پایتون ( Machine Learning | Data Science )
◀️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 ( Machine Learning | Data Science )
Channel پایتون ( Machine Learning | Data Science ) (@python4all_pro) in the Farsi language segment is an active participant. Currently, the community unites 24 627 subscribers, ranking 5 609 in the Technologies & Applications category and 13 840 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 627 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 261 over the last 30 days and by 14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.08%. Within the first 24 hours after publication, content typically collects 2.21% reactions from the total number of subscribers.
- Post reach: On average, each post receives 994 views. Within the first day, a publication typically gains 539 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 12 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.
import pandas as pd
# فایل CSV خودتون رو بخونید
df = pd.read_csv('your_data.csv')
# گزارش سریع کیفیت داده
print(f"تعداد سطرها: {len(df)}")
print(f"تعداد ستونها: {len(df.columns)}")
print(f"مقادیر خالی: {df.isnull().sum().sum()}")
print(f"سطرهای تکراری: {df.duplicated().sum()}")
# آماره سریع
print("\nخلاصه آماری:")
print(df.describe())
# مقادیر خالی به تفکیک ستون
print("\nمقادیر خالی هر ستون:")
۵ دقیقه وقت بذارید، اجراش کنید نتیجه رو ببینید شاید متوجه بشید چرا مدلتون خوب نتیجه نمیده
داده کثیف = نتیجه کثیف
این قانون طلایی علم دادس
#پایتون #Python
🆔 @Python4all_pro
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