hcharts（替代Matplotlib图表）

对于日常的python可视化过去用的都是Matplotlib可视化图表，但是这都是静态的图表，无法对图表进行交互。

今天介绍的pyecharts是经过网页渲染的可视化可交互的web页面图表，可以更好的时间选择或者维度选择进行交互，得到不同的动态图表。

汇总最常见的图表及代码：

1、柱状图

代码如下：

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 = ( Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add_xaxis([1, 2, 3, 4, 5]) .add_yaxis("product1", list2, stack="stack1", category_gap="50%") .add_yaxis("product2", list3, stack="stack1", category_gap="50%") .set_series_opts( label_opts=opts.LabelOpts( position="right", formatter=JsCode( "function(x){return Number(x.data.percent * 100).toFixed() + '%';}" ), ) ) .render("stack_bar_percent.html") )

2、折线图

代码如下：

import pyecharts.options as opts from pyecharts.charts import Line week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"] high_temperature = [11, 11, 15, 13, 12, 13, 10] low_temperature = [1, -2, 2, 5, 3, 2, 0] ( Line(init_opts=opts.InitOpts(width="1600px", height="800px")) .add_xaxis(xaxis_data=week_name_list) .add_yaxis( series_name="最高气温", y_axis=high_temperature, markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值"), ] ), markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_="average", name="平均值")] ), ) .add_yaxis( series_name="最低气温", y_axis=low_temperature, markpoint_opts=opts.MarkPointOpts( data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)] ), markline_opts=opts.MarkLineOpts( data=[ opts.MarkLineItem(type_="average", name="平均值"), opts.MarkLineItem(symbol="none", x="90%", y="max"), opts.MarkLineItem(symbol="circle", type_="max", name="最高点"), ] ), ) .set_global_opts( title_opts=opts.TitleOpts(title="未来一周气温变化", subtitle="纯属虚构"), tooltip_opts=opts.TooltipOpts(trigger="axis"), toolbox_opts=opts.ToolboxOpts(is_show=True), xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False), ) .render("temperature_change_line_chart.html") )

3、漏斗图

代码如下：

from pyecharts import options as opts from pyecharts.charts import Funnel from pyecharts.faker import Faker c = ( Funnel() .add("商品", [list(z) for z in zip(Faker.choose(), Faker.values())]) .set_global_opts(title_opts=opts.TitleOpts(title="Funnel-基本示例")) .render("funnel_base.html") )

4、仪表盘

代码如下：

from pyecharts import options as opts from pyecharts.charts import Gauge c = ( Gauge() .add("", [("完成率", 66.6)]) .set_global_opts(title_opts=opts.TitleOpts(title="Gauge-基本示例")) .render("gauge_base.html") )

5、地理坐标

代码如下：

from pyecharts import options as opts from pyecharts.charts import Geo from pyecharts.globals import ChartType, SymbolType c = ( Geo() .add_schema( maptype="china", itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"), ) .add( "", [("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)], type_=ChartType.EFFECT_SCATTER, color="white", ) .add( "geo", [("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆")], type_=ChartType.LINES, effect_opts=opts.EffectOpts( symbol=SymbolType.ARROW, symbol_size=6, color="blue" ), linestyle_opts=opts.LineStyleOpts(curve=0.2), ) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts(title_opts=opts.TitleOpts(title="Geo-Lines-background")) .render("geo_lines_background.html") )

6、关系图

代码如下：

from pyecharts import options as opts from pyecharts.charts 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")}) c = ( Graph() .add("", nodes, links, repulsion=8000) .set_global_opts(title_opts=opts.TitleOpts(title="Graph-基本示例")) .render("graph_base.html") )

7、地理坐标

代码如下：

from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map() .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china") .set_global_opts(title_opts=opts.TitleOpts(title="Map-基本示例")) .render("map_base.html") )

8、饼状图

代码如下：

from pyecharts import options as opts from pyecharts.charts import Pie from pyecharts.faker import Faker c = ( Pie() .add("", [list(z) for z in zip(Faker.choose(), Faker.values())]) .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) .render("pie_base.html") )

9、表格图

代码如下：

from pyecharts.components import Table from pyecharts.options import ComponentTitleOpts table = Table() headers = ["City name", "Area", "Population", "Annual Rainfall"] rows = [ ["Brisbane", 5905, 1857594, 1146.4], ["Adelaide", 1295, 1158259, 600.5], ["Darwin", 112, 120900, 1714.7], ["Hobart", 1357, 205556, 619.5], ["Sydney", 2058, 4336374, 1214.8], ["Melbourne", 1566, 3806092, 646.9], ["Perth", 5386, 1554769, 869.4], ] table.add(headers, rows) table.set_global_opts( title_opts=ComponentTitleOpts(title="Table-基本示例", subtitle="我是副标题支持换行哦") ) table.render("table_base.html")

10、时间轴图表

代码如下：

from pyecharts import options as opts from pyecharts.charts import Bar, Timeline from pyecharts.faker import Faker x = Faker.choose() tl = Timeline() for i in range(2015, 2020): bar = ( Bar() .add_xaxis(x) .add_yaxis("商家A", Faker.values()) .add_yaxis("商家B", Faker.values()) .set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i))) ) tl.add(bar, "{}年".format(i)) tl.render("timeline_bar.html")

11、树图

代码如下：

from pyecharts import options as opts from pyecharts.charts import Tree data = [ { "children": [ {"name": "B"}, { "children": [{"children": [{"name": "I"}], "name": "E"}, {"name": "F"}], "name": "C", }, { "children": [ {"children": [{"name": "J"}, {"name": "K"}], "name": "G"}, {"name": "H"}, ], "name": "D", }, ], "name": "A", } ] c = ( Tree() .add("", data) .set_global_opts(title_opts=opts.TitleOpts(title="Tree-基本示例")) .render("tree_base.html") )

12、矩形树图

代码如下：

from pyecharts import options as opts from pyecharts.charts import TreeMap data = [ {"value": 40, "name": "我是A"}, { "value": 180, "name": "我是B", "children": [ { "value": 76, "name": "我是B.children", "children": [ {"value": 12, "name": "我是B.children.a"}, {"value": 28, "name": "我是B.children.b"}, {"value": 20, "name": "我是B.children.c"}, {"value": 16, "name": "我是B.children.d"}, ], } ], }, ] c = ( TreeMap() .add("演示数据", data) .set_global_opts(title_opts=opts.TitleOpts(title="TreeMap-基本示例")) .render("treemap_base.html") )

13、雷达图

代码如下：

import pyecharts.options as opts from pyecharts.charts import Radar 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") )

14、散点图

代码如下：

import pyecharts.options as opts from pyecharts.charts import Scatter data = [ [10.0, 8.04], [8.0, 6.95], [13.0, 7.58], [9.0, 8.81], [11.0, 8.33], [14.0, 9.96], [6.0, 7.24], [4.0, 4.26], [12.0, 10.84], [7.0, 4.82], [5.0, 5.68], ] data.sort(key=lambda x: x[0]) x_data = [d[0] for d in data] y_data = [d[1] for d in data] ( Scatter(init_opts=opts.InitOpts(width="1600px", height="1000px")) .add_xaxis(xaxis_data=x_data) .add_yaxis( series_name="", y_axis=y_data, symbol_size=20, label_opts=opts.LabelOpts(is_show=False), ) .set_series_opts() .set_global_opts( xaxis_opts=opts.AxisOpts( type_="value", splitline_opts=opts.SplitLineOpts(is_show=True) ), yaxis_opts=opts.AxisOpts( type_="value", axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=True), ), tooltip_opts=opts.TooltipOpts(is_show=False), ) .render("basic_scatter_chart.html") )

除此之外还有桑基图、旭日图、河流图、词云图、极坐标系、象形柱图、水球图等等常用图形。

以上的图形都可以进行组合，可以通过python代码自己根据需要设计自己的动态监控大屏。

以下是我自己爬取汽车数据，设计的汽车营销BI数据监控动态大屏。

以上类型的动态大屏还可以补充更多不同类型且炫丽的动态大屏，可以根据不同的行业和分析目的设计更多酷炫的动态大屏。

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