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 24 732 subscribers, ranking 5 513 in the Technologies & Applications category and 13 706 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 732 subscribers.
According to the latest data from 16 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 621 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.69%. Within the first 24 hours after publication, content typically collects 2.32% reactions from the total number of subscribers.
- Post reach: On average, each post receives 912 views. Within the first day, a publication typically gains 573 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 17 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.
# class ListNode:
# def init(self, x):
# self.val = x
# self.next = None
class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
if head.next == None:
return None
tmp = head
size = 0
# find the size of the linked list
while tmp:
size += 1
tmp = tmp.next
tmp = head
#if we have to remove the first node:
if n == size:
return head.next
for i in range(size-n-1):
tmp = tmp.next
tmp.next = tmp.next.next
return head
Explanation:
Determining the list size:
First we go through the entire list to find its size. This is necessary to determine which node needs to be removed.
The size variable keeps track of the number of nodes in the list.
Removing the first node:
If n is equal to the size of the list, it means the first node should be removed. In this case, we return the next node from the head of the list.
Search for a node before the one you want to delete:
If n is not equal to the size of the list, we need to find the node that comes before the nth node from the end. We do this by moving size-n-1 nodes forward in the list.
Removing a node:
We set the next node for the found node so that it points to the next node after the next one, effectively bypassing the node to be deleted.
Returning a new header:
Returning the head of the list. If the first node was deleted, the new header will be the starting node of the list after the delete.
Time and space complexity:
Time complexity: O(L), where L is the length of the list. We go through the list twice: once to count nodes and again to remove a node.
Space complexity: O(1) because we use a constant amount of extra space independent of the size of the input data.
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