本节提要：接着上一章折线图，简要谈谈散点图scatter( )的常用关键参数，以及在气象绘图上的简单应用。
一、简要谈谈散点图
散点图scatter在气象绘图上主要用于地理信息的标注、站点值的检验、时间序列数据可视化等等，相比其他行业，气象上散点图的应用明显低频。当散点图的直径大小和内部填色改变时，有一个名谓“气泡图”的说法。 下面是在文献中截取的两种典型使用方式：
| scatter( )函数常用关键字参数 | |
x y | 传入scatter( )的横纵轴数据，用于确定散点位置 |
| s | 控制散点的大小 |
import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif']=['SimHei']#中文正常显示def sample_data():#编制实验数据 x=range(1,21)#横坐标数据 y=np.array([2,4,6,7,5,3,3,5,7,9,1115,10,8,4,7,8,3,2,5,7])#纵坐标数据 data1=np.array([22,13,24,26,30,31,36,20,27,15,17,19,24,27,30,15,21,22,10,27]) data2=np.array([120,132,143,151,109,149,125,119,120,158,171,101,106,108,126,149,127,151,143,102]) return x,y,data1,data2x,y,data1,data2=sample_data()#获得实验数据fig=plt.figure(figsize=(2.5,2),dpi=500)ax1=fig.add_axes([0,0,1,0.45])ax2=fig.add_axes([0,0.55,1,0.45])ax1.scatter(x,y,s=20,zorder=2)ax2.scatter(x,y,s=50,zorder=2)ax1.set(xlim=(0,21),ylim=(1,15))ax2.set(xlim=(0,21),ylim=(1,15))ax1.tick_params(axis='both',direction='in',length=3,width=0.5,labelsize=7)ax2.tick_params(axis='both',direction='in',length=3,width=0.5,labelsize=7)ax1.grid(alpha=0.75,ls=':')ax2.grid(alpha=0.75,ls=':')
| c | 控制散点颜色 |
ax1.scatter(x,y,c='tab:green',zorder=2)ax2.scatter(x,y,c='tab:blue',zorder=2)
| marker | 指定散点样式 |
ax1.scatter(x,y,marker='*',zorder=2)ax2.scatter(x,y,marker='s',zorder=2)
| cmap | 与c参数配合，修改等级颜色 |
ax1.scatter(x,y,c=data1,cmap='viridis',zorder=2)ax2.scatter(x,y,c=data1,cmap='rainbow',zorder=2)
| alpha | 指定散点透明度 |
ax1.scatter(x,y,alpha=0.75,zorder=2)ax2.scatter(x,y,alpha=0.25,zorder=2)
| facecolor,edgecolor | 强制指定颜色(不推荐) |
ax1.scatter(x,y,facecolor='none',edgecolor='k',zorder=2)ax2.scatter(x,y,facecolor='red',edgecolor='k',zorder=2)
| linewidths | 指定散点边界粗细 |
ax1.scatter(x,y,linewidths=1,facecolor='none',edgecolor='k',zorder=2)ax2.scatter(x,y,linewidths=20,facecolor='none',edgecolor='k',zorder=2)
| hatch | 内部填充样式 |
ax1.scatter(x,y,s=100,hatch='xxxx',zorder=2)ax2.scatter(x,y,s=100,hatch='**',zorder=2)
| vmin,vmax | 裁剪色条上下限 |
还有部分少见参数需要读者翻阅官方文档
四、散点图应用实例
A、最基础的传入数据，赋值给参数s，仅以散点大小表示数据变化
def sample_data():#编制实验数据 x=range(1,21)#横坐标数据 y=np.array([2,4,6,7,5,3,3,5,7,9,1115,10,8,4,7,8,3,2,5,7])#纵坐标数据 data1=np.array([22,13,24,26,30,40,45,20,27,15,17,19,24,27,30,15,21,22,10,27]) data2=np.array([32,19,27,29,30,44,120,39,67,45,17,19,24,87,30,15,61,22,26,37]) data3=np.array([120,132,143,151,109,149,125,119,120,158,171,101,106,108,126,149,127,151,143,102]) return x,y,data1,data2,data3x,y,data1,data2,data3=sample_data()#获得实验数据
B、最基础的传入数据，赋值给c和cmap，仅以散点颜色表示大小
ax1.scatter(x,y,s=100,c=data2,cmap='Blues',zorder=2)ax2.scatter(x,y,s=100,c=data3,cmap='Blues',zorder=2)
C、以散点大小和颜色同时表示一种数据的变化
ax1.scatter(x,y,s=data2,c=data2,cmap='Blues',zorder=2)ax2.scatter(x,y,s=data3,c=data3,cmap='Reds',zorder=2)
这种方式是目前最为流行的散点数据显示方式，如果一个数据越大，那么他的填色越深并且散点面积越大，这样最能展示数据的大小变化情况。人眼捕获大小最为敏感，填色图能显示其等级程度，这种两结合是比较科学的数据分析方法。
D、以散点颜色展示一种数据，以散点大小展示另一种数据
ax1.scatter(x,y,s=data3,c=data2,cmap='Blues',edgecolor='k',zorder=2)ax2.scatter(x,y,s=data1,c=data3,cmap='Reds',edgecolor='k',zorder=2)
这种数据显示方式是不推荐的，由于大小和颜色深浅不匹配，容易让读图者产生混淆。例如在蓝色子图中，颜色最深的散点其面积明显不是最大的，颜色和大小的不一致，容易使读图者产生误解。
五、带地图的散点图使用
A、最常用的方法——标记地理信息，如站点位置，站点海拔高度等
nameandstation={"恩施":[109.5,30.2],"利川":[109,30.3],"巴东":[110.34,31.04],"建始":[109.72,30.6],"宣恩":[109.49,29.987],"来凤":[109.407,29.493],"咸丰":[109.14,29.665],"鹤峰":[110.034,29.89]}for key,value in nameandstation.items(): ax.scatter(value[0] , value[1] , marker='.' , s=70 , color = "k" , zorder = 3) ax.text(value[0]-0.07 , value[1]+0.03 , key , fontsize = 6, color = "k")
站点海拔高度的标注参考链接：https://www.jianshu.com/p/2083fac7f2ce
原作者为简书大佬摸鱼咯。
B、进行显著性检验打点
气象家园上有相当多的高手讲解，我就不献丑了。比较火的参考链接：
http://bbs.06climate.com/forum.php?mod=viewthread&tid=92816&extra=page%3D1
原作者为气象家园大佬好久不见。
C、显示站点的大小和数值
可参考Python气象绘图教程(十四)
六、散点图的图例
散点图的图例添加，可供参考的有matplotlib官网实例，《Python数据科学手册》第四章p422，鄙号文章Python气象绘图教程(十四)
A、和折线图类似，默认的legend命令生成图例
ax.scatter(······,label='xxx')
ax.legend( )
B、使用legend_elements()命令获取当前scatter的图例句柄
N = 45x, y = np.random.rand(2, N)c = np.random.randint(1, 5, size=N)s = np.random.randint(10, 220, size=N)fig, ax = plt.subplots()scatter = ax.scatter(x, y, c=c, s=s)# produce a legend with the unique colors from the scatterlegend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")ax.add_artist(legend1)# produce a legend with a cross section of sizes from the scatterhandles, labels = scatter.legend_elements(prop="sizes", alpha=0.6)legend2 = ax.legend(handles, labels, loc="upper right", title="Sizes")plt.show()
volume = np.random.rayleigh(27, size=40)amount = np.random.poisson(10, size=40)ranking = np.random.normal(size=40)price = np.random.uniform(1, 10, size=40)fig, ax = plt.subplots()# Because the price is much too small when being provided as size for ``s``,# we normalize it to some useful point sizes, s=0.3*(price*3)**2scatter = ax.scatter(volume, amount, c=ranking, s=0.3*(price*3)**2, vmin=-3, vmax=3, cmap="Spectral")# Produce a legend for the ranking (colors). Even though there are 40 different# rankings, we only want to show 5 of them in the legend.legend1 = ax.legend(*scatter.legend_elements(num=5), loc="upper left", title="Ranking")ax.add_artist(legend1)# Produce a legend for the price (sizes). Because we want to show the prices# in dollars, we use the *func* argument to supply the inverse of the function# used to calculate the sizes from above. The *fmt* ensures to show the price# in dollars. Note how we target at 5 elements here, but obtain only 4 in the# created legend due to the automatic round prices that are chosen for us.kw = dict(prop="sizes", num=5, color=scatter.cmap(0.7), fmt="$ {x:.2f}", func=lambda s: np.sqrt(s/.3)/3)legend2 = ax.legend(*scatter.legend_elements(**kw), loc="lower right", title="Price")plt.show()
C、使用Python气象绘图教程(十四)中的方法，绘制空数据散点图
ax.scatter( [ ] , [ ] , s=datamin)
ax.scatter( [ ] , [ ] , s=datamed)
ax.scatter( [ ] , [ ] , s=datamax)
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