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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India14 432 Technologies & Applications4 615...

📈 Analytical overview of Telegram channel Python Interviews

Channel Python Interviews (@pythoninterviews) in the English language segment is an active participant. Currently, the community unites 28 836 subscribers, ranking 4 615 in the Technologies & Applications category and 14 432 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 28 836 subscribers.

According to the latest data from 26 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 78 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.43%. Within the first 24 hours after publication, content typically collects 0.57% reactions from the total number of subscribers.
Post reach: On average, each post receives 701 views. Within the first day, a publication typically gains 163 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as |--, link:-, learning, sql, analytic.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 27 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

28 836

Subscribers

-124 hours

+417 days

+7830 days

701

Post views

~ 16324 hours

~ 22248 hours

2.43%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

28 836

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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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