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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Сеть:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert Индия14 432 Технологии и приложения4 615...

📈 Аналитический обзор Telegram-канала Python Interviews

Канал Python Interviews (@pythoninterviews) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 28 836 подписчиков, занимая 4 615 место в категории Технологии и приложения и 14 432 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 28 836 подписчиков.

Согласно последним данным от 26 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 78, а за последние 24 часа — -1, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.43%. В первые 24 часа после публикации контент обычно набирает 0.57% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 701 просмотров. В течение первых суток публикация набирает 163 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как |--, link:-, learning, sql, analytic.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”

Благодаря высокой частоте обновлений (последние данные получены 27 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

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Подписчики

-124 часа

+417 дней

+7830 день

701

Просмотры поста

~ 16324 часа

~ 22248 часов

2.43%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

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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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