Check Battery status using Python



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Master Python programming in 15 days with Free Resources 👇

Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
- Day 3: Explore Python's built-in functions and operators.

Days 4-6: Control Structures
- Day 4: Understand conditional statements (if, elif, else).
- Day 5: Learn about loops (for and while) and iterators.
- Day 6: Work on small projects to practice using conditionals and loops.

Days 7-9: Data Structures
- Day 7: Learn about lists and how to manipulate them.
- Day 8: Explore dictionaries and sets.
- Day 9: Understand tuples and lists comprehensions.

Days 10-12: Functions and Modules
- Day 10: Learn how to define functions in Python.
- Day 11: Understand scope and global vs. local variables.
- Day 12: Explore Python's module system and create your own modules.

Days 13-15: Intermediate Concepts
- Day 13: Work with file handling and I/O operations.
- Day 14: Learn about exceptions and error handling.
- Day 15: Explore more advanced topics like object-oriented programming and libraries such as NumPy, pandas, and Matplotlib.

FREE RESOURCES TO LEARN PYTHON 👇

Microsoft course for Python: https://learn.microsoft.com/en-us/training/paths/beginner-python/

Harvard course for Python: http://cs50.harvard.edu/python/2022/

Freecodecamp Python course with certificate: https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course



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Build a Business Analytics Dashboard  Website using Python and MySQL



https://morioh.com/p/4dc41fee9160?f=5c21fb01c16e2556b555ab32

#python #mysql

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تبدیل متن به مقادیر عددی در پایتون

Numerizer

$ pip install numerizer

>>> from numerizer import numerize
>>> numerize('forty two')
'42'
>>> numerize('forty-two')
'42'
>>> numerize('four hundred and sixty two')
'462'
>>> numerize('one fifty')
'150'
>>> numerize('twelve hundred')
'1200'
>>> numerize('twenty one thousand four hundred and seventy three')
'21473'
>>> numerize('one million two hundred and fifty thousand and seven')
'1250007'
>>> numerize('one billion and one')
'1000000001'
>>> numerize('nine and three quarters')
'9.75'
>>> numerize('platform nine and three quarters')
'platform 9.75'

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Desktop Notification with Python


#code #Python
🆔 @Python4all_pro

🎵 UVR5 UI 🎵

The perfect Python tool to remove voice from audio using the user-friendly Gradio UI.

👉 This project is based on python-audio-separator (CLI version of UVR5).



▪Github
▪Colab


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🆔 @Python4all_pro

Calendar month using Python



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21 پروژه پایتون از سطح مبتدی تا پیشرفته

https://morioh.com/p/bab182ed697a?f=5c21fb01c16e2556b555ab32



#پروژه #Python
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⌨️ Useful Python Modules



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365 Days Python Challenge https://bit.ly/3Rr3OrM




#پروژه #Python
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IP Address Information using Python


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Build an AI RAG agent with web access using GPT-4o in just 15 lines of Python Code (step-by-step instructions):


https://theunwindai.com/p/build-an-ai-rag-agent-with-web-access-using-gpt-4o



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Python Pandas Cheat Sheet


#cheat_sheet #Python
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🖥 Hallo2:Long-Duration and High-Resolution Audio-driven Portrait Image Animation

Python project for long duration, high resolution portrait animation.

▪ GitHub: https://github.com/fudan-generative-vision/hallo2
▪ Project: https://fudan-generative-vision.github.io/hallo2/#/


#Python
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🖥 py2many: Python to many CLike languages ​​transpiler

The py2many tool helps you translate Python code into code in various languages, including Rust.

It supports many languages ​​such as Rust, C++, Julia, Kotlin, and others, and is also capable of generating Python code with type annotations.

To translate the code, use a command in the terminal, after which the generated code is compiled.

Documentation with instructions for installing the necessary libraries and formatters is available on the project website.


🔗 GitHub


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#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

ادامه در پست بعد👇

#cheat_sheet #Python
🆔 @Python4all_pro

#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

1. arr[i] - Access ith element.
2. arr[i:j] - Access slice from ith to jth element.
3. arr[i:j:k] - Access slice with step k.

*Array Reshaping*

1. arr.reshape() - Reshape array.
2. arr.flatten() - Flatten array.
3. arr.ravel() - Flatten array.

*Array Manipulation*

1. numpy.concatenate() - Concatenate arrays.
2. numpy.split() - Split array.
3. numpy.transpose() - Transpose array.
4. numpy.flip() - Flip array.

*Mathematical Functions*

1. numpy.sin() - Sine.
2. numpy.cos() - Cosine.
3. numpy.tan() - Tangent.
4. numpy.exp() - Exponential.
5. numpy.log() - Natural logarithm.

*Statistical Functions*

1. numpy.min() - Minimum value.
2. numpy.max() - Maximum value.
3. numpy.percentile() - Percentile.
4. numpy.quantile() - Quantile.

*Random Number Generation*

1. numpy.random.rand() - Random numbers.
2. numpy.random.normal() - Normal distribution.
3. numpy.random.uniform() - Uniform distribution.

*Linear Algebra*

1. numpy.dot() - Dot product.
2. numpy.matmul() - Matrix multiplication.
3. numpy.linalg.inv() - Matrix inverse.

#cheat_sheet #Python
🆔 @Python4all_pro

Python Tip

#python
#Python_tricks

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Create a Progress Bars using Python



#code #Python
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Google Earth Location using Python



#code #Python
🆔 @Python4all_pro

Build 50+ Python Applications for Beginners | 10 Lines of Code

https://morioh.com/p/351b8ec6db7f




#code #Python
🆔 @Python4all_pro