'Size':np.random.rand(50)*30})# Create a scatter plot with color gradientfig=px.scatter(df,x='X',y='Y',size='Size',color='Size',title='ScatterPlotwithColorGradient')# Show the plotfig.show()运行后得到结果如下：在本例中，我们
2*np.pi,len(labels),endpoint=False).tolist()stats+=stats[:1]angles+=angles[:1]fig,ax=plt....
cell.alignment = Alignment(horizontal='center', vertical='center') cell.font = Font(b=True, color="F8F8F8",size = 46)cell.fill = PatternFill("solid", fgColor="2591DB")# 将绘制出来的图表放置到Excel文档中sheet.add_chart(chart1,'A5')sheet.add_chart(chart2,'J5')chart3.width = 31sheet...
defmap_world()->Map:c=(Map(init_opts=opts.InitOpts(chart_id=2,bg_color='#ADD8E6')).add("",data_list,"world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="2020东京奥运会各国金牌分布图"),vi...
values[9][1:]: num+=1 name=list(df)[num] if name != 'y5': plt.text(10.2, i, name, horizontalalignment='left', size='small', color='grey') # And add a special annotation for the group we are interested in plt.text(10.2, df.y5.tail(1), 'Mr Orange', horizontalalignment='...
import matplotlib.animation as anianimator = ani.FuncAnimation(fig, chartfunc, interval = 100) 1. 从中我们可以看到 FuncAnimation 的几个输入: fig 是用来 「绘制图表」的 figure 对象; chartfunc 是一个以数字为输入的函数,其含义为时间序列上的时间; ...
5 x = data['年龄'].tolist()6 y = data['消费金额(元)'].tolist()7 chart = EffectScatter()8 chart.add_xaxis(x)9 chart.add_yaxis(series_name='年龄,消费金额(元)', y_axis=y, label_opts=opts.LabelOpts(is_show=False), symbol_size=15)10 chart.set_global_opts(title_opts=opts....
import altair as altfrom vega_datasets import datasource = data.iris()alt.Chart(source).mark_circle().encode( alt.X('sepalLength').scale(zero=False), alt.Y('sepalWidth').scale(zero=False, padding=1), color='species', size='petalWidth')4. Bokeh Bokeh主打web交互式可视化，...
(horizontal='center', vertical='center')#定义border 边框样式left, right, top, bottom = [Side(style='thin', color='000000')]*4self.border_style = Border(left=left, right=right, top=top, bottom=bottom)#定义字体self.font_size = Font(size=9)forcolinself.header_upper_string_list :...
可视化一个离散分类型数据属性稍有不同,条形图是(bar plot)最有效的方法之一。你也可以使用饼图(pie-chart),但一般来说要尽量避免,尤其是当不同类别的数量超过 3 个时。 # Histogramfig = plt.figure(figsize = (6,4))title = fig.suptitle("Sulphates...