def question6(df_clean):
'''
For question 6 ,
I think this data set can help us to find the most popular cuisine in new york city, based on my test grade functiion,
I think we can get the value for each kind of cuisine type, and then find the highest value, which means the most popular
'''
print 'Question 6:'
print ' The value for each type of cuisine:'
df_resturant=df_clean.set_index('CAMIS')
#make the data set to many groups by cuisine description
cuisine_sum=df_resturant.groupby('CUISINE DESCRIPTION')
#create a dictionay
cuisine_dic={'cuisine':[],'value':[]}
#a double for loop to get the value for each group
for cuisine,group in cuisine_sum:
cuisine_dic['cuisine'].append(cuisine)
df_grouped_cuisine=group.groupby(level='CAMIS',sort=False)
x=0
for CAMIS, groupx in df_grouped_cuisine:
x=x+test_restaurant_grades(groupx,CAMIS)
cuisine_dic['value'].append(x)
#convert the dictionary to dataframe
df_cuisine=pd.DataFrame.from_dict(cuisine_dic,orient='columns', dtype=None)
df_s=df_cuisine.sort(['value'],ascending=False)
# print the data frame of the value for each cuisine type
print df_s
#print df_cuisine_value
print 'By this data frame,and there almost have 84 different kind of cuisines \n I calculate the value for each kind of cuisine, which is the rank of value \n and we canfind the most popular is American, and then is chinese cuisine'
print 'this data set is useful to assessing the quality of restaurants in NYC, we can find all the value for all boroughs are positive,\n that means generally speaking the quilty for all the restaurant in NYC are improving.'
def main():
'''
clean data set
1) remove invalied grade rows
2)change the format of column GRADE DATE to a sortable kind Datw format
'''
#import data as dataframe
data=pd.read_csv('DOHMH_New_York_City_Restaurant_Inspection_Results.csv')
data=pd.DataFrame(data)
#df=data.dropna(subset=['GRADE'])
# clean the missing data and invalid data
keep=['A','B','C']
df_clean=data[data['GRADE'].isin(keep)]
#convert the GRADE column to list
list_d=list(df_clean['GRADE DATE'])
#change the format of GRADE DATE column to a datetime format for sorting
x=format_date(list_d)
df_clean['Date']=x
df_resturant=df_clean.set_index('CAMIS')
'''
make the dataframe for each boroughs with the total values
'''
#group the data set to sevral groups by the colum label boro
df_boro=df_resturant.groupby('BORO')
#df_grouped=df_resturant.groupby(level='CAMIS',sort=False)
#x=0
print 'Value of each boroughs:'
#use a double for loop to calculate value for each camis for each boroughs
for boroughs,group in df_boro:
#print boroughs
df_grouped=group.groupby(level='CAMIS',sort=False)
x=0
for CAMIS, groupx in df_grouped:
#use the test_restaurant_grads function to get value
x=x+test_restaurant_grades(groupx,CAMIS)
print boroughs
print x
'''
plot 6 each graphs
'''
plot_boro(df_clean,'nyc')
#change the name of boro values there is do not need missing boro ,so remove
boro=['BRONX','BROOKLYN','MANHATTAN','QUEENS','STATEN ISLAND']
df_clean_remove_missing=df_clean[df_clean['BORO'].isin(boro)]
df_rename=df_clean_remove_missing.replace(['BRONX','BROOKLYN','MANHATTAN','QUEENS','STATEN ISLAND'] , ['bronx','brooklyn','manhattan','queens','staten'])
#print df_rename
df_boroughs=df_rename.groupby('BORO')
# get the value for each boro
for boroughs,group in df_boroughs:
plot_boro(group,boroughs)
question6(df_clean)
