Collections in python.

# Collections are the inbuilt module in python

#which give more functionalites and features than inbuilt data types — list, set, dictionary,tuple etc.

#For using collections you have to import collections module

#Few the popular collections functions.

Counter

defaultdict

OrderedDict

deque

ChainMap

namedtuple()

#Counter function of Collections module

#counter function argument take sequences and return dictionary with the key and values as there number of

#occurences.
import random
l1=[random.randint(1,5) for i in range(8)]
print(“List1:”,l1)
from collections import Counter
count1=Counter(l1)
print(“count1:”,count1)
###################
List1: [5, 1, 3, 4, 2, 3, 5, 3] 
count1: Counter({3: 3, 5: 2, 1: 1, 4: 1, 2: 1})

# using dictinary comprehension checking the count

from collections import Counter
import random
dict1={k:v for k in range(10) for v in range(10)}
#print(dict1)
dict2={random.randint(1,7):random.randint(15,18) for k in range(10) for v in range(10)}
print(dict2)
count2=Counter(dict2)
print(“count2:”,count2)
count3=Counter(dict2.values())
print(“Count3:”,count3)
#################################
{3: 18, 6: 17, 4: 15, 1: 17, 5: 17, 2: 15, 7: 17}
count2: Counter({3: 18, 6: 17, 1: 17, 5: 17, 7: 17, 4: 15, 2: 15}) Count3: Counter({17: 4, 15: 2, 18: 1})

#Element- most_common

#You can sort it according to the number
#of counts in each element using most_common() function of the Counter object.
l3=[random.randint(1,3) for k in range(15)]
print(l3)
from collections import Counter
cur=Counter(l3)
print(“cur:”,cur)
print(“cur.most_common:”,cur.most_common())
#################################################
[3, 3, 1, 1, 1, 1, 2, 3, 2, 2, 1, 3, 3, 3, 1]
 cur: Counter({3: 6, 1: 6, 2: 3})
 cur.most_common: [(3, 6), (1, 6), (2, 3)]

# defaultdict second example

from collectors import defaultdict
list1=”hi hello hi hi hi helllo hi bye python great programming”.split()
count=defaultdict(int)
for name in list1:
count[name]+=1
print(“count[name]:”,count[name])
print(“count:”,count)
####################################
count[name]: 1 count[name]: 1 count[name]: 2 count[name]: 3 count[name]: 4 count[name]: 1 count[name]: 5 count[name]: 1 count[name]: 1 count[name]: 1 count[name]: 1 count: defaultdict(<class 'int'>, {'hi': 5, 'hello': 1, 'helllo': 1, 'bye': 1, 'python': 1, 'great': 1, 'programming': 1})

#nameddtuple()

The namedtuple() returns a tuple with names for each position

in the tuple. One of the biggest problems with ordinary tuples is that you have to remember the index of each field of a tuple object.

#simple tuple

tup1=(1,2,3,”hi”,”hello”,”bye”,”78",998.667,”oy”)
print(tup1)
print(tup1[4])
############
(1, 2, 3, 'hi', 'hello', 'bye', '78', 998.667, 'oy')
hello

################

from collections import namedtuple
tup1=namedtuple(‘student’,”name,roll,address”)
student1=tup1(“ram1”,3,”india”)
student2=tup1(“stud2”,4,”USA”)
print(“tup1:”,tup1)
print(“student1.name:”,student1.name)
print(“student2.address:”,student2.address)
#################################
tup1: <class '__main__.student'>
 student1.name: ram1
 student2.address: USA