Matplotlib_15.py

center_type_name = ['TYPE_A','TYPE_B','TYPE_C'] 

#relation between op area and number of orders 
op_table=pd.pivot_table(df,index='op_area',values='num_orders',aggfunc=np.sum) 

#relation between center type and op area 
c_type = {} 
for i in center_type_name: 
    c_type[i] = df[df['center_type']==i].op_area 

#relation between center type and num of orders 
center_table=pd.pivot_table(df,index='center_type',values='num_orders',aggfunc=np.sum) 

#subplots 
fig,ax = plt.subplots(nrows=3,ncols=1,figsize=(8,12)) 

#scatter plots 
ax[0].scatter(op_table.index,op_table['num_orders'],color='pink') 
ax[0].set_xlabel('Operation area') 
ax[0].set_ylabel('Number of orders') 
ax[0].set_title('Does operation area affect num of orders?') 
ax[0].annotate('optimum operation area of 4 km^2',xy=(4.2,1.1*10**7),xytext=(7,1.1*10**7),arrowprops=dict(facecolor='black', shrink=0.05),fontsize=12) 

#boxplot 
ax[1].boxplot([x for x in c_type.values()], labels=[x for x in c_type.keys()]) 
ax[1].set_xlabel('Center type') 
ax[1].set_ylabel('Operation area') 
ax[1].set_title('Which center type had the optimum operation area?') 

#bar graph 
ax[2].bar(center_table.index,center_table['num_orders'],alpha=0.7,color='orange',width=0.5) 
ax[2].set_xlabel('Center type') 
ax[2].set_ylabel('Number of orders') 
ax[2].set_title('Orders per center type') 

#show figure 
plt.tight_layout() 
plt.savefig('C:\\Users\\Dell\\Desktop\\AV Plotting images\\matplotlib_plotting_12.png',dpi=300,bbox_inches='tight') 
plt.show();