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Python Interviews

Python Interviews

Kanalga Telegram’da oā€˜tish

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

Ko'proq ko'rsatish

šŸ“ˆ Telegram kanali Python Interviews analitikasi

Python Interviews (@pythoninterviews) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 28 836 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 615-o'rinni va Hindiston mintaqasida 14 432-o'rinni egallagan.

šŸ“Š Auditoriya koā€˜rsatkichlari va dinamika

невіГомо sanasidan buyon loyiha tez oā€˜sib, 28 836 obunachiga ega boā€˜ldi.

26 Iyul, 2026 dagi oxirgi ma’lumotlarga koā€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 78 ga, soā€˜nggi 24 soatda esa -1 ga oā€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oā€˜rtacha 2.43% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.57% ini tashkil etuvchi reaksiyalarni toā€˜playdi.
  • Post qamrovi: Har bir post oā€˜rtacha 701 marta koā€˜riladi; birinchi sutkada odatda 163 ta koā€˜rish yigā€˜iladi.
  • Reaksiyalar va oā€˜zaro ta’sir: Auditoriya faol: har bir postga oā€˜rtacha 2 ta reaksiya keladi.
  • Tematik yoā€˜nalishlar: Kontent |--, link:-, learning, sql, analytic kabi asosiy mavzularga jamlangan.

šŸ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
ā€œJoin 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: @coderfunā€

Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boā€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini koā€˜rsatadi.

28 836
Obunachilar
-124 soatlar
+417 kunlar
+7830 kunlar
Postlar arxiv
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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()
  
This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features. I have curated the best resources to learn Python šŸ‘‡šŸ‘‡ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Hope you'll like it Like this post if you need more resources like this šŸ‘ā¤ļø

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