Available now! Telegram Research 2025 โ the year's key insights 
Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Show more๐ Analytical overview of Telegram channel Python for Data Analysts
Channel Python for Data Analysts (@pythonanalyst) in the English language segment is an active participant. Currently, the community unites 51 502 subscribers, ranking 2 594 in the Technologies & Applications category and 7 077 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 51 502 subscribers.
According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 104 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.99%. Within the first 24 hours after publication, content typically collects 0.83% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 570 views. Within the first day, a publication typically gains 425 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- Thematic interests: Content is focused on key topics such as visualization, panda, analyst, sql, analytic.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โFind top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalyticsโ
Thanks to the high frequency of updates (latest data received on 25 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.
51 502
Subscribers
No data24 hours
-547 days
+10430 days
Data loading in progress...
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Channel Posts
| 2 |  ๐ฐ Piechart using matplotlib in Python | 1 128 |
| 3 | โ
Python Basics for Data Analytics ๐๐
Python is one of the most in-demand languages for data analytics due to its simplicity, flexibility, and powerful libraries. Here's a detailed guide to get you started with the basics:
๐ง 1. Variables Data Types
You use variables to store data.
name = "Alice" # String
age = 28 # Integer
height = 5.6 # Float
is_active = True # Boolean
Use Case: Store user details, flags, or calculated values.
๐ 2. Data Structures
โ
List โ Ordered, changeable
fruits = ['apple', 'banana', 'mango']
print(fruits[0]) # apple
โ
Dictionary โ Key-value pairs
person = {'name': 'Alice', 'age': 28}
print(person['name']) # Alice
โ
Tuple Set
Tuples = immutable, Sets = unordered unique
โ๏ธ 3. Conditional Statements
score = 85
if score >= 90:
print("Excellent")
elif score >= 75:
print("Good")
else:
print("Needs improvement")
Use Case: Decision making in data pipelines
๐ 4. Loops
For loop
for fruit in fruits:
print(fruit)
While loop
count = 0
while count < 3:
print("Hello")
count += 1
๐ฃ 5. Functions
Reusable blocks of logic
def add(x, y):
return x + y
print(add(10, 5)) # 15
๐ 6. File Handling
Read/write data files
with open('data.txt', 'r') as file:
content = file.read()
print(content)
๐งฐ 7. Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Use Case: These libraries supercharge Python for analytics.
๐งน 8. Real Example: Analyzing Data
import pandas as pd
df = pd.read_csv('sales.csv') # Load data
print(df.head()) # Preview
# Basic stats
print(df.describe())
print(df['Revenue'].mean())
๐ฏ Why Learn Python for Data Analytics?
โ
Easy to learn
โ
Huge library support (Pandas, NumPy, Matplotlib)
โ
Ideal for cleaning, exploring, and visualizing data
โ
Works well with SQL, Excel, APIs, and BI tools
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
๐ฌ Double Tap โค๏ธ for more! | 1 134 |
| 4 | Expand your job search to increase your chances of becoming a data analyst.
Here are alternative roles to explore:
1. ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐: Focuses on using data to improve business processes and decision-making.
ย ย ย
2. ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ป๐ฎ๐น๐๐๐: Specializes in analyzing operational data to optimize efficiency and performance.
ย ย ย
3. ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ถ๐ป๐ด ๐๐ป๐ฎ๐น๐๐๐: Uses data to drive marketing strategies and measure campaign effectiveness.
ย ย ย
4. ๐๐ถ๐ป๐ฎ๐ป๐ฐ๐ถ๐ฎ๐น ๐๐ป๐ฎ๐น๐๐๐: Analyzes financial data to support investment decisions and financial planning.
ย ย ย
5. ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐ ๐๐ป๐ฎ๐น๐๐๐: Evaluates product performance and user data to help product development.
ย ย ย
6. ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐๐ป๐ฎ๐น๐๐๐: Conducts data-driven research to support strategic decisions and policy development.
ย ย ย
7. ๐๐ ๐๐ป๐ฎ๐น๐๐๐: Transforms data into actionable business insights through reporting and visualization.
ย ย ย
8. ๐ค๐๐ฎ๐ป๐๐ถ๐๐ฎ๐๐ถ๐๐ฒ ๐๐ป๐ฎ๐น๐๐๐: Utilizes statistical and mathematical models to analyze large datasets, often in finance.
ย ย ย
9. ๐๐๐๐๐ผ๐บ๐ฒ๐ฟ ๐๐ป๐๐ถ๐ด๐ต๐๐ ๐๐ป๐ฎ๐น๐๐๐: Analyzes customer data to improve customer experience and drive retention.
ย ย ย
10. ๐๐ฎ๐๐ฎ ๐๐ผ๐ป๐๐๐น๐๐ฎ๐ป๐: Provides expert advice on data strategies, data management, and analytics to organizations.
ย ย ย
11. ๐ฆ๐๐ฝ๐ฝ๐น๐ ๐๐ต๐ฎ๐ถ๐ป ๐๐ป๐ฎ๐น๐๐๐: Analyzes supply chain data to optimize logistics, reduce costs, and improve efficiency.
ย ย ย
12. ๐๐ฅ ๐๐ป๐ฎ๐น๐๐๐: Uses data to improve human resources processes, from recruitment to employee retention and performance management.
Data Analyst Roadmap ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐ | 2 670 |
| 5 | ๐ฅ Pandas Scenario-Based Interview Question ๐ผ
๐ Scenario:
You have an orders dataset with:
order_id
customer_id
order_date
category
sales
๐ฏ Task:
Find the top-selling category for each month based on total sales.
โ
Pandas Solution:
import pandas as pd
# Convert to datetime
df['order_date'] = pd.to_datetime(df['order_date'])
# Extract month
df['month'] = df['order_date'].dt.strftime('%b-%Y')
# Total sales by month & category
sales_summary = (
df.groupby(['month', 'category'])['sales']
.sum()
.reset_index()
)
# Rank categories within each month
sales_summary['rank'] = (
sales_summary.groupby('month')['sales']
.rank(method='dense', ascending=False)
)
# Top category per month
result = sales_summary[sales_summary['rank'] == 1]
print(result)
๐ก Concepts Tested:
โ๏ธ groupby()
โ๏ธ Date handling
โ๏ธ Aggregation
โ๏ธ Ranking within groups
React โฅ๏ธ for more interview questions | 3 789 |
| 6 | Excel Basics for Data Analytics
Excel sits at the start of most analysis work.
What you use Excel for
โข Cleaning raw data
โข Exploring patterns
โข Quick summaries for teams
Core concepts you must know
โข Data setup
โ Freeze header row. View โ Freeze Top Row.
โ Convert range to table. Ctrl + T.
โ Use proper headers. No merged cells. One value per cell.
โข Data cleaning
โ Remove duplicates. Data โ Remove Duplicates.
โ Trim extra spaces. =TRIM(A2)
โ Convert text to numbers. =VALUE(A2)
โ Fix date format. Format Cells โ Date.
โ Handle blanks. Filter blanks, fill or delete.
โ Find and replace. Ctrl + H.
โข Essential formulas
โ Math and counts
โช SUM. =SUM(A2:A100)
โช AVERAGE. =AVERAGE(A2:A100)
โช MIN. =MIN(A2:A100)
โช MAX. =MAX(A2:A100)
โช COUNT. Counts numbers.
โช COUNTA. Counts non blanks.
โช COUNTBLANK. Counts blanks.
โ Conditional formulas
โช IF. =IF(A2>5000,"High","Low")
โช IFS. Multiple conditions.
โช AND. =AND(A2>5000,B2="West")
โช OR. =OR(A2>5000,A2<1000)
โ Lookup formulas
โช XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
โช VLOOKUP. Old but common.
โช INDEX + MATCH. Powerful alternative.
โ Text formulas
โช LEFT. =LEFT(A2,4)
โช RIGHT. =RIGHT(A2,2)
โช MID. =MID(A2,2,3)
โช LEN. =LEN(A2)
โช CONCAT or TEXTJOIN.
โช LOWER, UPPER, PROPER.
โ Date formulas
โช TODAY. Current date.
โช NOW. Date and time.
โช YEAR, MONTH, DAY.
โช DATEDIF. Date difference.
โช EOMONTH. Month end.
โข Sorting and filtering
โ Sort by multiple columns.
โ Filter by value, color, condition.
โ Top 10 filter for quick insights.
โข Conditional formatting
โ Highlight duplicates.
โ Color scales for trends.
โ Rules for thresholds. Example. Sales > 10000 in green.
โข Pivot tables
โ Insert โ PivotTable.
โ Rows. Category or Product.
โ Values. Sum, Count, Average.
โ Filters. Date, Region.
โ Refresh after data update.
โข Charts you must know
โ Column. Comparison.
โ Bar. Ranking.
โ Line. Trends over time.
โ Pie. Share or percentage.
โ Combo. Actual vs target.
โข Data validation
โ Dropdown list. Data โ Data Validation โ List.
โ Prevent wrong entries.
โข Useful shortcuts
โ Ctrl + Arrow. Jump data.
โ Ctrl + Shift + Arrow. Select range.
โ Ctrl + 1. Format cells.
โ Ctrl + L. Apply filter.
โ Alt + =. Auto sum.
โ Ctrl + Z / Y. Undo redo.
โข Common analyst mistakes to avoid
โ Merged cells.
โ Hard coded totals.
โ Mixed data types in one column.
โ No backup before cleaning.
โข Daily practice task
โ Download any sales CSV.
โ Clean it.
โ Build one pivot table.
โ Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap โฅ๏ธ For More | 3 586 |
| 7 | Read this once. There won't be a second message.
Brainlancer just launched today.
Investor-backed marketplace for ALL AI freelancers. Designers, builders, copywriters, marketers, video creators, automation experts, consultants.
If you build, design, write, or sell anything with AI, this is your moment.
How it works:
โข Register free at brainlancer.com
โข Stripe verification, 5 minutes, instant approval
โข List up to 5 services from $49 to $4,999
โข Add monthly subscriptions on top if you want
โข We bring the clients. You keep 80%.
The deal:
No subscription.
No bidding.
No chasing.
We pay all marketing.
Real talk: no services live yet. We just launched. Whoever joins first gets seen first.
The first 100 Brainlancers are onboarding right now.
In 6 months others will have founding status, recurring income, featured services on the homepage.
You'll scroll past and remember this post.
Don't.
โ brainlancer.com | 0 |
| 8 | ๐ฅ Python Case Study-Based Interview Q&A (Top 5 ๐ฅ)
๐ Q1. Sales Drop Analysis
Scenario: Sales dropped last month. How will you analyze?
๐ Check monthly trends using groupby()
๐ Compare MoM performance
๐ Identify drop by region/product
๐ Drill down to root cause
๐ Q2. Customer Segmentation
Scenario: Segment customers based on purchase behaviour
๐ Group by customer ID
๐ Calculate total spend / frequency
๐ Create segments (High, Medium, Low)
๐ Useful for business decisions
๐ Q3. Data Cleaning Case
Scenario: Dataset has missing values, duplicates, inconsistent formats
๐ Handle missing โ fillna()/dropna()
๐ Remove duplicates โ drop_duplicates()
๐ Standardize formats (dates, text)
๐ Ensure clean dataset before analysis
๐ Q4. Top Performing Products
Scenario: Find best-selling products
๐ groupby(product) + sum(sales)
๐ Sort descending
๐ Use head() for top results
๐ Can also analyze category-wise
๐ Q5. Conversion Rate Analysis
Scenario: Calculate conversion rate from visits to purchases
๐ Conversion Rate = purchases / total visits
๐ Aggregate data properly
๐ Analyze by channel/source
๐ Helps optimize marketing
๐ฅ React with โฅ๏ธ for more case-study questions | 0 |
| 9 |  ๐ฐ Local vs global variable in python | 0 |
