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Awesome Team
Philippe Cholet
https://github.com/Philippe-Cholet
Last seen 20 minutes ago
Member for 6 years, 2 months, 27 days
Difficulty Advanced
I was a math teacher in France for two years. I'm currently reconverting to IT, I'm here to improve my Python skills, and practice English.
First-mihail.ponomaryov
I didn't know `partition`, like to finally know it, so `+2`.
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I suggested you OOP thanks to veky's solution. You can look it, it also defines a `__lt__` method, but it's more readable/simpler.
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"Take the least taken detais" (with counter stock most_common) is a good strategy with really readable code IMO (if we know Counter of course).
join decorator was copied from another of my scripts making what remains pretty IMO.
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You could use `float('inf')` or `from math import inf` for min_cost.
And global variable are tricky to use. You can define a function into another. Then you won't have to use a global variable.
def cheapest_flights():
min_cost = float('inf')
def cost_calc():
# cost_
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Great use of numpy and scipy! I like the simplicity that these modules offer to your code, except lines 24-31, I would like a function similar to `pad` to reduce the matrix. :-) Or apply pad function only if it's necessary.
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with `sum([...])` instead of `sum(...)`, you store an entire list before doing the sum.
`for p in list_of_one_element` to not write the expression twice is a good idea but here, `float_expression.is_integer()` can do the job.
yours: sum([p==int(p)for a in range(int((2*n)**.5))for p in[(2*n-
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Lines 30-33 could be a single line with future python 3.8:
while next_cells := neighboring_area(cells) - obstacles:
...
See more in the description of [PEP 572](https://www.python.org/dev/peps/pep-0572/).
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For my solution, I finally compared the use of a priority queue with a stack (so a list, like a DFS implementation) and a queue (like a BFS implementation). To do minimal changes, here my changes:
```python
# Instead of
from heapq import heappop, heappush
# For use a list instead of the priority qu
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### Timings
Well first, I executed your code on all check tests. It is (with debug lines commented, because create strings for these lines took time) **2.4 times** slower than mine. Thanks to `import cProfile; cProfile.run('''tests block''', sort='tottime')`, I know it's because of `next_living_war
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write a function to avoid to write the "same thing" three times:
def some_function_name(a, b, c):
return round(m.degrees(m.acos((a ** 2 + b ** 2 - c ** 2) / (2 * a * b))))
`while` should be `if`
`return [0, 0, 0]` is enough, no need to write `list(some_list)`.
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More creative than clear IMO.
No need of parenthesis around `float` or around no rounded result: `round((...), 2)` -> `round(..., 2)`.
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L[0 if (20 > age or 40 < age) else 1]
# or just
L[20 <= age <= 40]
Why does it work? `20 <= age <= 40` is a boolean True/False, which are integers: `True == 1 and False == 0`.
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Personaly I don't like too much "append", I prefer to yield things. Less code can be more readable. It still not readable enough for me (too much indexes) but it's a start.
def rectdiff(r1, r2):
if r1[0] < r2[0]:
yield r1[0], r1[1], min(r2[0], r1[2]), r1[3]
if r1[2]
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First-colinmcnicholl
Ahah, we wrote the same code. They are minor changes obviously but it is the same.
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* Use else when needed, we exit the function when we `return` something.
* `not ... == ...` is the same as `... != ...`.
* A number that does not finish with a zero does not seem to be a special case to me, but 0 does.
* You do not really use the index, but you want to iterate on the string in a rev
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Do not overuse parentheses, while, return and ... are not functions:
def beginning_zeros(number: str) -> int:
i = 0
while number[i] == "0":
i += 1
if len(number) <= i:
break
return i
# It can even be
def beginning_z
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