使用pip install pyecharts 安装，安装后的版本为 v1.6
pyecharts几行代码就能绘制出有特色的的图形，绘图API链式调用，使用方便。
1 仪表盘
from pyecharts import charts
# 仪表盘gauge = charts.Gauge()gauge.add(‘Python小例子‘, [(‘Python机器学习‘, 30), (‘Python基础‘, 70.), (‘Python正则‘, 90)])gauge.render(path="./data/仪表盘.html")print(‘ok‘)
仪表盘中共展示三项，每项的比例为30%,70%,90%，如下图默认名称显示第一项：Python机器学习，完成比例为30%
2 漏斗图
from pyecharts import options as optsfrom pyecharts.charts import Funnel, Pagefrom random import randint
def funnel_base() -> Funnel: c = ( Funnel() .add("豪车", [list(z) for z in zip([‘宝马‘, ‘法拉利‘, ‘奔驰‘, ‘奥迪‘, ‘大众‘, ‘丰田‘, ‘特斯拉‘], [randint(1, 20) for _ in range(7)])]) .set_global_opts(title_opts=opts.TitleOpts(title="豪车漏斗图")) ) return c
funnel_base().render(‘./img/car_funnel.html‘)print(‘ok‘)以7种车型及某个属性值绘制的漏斗图，属性值大越靠近漏斗的大端。
3 日历图
import datetimeimport random
from pyecharts import options as optsfrom pyecharts.charts import Calendar
def calendar_interval_1() -> Calendar: begin = datetime.date(2019, 1, 1) end = datetime.date(2019, 12, 27) data = [ [str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)] for i in range(0, (end - begin).days + 1, 2) # 隔天统计 ]
calendar = ( Calendar(init_opts=opts.InitOpts(width="1200px")).add( "", data, calendar_opts=opts.CalendarOpts(range_="2019")) .set_global_opts( title_opts=opts.TitleOpts(title="Calendar-2019年步数统计"), visualmap_opts=opts.VisualMapOpts( max_=25000, min_=1000, orient="horizontal", is_piecewise=True, pos_top="230px", pos_left="100px", ), ) ) return calendar
calendar_interval_1().render(‘./img/calendar.html‘)print(‘ok‘)
绘制2019年1月1日到12月27日的步行数，官方给出的图形宽度900px不够，只能显示到9月份，本例使用opts.InitOpts(width="1200px")做出微调，并且visualmap显示所有步数，每隔一天显示一次：
4 关系图(graph)
import jsonimport os
from pyecharts import options as optsfrom pyecharts.charts import Graph, Page
def graph_base() -> Graph: nodes = [ {"name": "cus1", "symbolSize": 10}, {"name": "cus2", "symbolSize": 30}, {"name": "cus3", "symbolSize": 20} ] links = [] for i in nodes: if i.get(‘name‘) == ‘cus1‘: continue for j in nodes: if j.get(‘name‘) == ‘cus1‘: continue links.append({"source": i.get("name"), "target": j.get("name")}) c = ( Graph() .add("", nodes, links, repulsion=8000) .set_global_opts(title_opts=opts.TitleOpts(title="customer-influence")) ) return c构建图，其中客户点1与其他两个客户都没有关系(link)，也就是不存在有效边：
5 水球图
from pyecharts import options as optsfrom pyecharts.charts import Liquid, Pagefrom pyecharts.globals import SymbolType
def liquid() -> Liquid: c = ( Liquid() .add("lq", [0.67, 0.30, 0.15]) .set_global_opts(title_opts=opts.TitleOpts(title="Liquid")) ) return c
liquid().render(‘./img/liquid.html‘)
水球图的取值[0.67, 0.30, 0.15]表示下图中的三个波浪线，一般代表三个百分比:
6 饼图
from pyecharts import options as optsfrom pyecharts.charts import Piefrom random import randint
def pie_base() -> Pie: c = ( Pie() .add("", [list(z) for z in zip([‘宝马‘, ‘法拉利‘, ‘奔驰‘, ‘奥迪‘, ‘大众‘, ‘丰田‘, ‘特斯拉‘], [randint(1, 20) for _ in range(7)])]) .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) ) return c
pie_base().render(‘./img/pie_pyecharts.html‘)
7 极坐标
import randomfrom pyecharts import options as optsfrom pyecharts.charts import Page, Polar
def polar_scatter0() -> Polar: data = [(alpha, random.randint(1, 100)) for alpha in range(101)] # r = random.randint(1, 100) print(data) c = ( Polar() .add("", data, type_="bar", label_opts=opts.LabelOpts(is_show=False)) .set_global_opts(title_opts=opts.TitleOpts(title="Polar")) ) return c
polar_scatter0().render(‘./img/polar.html‘)极坐标表示为(夹角,半径)，如(6,94)表示"夹角"为6，半径94的点：
8 词云图
from pyecharts import options as optsfrom pyecharts.charts import Page, WordCloudfrom pyecharts.globals import SymbolType
words = [ ("Python", 100), ("C++", 80), ("Java", 95), ("R", 50), ("JavaScript", 79), ("C", 65)]
def wordcloud() -> WordCloud: c = ( WordCloud() # word_size_range: 单词字体大小范围 .add("", words, word_size_range=[20, 100], shape=‘cardioid‘) .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud")) ) return c
wordcloud().render(‘./img/wordcloud.html‘)
("C",65)表示在本次统计中C语言出现65次
9 热力图
import randomfrom pyecharts import options as optsfrom pyecharts.charts import HeatMap
def heatmap_car() -> HeatMap: x = [‘宝马‘, ‘法拉利‘, ‘奔驰‘, ‘奥迪‘, ‘大众‘, ‘丰田‘, ‘特斯拉‘] y = [‘中国‘,‘日本‘,‘南非‘,‘澳大利亚‘,‘阿根廷‘,‘阿尔及利亚‘,‘法国‘,‘意大利‘,‘加拿大‘] value = [[i, j, random.randint(0, 100)] for i in range(len(x)) for j in range(len(y))] c = ( HeatMap() .add_xaxis(x) .add_yaxis("销量", y, value) .set_global_opts( title_opts=opts.TitleOpts(title="HeatMap"), visualmap_opts=opts.VisualMapOpts(), ) ) return c
heatmap_car().render(‘./img/heatmap_pyecharts.html‘)
10 地图
结语
pyecharts有30多种不同的可视化图形，开源免费且文档案例详细，可作为数据可视化首选！
中文文档：
https://pyecharts.org/#/zh-cn/intro
源码：
https://github.com/pyecharts/pyecharts
来源: https://mp.weixin.qq.com/s/ZJWQsNp58ftp-1YFvtts8g
原文地址：https://www.cnblogs.com/kaibindirver/p/12229913.html
时间： 2024-11-01 11:22:06