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#pie plot 
plt.pie([x*100 for x in d_cuisine.values()],labels=[x for x in d_cuisine.keys()],autopct='%0.1f',explode=[0,0,0.1,0]) 

#label the plot 
plt.title('Cuisine share %') 
plt.savefig('C:\\Users\\Dell\\Desktop\\AV Plotting images\\matplotlib_plotting_8.png',dpi=300,bbox_inches='tight') 
plt.show();

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#dictionary for base price per cuisine
c_price = {}
for i in df['cuisine'].unique():
    c_price[i] = df[df['cuisine']==i].base_price

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#plotting boxplot 
plt.boxplot([x for x in c_price.values()],labels=[x for x in c_price.keys()]) 

#x and y-axis labels 
plt.xlabel('Cuisine') 
plt.ylabel('Price') 

#plot title 
plt.title('Analysing cuisine price') 

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#plotting histogram 
plt.hist(df['base_price'],rwidth=0.9,alpha=0.3,color='blue',bins=15,edgecolor='red') 

#x and y-axis labels 
plt.xlabel('Base price range') 
plt.ylabel('Distinct order') 

#plot title 
plt.title('Inspecting price effect') 

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#new revenue column 
df['revenue'] = df.apply(lambda x: x.checkout_price*x.num_orders,axis=1) 

#new month column 
df['month'] = df['week'].apply(lambda x: x//4) 

#list to store month-wise revenue 
month=[] 
month_order=[] 

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#subplots returns a Figure and an Axes object 
fig,ax=plt.subplots(nrows=1,ncols=2,figsize=(20,5)) 

#manipulating the first Axes 
ax[0].plot(week,week_order) 
ax[0].set_xlabel('Week') 
ax[0].set_ylabel('Revenue') 
ax[0].set_title('Weekly income') 

#manipulating the second Axes 

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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 

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train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')

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train.head()

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train.info()