Text | Parson Sauce
Source: Python Technology
Why challenge yourself not to write a for loop in your code? Because this forces you to learn to use more advanced and more idiomatic syntax or libraries. The article uses python as an example to talk about a lot of syntax that everyone has seen in other people’s code but rarely uses it themselves.
It’s been a while since I started exploring the amazing language features in Python. In the beginning, I gave myself a challenge with the goal of allowing me to practice more Python language features than I would have had programming experience with other programming languages. This makes things more and more interesting! The code becomes more and more concise, and the code looks more structured and standardized. I will describe these benefits below.
Usually the for loop is used in the following usage scenarios:
-
to extract some information in a sequence.
-
Generate one sequence from another.
-
Writing for has become a habit.
Fortunately, Python already has a lot of tools to help you do this, you just need to shift your mind and think about it from a different perspective.
What you gain by avoiding writing for loops:
-
Less code
-
Better code readability
-
Less indentation (still makes sense for Python)
Let’s take a look at the following code structure:
# 1
with ...:
for ...:
if ...:
try:
except:
else:
In this example, we are dealing with multiple levels of nested code, which is difficult to read. This example uses multiple levels of nested code. What I found in this code was the indiscriminate use of indentation to mix management logic (with, try-except) and business logic (for, if). If you adhere to the convention of only using indentation for administrative logic, then the core business logic should be taken out immediately.
“Flat structures are better than nested structures” – The Zen of Python
Existing tools you can use to replace for loops
1.List Comprehension / Generator expression
Let’s look at a simple example. If you want to convert an array to another array:
result = []
for item in item_list:
new_item = do_something_with(item)
result.append(item)
If you like MapReduce, you can also use map, or List Comprehension in Python:
result = [do_something_with(item) for item in item_list]
Likewise, if you just want to iterate over the elements in an array, you can use the same code Generator Expression. result = (do_something_with(item) for item in item_list)
2. Function
If you want to map one array into another array, you can solve this problem in a more advanced and practical programming way by simply calling the map function.
doubled_list = map(lambda x: x * 2, old_list)
If you want to reduce a sequence to a single one, use reduce
from functools import reduce summation = reduce(lambda x, y: x + y, numbers)
Additionally, many Python built-in functions use iterables:
>>> a = list(range(10))
>>> a
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> all(a)
False
>>> any(a)
True
>>> max(a)
9
>>>min(a)
0
>>> list(filter(bool, a))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> set(a)
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
>>> dict(zip(a,a))
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
>>> sorted(a, reverse=True)
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
>>> str(a)
'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]'
>>> sum(a)
45
3.Extract Functions or Generators
The above two methods are good for handling simpler logic. How about more complex logic? As programmers, we write functions to abstract away complex business. The same idea applies here. If you write like this:
results = []
for item in item_list:
# setups
# condition
#processing
# calculation
results.append(result)
Clearly you’re adding too much responsibility to a block of code. Instead, I suggest you do:
def process_item(item):
# setups
# condition
#processing
# calculation
return result
results = [process_item(item) for item in item_list]
What would happen if we changed to a nested function?
results = []
for i in range(10):
for j in range(i):
results.append((i, j))
Replace it with List Comprehension to achieve this:
results = [(i, j)
for i in range(10)
for j in range(i)]
If your code block needs to log some internal state
# finding the max prior to the current item
a = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
results = []
current_max = 0
for i in a:
current_max = max(i, current_max)
results.append(current_max)
# results = [3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
We use a generator to achieve this:
def max_generator(numbers):
current_max = 0
for i in numbers:
current_max = max(i, current_max)
yield current_max
a = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
results = list(max_generator(a))
Readers may ask, “Wait a minute! You used a for loop in the generator, that’s cheating! Don’t worry, look at the code below.
Don’t write it yourself, itertools does it for you.
This module is very simple and I believe this module can replace your original for loop in most scenarios.
For example, the last example could be rewritten as:
from itertools import accumulate a = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8] resutls = list(accumulate(a, max))
In addition, if you want to iterate over a combination sequence, you need to use product(), permutations(), combinations().
Conclusion
In most cases, you don’t need to write a for loop.
You should avoid writing for loops for better code readability.
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