Python Interviews
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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
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Red:📈 Análisis del canal de Telegram Python Interviews
El canal Python Interviews (@pythoninterviews) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 28 836 suscriptores, ocupando la posición 4 615 en la categoría Tecnologías y Aplicaciones y el puesto 14 432 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 28 836 suscriptores.
Según los últimos datos del 26 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 78, y en las últimas 24 horas de -1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.43%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.57% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 701 visualizaciones. En el primer día suele acumular 163 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como |--, link:-, learning, sql, analytic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
28 836
Suscriptores
-124 horas
+417 días
+7830 días
Carga de datos en curso...
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julio '26
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+158
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+229
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+106
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+192
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 27 julio | +13 | |||
| 26 julio | +1 | |||
| 25 julio | +8 | |||
| 24 julio | +12 | |||
| 23 julio | +10 | |||
| 22 julio | +3 | |||
| 21 julio | +19 | |||
| 20 julio | +8 | |||
| 19 julio | +5 | |||
| 18 julio | +9 | |||
| 17 julio | +5 | |||
| 16 julio | 0 | |||
| 15 julio | +17 | |||
| 14 julio | 0 | |||
| 13 julio | +5 | |||
| 12 julio | +8 | |||
| 11 julio | 0 | |||
| 10 julio | +1 | |||
| 09 julio | +7 | |||
| 08 julio | +6 | |||
| 07 julio | 0 | |||
| 06 julio | +12 | |||
| 05 julio | +2 | |||
| 04 julio | +6 | |||
| 03 julio | +4 | |||
| 02 julio | +9 | |||
| 01 julio | +14 |
Publicaciones del Canal
To be GOOD in Data Science you need to learn:
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| 3 | 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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| 8 | Top linked list questions to practice:
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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
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