比较（八）利用python绘制指示器

比较（八）利用python绘制指示器

指示器（Indicators）简介

指示器是一系列相关图的统称，主要用于突出展示某一变量的实际值与目标值的差异，例如常见的数据delta、仪表盘、子弹图、水滴图等。

快速绘制

1. 基于plotly

   更多用法可参考Indicators in Python

   import plotly.graph_objects as gofig = go.Figure()# 自定义仪表盘
   fig.add_trace(go.Indicator(domain = {'row': 0, 'column': 0},value = 450,mode = "gauge+number+delta",title = {'text': "Speed"},delta = {'reference': 380},gauge = {'axis': {'range': [None, 500]},'steps' : [{'range': [0, 250], 'color': "lightgray"},{'range': [250, 400], 'color': "gray"}],'threshold' : {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 490}}))# 自定义子弹图
   fig.add_trace(go.Indicator(domain = {'x': [0.05, 0.5], 'y': [0.15, 0.35]},mode = "number+gauge+delta", value = 180,delta = {'reference': 200},gauge = {'shape': "bullet",'axis': {'range': [None, 300]},'threshold': {'line': {'color': "black", 'width': 2},'thickness': 0.75,'value': 170},'steps': [{'range': [0, 150], 'color': "gray"},{'range': [150, 250], 'color': "lightgray"}],'bar': {'color': "black"}}))# 基本delta
   fig.add_trace(go.Indicator(domain = {'row': 0, 'column': 1},mode = "delta",value = 300,))# 自定义delta
   fig.add_trace(go.Indicator(domain = {'row': 1, 'column': 1},mode = "number+delta",value = 40,))fig.update_layout(grid = {'rows': 2, 'columns': 2, 'pattern': "independent"},template = {'data' : {'indicator': [{'title': {'text': "Speed"},'mode' : "number+delta+gauge",'delta' : {'reference': 90}}]}})
   

   

2. 基于pyecharts

   仪表盘只能居中，无法改变位置。水球图可以通过center参数改变位置。

   更多用法可以参考pyecharts-gallery

   from pyecharts import options as opts
   from pyecharts.charts import Gauge, Liquid, Grid, Page# 创建仪表盘图表
   g = (Gauge().add("基本仪表盘", [("完成率", 66.6)],title_label_opts=opts.GaugeTitleOpts(font_size=20, offset_center=[0,35]),detail_label_opts=opts.GaugeDetailOpts(formatter='{value}%', offset_center=[0,60]),radius="50%").set_global_opts(legend_opts=opts.LegendOpts(is_show=False),tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{a} <br/>{b} : {c}%"))
   )# 创建水球图
   c = (Liquid().add("lq", [0.6, 0.7], center=["80%", "50%"])
   )# 在一个页面中显示两个图表，调整每个图的宽度
   grid = (Grid().add(g, grid_opts=opts.GridOpts(pos_left="10%", pos_right="55%")) .add(c, grid_opts=opts.GridOpts(pos_left="60%", pos_right="10%")) 
   )grid.render_notebook()
   

   

3. 基于matplotlib

   参考：Building a Bullet Graph in Python

   # 创建子弹图的函数
   def bulletgraph(data=None, limits=None, labels=None, axis_label=None, title=None,size=(5, 3), palette=None, formatter=None, target_color="gray",bar_color="black", label_color="gray"):'''data: 需要绘制的数据，通常是一个二维列表，列表的元素为三元组，分别表示图例、目标值和实际值。例如：[["图例A", 60, 75]]limits: 子弹图的分段标准，例如：[20, 60, 100] 表示图形将被分成 <20, 20-60, >60-100 这几个区段labels: 子弹图中每个区段的名称axis_label: x轴的标签title: 图形标题size: 图形大小palette: 子弹图的颜色板formatter: 用于格式化x轴刻度的格式器target_color: 目标值线条的颜色，默认是灰色bar_color: 实际值条形的颜色，默认黑色label_color: 标签文本颜色，默认灰色'''# 确定最大值来调整条形图的高度（除以10似乎效果不错）h = limits[-1] / 10# 默认使用sns的绿色调色板if palette is None:palette = sns.light_palette("green", len(limits), reverse=False)# 如果只有一组数据，创建一个子图；否则，根据数据的数量创建多个子图if len(data) == 1:fig, ax = plt.subplots(figsize=size, sharex=True)else:fig, axarr = plt.subplots(len(data), figsize=size, sharex=True)# 针对每个子图，添加一个子弹图条形for idx, item in enumerate(data):# 从创建的子图数组中获取轴对象if len(data) > 1:ax = axarr[idx]# 格式设置，移除多余的标记杂项ax.set_aspect('equal')ax.set_yticks([1])ax.set_yticklabels([item[0]])ax.spines['bottom'].set_visible(False)ax.spines['top'].set_visible(False)ax.spines['right'].set_visible(False)ax.spines['left'].set_visible(False)prev_limit = 0# 画出各个区段的柱形图for idx2, lim in enumerate(limits):ax.barh([1], lim - prev_limit, left=prev_limit, height=h,color=palette[idx2])  prev_limit = limrects = ax.patches# 画出表示实际值的条形图ax.barh([1], item[1], height=(h / 3), color=bar_color)# 计算y轴的范围，确保目标线条的长度适应ymin, ymax = ax.get_ylim()# 画出表示目标值的线条ax.vlines(item[2], ymin * .9, ymax * .9, linewidth=1.5, color=target_color)  # 添加标签if labels is not None:for rect, label in zip(rects, labels):height = rect.get_height()ax.text(rect.get_x() + rect.get_width() / 2, -height * .4, label, ha='center', va='bottom', color=label_color) # 设置x轴刻度的格式if formatter:ax.xaxis.set_major_formatter(formatter)# 设置x轴的标签if axis_label:ax.set_xlabel(axis_label)# 设置子图的标题if title:fig.suptitle(title, fontsize=14)# 调整子图之间的间隔fig.subplots_adjust(hspace=0)  
   

   import matplotlib.pyplot as plt
   import seaborn as sns
   from matplotlib.ticker import FuncFormatter
   import matplotlib as mplplt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签# 格式化为货币
   def money(x, pos):return "${:,.0f}".format(x)
   # 通过自定义函数格式化刻度值
   money_fmt = FuncFormatter(money)# 自定义数据
   data = [("HR", 50000, 60000),("Marketing", 75000, 65000),("Sales", 125000, 80000),("R&D", 195000, 115000)]
   # 自定义颜色
   palette = sns.light_palette("grey", 3, reverse=False)# 绘制子弹图
   bulletgraph(data, limits=[50000, 125000, 200000],labels=["Below", "On Target", "Above"], size=(10,5),axis_label="年终预算", label_color="black",bar_color="#252525", target_color='#f7f7f7', palette=palette,title="营销渠道预算绩效",formatter=money_fmt)
   

   

定制多样化的指示器

参考：Gauge Chart with Python

利用plotly模拟仪表盘

import plotly.graph_objects as go
import numpy as npplot_bgcolor = "white"
quadrant_colors = [plot_bgcolor, "#f25829", "#f2a529", "#eff229", "#85e043", "#2bad4e"] 
quadrant_text = ["", "<b>Very high</b>", "<b>High</b>", "<b>Medium</b>", "<b>Low</b>", "<b>Very low</b>"]
n_quadrants = len(quadrant_colors) - 1current_value = 450
min_value = 0
max_value = 600
# 指针长度
hand_length = np.sqrt(2) / 4 
# 指针角度
hand_angle = np.pi * (1 - (max(min_value, min(max_value, current_value)) - min_value) / (max_value - min_value))fig = go.Figure(data=[# 半圆型饼图构造仪表盘go.Pie(values=[0.5] + (np.ones(n_quadrants) / 2 / n_quadrants).tolist(),rotation=90,hole=0.5,marker_colors=quadrant_colors,text=quadrant_text,textinfo="text",hoverinfo="skip",),],layout=go.Layout(showlegend=False,margin=dict(b=0,t=10,l=10,r=10),width=450,height=450,paper_bgcolor=plot_bgcolor,# 注释annotations=[go.layout.Annotation(text=f"Speed Now: {current_value}",x=0.5, xanchor="center", xref="paper",y=0.4, yanchor="bottom", yref="paper",showarrow=False,)],# 指针shapes=[# 圆点go.layout.Shape(type="circle",x0=0.48, x1=0.48,y0=0.52, y1=0.52,fillcolor="#333",line_color="#333",),# 线go.layout.Shape(type="line",x0=0.5, x1=0.5 + hand_length * np.cos(hand_angle),y0=0.5, y1=0.5 + hand_length * np.sin(hand_angle),line=dict(color="#333", width=4))])
)
fig.show()

总结

以上通过plotly、pyecharts和matplotlib绘制了各种各样的指示器。也利用plotly通过自定义方式模拟出仪表盘的效果。

共勉～