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

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网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert 印度6 992 技术与应用2 557...

📈 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 51 622 名订阅者，在 技术与应用 类别中位列第 2 557，并在印度地区排名第 6 992 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 51 622 名订阅者。

根据 16 七月, 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 59，过去 24 小时变化为 6，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 4.30%。内容发布后 24 小时内通常能获得 1.10% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 218 次浏览，首日通常累积 566 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 17 七月, 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。

51 622

订阅者

+624 小时

+357 天

+5930 天

2 218

帖子浏览量

~ 56624 小时

~ 76448 小时

4.30%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

吸引订阅者

七月 '26

+174

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+223

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+373

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四月 '26

+226

在1个频道中

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三月 '26

+176

在3个频道中

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二月 '26

+507

在7个频道中

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一月 '26

+679

在1个频道中

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十二月 '25

+606

在1个频道中

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十一月 '25

+659

在6个频道中

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十月 '25

+766

在0个频道中

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九月 '25

+880

在12个频道中

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八月 '25

+963

在28个频道中

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七月 '25

+691

在28个频道中

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六月 '25

+1 423

在26个频道中

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五月 '25

+2 819

在29个频道中

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四月 '25

+3 765

在24个频道中

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三月 '25

+689

在32个频道中

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二月 '25

+798

在27个频道中

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一月 '25

+1 116

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十二月 '24

+687

在8个频道中

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+1 160

在11个频道中

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十月 '24

+1 440

在18个频道中

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+1 558

在16个频道中

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八月 '24

+2 026

在13个频道中

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七月 '24

+3 523

在12个频道中

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六月 '24

+2 963

在11个频道中

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五月 '24

+3 323

在10个频道中

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四月 '24

+3 084

在8个频道中

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三月 '24

+3 970

在7个频道中

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二月 '24

+3 196

在1个频道中

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一月 '24

+3 989

在4个频道中

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十二月 '23

+2 324

在6个频道中

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十一月 '23

+3 962

在4个频道中

日期	订阅者增长	提及	频道
17 七月	+3
16 七月	+13
15 七月	+25
14 七月	0
13 七月	+11
12 七月	+10
11 七月	+7
10 七月	+4
09 七月	+13
08 七月	+16
07 七月	+6
06 七月	+14
05 七月	+1
04 七月	+12
03 七月	+4
02 七月	+15
01 七月	+20

频道帖子

Aaj hi ek certified Hackar bano!💻 Shuru se saari cheeze seekho bilkul basic se!! PW skills leke aaya h certified Ethical Hacking ka course!! Isme milega : ✅ Hands on Practice ✅ LIVE Hacking Labs ✅ Certificate after Completion Sirf Rs 4999 mai Abhi enroll karo HACK30 Coupon code use karke 30% OFF milega! Enroll NOW : https://pwskills.com/web-development/certified-ethical-hacking-course-035473/?source=pwskills.com&position=course_dropdown&from=home_page&utm_source=pwskills&utm_medium=telegram&utm_campaign=ethical_hacking

2	📚 Frequently Asked Pandas Interview Questions (Beginner Level) 1️⃣ What is the difference between a Series and a DataFrame? 💡 Answer: Series → A one-dimensional labeled array. DataFrame → A two-dimensional table with rows and columns. 2️⃣ How do you find missing values in a DataFrame? 💡 Answer: df.isnull().sum() This returns the number of missing values in each column. 3️⃣ What is the difference between loc and iloc? 💡 Answer: loc → Label-based indexing. iloc → Integer position-based indexing. 4️⃣ What is the difference between merge() and concat()? 💡 Answer: merge() combines DataFrames using a common key (similar to an SQL JOIN). concat() combines DataFrames by stacking them vertically or horizontally. React ♥️ for more interview questions	973
3	GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra. What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%. The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team. ➡️ HuggingFace	1 005
4	+5 Data Visualization with Pandas	2 187
5	🎯 5 Playlists = 5 courses 👇 1/ Generative AI (freecodecamp): https://youtu.be/mEsleV16qdo?si=PgiaT2kx43xMI78O 2/ Machine Learning (freecodecamp): https://youtu.be/i_LwzRVP7bg?si=iQfXCjLOSLYfVukE 3/ Ethical Hacking: https://youtu.be/Rgvzt0D8bR4?si=W5lskoyT88a18ppU 4/ Data Analytics (WSCube Tech): https://youtu.be/VaSjiJMrq24?si=ipirg6bbI68w7YeF 3/ Cyber Security (WSCube): https://youtu.be/Zdk01t_VTOA?si=MAKJccpTvKrvQ8Td	2 802
6	+5 Data Visualization with Pandas	3 942
7	🔰 Piechart using matplotlib in Python	3 593
8	✅ 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!	3 286
9	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 😊	3 430
10	🔥 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
11	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
12	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
13	🔥 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

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