Python Interviews
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Ko'proq ko'rsatishš 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.
š Auditoriya koārsatkichlari va dinamika
Š½ŠµŠ²ŃŠ“омо sanasidan buyon loyiha tez oāsib, 28 836 obunachiga ega boāldi.
26 Iyul, 2026 dagi oxirgi maālumotlarga koāra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 78 ga, soānggi 24 soatda esa -1 ga oāzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oārtacha 2.43% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.57% ini tashkil etuvchi reaksiyalarni toāplaydi.
- Post qamrovi: Har bir post oārtacha 701 marta koāriladi; birinchi sutkada odatda 163 ta koārish yigāiladi.
- Reaksiyalar va oāzaro taāsir: Auditoriya faol: har bir postga oārtacha 2 ta reaksiya keladi.
- Tematik yoānalishlar: Kontent |--, link:-, learning, sql, analytic kabi asosiy mavzularga jamlangan.
š Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taāriflaydi:
āJoin 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 maālumot 27 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boālib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taāsir nuqtasiga aylantirishini koārsatadi.
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.
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