Python iterators and generators
last modified September 2, 2020
In this part of the Python tutorial, we work with interators and generators. Iterator is an object which allows a programmer to traverse through all the elements of a collection, regardless of its specific implementation.
In Python, an iterator is an object which implements the
iterator protocol. The iterator protocol consists of
two methods. The
__iter__() method, which must return
the iterator object, and the
next() method, which returns
the next element from a sequence.
Iterators have several advantages:
- Cleaner code
- Iterators can work with infinite sequences
- Iterators save resources
Python has several built-in objects, which implement the iterator protocol. For example lists, tuples, strings, dictionaries or files.
#!/usr/bin/env python # iterator.py str = "formidable" for e in str: print(e, end=" ") print() it = iter(str) print(next(it)) print(next(it)) print(next(it)) print(list(it))
In the code example, we show a built-in iterator on a string. In Python a
string is an immutable sequence of characters. The
returns an iterator on object. We can also use the
tuple() functions on iterators.
$ ./iterator.py f o r m i d a b l e f o r ['m', 'i', 'd', 'a', 'b', 'l', 'e']
Python reading lines
By saving system resources we mean that when working with iterators, we can get the next element in a sequence without keeping the entire dataset in memory.
#!/usr/bin/env python # read_data.py with open('data.txt', 'r') as f: while True: line = f.readline() if not line: break else: print(line.rstrip())
This code prints the contents of the
data.txt file. Instead of
using a while loop, we can apply an iterator, which simplifies our task.
#!/usr/bin/env python # read_data_iterator.py with open('data.txt', 'r') as f: for line in f: print(line.rstrip())
open() function returns a file object, which is an iterator.
We can use it in a for loop. With the usage of an iterator, the code is cleaner.
Python iterator protocol
In the following example, we create a custom object that implements the iterator protocol.
#!/usr/bin/env python # iterator_protocol.py class Seq: def __init__(self): self.x = 0 def __next__(self): self.x += 1 return self.x**self.x def __iter__(self): return self s = Seq() n = 0 for e in s: print(e) n += 1 if n > 10: break
In the code example, we create a sequence of numbers 1, 4, 27, 256, ... . This demonstrates that with iterators, we can work with infinite sequences.
def __iter__(self): return self
The for statement calls the
__iter__() function on the
container object. The function returns an iterator object that defines the
__next__(), which accesses elements in the container one at a time.
def next(self): self.x += 1 return self.x**self.x
next() method returns the next element of a sequence.
if n > 10: break
Because we are working with an infinite sequence, we must interrupt the for loop.
$ ./iterator.py 1 4 27 256 3125 46656 823543 16777216 387420489 10000000000 285311670611
The loop can be interrupted in another way. In the class definition we must
StopIteration exception. In the following
example, we redo our previous example.
#!/usr/bin/env python # stopiter.py class Seq14: def __init__(self): self.x = 0 def __next__(self): self.x += 1 if self.x > 14: raise StopIteration return self.x ** self.x def __iter__(self): return self s = Seq14() for e in s: print(e)
The code example will print first 14 numbers of a sequence.
if self.x > 14: raise StopIteration
StopIteration exception will cease the for loop.
$ ./stop_iter.py 1 4 27 256 3125 46656 823543 16777216 387420489 10000000000 285311670611 8916100448256 302875106592253 11112006825558016
This is the output of the example.
Generator is a special routine that can be used to control the iteration behaviour of a loop. A generator is similar to a function returning an array. A generator has parameters, it can be called and it generates a sequence of numbers. But unlike functions, which return a whole array, a generator yields one value at a time. This requires less memory.
Generators in Python:
- Are defined with the def keyword
- Use the
- May use several
- Return an iterator
Let's look at an generator example.
#!/usr/bin/env python # simple_generator.py def gen(): x, y = 1, 2 yield x, y x += 1 yield x, y g = gen() print(next(g)) print(next(g)) try: print(next(g)) except StopIteration: print("Iteration finished")
The program creates a very simple generator.
def gen(): x, y = 1, 2 yield x, y x += 1 yield x, y
A generator is defined with a
keyword, just like normal functions. We use two
inside the body of a generator. The
yield keyword exits the
generator and returns a value. Next time the
function of an iterator is called, we continue on the line following the
keyword. Note that the local variables are preserved throughout the iterations.
When there is nothing left to yield, a
exception is raised.
$ ./generator.py (1, 2) (2, 2) Iteration finished
The following example we calculates Fibonacci numbers. The first number of the sequence is 0, the second number is 1, and each subsequent number is equal to the sum of the previous two numbers of the sequence itself.
#!/usr/bin/env python # fibonacci_gen.py import time def fib(): a, b = 0, 1 while True: yield b a, b = b, a + b g = fib() try: for e in g: print(e) time.sleep(1) except KeyboardInterrupt: print("Calculation stopped")
The script continuously prints Fibonacci numbers to the console. It is terminated with Ctrl + C key combination.
Python generator expression
Generator expression is similar to a list comprehension. The difference is that a generator expression returns a generator, not a list.
#!/usr/bin/env python # generator_expression.py n = (e for e in range(50000000) if not e % 3) i = 0 for e in n: print(e) i += 1 if i > 100: raise StopIteration
The example calculates values that can be divided by 3 without a remainder.
n = (e for e in range(50000000) if not e % 3)
A generator expression is created with round brackets. Creating a list comprehension in this case would be very inefficient because the example would occupy a lot of memory unnecessarily. Insted of this, we create a generator expression, which generates values lazily on demand.
i = 0 for e in n: print(e) i += 1 if i > 100: raise StopIteration
In the for loop, we generate 100 values with a generator. We have done this without extensive usage of memory.
In the next example, we create a grep-like utility in Python using generator expression.
The Roman Empire (Latin: Imperium Rōmānum; Classical Latin: [ɪmˈpɛ.ri.ũː roːˈmaː.nũː] Koine and Medieval Greek: Βασιλεία τῶν Ῥωμαίων, tr. Basileia tōn Rhōmaiōn) was the post-Roman Republic period of the ancient Roman civilization, characterized by government headed by emperors and large territorial holdings around the Mediterranean Sea in Europe, Africa and Asia. The city of Rome was the largest city in the world c. 100 BC – c. AD 400, with Constantinople (New Rome) becoming the largest around AD 500, and the Empire's populace grew to an estimated 50 to 90 million inhabitants (roughly 20% of the world's population at the time).[n 7] The 500-year-old republic which preceded it was severely destabilized in a series of civil wars and political conflict, during which Julius Caesar was appointed as perpetual dictator and then assassinated in 44 BC. Civil wars and executions continued, culminating in the victory of Octavian, Caesar's adopted son, over Mark Antony and Cleopatra at the Battle of Actium in 31 BC and the annexation of Egypt. Octavian's power was then unassailable and in 27 BC the Roman Senate formally granted him overarching power and the new title Augustus, effectively marking the end of the Roman Republic.
We use this text file.
#!/usr/bin/env python # gen_grep.py import sys def grep(pattern, lines): return ((line, lines.index(line)+1) for line in lines if pattern in line) file_name = sys.argv pattern = sys.argv with open(file_name, 'r') as f: lines = f.readlines() for line, n in grep(pattern, lines): print(n, line.rstrip())
The example reads data from a file and prints lines that contain the specified pattern and their line numbers.
def grep(pattern, lines): return ((line, lines.index(line)+1) for line in lines if pattern in line)
The grep-like utility uses this generator expression. The expression goes through the list of lines and picks those, which contain the patter. It calculates the index of the line in the list, which is its line number in the file.
with open(file_name, 'r') as f: lines = f.readlines() for line, n in grep(pattern, lines): print(n, line.rstrip())
We open the file for reading and call the
grep() function on
the data. The function returns a generator, which is traversed with the for loop.
$ ./gen_grep.py Roman roman_empire.txt 1 The Roman Empire (Latin: Imperium Rōmānum; Classical Latin: [ɪmˈpɛ.ri.ũː roːˈmaː.nũː] 3 post-Roman Republic period of the ancient Roman civilization, characterized by government 13 then unassailable and in 27 BC the Roman Senate formally granted him overarching power and 14 the new title Augustus, effectively marking the end of the Roman Republic.
There are four lines that contain the 'Roman' word in the file.
In this chapter, we have covered iterators and generators in Python.