A generator function uses yield instead of return:
def evens():
num = 0
while num < 10:
yield num
num += 2
A generator is a type of iterator that yields results from a generator function.
Just call the generator function to get back a generator:
evengen = evens()
next(evengen) # 0
next(evengen) # 2
next(evengen) # 4
next(evengen) # 6
next(evengen) # 8
next(evengen) # ❌ StopIteration exception
def evens():
num = 0
while num < 2:
yield num
num += 2
gen = evens()
next(gen)
next(gen)
next()is called on the iterator, it executes the body of the generator
from the last stopping point up to the next yield statement.
yield statement, it pauses on the next statement and returns the value of the
yielded expression.
StopIteration exception.We can use for loops on generators, since generators are just special types of iterators.
def evens(start, end):
num = start + (start % 2)
while num < end:
yield num
num += 2
for num in evens(12, 60):
print(num)
Looks a lot like...
evens = [num for num in range(12, 60) if num % 2 == 0]
# Or = filter(lambda x: x % 2 == 0, range(12, 60))
for num in evens:
print(num)
Generators are lazy: they only generate the next item when needed.
Why generate the whole sequence...
def find_matches(filename, match):
matched = []
for line in open(filename):
if line.find(match) > -1:
matched.append(line)
return matched
matched_lines = find_matches('frankenstein.txt', "!")
matched_lines[0]
matched_lines[1]
...if you only want some elements?
def find_matches(filename, match):
for line in open(filename):
if line.find(match) > -1:
yield line
line_iter = find_matches('frankenstein.txt', "!")
next(line_iter)
next(line_iter)
A large list can cause your program to run out of memory!
def countdown(n):
"""
Generate a countdown of numbers from N down to 'blast off!'.
>>> c = countdown(3)
>>> next(c)
3
>>> next(c)
2
>>> next(c)
1
>>> next(c)
'blast off!'
"""
def countdown(n):
"""
Generate a countdown of numbers from N down to 'blast off!'.
>>> c = countdown(3)
>>> next(c)
3
>>> next(c)
2
>>> next(c)
1
>>> next(c)
'blast off!'
"""
while n > 0:
yield n
n -= 1
yield "blast off!"
Let's transform this function...
def virfib(n):
"""Compute the nth Virahanka-Fibonacci number, for N >= 1.
>>> virfib(6)
8
"""
prev = 0 # First Fibonacci number
curr = 1 # Second Fibonacci number
k = 1
while k < n:
(prev, curr) = (curr, prev + curr)
k += 1
return curr
..into a generator function!
def generate_virfib():
"""Generate the next Virahanka-Fibonacci number.
>>> g = generate_virfib()
>>> next(g)
0
>>> next(g)
1
>>> next(g)
1
>>> next(g)
2
"""
def generate_virfib():
"""Generate the next Virahanka-Fibonacci number.
>>> g = generate_virfib()
>>> next(g)
0
>>> next(g)
1
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
"""
prev = 0 # First Fibonacci number
curr = 1 # Second Fibonacci number
while True:
yield prev
(prev, curr) = (curr, prev + curr)
A yield from statement can be used to yield the values from an iterable one at a time.
Instead of...
def a_then_b(a, b):
for item in a:
yield item
for item in b:
yield item
list(a_then_b(["Apples", "Aardvarks"], ["Bananas", "BEARS"]))
We can write...
def a_then_b(a, b):
yield from a
yield from b
list(a_then_b(["Apples", "Aardvarks"], ["Bananas", "BEARS"]))
A yield from can also yield the results of another generator function
(which could be itself).
def countdown(k):
if k > 0:
yield k
yield from countdown(k - 1)
| Calls | Executed code | Bindings | Yields | |
|---|---|---|---|---|
>>> c = countdown(3) | def countdown(k): | k = 3 | ||
>>> next(c) | if k > 0: | |||
yield k | 3 | |||
>>> next(c) | yield from countdown(k - 1) | |||
def countdown(k): | k = 2 | |||
if k > 0: | ||||
yield k | 2 | |||
>>> next(c) | yield from countdown(k - 1) | |||
def countdown(k): | k = 1 | |||
if k > 0: | ||||
yield k | 1 | |||
>>> next(c) | yield from countdown(k - 1) | |||
def countdown(k): | k = 0 | |||
if k > 0: | ||||
yield k | ||||
yield from countdown(k - 1) | ||||
| StopIteration |
When a generator function executes a return statement, it exits and cannot yield more values.
def f(x):
yield x
yield x + 1
return
yield x + 3
list(f(2)) # [2, 3]
Python allows you to specify a value to be returned, but this value is not yielded.
def g(x):
yield x
yield x + 1
return x + 2
yield x + 3
list(g(2)) # [2, 3]
It is possible to access that return value, with this one weird trick. But you won't ever need this in 61A!
def h(x):
y = yield from g(x)
yield y
list(h(2)) # [2, 3, 4]
The number of partitions of a positive integer n, using parts up to size m, is the number
of ways in which n can be expressed as the sum of positive integer parts up to m in
increasing order.
count_partitions(6, 4)
def count_partitions(n, m):
"""
>>> count_partitions(6, 4)
9
"""
if n < 0 or m == 0:
return 0
else:
exact_match = 0
if n == m:
exact_match = 1
with_m = count_partitions(n-m, m)
without_m = count_partitions(n, m-1)
return exact_match + with_m + without_m
Each call to the generator should yield a partition.
def partitions(n, m):
"""List partitions.
>>> for p in partitions(6, 4): print(p)
4 + 2
4 + 1 + 1
3 + 3
3 + 2 + 1
3 + 1 + 1 + 1
2 + 2 + 2
2 + 2 + 1 + 1
2 + 1 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1 + 1
"""
def partitions(n, m):
"""List partitions.
>>> for p in partitions(6, 4): print(p)
4 + 2
4 + 1 + 1
3 + 3
3 + 2 + 1
3 + 1 + 1 + 1
2 + 2 + 2
2 + 2 + 1 + 1
2 + 1 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1 + 1
"""
if n < 0 or m == 0:
return
else:
if n == m:
yield str(m)
for p in partitions(n-m, m):
yield str(m) + " + " + p
yield from partitions(n, m - 1)