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df_center = pd.read_csv('C:\\Users\Dell\\Desktop\\train_food\\fulfilment_center_info.csv') 
df_center.head()

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import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
plt.style.use('seaborn')

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df_meal = pd.read_csv('C:\\Users\Dell\\Desktop\\train_food\\meal_info.csv') 
df_meal.head()

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df_center = pd.read_csv('C:\\Users\Dell\\Desktop\\train_food\\fulfilment_center_info.csv') 
df_center.head()

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df_food = pd.read_csv('C:\\Users\Dell\\Desktop\\train_food\\train_food.csv') 
df_food.head()

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df = pd.merge(df_food,df_center,on='center_id') 
df = pd.merge(df,df_meal,on='meal_id')

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table = pd.pivot_table(data=df,index='category',values='num_orders',aggfunc=np.sum)
table

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#bar graph
plt.bar(table.index,table['num_orders'])

#xticks 
plt.xticks(rotation=70) 

#x-axis labels 
plt.xlabel('Food item') 

#y-axis labels 

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#dictionary for meals per food item
item_count = {}

for i in range(table.index.nunique()):
    item_count[table.index[i]] = table.num_orders[i]/df_meal[df_meal['category']==table.index[i]].shape[0]

#bar plot 
plt.bar([x for x in item_count.keys()],[x for x in item_count.values()],color='orange')

#adjust xticks

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#dictionary for cuisine and its total orders
d_cuisine = {}

#total number of order
total = df['num_orders'].sum()

#find ratio of orders per cuisine
for i in range(df['cuisine'].nunique()):

#cuisine