python可视化利器：pyecharts

 

前言

前面我们提及ggplot在R和Python中都是数据可视化的利器，在机器学习和数据分析领域得到了广泛的应用。pyecharts结合了Python和百度开源的Echarts工具，基于其交互性和便利性得到了众多开发者的认可。拥有如下的特点：

• 可集成至Flask、Django等主流web框架
• 相较于matplotlib等传统绘图库，pyecharts语法更加简洁，更加注重数据的呈现方式而非图形细节
• 包含原生的百度地图，方便绘制地理可视化图形

本文主要整理自pyecharts官网github文档：https://github.com/pyecharts/pyecharts/

安装

# pip安装 $ pip(3) install pyecharts # 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 

简单的实例

首先绘制第一个图表：

from pyecharts.charts import Bar bar = Bar() bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]) bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90]) # 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() # pyechart所有方法均支持链式调用, 因此上面的代码也可以改写成如下形式 from pyecharts.charts import Bar bar = ( Bar() .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]) .add_yaxis("商家A", [5, 20, 36, 10, 75, 90]) ) bar.render() # 使用options配置项添加主标题和副标题 from pyecharts.charts import Bar from pyecharts import options as opts bar = Bar() bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]) bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90]) bar.set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")) bar.render() 

 

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基本图表

1. 柱状图

from pyecharts import options as opts from pyecharts.charts import Bar from pyecharts.commons.utils import JsCode from pyecharts.globals import ThemeType list2 = [ {"value": 12, "percent": 12 / (12 + 3)}, {"value": 23, "percent": 23 / (23 + 21)}, {"value": 33, "percent": 33 / (33 + 5)}, {"value": 3, "percent": 3 / (3 + 52)}, {"value": 33, "percent": 33 / (33 + 43)}, ] list3 = [ {"value": 3, "percent": 3 / (12 + 3)}, {"value": 21, "percent": 21 / (23 + 21)}, {"value": 5, "percent": 5 / (33 + 5)}, {"value": 52, "percent": 52 / (3 + 52)}, {"value": 43, "percent": 43 / (33 + 43)}, ] c = ( # 设置主题: 默认是黑红风格, 其他风格大部分还不如黑红风格好看 Bar(init_opts=opts.InitOpts()) # 新增x轴数据, 这里有五列柱状图 .add_xaxis( [ "名字很长的X轴标签1", "名字很长的X轴标签2", "名字很长的X轴标签3", "名字很长的X轴标签4", "名字很长的X轴标签5", ] ) # 参数一: 系列名称; 参数二: 系列数据; stack: 数据堆叠; category_gap: 柱间距离 .add_yaxis("product1", list2, stack="stack1", category_gap="50%") .add_yaxis("product2", list3, stack="stack1", category_gap="50%") # set_series_opts系列配置项，可配置图元样式、文字样式、标签样式、点线样式等; 其中opts.LabelOpts指标签配置项 .set_series_opts( label_opts=opts.LabelOpts( position="right", # 数据标签的位置 formatter=JsCode( # 标签内容的格式器, 这里展示了百分比 "function(x){return Number(x.data.percent * 100).toFixed() + '%';}" ), ) ) # set_global_opts全局配置项 .set_global_opts( # 旋转坐标轴: 解决坐标轴名字过长的问题 xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)), title_opts=opts.TitleOpts(title="Bar-柱状图展示", subtitle="Bar-副标题"), ) .render("stack_bar_percent.html") ) 

 

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2. 特效散点图

from pyecharts import options as opts from pyecharts.charts import EffectScatter from pyecharts.faker import Faker from pyecharts.globals import SymbolType c = ( # 特效散点图 EffectScatter() # Faker返回假数据 .add_xaxis(Faker.choose()) # symbol=SymbolType.ARROW修改特效类型: 这里指箭头特效 .add_yaxis("", Faker.values(), symbol=SymbolType.ARROW) .set_global_opts( title_opts=opts.TitleOpts(title="EffectScatter-显示分割线"), # 显示横纵轴分割线 xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), ) .render("effectscatter_splitline.html") ) 

 

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3. 漏斗图

研发岗涉及业务分析时经常需要绘制漏斗图，用pyecharts可以一键生成

data = [[x_data[i], y_data[i]] for i in range(len(x_data))] ( # InitOpts初始化配置项: 配置画布长宽 Funnel(init_opts=opts.InitOpts(width="800px", height="500px")) .add( series_name="网页访问数据", data_pair=data, # gap: 数据图形间距, 默认0 gap=2, # tooltip_opts: 鼠标提示框组件配置项, a: series_name, b: x_data, c: y_data tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b} : {c}%"), # label_opts: 标签配置项, inside指标签在图层内部 label_opts=opts.LabelOpts(is_show=True, position="inside"), # 图元样式配置项 itemstyle_opts=opts.ItemStyleOpts(border_color="#fff", border_width=1), ) .set_global_opts(title_opts=opts.TitleOpts(title="漏斗图", subtitle="纯属虚构")) .render("funnel_chart.html") ) 

 

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4. 关系图

from pyecharts import options as opts from pyecharts.charts import Graph # 构造数据: nodes表示节点信息和对应的节点大小; links表示节点之间的关系 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 = [] # fake节点之间的两两双向关系 for i in nodes: for j in nodes: links.append({"source": i.get("name"), "target": j.get("name")}) c = ( Graph() # repulsion: 节点之间的斥力因子, 值越大表示节点之间的斥力越大 .add("", nodes, links, repulsion=8000) .set_global_opts(title_opts=opts.TitleOpts(title="Graph-基本示例")) .render("graph_base.html") ) 

 

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数据分析中常见的微博转发图也是通过关系图转化来的：

 

 

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5. 组合组件Grid

最常用的是组合直方图和折点图。

from pyecharts import options as opts from pyecharts.charts import Bar, Grid, Line from pyecharts.faker import Faker bar = ( Bar() .add_xaxis(Faker.choose()) .add_yaxis("商家A", Faker.values()) .add_yaxis("商家B", Faker.values()) .set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar")) ) line = ( Line() .add_xaxis(Faker.choose()) .add_yaxis("商家A", Faker.values()) .add_yaxis("商家B", Faker.values()) .set_global_opts( title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"), legend_opts=opts.LegendOpts(pos_top="48%"), ) ) grid = ( Grid() # GridOpts: 直角坐标系网格配置项 # pos_bottom: grid组件离容器底部的距离 # pos_top: grid组件离容器顶部的距离 .add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")) .add(line, grid_opts=opts.GridOpts(pos_top="60%")) .render("grid_vertical.html") ) 

 

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6. 折线图

import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.faker import Faker c = ( Line() # Faker: 获取伪造数据集 .add_xaxis(Faker.choose()) .add_yaxis("商家A", Faker.values()) .add_yaxis("商家B", Faker.values()) .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例")) .render("line_base.html") ) 

 

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7. 地图

from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map() # Faker: 伪造数据集, 包括国家和对应的value .add("商家A", [list(z) for z in zip(Faker.country, Faker.values())], "world") # 显示label .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="Map-世界地图"), # VisualMapOpts: 视觉映射配置项, 指定组件的最大值 visualmap_opts=opts.VisualMapOpts(max_=200), ) .render("map_world.html") ) 

 

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8. 层叠组件

from pyecharts import options as opts from pyecharts.charts import Bar, Line from pyecharts.faker import Faker 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] v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2] bar = ( Bar() .add_xaxis(Faker.months) .add_yaxis("蒸发量", v1) .add_yaxis("降水量", v2) .extend_axis( # 新增y坐标轴配置项: 因为有三个纵轴数据, 包括蒸发量/降水量(单位是ml), 平均温度(单位是°C) yaxis=opts.AxisOpts( axislabel_opts=opts.LabelOpts(formatter="{value} °C"), interval=5 ) ) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="Overlap-bar+line"), # 设置y坐标轴配置项 yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} ml")), ) ) # 新增折线图 line = Line().add_xaxis(Faker.months).add_yaxis("平均温度", v3, yaxis_index=1) # 使用层叠组件组合图形 bar.overlap(line) bar.render("overlap_bar_line.html") 

 

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9. 饼状图

from pyecharts import options as opts from pyecharts.charts import Pie from pyecharts.faker import Faker c = ( Pie() .add( "", # 设置数据集 [list(z) for z in zip(Faker.choose(), Faker.values())], radius=["40%", "55%"], # 设置标签配置项 label_opts=opts.LabelOpts( # 标签位置 position="outside", # 标签内容格式器: {a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比） formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ", # 文字块背景色 background_color="#eee", # 文字块边框颜色 border_color="#aaa", border_width=1, border_radius=4, # 在 rich 里面，可以自定义富文本样式。利用富文本样式，可以在标签中做出非常丰富的效果 rich={ "a": {"color": "#999", "lineHeight": 22, "align": "center"}, "abg": { "backgroundColor": "#e3e3e3", "width": "100%", "align": "right", "height": 22, "borderRadius": [4, 4, 0, 0], }, "hr": { "borderColor": "#aaa", "width": "100%", "borderWidth": 0.5, "height": 0, }, "b": {"fontSize": 16, "lineHeight": 33}, "per": { "color": "#eee", "backgroundColor": "#334455", "padding": [2, 4], "borderRadius": 2, }, }, ), ) .set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例")) .render("pie_rich_label.html") ) 

10. 雷达图

import pyecharts.options as opts from pyecharts.charts import Radar """ Gallery 使用 pyecharts 1.1.0 参考地址: https://echarts.baidu.com/examples/editor.html?c=radar 目前无法实现的功能: 1、雷达图周围的图例的 textStyle 暂时无法设置背景颜色 """ v1 = [[4300, 10000, 28000, 35000, 50000, 19000]] v2 = [[5000, 14000, 28000, 31000, 42000, 21000]] ( Radar(init_opts=opts.InitOpts(width="1280px", height="720px", bg_color="#CCCCCC")) .add_schema( schema=[ opts.RadarIndicatorItem(name="销售（sales）", max_=6500), opts.RadarIndicatorItem(name="管理（Administration）", max_=16000), opts.RadarIndicatorItem(name="信息技术（Information Technology）", max_=30000), opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000), opts.RadarIndicatorItem(name="研发（Development）", max_=52000), opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000), ], splitarea_opt=opts.SplitAreaOpts( is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1) ), textstyle_opts=opts.TextStyleOpts(color="#fff"), ) .add( series_name="预算分配（Allocated Budget）", data=v1, linestyle_opts=opts.LineStyleOpts(color="#CD0000"), ) .add( series_name="实际开销（Actual Spending）", data=v2, linestyle_opts=opts.LineStyleOpts(color="#5CACEE"), ) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="基础雷达图"), legend_opts=opts.LegendOpts() ) .render("basic_radar_chart.html") ) 

 

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11. 普通散点图

from pyecharts import options as opts from pyecharts.charts import Scatter from pyecharts.faker import Faker c = ( Scatter() .add_xaxis(Faker.choose()) .add_yaxis("商家A", Faker.values()) .set_global_opts( title_opts=opts.TitleOpts(title="Scatter-显示分割线"), xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), ) .render("scatter_splitline.html") ) 

 

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其他图形

其他的图形示例可以在官方文档中查询：http://gallery.pyecharts.org/。