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age = 25
name = "John"
- Data Types: Python supports various data types, including int, float, str, list, tuple, and more. Example:
height = 1.75 # float
colors = ['red', 'green', 'blue'] # list
- Basic Operations: You can perform basic arithmetic operations:
result = 10 + 5
2. Control Structures (If Statements, Loops):
- If Statements: Conditional statements allow you to make decisions in your code.
age = 18
if age >= 18:
print("You are an adult.")
else:
print("You are a minor.")
- Loops (For and While): Loops are used for iterating over a sequence (string, list, tuple, dictionary, etc.).
fruits = ['apple', 'banana', 'orange']
for fruit in fruits:
print(fruit)
3. Functions and Modules:
- Functions: Functions are blocks of reusable code. Example:
def greet(name):
return f"Hello, {name}!"
result = greet("Alice")
- Modules: Modules allow you to organize code into separate files. Example:
# mymodule.py
def multiply(x, y):
return x * y
# main script
import mymodule
result = mymodule.multiply(3, 4)
Understanding these basics is crucial as they lay the foundation for more advanced topics.
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Hope it helps :)
🕸️ https://iai-hub.comComplete Python Topics for Data Analysts 😄👇 Python for Data Analysis:
https://t.me/pythonanalyst1. Introduction to Python: - Variables, data types, and basic operations. - Control structures (if statements, loops). - Functions and modules. 2. NumPy: - Array creation and manipulation. - Mathematical operations on arrays. - Indexing and slicing. 3. Pandas: - Series and DataFrame basics. - Data cleaning and manipulation. - Grouping and aggregation. 4. Matplotlib and Seaborn: - Data visualization using line plots, bar charts, and scatter plots. - Customizing plots and adding labels. 5. Data Cleaning and Preprocessing: - Handling missing data. - Removing duplicates. - Data normalization and scaling. 6. Statistical Analysis with Python: - Descriptive statistics. - Inferential statistics and hypothesis testing. 7. Scikit-Learn: - Introduction to machine learning. - Supervised and unsupervised learning algorithms. - Model evaluation and validation. 8.…
"""
Created on Thu Dec 28 12:55:48 2023
@author: [email protected]
"""
from pyzbar.pyzbar import *
import cv2
img = cv2.imread("barcode(3).png")
Barcode = decode(img)
print("Decode:", Barcode[0].data)
cv2.imshow("img", img)
cv2.waitKey(0)
💻Creating a Barcode reader using the Python language
🔻share with your friends🔻
🔹@OpenCV_olc🔹تسمح خطتك الحالية بتحليلات لما لا يزيد عن 5 قنوات. للحصول على المزيد، يُرجى اختيار خطة مختلفة.