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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
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 51 491 名订阅者，在 技术与应用 类别中位列第 2 618，并在 印度 地区排名第 7 413 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 51 491 名订阅者。
根据 04 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 240，过去 24 小时变化为 11，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 4.08%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 100 次浏览，首日通常累积 0 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 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 05 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。
51 491
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+1124 小时
+467 天
+24030 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 05 六月 | +20 | |||
| 04 六月 | +11 | |||
| 03 六月 | +9 | |||
| 02 六月 | +11 | |||
| 01 六月 | +7 |
频道帖子
🔥 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| 2 | 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 | 2 490 |
| 3 | Read this once. There won't be a second message.
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You'll scroll past and remember this post.
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→ brainlancer.com | 2 837 |
| 4 | 🔥 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 | 3 686 |
| 5 |  🔰 Local vs global variable in python | 3 901 |
| 6 | If you are trying to transition into the data analytics domain and getting started with SQL, focus on the most useful concept that will help you solve the majority of the problems, and then try to learn the rest of the topics:
👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX
👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)
👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions
👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS
Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences
👉🏻CTE
👉🏻Views & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 4 090 |
| 7 | 🚀 Roadmap to Master Data Analytics in 50 Days! 📊📈
📅 Week 1–2: Foundations
🔹 Day 1–3: What is Data Analytics? Tools overview
🔹 Day 4–7: Excel/Google Sheets (formulas, pivot tables, charts)
🔹 Day 8–10: SQL basics (SELECT, WHERE, JOIN, GROUP BY)
📅 Week 3–4: Programming Data Handling
🔹 Day 11–15: Python for data (variables, loops, functions)
🔹 Day 16–20: Pandas, NumPy – data cleaning, filtering, aggregation
📅 Week 5–6: Visualization EDA
🔹 Day 21–25: Data visualization (Matplotlib, Seaborn)
🔹 Day 26–30: Exploratory Data Analysis – ask questions, find trends
📅 Week 7–8: BI Tools Advanced Skills
🔹 Day 31–35: Power BI / Tableau – dashboards, filters, DAX
🔹 Day 36–40: Real-world case studies – sales, HR, marketing data
🎯 Final Stretch: Projects Career Prep
🔹 Day 41–45: Capstone projects (end-to-end analysis + report)
🔹 Day 46–48: Resume, GitHub portfolio, LinkedIn optimization
🔹 Day 49–50: Mock interviews + SQL + Excel + scenario questions
💬 Tap ❤️ for more! | 3 182 |
| 8 | 10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍 | 3 329 |
