Python Projects & Free Books

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Python Interview Projects & Free Courses Admin: @Coderfun

Red:Data Analytics India10 047 Tecnologías y Aplicaciones3 337...

📈 Análisis del canal de Telegram Python Projects & Free Books

El canal Python Projects & Free Books (@pythonfreebootcamp) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 926 suscriptores, ocupando la posición 3 337 en la categoría Tecnologías y Aplicaciones y el puesto 10 047 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 926 suscriptores.

Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 175, y en las últimas 24 horas de 29, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.03%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.77% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 649 visualizaciones. En el primer día suele acumular 314 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como learning, analyst, framework, link:-, structure.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Python Interview Projects & Free Courses Admin: @Coderfun”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 07 junio, 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.

40 926

Suscriptores

+2924 horas

+517 días

+17530 días

1 649

Visitas de la publicación

~ 31424 horas

~ 55048 horas

4.03%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Building Modern GUIs with Tkinter and Python

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https://topmate.io/analyst/907371 If you're a job seeker, these well structured document resources will help you to know and learn all the real time Python Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide! Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

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Do you enjoy reading this channel? Perhaps you have thought about placing ads on it? To do this, follow three simple steps: 1) Sign up: https://telega.io/c/pythonfreebootcamp 2) Top up the balance in a convenient way 3) Create an advertising post If the topic of your post fits our channel, we will publish it with pleasure.

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Python code To download from Youtube ⚙

from pytube import YouTube

# Enter the YouTube video URL
url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Create a YouTube object with the URL
yt = YouTube(url)

# Select the highest resolution video
video = yt.streams.get_highest_resolution()

# Set the output directory and filename
output_dir = "/storage/emulated/0/Documents/"
filename = yt.title+".mp4"

# Download the video
video.download(output_dir, filename)

print(f"Download complete: {filename}")

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Essential Python Libraries for Data Analytics 😄👇 Python Free Resources: https://t.me/pythondevelopersindia 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 5. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 6. PyTorch: - Deep learning library, particularly popular for neural network research. 7. Django: - High-level web framework for building robust, scalable web applications. 8. Flask: - Lightweight web framework for building smaller web applications and APIs. 9. Requests: - HTTP library for making HTTP requests. 10. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects. Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Tips to Merge two dictionary

boy={"ram":70,"Sundar":80}

girl={"riya":80,"Sonali":70}

student=boy | girl

print(student)

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Scrap Image from bing using BeautifulSoup

import requests
from bs4 import BeautifulSoup as BSP

def split_url(url):
    return url.split('&')[0]

def get_image_urls(search_query):
    url = f"https://cn.bing.com/images/search?q={search_query}&first=1&cw=1177&ch=678"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
    }
    rss = requests.get(url, headers=headers)
    soup = BSP(rss.content, "html.parser")

    all_img = []
    for img in soup.find_all('img'):
        img_url = img.get('src2')
        if img_url and img_url.startswith('https://tse2.mm.bing.net/'):
            img_url = split_url(img_url)
            all_img.append(img_url)

    return all_img

print(get_image_urls("cat"))

sample response :

['https://tse2.mm.bing.net/th?q=Cat+Portrait', ...']

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Scrap Image from google using BeautifulSoup

import requests
from bs4 import BeautifulSoup as BSP

def get_image_urls(search_query):
    url = f"https://www.google.com/search?q={search_query}&tbm=isch"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
    }
    rss = requests.get(url, headers=headers)
    soup = BSP(rss.content, "html.parser")

    all_img = []
    for img in soup.find_all('img'):
        src = img['src']
        if not src.endswith("gif"):
            all_img.append(src)

    return all_img

print(get_image_urls("boy"))

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