01 引言：
02 样例数据生产：
由于文章中未提供数据，这边我们借助numpy的random函数生成一个样例数据，20列对应2001-2020年，12行对应每年的12个月份。
np.random.seed(3)x = np.arange(1,13)df= pd.DataFrame()# 构造2001年的数据df['y2001'] = -2*x**2+5*x+np.random.rand(12)*np.random.rand(1)*100+500# 在2001年的数据基础上增加随机数构造剩余年份数据for i in range(2,21):df['y'+str(2000+i)] = df['y2001']+np.random.rand(12)*np.random.rand(1)**2*100print(df)
03可视化数据：
图表中主要用到了均值和标准差数据，这边可以借助pandas的describe直接获取。
fig,axs = plt.subplots(2,1,figsize = (8,6))axs[0].plot(x,df['y2019'],color = 'r',marker = '.',label = '2019')axs[0].plot(x,df.T.describe().loc['mean'],color = 'k',marker = '.',label = 'mean')axs[0].fill_between(x,df.T.describe().loc['mean']-df.T.describe().loc['std'],df.T.describe().loc['mean']+df.T.describe().loc['std'],alpha = 0.3,color = 'k',label = 'std')axs[1].plot(x,df['y2019']-df.T.describe().loc['mean'],color = 'k')axs[1].axhline(y = 0,c = 'k')y1 ,y2 = df['y2019']-df.T.describe().loc['mean'],0axs[1].fill_between(x,y1 ,y2,where = (y2<y1),color = 'r',alpha = 1,interpolate = True,label = 'increase')axs[1].fill_between(x,y1 ,y2,where = (y2>y1),color = 'b',alpha = 1,interpolate = True,label = 'decrease')plt.show()
034完整代码如下：
# -*- encoding: utf-8 -*-'''@File : GZH0401.py@Time : 2023/04/01 20:37:58@Author : HMX@Version : 1.0@Contact : kzdhb8023@163.com'''# here put the import libimport matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdsize = 15fontdict = {'weight': 'normal','size':size,'color':'k','family':'SimHei'}mpl.rcParams.update({'text.usetex': False,'font.family': 'stixgeneral','mathtext.fontset': 'stix',"font.family":'serif',"font.size": size,"mathtext.fontset":'stix',"font.serif": ['Times New Roman'],})np.random.seed(3)x = np.arange(1,13)df= pd.DataFrame()# 构造2001年的数据df['y2001'] = -2*x**2+5*x+np.random.rand(12)*np.random.rand(1)*100+500# 在2001年的数据基础上增加随机数构造剩余年份数据for i in range(2,21):df['y'+str(2000+i)] = df['y2001']+np.random.rand(12)*np.random.rand(1)**2*100print(df)print(df.T.describe())# 绘制fig,axs = plt.subplots(2,1,figsize = (8,6))axs[0].plot(x,df['y2019'],color = 'r',marker = '.',label = '2019')axs[0].plot(x,df.T.describe().loc['mean'],color = 'k',marker = '.',label = 'mean')axs[0].fill_between(x,df.T.describe().loc['mean']-df.T.describe().loc['std'],df.T.describe().loc['mean']+df.T.describe().loc['std'],alpha = 0.3,color = 'k',label = 'std')axs[1].plot(x,df['y2019']-df.T.describe().loc['mean'],color = 'k')axs[1].axhline(y = 0,c = 'k')y1 ,y2 = df['y2019']-df.T.describe().loc['mean'],0axs[1].fill_between(x,y1 ,y2,where = (y2<y1),color = 'r',alpha = 1,interpolate = True,label = 'increase')axs[1].fill_between(x,y1 ,y2,where = (y2>y1),color = 'b',alpha = 1,interpolate = True,label = 'decrease')# 完善一下细节titles = ['(a)','(b)']for j,iax in enumerate(axs):iax.set_ylabel('森气笔记',fontdict = fontdict)iax.set_xticks(x)iax.legend(loc='lower left',frameon = False)iax.set_title(titles[j]+' ',loc= 'right',y = 0.82,fontsize =size)axs[1].set_xticklabels(["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"])axs[0].set_xticklabels([])axs[1].set_xlabel('Month')axs[0].set_ylim(300,600)axs[1].set_ylim(-30,30)axs[1].set_yticks(np.arange(-30,31,15))plt.tight_layout()plt.savefig(r'D:\ForestMeteorology\GZH230401\data\GZH0401.png',dpi = 600)plt.show()
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