pyecharts运行了但是没有图_python数据可视化之pyecharts
Python ❤️ Echarts = pyecharts
pyecharts 是 Python 和 Echarts 的结合，绘制出的图表十分精美且种类丰富
pyecharts目前更新到 1.7.0，1.x版本与0.5.x版本在语法上有很大区别
安装
这里我们直接安装最新版本：
pip(3) install pyecharts如果安装后导入有问题，可以尝试源码安装：
$ git clone https://github.com/pyecharts/pyecharts.git$ cd pyecharts$ pip install -r requirements.txt$ python setup.py install# 或者执行 python install.py柱形图
from pyecharts.charts import Barfrom pyecharts import options as optsx = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]y1 = [5, 20, 36, 10, 75, 90]y2 = [15, 6, 45, 20, 35, 66]bar = ( Bar() .add_xaxis(x) .add_yaxis("商家A", y1) .add_yaxis("商家B", y2) .set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")))# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件# 也可以传入路径参数，如 bar.render("mycharts.html")bar.render()
在notebook中显示图表
pyecharts也支持Jupyter Notebook 和 JupyterLab，但是在notebook中渲染图表时要求不同：
Jupyter Notebook 用 bar.render_notebook()直接渲染
JupyterLab 则稍微复杂一些：
必须在引入 pyecharts.charts 等模块前声明Notebook 类型
from pyecharts.globals import CurrentConfig, NotebookTypeCurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB第一次渲染时要先用 bar.load_javascript() 预先加载基本 JavaScript 文件到 Notebook 中
然后在不同的cell中运行bar.render_notebook()
这样就可以一边运行代码，一边查看图片结果啦。
Bar-DataZoom(slider+inside)
from pyecharts.faker import Fakerfrom pyecharts import options as optsfrom pyecharts.charts import Barbar = ( Bar() .add_xaxis(Faker.days_attrs) .add_yaxis("", Faker.days_values, color=Faker.rand_color()) .set_global_opts( title_opts=opts.TitleOpts(title="Bar-DataZoom"), datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], ) )bar.render_notebook()
效果比较炫酷
堆叠柱形图
from pyecharts.charts import Barfrom pyecharts import options as optsx = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]y1 = [5, 20, 36, 10, 75, 90]y2 = [15, 6, 45, 20, 35, 66]#通过stack="stack1"设置堆叠bar = ( Bar() .add_xaxis(x) .add_yaxis("商家A", y1, stack="stack1") .add_yaxis("商家B", y2, stack="stack1") .set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")))bar.render_notebook()
更换图表风格
以上图表都是pyecharts的默认主题，其实pyecharts内置10+种不同主题，可以自行选用。
from pyecharts.charts import Barfrom pyecharts import options as opts#引入主题模块from pyecharts.globals import ThemeTypex = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]y1 = [5, 20, 36, 10, 75, 90]y2 = [15, 6, 45, 20, 35, 66]# init_opts=opts.InitOpts(theme=ThemeType.LIGHT) 选用LIGHT主题# .reversal_axis()交换 XY 轴# .set_series_opts(label_opts=opts.LabelOpts(position="right")) 指定数据显示位置为右侧bar = ( Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add_xaxis(x) .add_yaxis("商家A", y1) .add_yaxis("商家B", y2) .reversal_axis() .set_series_opts(label_opts=opts.LabelOpts(position="right")) .set_global_opts(title_opts=opts.TitleOpts(title="主题为LIGHT", subtitle="副标题")))bar.render_notebook()
添加标记点
from pyecharts.charts import Barfrom pyecharts import options as optsfrom pyecharts.globals import ThemeTypex = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]y1 = [5, 20, 36, 10, 75, 90]y2 = [15, 6, 45, 20, 35, 66]# 通过markpoint_opts添加标记点bar = ( Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK)) .add_xaxis(x) .add_yaxis("商家A", y1) .add_yaxis("商家B", y2) .set_series_opts( label_opts=opts.LabelOpts(is_show=False), markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值"), ] ), ) .set_global_opts(title_opts=opts.TitleOpts(title="主题为DARK", subtitle="最大值、最小值标记点")))bar.render_notebook()
饼图
from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.globals import ThemeTypelist = [['可乐', 76], ['雪碧', 49], ['橙汁', 115]]bar = ( Pie(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add("", list) .set_global_opts(title_opts=opts.TitleOpts(title="饼图")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b} : {c} {d}%")))bar.render_notebook()
多饼图
from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.commons.utils import JsCodefn = """ function(params) { if(params.name == '其他') return '\\n\\n\\n' + params.name + ' : ' + params.value + '%'; return params.name + ' : ' + params.value + '%'; } """def new_label_opts(): return opts.LabelOpts(formatter=JsCode(fn), position="center")c = ( Pie() .add( "", [list(z) for z in zip(["剧情", "其他"], [25, 75])], center=["20%", "30%"], radius=[60, 80], label_opts=new_label_opts(), ) .add( "", [list(z) for z in zip(["奇幻", "其他"], [24, 76])], center=["55%", "30%"], radius=[60, 80], label_opts=new_label_opts(), ) .add( "", [list(z) for z in zip(["爱情", "其他"], [14, 86])], center=["20%", "70%"], radius=[60, 80], label_opts=new_label_opts(), ) .add( "", [list(z) for z in zip(["惊悚", "其他"], [11, 89])], center=["55%", "70%"], radius=[60, 80], label_opts=new_label_opts(), ) .set_global_opts( title_opts=opts.TitleOpts(title="Pie-多饼图示例"), legend_opts=opts.LegendOpts( type_="scroll", pos_top="20%", pos_left="80%", orient="vertical" ), ) )c.render_notebook()
多维散点图
from pyecharts import options as optsfrom pyecharts.charts import Scatterfrom pyecharts.faker import Fakerfrom pyecharts.globals import ThemeTypefrom pyecharts.commons.utils import JsCodeScatter = ( Scatter(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add_xaxis(Faker.choose()) .add_yaxis( "商家A", [list(z) for z in zip(Faker.values(), Faker.choose())], label_opts=opts.LabelOpts( formatter=JsCode( "function(params){return params.value[1] +' : '+ params.value[2];}" ), #position="inside" ), ) .set_global_opts( title_opts=opts.TitleOpts(title="Scatter-多维度数据"), tooltip_opts=opts.TooltipOpts( formatter=JsCode( "function (params) {return params.name + ' : ' + params.value[2];}" ) ), visualmap_opts=opts.VisualMapOpts( type_="size", max_=150, min_=20, dimension=1 ), ) )Scatter.render_notebook()
漏斗图
from pyecharts import options as optsfrom pyecharts.charts import Funnel, Pagefrom pyecharts.globals import ThemeTypelabels = ['浏览人数', '加购人数', '购买人数']data = [100, 50, 30]funnel = ( Funnel(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add("", [list(z) for z in zip(labels, data)], label_opts=opts.LabelOpts(position="inside")) .set_global_opts(title_opts=opts.TitleOpts(title="销售转化漏斗")) ) funnel.render_notebook()
词云
from pyecharts import options as optsfrom pyecharts.charts import Page, WordCloudfrom pyecharts.globals import SymbolTypewords = [ ("火锅", 1446), ("面包", 928), ("油条", 906), ("牛奶", 825), ("凉皮", 514), ("早餐", 486), ("吐司", 53), ("芝士", 163), ("酸奶", 86), ("奶茶", 17), ("蛋糕", 678), ("烧烤", 567), ("土豆", 437), ("巧克力", 422), ("甜甜圈", 353), ("苹果", 331)]wordcloud = ( WordCloud() .add( "", words, word_size_range=[20, 100], rotate_step = 90,#设置文字角度 textstyle_opts=opts.TextStyleOpts(font_family="cursive"), ) .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud")) )wordcloud.render_notebook()
中国疫情地图
from pyecharts import options as optsfrom pyecharts.charts import Map#数据截至 2020/2/29 22:14 现存确诊data = [['湖北', 34617], ['广东', 366], ['山东', 332], ['浙江', 188], ['四川', 184], ['湖南', 170], ['黑龙江', 166], ['重庆', 148],['北京', 132], ['江西', 123], ['安徽', 116], ['江苏', 108], ['河南', 81], ['广西', 74], ['香港', 62], ['福建', 53],['上海', 47], ['陕西', 37], ['贵州', 32], ['河北', 31], ['台湾', 29], ['内蒙古', 26], ['辽宁', 25], ['天津', 24],['山西', 20], ['吉林', 17], ['海南', 16], ['云南', 15], ['新疆', 11], ['甘肃', 7], ['宁夏', 4], ['澳门', 2], ['青海', 0], ['西藏', 0]]map = ( Map() .add("现存确诊", data, "china") .set_global_opts( title_opts=opts.TitleOpts(title="现存确诊疫情地图"), visualmap_opts=opts.VisualMapOpts(max_=35000, is_piecewise=True, pieces=[ {"min": 10000, "label": '>10000人', "color": "#6666CC"}, {"min": 1000, "max": 10000, "label": '1001-10000人', "color": "#9999FF"}, {"min": 500, "max": 999, "label": '999-1000人', "color": "#CCCCFF"}, {"min": 100, "max": 499, "label": '100-499人', "color": "#FF9999"}, {"min": 10, "max": 99, "label": '10-99人', "color": "#FFCCCC"}, {"min": 1, "max": 9, "label": '0-9人', "color": "#CCCCCC"}, {"min": 0, "max": 0, "label": '0人', "color": "#ffffff"}, ],), ) )map.render_notebook()
以上只是pyecharts的一些基本示例，然而它的强大之处远不止于此。
具体可参考官方文档：https://pyecharts.org/#/zh-cn/intro