现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Python for Data Analysts
前往频道在 Telegram
Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
显示更多📈 Telegram 频道 Python for Data Analysts 的分析概览
频道 Python for Data Analysts (@pythonanalyst) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 51 502 名订阅者，在 技术与应用 类别中位列第 2 594，并在 印度 地区排名第 7 077 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 51 502 名订阅者。
根据 24 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 104，过去 24 小时变化为 0，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 4.99%。内容发布后 24 小时内通常能获得 0.83% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 570 次浏览，首日通常累积 425 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 8。
- 主题关注点： 内容集中在 visualization, panda, analyst, sql, analytic 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Find top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalytics”
凭借高频更新（最新数据采集于 25 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
51 502
订阅者
无数据24 小时
-547 天
+10430 天
数据加载中...
相似频道
标签云
进出提及
---
---
---
---
---
---
吸引订阅者
六月 '26
六月 '26
+188
在2个频道中
五月 '26
+373
在4个频道中
Get PRO
四月 '26
+226
在1个频道中
Get PRO
三月 '26
+176
在3个频道中
Get PRO
二月 '26
+507
在7个频道中
Get PRO
一月 '26
+679
在1个频道中
Get PRO
十二月 '25
+606
在1个频道中
Get PRO
十一月 '25
+659
在6个频道中
Get PRO
十月 '25
+766
在0个频道中
Get PRO
九月 '25
+880
在12个频道中
Get PRO
八月 '25
+963
在28个频道中
Get PRO
七月 '25
+691
在28个频道中
Get PRO
六月 '25
+1 423
在26个频道中
Get PRO
五月 '25
+2 819
在29个频道中
Get PRO
四月 '25
+3 765
在24个频道中
Get PRO
三月 '25
+689
在32个频道中
Get PRO
二月 '25
+798
在27个频道中
Get PRO
一月 '25
+1 116
在22个频道中
Get PRO
十二月 '24
+687
在8个频道中
Get PRO
十一月 '24
+1 160
在11个频道中
Get PRO
十月 '24
+1 440
在18个频道中
Get PRO
九月 '24
+1 558
在16个频道中
Get PRO
八月 '24
+2 026
在13个频道中
Get PRO
七月 '24
+3 523
在12个频道中
Get PRO
六月 '24
+2 963
在11个频道中
Get PRO
五月 '24
+3 323
在10个频道中
Get PRO
四月 '24
+3 084
在8个频道中
Get PRO
三月 '24
+3 970
在7个频道中
Get PRO
二月 '24
+3 196
在1个频道中
Get PRO
一月 '24
+3 989
在4个频道中
Get PRO
十二月 '23
+2 324
在6个频道中
Get PRO
十一月 '23
+3 962
在4个频道中
| 日期 | 订阅者增长 | 提及 | 频道 | |
| 25 六月 | +10 | |||
| 24 六月 | +5 | |||
| 23 六月 | +1 | |||
| 22 六月 | 0 | |||
| 21 六月 | +1 | |||
| 20 六月 | 0 | |||
| 19 六月 | +2 | |||
| 18 六月 | +8 | |||
| 17 六月 | 0 | |||
| 16 六月 | +8 | |||
| 15 六月 | +9 | |||
| 14 六月 | +10 | |||
| 13 六月 | +22 | |||
| 12 六月 | +15 | |||
| 11 六月 | +1 | |||
| 10 六月 | +16 | |||
| 09 六月 | +5 | |||
| 08 六月 | 0 | |||
| 07 六月 | +5 | |||
| 06 六月 | +10 | |||
| 05 六月 | +22 | |||
| 04 六月 | +11 | |||
| 03 六月 | +9 | |||
| 02 六月 | +11 | |||
| 01 六月 | +7 |
频道帖子
| 2 |  🔰 Piechart using matplotlib in Python | 1 128 |
| 3 | ✅ Python Basics for Data Analytics 📊🐍
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:
🧠 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.
🔄 2. Data Structures
✅ List – Ordered, changeable
fruits = ['apple', 'banana', 'mango']
print(fruits[0]) # apple
✅ Dictionary – Key-value pairs
person = {'name': 'Alice', 'age': 28}
print(person['name']) # Alice
✅ Tuple Set
Tuples = immutable, Sets = unordered unique
⚙️ 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
🔁 4. Loops
For loop
for fruit in fruits:
print(fruit)
While loop
count = 0
while count < 3:
print("Hello")
count += 1
🔣 5. Functions
Reusable blocks of logic
def add(x, y):
return x + y
print(add(10, 5)) # 15
📂 6. File Handling
Read/write data files
with open('data.txt', 'r') as file:
content = file.read()
print(content)
🧰 7. Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Use Case: These libraries supercharge Python for analytics.
🧹 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())
🎯 Why Learn Python for Data Analytics?
✅ Easy to learn
✅ Huge library support (Pandas, NumPy, Matplotlib)
✅ Ideal for cleaning, exploring, and visualizing data
✅ Works well with SQL, Excel, APIs, and BI tools
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
💬 Double Tap ❤️ 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. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Focuses on using data to improve business processes and decision-making.
2. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Specializes in analyzing operational data to optimize efficiency and performance.
3. 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Uses data to drive marketing strategies and measure campaign effectiveness.
4. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes financial data to support investment decisions and financial planning.
5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Evaluates product performance and user data to help product development.
6. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Conducts data-driven research to support strategic decisions and policy development.
7. 𝗕𝗜 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Transforms data into actionable business insights through reporting and visualization.
8. 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Utilizes statistical and mathematical models to analyze large datasets, often in finance.
9. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes customer data to improve customer experience and drive retention.
10. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁: Provides expert advice on data strategies, data management, and analytics to organizations.
11. 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes supply chain data to optimize logistics, reduce costs, and improve efficiency.
12. 𝗛𝗥 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Uses data to improve human resources processes, from recruitment to employee retention and performance management.
Data Analyst Roadmap 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊 | 2 670 |
| 5 | 🔥 Pandas Scenario-Based Interview Question 🐼
📊 Scenario:
You have an orders dataset with:
order_id
customer_id
order_date
category
sales
🎯 Task:
Find the top-selling category for each month based on total sales.
✅ 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)
💡 Concepts Tested:
✔️ groupby()
✔️ Date handling
✔️ Aggregation
✔️ Ranking within groups
React ♥️ 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
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams
Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.
• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.
• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.
• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.
• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.
• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.
• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.
• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.
• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.
• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.
• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap ♥️ 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:
• Register free at brainlancer.com
• Stripe verification, 5 minutes, instant approval
• List up to 5 services from $49 to $4,999
• Add monthly subscriptions on top if you want
• 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.
→ brainlancer.com | 0 |
| 8 | 🔥 Python Case Study-Based Interview Q&A (Top 5 🔥)
📊 Q1. Sales Drop Analysis
Scenario: Sales dropped last month. How will you analyze?
👉 Check monthly trends using groupby()
👉 Compare MoM performance
👉 Identify drop by region/product
👉 Drill down to root cause
📊 Q2. Customer Segmentation
Scenario: Segment customers based on purchase behaviour
👉 Group by customer ID
👉 Calculate total spend / frequency
👉 Create segments (High, Medium, Low)
👉 Useful for business decisions
📊 Q3. Data Cleaning Case
Scenario: Dataset has missing values, duplicates, inconsistent formats
👉 Handle missing → fillna()/dropna()
👉 Remove duplicates → drop_duplicates()
👉 Standardize formats (dates, text)
👉 Ensure clean dataset before analysis
📊 Q4. Top Performing Products
Scenario: Find best-selling products
👉 groupby(product) + sum(sales)
👉 Sort descending
👉 Use head() for top results
👉 Can also analyze category-wise
📊 Q5. Conversion Rate Analysis
Scenario: Calculate conversion rate from visits to purchases
👉 Conversion Rate = purchases / total visits
👉 Aggregate data properly
👉 Analyze by channel/source
👉 Helps optimize marketing
🔥 React with ♥️ for more case-study questions | 0 |
| 9 |  🔰 Local vs global variable in python | 0 |
