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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شبکه:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert الهند15 254 فناوری و برنامه‌ها4 798...

📈 تحلیل کانال تلگرام Python Interviews

کانال Python Interviews (@pythoninterviews) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 28 751 مشترک است و جایگاه 4 798 را در دسته فناوری و برنامه‌ها و رتبه 15 254 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 28 751 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 03 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 101 و در ۲۴ ساعت گذشته برابر 11 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

وضعیت تأیید: تأیید نشده
نرخ تعامل (ER): میانگین تعامل مخاطب 0.62% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.75% واکنش نسبت به کل مشترکان کسب می‌کند.
دسترسی پست‌ها: هر پست به طور میانگین 178 بازدید دریافت می‌کند. در اولین روز معمولاً 215 بازدید جمع‌آوری می‌شود.
واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 1 است.
علایق موضوعی: محتوا بر موضوعات کلیدی مانند |--, link:-, learning, sql, analytic تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“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”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 04 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

28 751

مشترکین

+1124 ساعت

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+10130 روز

178

نمایش های پست

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0.62%

نرخ مشارکت

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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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