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

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India15 254 Tecnologías y Aplicaciones4 798...

📈 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 28 751 suscriptores, ocupando la posición 4 798 en la categoría Tecnologías y Aplicaciones y el puesto 15 254 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 28 751 suscriptores.

Según los últimos datos del 03 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 101, y en las últimas 24 horas de 11, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.62%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.75% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 178 visualizaciones. En el primer día suele acumular 215 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
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 04 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.

28 751

Suscriptores

+1124 horas

+187 días

+10130 días

178

Visitas de la publicación

~ 21524 horas

Sin datos48 horas

0.62%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

28 751

Virtual Env in Python

28 751

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Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use. 1. Python Basics - Variables: x = 10

y = "Hello"

- Data Types: - Integers:

 x = 10

- Floats:

 y = 3.14

- Strings:

 name = "Alice"

- Lists:

 my_list = [1, 2, 3]

- Dictionaries:

 my_dict = {"key": "value"}

- Tuples:

 my_tuple = (1, 2, 3)

- Control Structures: - if, elif, else statements - Loops:

    for i in range(5):
        print(i)

- While loop:

    while x < 5:
        print(x)
        x += 1

2. Importing Libraries - NumPy:

  import numpy as np

- Pandas:

  import pandas as pd

- Matplotlib:

  import matplotlib.pyplot as plt

- Seaborn:

  import seaborn as sns

3. NumPy for Numerical Data - Creating Arrays:

  arr = np.array([1, 2, 3, 4])

- Array Operations:

  arr.sum()
  arr.mean()

- Reshaping Arrays:

  arr.reshape((2, 2))

- Indexing and Slicing:

  arr[0:2]  # First two elements

4. Pandas for Data Manipulation - Creating DataFrames:

  df = pd.DataFrame({
      'col1': [1, 2, 3],
      'col2': ['A', 'B', 'C']
  })

- Reading Data:

  df = pd.read_csv('file.csv')

- Basic Operations:

  df.head()          # First 5 rows
  df.describe()      # Summary statistics
  df.info()          # DataFrame info

- Selecting Columns:

  df['col1']
  df[['col1', 'col2']]

- Filtering Data:

  df[df['col1'] > 2]

- Handling Missing Data:

  df.dropna()        # Drop missing values
  df.fillna(0)       # Replace missing values

- GroupBy:

  df.groupby('col2').mean()

5. Data Visualization - Matplotlib:

  plt.plot(df['col1'], df['col2'])
  plt.xlabel('X-axis')
  plt.ylabel('Y-axis')
  plt.title('Title')
  plt.show()

- Seaborn:

  sns.histplot(df['col1'])
  sns.boxplot(x='col1', y='col2', data=df)

6. Common Data Operations - Merging DataFrames:

  pd.merge(df1, df2, on='key')

- Pivot Table:

  df.pivot_table(index='col1', columns='col2', values='col3')

- Applying Functions:

  df['col1'].apply(lambda x: x*2)

7. Basic Statistics - Descriptive Stats:

  df['col1'].mean()
  df['col1'].median()
  df['col1'].std()

- Correlation:

  df.corr()

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