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Python Itertools ModuleUse the itertools module, invoking takewhile and other methods. Implement advanced iteration logic.
Itertools. Iteration logic can be expressed with imperative loops. But a shared library of code is simpler to maintain. We find these functions in the itertools module.
With itertools, we can express iteration in a more elegant way. This is declarative code as opposed to imperative code. Even complex things like permutations can be done.
Cycle. This method generates an infinitely repeating series of values. It receives an iterable collection. And it endlessly repeats those elements, in a cycle.
List We pass a 3-element list to cycle(). We then loop over the first ten elements of the result, which are 1, 2 and 3 repeated.
Caution If you try to fully evaluate the result of cycle(), as by passing it to the list() built-in, your program will freeze.
import itertools # Cycle through these values. result = itertools.cycle([1, 2, 3]) # Display first ten results. i = 0 for value in result: print(value) i += 1 if i >= 10: break
1 2 3 1 2 3 1 2 3 1
Count. With count() we generate a count. We specify the start value (here it is zero) and then advance by a step. But using the default step of 1 is more common.
import itertools # Generate count from 0 to infinity, with step of 2. result = itertools.count(0, 2) # Display until value 10. for value in result: print(value) if value >= 10: break
0 2 4 6 8 10
Repeat. Repeat() receives 2 arguments. The first argument is the value you want to repeat. And the second argument is the number of times you wish to repeat that value.
Tip We convert the result of the repeat method with the list built-in function. This yields a 4-element list.
import itertools # Repeat the value 5 four times. result = itertools.repeat(5, 4) print(list(result))
[5, 5, 5, 5]
Takewhile. This program uses takewhile. With takewhile, we continue "taking" elements from the start until one does not match the predicate condition.
Argument We specify a lambda expression that returns true when the argument is less than 10. This is the first argument to takewhile.
And The takewhile method returns an iterator that contains four elements, but not the fifth (10) because it is not less than 10.
Also The final element, with value 1, is not included, because takewhile stopped processing after the first element that does not match.
import itertools # A list with seven values. values = [1, 5, 6, 8, 10, 12, 1] # Take values until one is higher than 9. result = itertools.takewhile(lambda v: v < 10, values) for value in result: print(value)
1 5 6 8
Dropwhile. Dropwhile() eliminates elements at the start of an iterable. The predicate lambda is evaluated—while it evaluates to true, elements are removed.
Finally When the first element that is not "dropped" is encountered, dropwhile() returns the remaining elements as an iterator.
Tip The term "skip" is often used instead of "drop." Other that the term, the idea is the same.
import itertools values = ["cat", "dog", "turnip", "carrot", "fish"] # Drop values while they are less than length 3. result = itertools.dropwhile(lambda s: len(s) <= 3, values) for value in result: print(value)
turnip carrot fish
Permutations. A permutation treats differently-ordered values as a separate result. It generates all possible sequences within an argument.
Arguments We pass the permutations() method an iterable argument. The second argument is the desired length of the results.
Here We find 2-element permutations within the list. The permutations method returns a tuple of the data.
Tuple
Tip Permutations are useful too in real programs. If you want to find all possible selections within a set of data, consider them.
import itertools values = [1, 2, 3] # Get all permutations of the three numbers. result = itertools.permutations(values, 2) for value in result: print(value)
(1, 2) (1, 3) (2, 1) (2, 3) (3, 1) (3, 2)
Chain. Chain combines many iterable collections into a single one. Here we pass chain() 3 lists, and it returns one sequence of those values. Lists are not required.
Info Chain() can accept any number of arguments. Three lists are accepted, but so are fewer or more.
import itertools values1 = [1, 2, 3, 4] values2 = [5, 6, 7, 8] values3 = [9, 10] # Chain three lists into one iterable. result = itertools.chain(values1, values2, values3) print(list(result))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Filterfalse. With filterfalse, we specify a predicate, often a lambda expression. Elements where the predicate evaluates to true are removed by filterfalse.
So The predicate matches (and removes matching) elements. Here we remove longer strings, like "parrot."
import itertools values = ["cat", "parrot", "dog", "bird"] # Filter out values with length greater than or equal to 4. result = itertools.filterfalse(lambda e: len(e) >= 4, values) for element in result: print(element)
cat dog
Discussion. With itertools we write programs in a more functional way. We can specify what we want, and have itertools do the looping and processing—this is sometimes an improvement.
However In my experience, imperative style, as with for-loops and statements, is often faster.
And It is easier for other developers to understand. I prefer using for-loops and dictionaries in programs for these reasons.
Dictionary
for
A summary. Itertools functions are powerful. A key is advantage is universality: you do not need to write them for each program. Instead you can use these functions in many programs.
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