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.
# pip install stegano
from stegano import lsb
secret = lsb.hide('image.png', 'очень секретный текст')
secret.save('secret_image.png')
print(lsb.reveal('secret_image.png'))
🖥 GitHubfrom PIL import Image, ImageFilter
def blur_image(image_path, output_path, radius):
image = Image.open(image_path)
blurred_image = image.filter(ImageFilter.GaussianBlur(radius))
blurred_image.save(output_path)
blur_image('cat.jpg', 'cat_out.jpg', 3)
Image.open('cat_out.jpg')
🟡 In general, the PIL library has a lot of features and excellent documentation
#library
#python
🆔 @Python4all_propip3 install aurora-ssg
• Githubclass Solution(object):
def subsetsWithDup(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
res = []
nums.sort()
self.dfs(nums, [], res)
return res
def dfs(self, nums, path, res):
res.append(path)
for i in range(len(nums)):
if i > 0 and nums[i] == nums[i-1]:
continue
self.dfs(nums[i+1:], path + [nums[i]], res)
Explanation:
Sorting:
First we sort the nums array. Sorting makes it easy to handle duplicates because similar items will be placed next to each other.
Recursive dfs method:
The dfs method is used to recursively construct all possible subsets. In dfs, path represents the current subset, and res is a list of all subsets.
Adding a subset to the result:
At each level of recursion, we add the current subset of path to the result of res.
Handling duplicates:
Before continuing the recursion, we check whether the current element nums[i] is a duplicate of the previous element. If so, skip it to avoid adding identical subsets to res.
Recursive construction of subsets:
For each element in nums, starting at the current index, we call dfs on the next elements of the array (nums[i+1:]). This means that we consider subsets that include the current element, and continue to build subsets without the current element.
Path (path) and compartment (nums):
Each time dfs is called, a new subset is created by adding the current element to path. This new list is then passed to the next level of recursion, allowing all possible subsets to be constructed.
Time and space complexity:
Time complexity: O(2^n), where n is the number of elements in nums. This is because there are 2^n subsets for an array of n elements.
Space complexity: O(2^n * n), since each of the 2^n possible subsets may require up to n elements to store.pip install --upgrade MukeshAPI
from MukeshAPI import api
response = api.ai_image("cute boy pic")
#print(response)
with open("mukesh.jpg", 'wb') as f:
f.write(response)
print("image generated successfully")
#code
🆔 @Python4all_proافراد زیادی با این سبک کار درآمد چند هزار دلاری دارند؛⁉️چرا شما نه⁉️ برای شروع این مسیر یک جلسه رایگان روز یکشنبه ساعت ۱۹ برگزار خواهد شد 🎙️توسط: علیرضا قیمتی دکتری مدیریت کسب و کار ۸ سال سابقه آموزش و فعالیت بینالمللی 🌐لینک ثبت نام: https://links.etekanesh.com/mrym10tr 📱 کانال تلگرام افراد موفق: https://t.me/TekaneshAcademy 📱 ارتباط با پشتیبانی در صورت بروز مشکل در ورود به جلسه: @Academy_Tekanesh
def threeSumClosest(self, num, target):
num.sort()
result = num[0] + num[1] + num[2]
for i in range(len(num) - 2):
j, k = i+1, len(num) - 1
while j < k:
sum = num[i] + num[j] + num[k]
if sum == target:
return sum
if abs(sum - target) < abs(result - target):
result = sum
if sum < target:
j += 1
elif sum > target:
k -= 1
else:
return result
return result
Explanation:
Sort an array:
First we sort the num array. This will allow us to use two pointers to find the closest sum.
Initializing the result:
We initialize the result variable with the sum of the first three elements of the sorted array. This will be our starting closest amount.
Traversing the array:
We use a for loop to iterate through the array. For each element we use two pointers j and k:
j starts immediately after the current element i.
k starts from the end of the array.
Two pointers:
Inside the while loop, while j is less than k, we calculate the sum of the elements num[i], num[j] and num[k].
If sum equals target, then we return sum since we found an exact match.
Result update:
If the current sum sum is closer to target than the previous closest sum result, update result.
Pointer shift:
If sum is less than target, move pointer j to the right to increase the sum.
If sum is greater than target, shift pointer k to the left to decrease the sum.
Return result:
After completing all iterations, we return result, which will contain the sum of three numbers closest to target.
Time and space complexity:
Time complexity: O(n^2), where n is the length of the array. Sorting takes O(n log n) and the basic algorithm with two pointers runs in O(n^2).
Space complexity: O(1) since we only use a few additional variables, and do not use additional memory depending on the size of the input data.from os import remove,popen
import subprocess
def sprocess(a, b="utf-8"):
p = subprocess.Popen(a,shell=True,stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
return str(p[0].decode(b)+p[1].decode(b))
vid = await message.reply_to_message.download()
print(sprocess("ffmpeg -y -i '" + vid + "' -map_metadata -1 s.mp4"))
durasi = popen("ffprobe -i '" + vid + "' -show_entries format=duration -v quiet -of csv='p=0'").read()
await message.reply_video(video="s.mp4")
remove("s.mp4")
remove(vid)
#code
🆔 @Python4all_pro
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