پایتون | Data Science | Machine Learning
◀️اینجا با تمرین و چالش با هم پایتون رو یاد می گیریم ⏮بانک اطلاعاتی پایتون پروژه / code/ cheat sheet +ویدیوهای آموزشی +کتابهای پایتون تبلیغات: @alloadv 🔁ادمین : @maryam3771
Show more📈 Analytical overview of Telegram channel پایتون | Data Science | Machine Learning
Channel پایتون | Data Science | Machine Learning (@python4all_pro) in the Farsi language segment is an active participant. Currently, the community unites 24 694 subscribers, ranking 5 515 in the Technologies & Applications category and 13 715 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 694 subscribers.
According to the latest data from 18 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 596 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.81%. Within the first 24 hours after publication, content typically collects 2.09% reactions from the total number of subscribers.
- Post reach: On average, each post receives 941 views. Within the first day, a publication typically gains 515 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as مصنوعی, دنیا, آموزش, پایتون, وبینار.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“◀️اینجا با تمرین و چالش با هم پایتون رو یاد می گیریم
⏮بانک اطلاعاتی پایتون
پروژه / code/ cheat sheet
+ویدیوهای آموزشی
+کتابهای پایتون
تبلیغات:
@alloadv
🔁ادمین :
@maryam3771”
Thanks to the high frequency of updates (latest data received on 19 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
مدرسین: دکتر پرستو فرنیا دکترای مهندسی پزشکی ، عضو هیات علمی دانشگاه علوم پزشکی تهران مهندس علی کاظمی دانشجوی دکترای تخصصی مهندسی پزشکی، دانشگاه علوم پزشکی تهران مهندس رضا نقنه دانشجوی دکترای تخصصی مهندسی پزشکی، دانشگاه علوم پزشکی تهران🕑زمان: پنجشنبه و جمعه، ۱۱و ۱۲ مرداد ۱۴۰۳ 📍مکان: آزمایشگاه ملی نقشه برداری مغز ‼️ظرفیت محدود‼️ 🌐برای ثبت نام و کسب اطلاعات بیشتر کلیک کنید 💠 تماس با ما: 02186093155 💠Telegram 💠Instagram 💠LinkedIn 🌐Website
nums.sort()
results = []
self.findNsum(nums, target, 4, [], results)
return results
def findNsum(self, nums, target, N, result, results):
if len(nums) < N or N < 2: return
# solve 2-sum
if N == 2:
l,r = 0,len(nums)-1
while l < r:
if nums[l] + nums[r] == target:
results.append(result + [nums[l], nums[r]])
l += 1
r -= 1
while l < r and nums[l] == nums[l - 1]:
l += 1
while r > l and nums[r] == nums[r + 1]:
r -= 1
elif nums[l] + nums[r] < target:
l += 1
else:
r -= 1
else:
for i in range(0, len(nums)-N+1): # careful about range
if target < nums[i]*N or target > nums[-1]*N: # take advantages of sorted list
break
if i == 0 or i > 0 and nums[i-1] != nums[i]: # recursively reduce N
self.findNsum(nums[i+1:], target-nums[i], N-1, result+[nums[i]], results)
return
Explanation:
Sorting:
First the nums array is sorted. Sorting makes it easier to handle duplicates and speeds up execution using binary search.
Recursive function findNsum:
The findNsum function recursively finds combinations whose sum is equal to the given target. Depending on the value of N, it handles different cases:
For N = 2: This is the "Two Sum" subtask. We use two pointers (l and r) to find pairs of numbers in the array that add up to target.
If the current pair of numbers nums[l] and nums[r] sums to target, add this pair to the results.
We move the pointers left and right, skipping duplicates to avoid repeated combinations.
For N > 2: The function calls itself recursively, decrementing N by 1 and continuing to search for combinations among the remaining elements of the array. We also check if the current element and its combinations are within a valid range (for optimization).
Conditions for exiting recursion:
If the length of the array is less than N or N is less than 2, the function terminates execution, since there is no point in further searching for combinations.
We use conditions to stop execution if the current element is too large or too small to achieve the target value for a given number of elements (N).
Unique combinations:
To avoid duplicates, we check whether the current element is unique compared to previous ones.
Collection of results:
The results for each call to the findNsum function are added to the results list.
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