pyecharts在数据可视化中的应用

pyecharts是一款非常好用的，基于python的，数据可视化工具包，那么如何来使用pyecharts，下面就来简单介绍一下pyecharts的使用方法，具体安装过程可以参考

pyecharts官方手册

注意事项

pyecharts 分为 v0.5.X 和 v1 两个大版本，v0.5.X 和 v1 间不兼容

简单的开发流程

1. 导入全局配置项和图表类型

from pyecharts import options as optsfrom pyecharts.charts import [name]

2. 添加或导入数据

x_data = ["1995","1996","1997","1998","1999","2000","2001","2002","","","","","","",""]y_data = [0.32,0.32,0.32,0.32,0.33,0.33,0.34,0.37,0.37,0.37,0.37,0.39,0.41,0.42,0.44]

3. 初始化图表(以Line为例)，配置全局设置 ，添加xy轴数据

Line().set_global_opts( title_opts=opts.TitleOpts(title="Line-折线图", subtitle="Chale"),).add_xaxis(xaxis_data = x_data).add_yaxis( y_axis = y_data）

4. 导出

.render("html/折线图.html")

render方法会在指定目录生成一个html文件,在浏览器打开可查看图例

下面有几个例题可以更详细的理解这个流程

项目实例

折线图(Line)

年份 ： [“1995”, “1996”, “1997”, “1998”, “1999”, “2000”, “2001”, “2002”, “”, “”, “”, “”,“”, “”, “”]

邮费： [0.32, 0.32, 0.32, 0.32, 0.33, 0.33, 0.34, 0.37, 0.37, 0.37, 0.37, 0.39, 0.41, 0.42, 0.44]

"""@desc:@author: Chale@software: PyCharm on /6/5 9:54 """import pyecharts.options as optsfrom pyecharts.charts import Line#原始数据x_data = ["1995","1996","1997","1998","1999","2000","2001","2002","","","","","","",""]y_data = [0.32,0.32,0.32,0.32,0.33,0.33,0.34,0.37,0.37,0.37,0.37,0.39,0.41,0.42,0.44]#折线图(#初始化Line()#全局配置.set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=True),xaxis_opts=opts.AxisOpts(type_="category"),title_opts=opts.TitleOpts(title="Line-折线图", subtitle="Chale"),yaxis_opts=opts.AxisOpts(type_="value",axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),))#xy轴配置.add_xaxis(xaxis_data=x_data).add_yaxis(series_name="美国1995年-邮费折线图",y_axis=y_data,symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=True),)#导出.render("./html/basic_line_chart.html"))#阶梯图c = (Line().set_global_opts(title_opts=opts.TitleOpts(title="Line-阶梯图", subtitle="Chale")).add_xaxis(xaxis_data=x_data).add_yaxis("美国1995年-邮费阶梯图", y_axis=y_data, is_step=True).render("./html/line_step.html"))

堆叠柱形图(bar)、极坐标系-堆叠柱状图

2000年-热狗大胃王比赛前三名成绩的堆叠柱形图、极坐标系-堆叠柱状图（南丁格尔玫瑰图）：

数据文件：hot-dog-places.csv

可以通过 pandas 来导入 csv 文件

"""@desc:@author: Chale@software: PyCharm on /6/5 10:57 """import pandas as pdfrom pyecharts import options as optsfrom pyecharts.charts import Barfrom pyecharts.charts import Polar#导入csv文件hot_dog = pd.read_csv('file/hot-dog-places.csv')#xy轴赋值x = hot_dog.columns.values.tolist()y1 = hot_dog.values[0:1][0].tolist()y2 = hot_dog.values[1:2][0].tolist()y3 = hot_dog.values[2:3][0].tolist()#堆叠柱形图c = (Bar()#设置x轴.add_xaxis(x)#设置堆叠数据.add_yaxis("Fst", y1, stack="stack1").add_yaxis("Sec", y2, stack="stack1").add_yaxis("Trd", y3, stack="stack1").set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠柱形图", subtitle="Chale")).render("html/堆叠柱形图.html"))#玫瑰图c = (Polar().add_schema(angleaxis_opts=opts.AngleAxisOpts(data=x, type_="category")).add("A", y1, type_="bar", stack="stack0").add("B", y2, type_="bar", stack="stack0").add("C", y3, type_="bar", stack="stack0").set_global_opts(title_opts=opts.TitleOpts(title="Polar-AngleAxis", subtitle="Chale")).render("html/南丁格尔玫瑰图.html"))

饼图(pie)

数据文件：vote_result.csv

"""@desc:@author: Chale@software: PyCharm on /6/5 11:30"""import pandas as pdfrom pyecharts import options as optsfrom pyecharts.charts import Pie#导入csv文件vote_result = pd.read_csv('file/vote_result.csv')#xy轴赋值x = vote_result['感兴趣的领域'].tolist()y= vote_result['票数'].tolist()c = (Pie().add("", [list(z) for z in zip(x,y)])#设置饼图各部分颜色.set_colors(["#3FA7D6", "#F3CA40", "#F2A541", "#F3752B", "#9bf6ff", "#D78A76", "#0CCE6B"]).set_global_opts(title_opts=opts.TitleOpts(title="投票结果", subtitle="Chale")).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")).render("html/饼图.html"))d = (Pie().add("",[list(z) for z in zip(x, y)],#设置圆环宽度和大小radius=["40%", "75%"],).set_global_opts(title_opts=opts.TitleOpts(title="环形饼图", subtitle="Chale"),legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),).set_colors(["#3FA7D6", "#F3CA40", "#F2A541", "#F3752B", "#9bf6ff", "#D78A76", "#0CCE6B"]).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")).render("html/环形饼图.html"))

堆积柱形图II

数据文件：approval_rate.csv

"""@desc:@author: Chale@software: PyCharm on /6/5 12:00"""import pandas as pdfrom pyecharts import options as optsfrom pyecharts.charts import Bar#导入csv文件approval_rate = pd.read_csv('file/approval_rate.csv')#xy轴赋值x = approval_rate['政治举措'].tolist()y1 = approval_rate['支持'].tolist()y2 = approval_rate['反对'].tolist()y3 = approval_rate['不发表意见'].tolist()c = (Bar()#设置x轴.add_xaxis(x)#设置y轴堆叠数据.add_yaxis("支持", y1, stack="stack1").add_yaxis("反对", y2, stack="stack1").add_yaxis("不发表意见", y3, stack="stack1").set_series_opts(label_opts=opts.LabelOpts(is_show=True)).set_global_opts(title_opts=opts.TitleOpts(title="奥巴马的政治举措民意调查结果", subtitle="Chale")).render("html/堆叠柱形图aobama.html"))

以上就是pyecharts在数据可视化中的应用中比较基础的部分

完。