Python Interviews
前往频道在 Telegram
Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun
显示更多📈 Telegram 频道 Python Interviews 的分析概览
频道 Python Interviews (@pythoninterviews) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 28 836 名订阅者,在 技术与应用 类别中位列第 4 615,并在 印度 地区排名第 14 432 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 28 836 名订阅者。
根据 26 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 78,过去 24 小时变化为 -1,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 2.43%。内容发布后 24 小时内通常能获得 0.57% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 701 次浏览,首日通常累积 163 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 |--, link:-, learning, 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:
“Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free
For collaborations: @coderfun”
凭借高频更新(最新数据采集于 27 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。
28 836
订阅者
-124 小时
+417 天
+7830 天
帖子存档
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To be GOOD in Data Science you need to learn:
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Quick Python Cheat Sheet for Beginners 🐍✍️
Python is widely used for data analysis, automation, and AI—perfect for beginners starting their coding journey.
Aggregation Functions 📊
• sum(list) → Adds all values
👉 sum([1,2,3]) = 6
• len(list) → Counts total elements
👉 len([1,2,3]) = 3
• max(list) → Highest value
👉 max([4,7,2]) = 7
• min(list) → Lowest value
👉 min([4,7,2]) = 2
• sum(list)/len(list) → Average
👉 sum([10,20])/2 = 15
Lookup / Searching 🔍
• in → Check existence
👉 5 in [1,2,5] = True
• list.index(value) → Position of value
👉 [10,20,30].index(20) = 1
• Dictionary lookup
👉 data = {"name": "John", "age": 25} data["name"] # John
Logical Operations 🧠
• if condition: → Decision making
👉 if x > 10: print("High") else: print("Low")
• and → All conditions true
• or → Any condition true
• not → Reverse condition
Text (String) Functions 🔤
• len(text) → Length
👉 len("hello") = 5
• text.lower() → Lowercase
• text.upper() → Uppercase
• text.strip() → Remove spaces
👉 " hi ".strip() = "hi"
• text.replace(old, new)
👉 "hi".replace("h","H") = "Hi"
• String concatenation
👉 "Hello " + "World"
Date Time Functions 📅
• from datetime import datetime
• datetime.now() → Current date time
• Extract values:
now = datetime.now() now.year now.month now.day
Math Functions ➗
• import math
• math.sqrt(x) → Square root
• math.ceil(x) → Round up
• math.floor(x) → Round down
• abs(x) → Absolute value
Conditional Aggregation (Like Excel SUMIF) ⚡
• Using list comprehension
nums = [10, 20, 30, 40] sum(x for x in nums if x > 20) # 70
• Count condition
len([x for x in nums if x > 20]) # 2
Pro Tip for Data Analysts 💡
👉 For real-world work, use libraries: pandas & numpy
Example:
import pandas as pd df["salary"].mean()
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Top linked list questions to practice:
1. 🔄 Reverse a Linked List
2. 🔁 Detect a Cycle in a Linked List
3. 🤝 Find the Merge Point of Two Linked Lists
4. 🚫 Remove N-th Node From End of List
5. 🔗 Merge Two Sorted Linked Lists
6. 🖼️ Check if a Linked List is a Palindrome
7. 🚨 Remove Duplicates from a Sorted List
8. 🎯 Find the Middle of a Linked List
9. 🔄 Rotate a Linked List
10. 📑 Implement a Doubly Linked List
11. 📊 Implement a Circular Linked List
12. 🛠️ Add Two Numbers Represented by Linked Lists
13. 🧹 Remove Linked List Elements
14. 🧩 Partition List around a value
15. 🔄 Reverse Nodes in k-Group
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