اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Data Analytics
رفتن به کانال در Telegram
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر📈 تحلیل کانال تلگرام Data Analytics
کانال Data Analytics (@dataanalyticsx) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 28 920 مشترک است و جایگاه 4 741 را در دسته فناوری و برنامهها و رتبه 22 829 را در منطقه روسيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 28 920 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 490 و در ۲۴ ساعت گذشته برابر 16 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 4.41% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.27% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 1 275 بازدید دریافت میکند. در اولین روز معمولاً 368 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 2 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند sellerflash, buybox, buyer, chaos, effortless تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
28 920
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28 920
Repost from Learn Python Coding
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28 920
SQL Cheat Sheet for Interview 2026
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28 920
Repost from Machine Learning with Python
This cheat sheet—part of our Complete Guide to #NumPy, #pandas, and #DataVisualization—offers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.
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28 920
This channels is for Programmers, Coders, Software Engineers.
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28 920
Repost from Machine Learning with Python
Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
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28 920
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Repost from Machine Learning with Python
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28 920
📊 5 Useful Python Scripts for Automated Data Quality Checks
📌 Introduction
Data quality issues are pervasive and can lead to incorrect business decisions, broken analysis, and pipeline failures. Manual data validation is time-consuming and prone to errors, making it essential to automate the process. This article discusses five useful Python scripts for automated data quality checks, addressing common issues such as missing data, invalid data types, duplicate records, outliers, and cross-field inconsistencies.
📌 Main Content / Discussion
The five Python scripts are designed to handle specific data quality issues.
import pandas as pd
import numpy as np
# Example 1: Missing data analyzer script
def analyze_missing_data(df):
missing_data = df.isnull().sum()
return missing_data
# Example 2: Data type validator script
def validate_data_types(df, schema):
for column, dtype in schema.items():
if df[column].dtype != dtype:
print(f"Invalid data type for column {column}")
return df
# Example 3: Duplicate record detector script
def detect_duplicates(df):
duplicates = df.duplicated().sum()
return duplicates
# Example 4: Outlier detection script
def detect_outliers(df, column):
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
return outliers
# Example 5: Cross-field consistency checker script
def check_cross_field_consistency(df):
# Check for temporal consistency
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
inconsistencies = df[df['start_date'] > df['end_date']]
return inconsistencies
28 920
Repost from Udemy Free Coupons
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#Polars Cheat Sheet with example
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://t.me/DataAnalyticsX 🔴
28 920
Cheatsheet for Pandas to Polar
Getting started with Polars? This post shows you how to convert some familar Pandas commands to #Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.
https://www.rhosignal.com/posts/polars-pandas-cheatsheet/
28 920
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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