#How to do aggregates using groupby, defaultdict and Counter on flat files
#This Example computes groups for:
#region,count(region),sum(sales),avg(sales),max(sales),min(sales)
#
#By Nestor Nissen
from csv import DictReader
from operator import itemgetter
from itertools import groupby
from collections import defaultdict, Counter
rawdata='''
Region,City,Sales
Scotland,Edinburgh,20000
Scotland,Glasgow,12500
Wales,Cardiff,29700
Wales,Bangor,12800
England,London,90000
England,Manchester,45600
England,Liverpool,29700
'''.splitlines()[1:]
indata=list(DictReader(rawdata))
print('Using sort and groupby:')
counts=[]
sums=[]
avgs=[]
maxs=[]
mins=[]
ordered_data=sorted(indata,key=itemgetter('Region'))
for region,group in groupby(ordered_data,key=itemgetter('Region')):
group_list=list(group)
count=sum(1 for city in group_list)
total=sum(int(city['Sales']) for city in group_list)
maxsale=max(int(city['Sales']) for city in group_list)
minsale=min(int(city['Sales']) for city in group_list)
counts.append((region,count))
sums.append((region,total))
avgs.append((region,total/count))
maxs.append((region,maxsale))
mins.append((region,minsale))
print('count:',counts,'\nsum:',sums,'\navg:',avgs,
'\nmax:',maxs,'\nmin:',mins,'\n')
print('Using defaultdict:')
dd_counts=defaultdict(int)
dd_sales=defaultdict(int)
dd_maxs=defaultdict(int)
dd_mins=defaultdict(lambda: 9**99)
for row in indata:
region=row['Region']
sales=int(row['Sales'])
dd_counts[region]+=1
dd_sales[region]+=sales
dd_maxs[region]=max(dd_maxs[region],sales)
dd_mins[region]=min(dd_mins[region],sales)
counts=list(dd_counts.items())
sums=list(dd_sales.items())
avgs=[(key,dd_sales[key]/count) for key,count in dd_counts.items()]
maxs=list(dd_maxs.items())
mins=list(dd_mins.items())
print('count:',counts,'\nsum:',sums,'\navg:',avgs,
'\nmax:',maxs,'\nmin:',mins,'\n')
print('Using counter:')
counts=list(Counter(map(itemgetter('Region'),indata)).items())
print('count:',counts)
