How to draw multiple subgraphs by Python Matplotlib
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
About "Python Matplotlib how to draw multiple subgraphs" This article is shared here, I hope the above content can be of some help to everyone, so that you can learn more knowledge, if you think the article is good, please share it for more people to see.
