logo
Splunk Team

python 3: checking next value of an iterator without iterating

  • Last Update :
  • Techknowledgy :

From https://docs.python.org/2/library/itertools.html

def pairwise(iterable):
   "s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return izip(a, b)

I think pairwise does everything you need here, so no fussing with your own generator function or tee.

reader = csv.DictReader(f, dialect = 'excel')

for current_row, next_row in pairwise(reader):
   if current_row[column] == next_row[column]:
   dosomething()

Personally, I would look back, instead of looking ahead, assuming the constraints of your scenario allow it:

it = iter(reader)
prev_row = it.next()
while True:
   try:
   cur_row = it.next()
if cur_row[column] == prev_row[column]:
   dosomething()
prev_row = cur_row
except StopIteration:
   break

i dont know if this might work but it works in android

reader = csv.DictReader(f, dialect = 'excel')
reader2 = csv.DictReader(f, dialect = 'excel')

for row in reader:
   current_row = row

for row2 in reader2
next_row = reader2.__next__()
if current_row[column] == next_row[column]:
   dosomething()
continue

Because dictionaries must retain unique keys (so cannot append rows with same keys) and csv.DictReader object is not subscriptable (so cannot reference column or row number), consider reading csv data into a list and then compare row to subsequent row:

import csv

with open(os.path.abspath(path_to_file), "r") as f:
   reader = csv.reader(f)

# APPEND READER LINES INTO LIST
csvList = []
for row in reader:
   csvList.append(row)

# ITERATE THROUGH LIST, CHECK AGAINST NEXT ROW
for i in range(len(csvList) - 1):
   # FIND THE COLUMN NUMBER(BELOW USES 1)
if (csvList[i][1] == csvList[i + 1][1]):
   doSomething()
def pairwise(iterable):
   "s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return izip(a, b)

Suggestion : 2

We use the next() function to manually iterate through all the items of an iterator. When we reach the end and there is no more data to be returned, it will raise the StopIteration Exception. Following is an example.,We can see that the int() function always returns 0. So passing it as iter(int,1) will return an iterator that calls int() until the returned value equals 1. This never happens and we get an infinite iterator.,Inside the loop, it calls next() to get the next element and executes the body of the for loop with this value. After all the items exhaust, StopIteration is raised which is internally caught and the loop ends. Note that any other kind of exception will pass through.,The built-in function iter() can be called with two arguments where the first argument must be a callable object (function) and second is the sentinel. The iterator calls this function until the returned value is equal to the sentinel.

We use the next() function to manually iterate through all the items of an iterator. When we reach the end and there is no more data to be returned, it will raise the StopIteration Exception. Following is an example.

# define a list
my_list = [4, 7, 0, 3]

# get an iterator using iter()
my_iter = iter(my_list)

# iterate through it using next()

# Output: 4
print(next(my_iter))

# Output: 7
print(next(my_iter))

# next(obj) is same as obj.__next__()

# Output: 0
print(my_iter.__next__())

# Output: 3
print(my_iter.__next__())

# This will raise error, no items left
next(my_iter)

Output

4
7
0
3
Traceback (most recent call last):
File "<string>", line 24, in <module>
      next(my_iter)
      StopIteration

A more elegant way of automatically iterating is by using the for loop. Using this, we can iterate over any object that can return an iterator, for example list, string, file etc.

>>>
for element in my_list:
   ...print(element)
   ...
   4
7
0
3

Is actually implemented as.

# create an iterator object from that iterable
iter_obj = iter(iterable)

# infinite loop
while True:
   try:
   # get the next item
element = next(iter_obj)
# do something with element
except StopIteration:
   #
if StopIteration is raised,
break from loop
break

If you do not have any idea about object-oriented programming, visit Python Object-Oriented Programming.

class PowTwo:
   ""
"Class to implement an iterator of powers of two ""
"

def __init__(self, max = 0):
   self.max = max

def __iter__(self):
   self.n = 0
return self

def __next__(self):
   if self.n <= self.max:
   result = 2 ** self.n
self.n += 1
return result
else:
   raise StopIteration

# create an object
numbers = PowTwo(3)

# create an iterable from the object
i = iter(numbers)

# Using next to get to the next iterator element
print(next(i))
print(next(i))
print(next(i))
print(next(i))
print(next(i))

Suggestion : 3

Last Updated : 03 Aug, 2022

Output:

1

Output: 

[Using next()] The contents of list are:
   1 2 3 4 5
Time taken when using next() is: 0.23 ms

[Using For - Loop] The contents of list are:
   1 2 3 4 5
Time taken when using
for loop is: 0.23 ms

Suggestion : 4

The key feature is that a generator function only runs in response to “next” operations carried out in iteration. Once a generator function returns, iteration stops. However, the for statement that’s usually used to iterate takes care of these details, so you don’t normally need to worry about them.,The mere presence of the yield statement in a function turns it into a generator. Unlike a normal function, a generator only runs in response to iteration. Here’s an experiment you can try to see the underlying mechanics of how such a function works:,If you want to implement a new kind of iteration pattern, define it using a generator function. Here’s a generator that produces a range of floating-point numbers:,If you want a generator to expose extra state to the user, don’t forget that you can easily implement it as a class, putting the generator function code in the __iter__() method. For example:

To manually consume an iterable, use the next() function and write your code to catch the StopIteration exception. For example, this example manually reads lines from a file:

with open('/etc/passwd') as f:
   try:
   while True:
   line = next(f)
print(line, end = '')
except StopIteration:
   pass

Normally, StopIteration is used to signal the end of iteration. However, if you’re using next() manually (as shown), you can also instruct it to return a terminating value, such as None, instead. For example:

with open('/etc/passwd') as f:
   while True:
   line = next(f, None)
if line is None:
   break
print(line, end = '')

The following interactive example illustrates the basic mechanics of what happens during iteration:

>>> items = [1, 2, 3]
>>> # Get the iterator
>>> it = iter(items) # Invokes items.__iter__()
>>> # Run the iterator
>>> next(it) # Invokes it.__next__()
1
>>> next(it)
2
>>> next(it)
3
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
      StopIteration
      >>>

Typically, all you need to do is define an __iter__() method that delegates iteration to the internally held container. For example:

class Node:
   def __init__(self, value):
   self._value = value
self._children = []

def __repr__(self):
   return 'Node({!r})'.format(self._value)

def add_child(self, node):
   self._children.append(node)

def __iter__(self):
   return iter(self._children)

# Example
if __name__ == '__main__':
   root = Node(0)
child1 = Node(1)
child2 = Node(2)
root.add_child(child1)
root.add_child(child2)
for ch in root:
   print(ch)
# Outputs Node(1), Node(2)

If you want to implement a new kind of iteration pattern, define it using a generator function. Here’s a generator that produces a range of floating-point numbers:

def frange(start, stop, increment):
   x = start
while x < stop:
   yield x
x += increment

To use such a function, you iterate over it using a for loop or use it with some other function that consumes an iterable (e.g., sum(), list(), etc.). For example:

>>>
for n in frange(0, 4, 0.5):
   ...print(n)
   ...
   0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
   >>>
   list(frange(0, 1, 0.125))[0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] >>>

The mere presence of the yield statement in a function turns it into a generator. Unlike a normal function, a generator only runs in response to iteration. Here’s an experiment you can try to see the underlying mechanics of how such a function works:

>>> def countdown(n):
... print('Starting to count from', n)
... while n > 0:
... yield n
... n -= 1
... print('Done!')
...

>>> # Create the generator, notice no output appears
>>> c = countdown(3)
>>> c
<generator object countdown at 0x1006a0af0>

   >>> # Run to first yield and emit a value
   >>> next(c)
   Starting to count from 3
   3

   >>> # Run to the next yield
   >>> next(c)
   2

   >>> # Run to next yield
   >>> next(c)
   1

   >>> # Run to next yield (iteration stops)
   >>> next(c)
   Done!
   Traceback (most recent call last):
   File "<stdin>", line 1, in <module>
         StopIteration
         >>>

By far, the easiest way to implement iteration on an object is to use a generator function. In Recipe 4.2, a Node class was presented for representing tree structures. Perhaps you want to implement an iterator that traverses nodes in a depth-first pattern. Here is how you could do it:

class Node:
   def __init__(self, value):
   self._value = value
self._children = []

def __repr__(self):
   return 'Node({!r})'.format(self._value)

def add_child(self, node):
   self._children.append(node)

def __iter__(self):
   return iter(self._children)

def depth_first(self):
   yield self
for c in self:
   yield from c.depth_first()

# Example
if __name__ == '__main__':
   root = Node(0)
child1 = Node(1)
child2 = Node(2)
root.add_child(child1)
root.add_child(child2)
child1.add_child(Node(3))
child1.add_child(Node(4))
child2.add_child(Node(5))

for ch in root.depth_first():
   print(ch)
# Outputs Node(0), Node(1), Node(3), Node(4), Node(2), Node(5)

Suggestion : 5

Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function.,Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to:,Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument).,Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Roughly equivalent to:

def accumulate(iterable, func = operator.add, *, initial = None):
   'Return running totals'
# accumulate([1, 2, 3, 4, 5]) -- > 1 3 6 10 15
# accumulate([1, 2, 3, 4, 5], initial = 100) -- > 100 101 103 106 110 115
# accumulate([1, 2, 3, 4, 5], operator.mul) -- > 1 2 6 24 120
it = iter(iterable)
total = initial
if initial is None:
   try:
   total = next(it)
except StopIteration:
   return
yield total
for element in it:
   total = func(total, element)
yield total
>>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8] >>>
   list(accumulate(data, operator.mul)) # running product[3, 12, 72, 144, 144, 1296, 0, 0, 0, 0] >>>
   list(accumulate(data, max)) # running maximum[3, 4, 6, 6, 6, 9, 9, 9, 9, 9]

# Amortize a 5 % loan of 1000 with 4 annual payments of 90 >>>
   cashflows = [1000, -90, -90, -90, -90] >>>
   list(accumulate(cashflows, lambda bal, pmt: bal * 1.05 + pmt))[1000, 960.0, 918.0, 873.9000000000001, 827.5950000000001]

# Chaotic recurrence relation https: //en.wikipedia.org/wiki/Logistic_map
   >>>
   logistic_map = lambda x, _: r * x * (1 - x) >>>
   r = 3.8 >>>
   x0 = 0.4 >>>
   inputs = repeat(x0, 36) # only the initial value is used >>>
   [format(x, '.2f') for x in accumulate(inputs, logistic_map)]
   ['0.40', '0.91', '0.30', '0.81', '0.60', '0.92', '0.29', '0.79', '0.63',
      '0.88', '0.39', '0.90', '0.33', '0.84', '0.52', '0.95', '0.18', '0.57',
      '0.93', '0.25', '0.71', '0.79', '0.63', '0.88', '0.39', '0.91', '0.32',
      '0.83', '0.54', '0.95', '0.20', '0.60', '0.91', '0.30', '0.80', '0.60'
   ]
def chain( * iterables):
   # chain('ABC', 'DEF') -- > A B C D E F
for it in iterables:
   for element in it:
   yield element
def from_iterable(iterables):
   # chain.from_iterable(['ABC', 'DEF']) -- > A B C D E F
for it in iterables:
   for element in it:
   yield element
def combinations(iterable, r):
   # combinations('ABCD', 2) -- > AB AC AD BC BD CD
# combinations(range(4), 3) -- > 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
   return
indices = list(range(r))
yield tuple(pool[i]
   for i in indices)
while True:
   for i in reversed(range(r)):
   if indices[i] != i + n - r:
   break
else:
   return
indices[i] += 1
for j in range(i + 1, r):
   indices[j] = indices[j - 1] + 1
yield tuple(pool[i]
   for i in indices)
def combinations(iterable, r):
   pool = tuple(iterable)
n = len(pool)
for indices in permutations(range(n), r):
   if sorted(indices) == list(indices):
   yield tuple(pool[i]
      for i in indices)

Suggestion : 6

To get the next value of the Iterable, call next() function on its iterator object. It will return the next value of the Iterable. Keep on calling this next() function to get all elements of iterable one by one. When iterator reaches the end of elements in iterable then it will throw StopIteration error.,As explained above next() function is equivalent to the hasnext() in Python. It is used to get the next value from the Iterable.,When next() function is called then it calls the __next__() function on the passed object and returns the value returned by it. Basically Iterator objects overloads the __next__() function and it returns the next value from the associated Iterable object.,When iter() function is called then it calls the __iter__() function on the passed object and returns the value returned by it. Basically a class that has overloaded __iter__() function is called Iterable and it is expected that it will return an Iterator object.

Suppose we have a list of Numbers i.e.

# List of Numbers
listOfNum = [11, 12, 13, 14, 15, 16]

# get the Iterator of list
listIterator = iter(listOfNum)

Python has a magical for loop that internally uses Iterator to iterate over the Iterable object. For example we can iterate over the elements in list (Iterable) using for loop too i.e.

# Iterate over the Iterable(list) using
for loop
for elem in listOfNum:
   print(elem)


Trending Technology


Most popular in python