i /180*math.pi 
    r =math.sin(2*t) *math.cos(2*t)  
    data.append([r, i]) 
    polar =Polar("极坐标系示例", width=1200, height=600) 
    polar.add("Color-Flower", data, start_angle=0, symbol=None, axis_range=[0, None], area_color="#f71f24", area_opacity=0.6) 
    polar.show_config() 
    polar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.4 用散点图画出一个爱心,代码:

from pyecharts import Scatter 
scatter =Scatter("散点图示例", width=800, height=480) 
v1 ,v2 =scatter.draw("love.png") 
scatter.add("Love", v1, v2) 
scatter.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.5 用散点图画出一个火辣的 Bra,代码:

from pyecharts import Scatter 
scatter =Scatter("散点图示例", width=1000, height=480) 
v1 ,v2 =scatter.draw("cup.png") 
scatter.add("Cup", v1, v2) 
scatter.render()

效果:

6.6 某地最低温和最高气温折线图,代码:

from pyecharts import Line 
attr =['周一', '周二', '周三', '周四', '周五', '周六', '周日', ] 
line =Line("折线图示例") 
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"]) 
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"], mark_line=["average"], yaxis_formatter="°C") 
line.show_config() 
line.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.7 饼图嵌套,代码:

from pyecharts import Pie 
pie =Pie("饼图示例", title_pos='center', width=1000, height=600) 
pie.add("", ['A', 'B', 'C', 'D', 'E', 'F'], [335, 321, 234, 135, 251, 148], radius=[40, 55],is_label_show=True) 
pie.add("", ['H', 'I', 'J'], [335, 679, 204], radius=[0, 30], legend_orient='vertical', legend_pos='left') 
pie.show_config() 
pie.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.8 饼图再嵌套,代码:

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("", attr, [random.randint(20, 100) for _ in range(6)], radius=[0, 45], center=[25, 50],rosetype='area') 
pie.add("", attr, [random.randint(0, 100) for _ in range(6)], radius=[50, 55], center=[65, 50],is_random=True) 
pie.add("", attr, [random.randint(20, 100) for _ in range(6)], radius=[0, 45], center=[65, 50],rosetype='radius') 
pie.show_config() 
pie.render()

效果:

 Python3:pyecharts数据可视化插件详解编程语言

6.9 某地的降水量和蒸发量柱状图,代码:

from pyecharts import Bar 
attr =["{}月".format(i) for i in range(1, 13)] 
v1 =[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3] 
v2 =[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3] 
bar =Bar("柱状图示例") 
bar.add("蒸发量", attr, v1, mark_line=["average"], mark_point=["max", "min"]) 
bar.add("降水量", attr, v2, mark_line=["average"], mark_point=["max", "min"]) 
bar.show_config() 
bar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.10 各类电影中”好片”所占的比例,代码:

from pyecharts import Pie 
pie =Pie('各类电影中"好片"所占的比例', "数据来着豆瓣", title_pos='center') 
pie.add("", ["剧情", ""], [25, 75], center=[10, 30], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None, ) 
pie.add("", ["奇幻", ""], [24, 76], center=[30, 30], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None, legend_pos='left') 
pie.add("", ["爱情", ""], [14, 86], center=[50, 30], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["惊悚", ""], [11, 89], center=[70, 30], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["冒险", ""], [27, 73], center=[90, 30], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["动作", ""], [15, 85], center=[10, 70], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["喜剧", ""], [54, 46], center=[30, 70], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["科幻", ""], [26, 74], center=[50, 70], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["悬疑", ""], [25, 75], center=[70, 70], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None) 
pie.add("", ["犯罪", ""], [28, 72], center=[90, 70], radius=[18, 24], label_pos='center', is_label_show=True, label_text_color=None, is_legend_show=True, legend_top="center") 
pie.show_config() 
pie.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

6.11 用极坐标系画出一个蜗牛壳,代码:

import math 
from pyecharts import Polar 
data =[] 
for i in range(5): 
    for j in range(101): 
        theta =j /100*360 
        alpha =i *360+theta  
        r =math.pow(math.e, 0.003*alpha)  
        data.append([r, theta]) 
polar =Polar("极坐标系示例") 
polar.add("", data, symbol_size=0, symbol='circle', start_angle=-25, is_radiusaxis_show=False, area_color="#f3c5b3", area_opacity=0.5, is_angleaxis_show=False) 
polar.show_config() 
polar.render()

效果:

Python3:pyecharts数据可视化插件详解编程语言

 

原创文章,作者:ItWorker,如若转载,请注明出处:https://blog.ytso.com/16716.html

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