- When compared with class level iterators, generators are very easy to use Improves memory utilization and performance.
- Generators are best suitable for reading data from large files.
- Generators work great for web scraping.
Performance – generator vs collections
Python
import random
import time
names = ['Ajay','Amit','Aman','Manish']
subjects = ['Python','Java','C']
def people_list(num_people):
results = []
for i in range(num_people):
person = {
'id':i,
'name': random.choice(names),
'subject':random.choice(subjects)
}
results.append(person)
return results
def people_generator(num_people):
for i in range(num_people):
person = {
'id':i,
'name': random.choice(names),
'major':random.choice(subjects)
}
yield person
t1 = time.localtime()
people = people_list(10000000)
t2 = time.localtime()
print('Took {}'.format(t2-t1))
t1 = time.localtime()
people = people_generator(10000000)
t2 = time.localtime()
print('Took {}'.format(t2-t1))Output
Memory – generator vs collections
Normal collection
Python
l=[x*x for x in range(10000000000000000)]
print(l[0])Output
- We will get MemoryError.
Generators
Python
g=(x*x for x in range(10000000000000000))
print(next(g))Output
PowerShell
0- We won’t get any MemoryError because the values won’t be stored at the beginning.
Ungraded Questions
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