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This article will explain in detail how Python Matplotlib draws multiple subgraphs. Xiaobian thinks it is quite practical, so share it with you as a reference. I hope you can gain something after reading this article.

Merge legends by getting labels and linetypes for subplots

Note Add label

#Import data (readers can ignore) pre_lp=total_res#Combination model true=diff1[-pre_day:]#True value pre_ph=results_data["yhat"]#prophetpre_lstm=reslut#lstmpre_ari=data_ari <$'data_pre']#arima#Set Chinese font rcParams <$'font.sans-serif'] = 'kaiti'#Generate a time series (readers can modify or delete according to the situation) time =pd.to_datetime(np.arange(0,21), unit='D', origin=pd.Timestamp ('2021 -10- 19')#Create canvas fig=plt.figure (figsize=(20,16))#figsize is canvas size # 1 ax1= fig. add_subplot(221)ax1.plot (time,pre_lp,color='#1bb9f6',marker='^',linestyle='-',label='1')# ax1.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax1.set_title ('1', fontsize=15)#Set title ax1.set_xlabel ('Date/day', fontsize=15)#Set abscissa name ax1.set_ylabel ('Infected people/person', fontsize=15)#Set ordinate name ax1.xaxis.set_major_formatter (mdate.DateFormatter ('% m-% d'))#Set abscissa scale (readers can ignore) plt.xticks (pd.date_range(time[0],time[-1],freq='D '),rotation=45)#Set abscissa scale (readers can ignore)# 2 ax2=fig.add_subplot(222)ax2.plot (time,pre_ph,color='#739b06',marker='o',linestyle='-',label='2')# ax2.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax2.set_title ('2',fontsize=15)ax2.set_xlabel ('Date/day', fontsize=15)ax2.set_ylabel ('infected/person', fontsize=15)ax2.xaxis.set_major_formatter (mdate.DateFormatter('%m-%d'))plt.xticks (pd.date_range(time[0],time[-1],freq='D'),rotation=45)# 3 ax3=fig.add_subplot(223)ax3.plot (time,pre_lstm,color='#38d9a9',marker='*',linestyle='-',label='3')# ax3.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax3.set_title ('3',fontsize=15)ax3.set_xlabel ('Date/day', fontsize=15)ax3.set_ylabel ('infected/person', fontsize=15)ax3.xaxis.set_major_formatter (mdate.DateFormatter('%m-%d'))plt.xticks (pd.date_range(time[0],time[-1],freq='D'),rotation=45)# 4 ax4=fig.add_subplot(224)ax4.plot (time,pre_ari,color='#e666ff',marker='x',linestyle='-',label='4')ax4.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax4.set_title ('4',fontsize=15)ax4.set_xlabel ('Date/day', fontsize=15)ax4.set_ylabel ('infected/person', fontsize=15)ax4.xaxis.set_major_formatter (mdate.DateFormatter ('% m-% d'))plt.xticks(pd.date_range(time[0],time[-1],freq='D '),rotation=45)#Initialize labels and linetype arrays lines=[]labels=[]#Get linetypes and labels for ax in fig.axes by looping: axLine, axLabel = ax.get_legend_handles_labels() lines.extend(axLine) labels.extend(axLabel)#Set legend and adjust legend position fig.legend(lines, labels,loc='lower center', ncol=5,framealpha=False,fontsize=25)

The results are shown below

At this time, we will comment out the label and line type in the original code. The code is as follows:

#Import data (readers can ignore) pre_lp=total_res#Combination model true=diff1[-pre_day:]#True value pre_ph=results_data["yhat"]#prophetpre_lstm=reslut#lstmpre_ari=data_ari <$'data_pre']#arima#Set Chinese font rcParams <$'font.sans-serif'] = 'kaiti'#Generate a time series (readers can modify or delete according to the situation) time =pd.to_datetime(np.arange(0,21), unit='D', origin=pd.Timestamp ('2021 -10- 19')#Create canvas fig=plt.figure (figsize=(20,16))#figsize is canvas size # 1 ax1= fig. add_subplot(221)ax1.plot (time,pre_lp,color='#1bb9f6',marker='^',linestyle='-',label='1')ax1.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax1.set_title ('1', fontsize=15)#Set title ax1.set_xlabel ('Date/day', fontsize=15)#Set abscissa name ax1.set_ylabel ('Infected people/person', fontsize=15)#Set ordinate name ax1.xaxis.set_major_formatter (mdate.DateFormatter ('% m-% d'))#Set abscissa scale (readers can ignore) plt.xticks (pd.date_range(time[0],time[-1],freq='D '),rotation=45)#Set abscissa scale (readers can ignore)# 2 ax2=fig.add_subplot(222)ax2.plot (time,pre_ph,color='#739b06',marker='o',linestyle='-',label='2')ax2.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax2.set_title ('2',fontsize=15)ax2.set_xlabel ('Date/day', fontsize=15)ax2.set_ylabel ('infected/person', fontsize=15)ax2.xaxis.set_major_formatter (mdate.DateFormatter('%m-%d'))plt.xticks (pd.date_range(time[0],time[-1],freq='D'),rotation=45)# 3 ax3=fig.add_subplot(223)ax3.plot (time,pre_lstm,color='#38d9a9',marker='*',linestyle='-',label='3')ax3.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax3.set_title ('3',fontsize=15)ax3.set_xlabel ('Date/day', fontsize=15)ax3.set_ylabel ('infected/person', fontsize=15)ax3.xaxis.set_major_formatter (mdate.DateFormatter('%m-%d'))plt.xticks (pd.date_range(time[0],time[-1],freq='D'),rotation=45)# 4 ax4=fig.add_subplot(224)ax4.plot (time,pre_ari,color='#e666ff',marker='x',linestyle='-',label='4')ax4.plot (time,true,color='#fd5749',marker='s',linestyle='-',label='true')ax4.set_title ('4',fontsize=15)ax4.set_xlabel ('Date/day', fontsize=15)ax4.set_ylabel ('infected/person', fontsize=15)ax4.xaxis.set_major_formatter (mdate.DateFormatter ('% m-% d'))plt.xticks(pd.date_range(time[0],time[-1],freq='D '),rotation=45)#Initialize labels and linetype arrays # lines=[]# labels=[]#Get linetypes and labels# for ax in fig.axes:# axLine, axLabel = ax.get_legend_handles_labels()# lines.extend(axLine)# labels.extend(axLabel)#Set legend and adjust legend position fig.legend(lines, labels,loc='lower center', ncol=5,framealpha=False,fontsize=25)

The results are shown below

Adjust subgraph spacing

plt.subplots_adjust(wspace=0.4,hspace=0.4)

wspace is the wide spacing between subgraphs, hspace is the high spacing between subgraphs

The comparison chart is as follows

Images with spacing set

Images with no set spacing

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