Tip : Use the zip() function to iterate over multiple iterables simultaneously

Example:

# Create two lists
names = ["John", "Mary", "Bob"]
ages = [30, 25, 40]

# Iterate over both lists using zip()
for name, age in zip(names, ages):
    print(f"{name} is {age} years old.")

Benefits:

* Simplifies the process of iterating over multiple lists or tuples
* Ensures that the elements from corresponding lists are aligned

Language: Python

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Scientific Python Lectures
Gaël Varoquaux et al, 2024

⌨️ Extract Text from PDFs with Python

Need to extract text from PDF files? Python makes it easy with the PyPDF2 library!

First, install the necessary module:

pip install PyPDF2

Then, use this script:

import PyPDF2

# https://t.me/pythonfreebootcamp

pdf_file = 'sample.pdf'
with open(pdf_file, 'rb') as file:
    reader = PyPDF2.PdfFileReader(file)
    text = ''
    for page_num in range(reader.numPages):
        page = reader.getPage(page_num)
        text += page.extractText()

print(text)

Effortlessly convert PDF content into text format for easy manipulation and analysis!

Python script that generates a QR code from user-provided text or a URL using the qrcode library:

import qrcode

def generate_qr_code(data, filename):
    # Create a QR code instance
    qr = qrcode.QRCode(
        version=1,
        error_correction=qrcode.constants.ERROR_CORRECT_L,
        box_size=10,
        border=4,
    )

    # Add the data to the QR code
    qr.add_data(data)
    qr.make(fit=True)

    # Generate the QR code image
    img = qr.make_image(fill_color="black", back_color="white")

    # Save the QR code image to a file
    img.save(filename)
    print(f"QR code saved as '{filename}'")

if __name__ == "__main__":
    print("QR Code Generator")
    data = input("Enter the text or URL to encode: ")
    filename = input("Enter the filename (e.g., qrcode.png): ")

    # Generate the QR code
    generate_qr_code(data, filename)

Here's how the script works:

1. The qrcode library is imported to generate QR codes.
2. The generate_qr_code function takes two arguments: data (the text or URL to encode) and filename (the name of the file to save the QR code image).
3. Inside the function, a QRCode instance is created with some configuration options, such as version, error correction level, box size, and border width.
4. The add_data method is called to add the provided data to the QR code.
5. The make method is called to generate the QR code image.
6. The make_image method is used to create a PIL image object from the QR code, specifying the fill color (black) and background color (white).
7. The save method is called on the image object to save the QR code image to the specified filename.
8. In the if __name__ == "__main__": block, the script prompts the user to enter the text or URL to encode and the filename for the QR code image.
9. The generate_qr_code function is called with the user-provided data and filename.

To run the script, save it to a file (e.g., qr_code_generator.py) and execute it using the Python interpreter. You'll be prompted to enter the text or URL to encode and the filename for the QR code image. After providing the necessary input, the script will generate the QR code image and save it to the specified filename.

Note that you need to have the qrcode library installed. You can install it using pip:

pip install qrcode

Feel free to modify the script according to your needs, such as adding error handling, customizing the QR code appearance, or integrating it with a graphical user interface (GUI) framework like Tkinter or PyQt.

Top 7 Python projects in 2024, offering a mix of web development, machine learning, data visualization, and utility applications:

1. Django 4.0 Web Development:
- Description: Utilize the latest version of Django to build scalable web applications. Django 4.0 brings new features and improvements, making it easier to develop robust web solutions.
- Skills: Web development, database management, Django ORM.
- Example: Building an e-commerce site or a blog platform [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

2. Transformers for Natural Language Processing:
- Description: Use transformer models like GPT-4 for various NLP tasks such as text classification, sentiment analysis, and text generation.
- Skills: Machine learning, NLP, working with pre-trained models.
- Example: Creating a chatbot or a sentiment analysis tool [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

3. Typer for CLI Applications:
- Description: Typer is a library for building command-line interfaces (CLIs) with ease using Python's type hints.
- Skills: CLI development, Python scripting.
- Example: Developing a CLI tool for managing tasks or interacting with APIs [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

4. Dash for Data Visualization:
- Description: Create interactive and customizable web-based data visualizations using Dash, which is built on Flask and Plotly.
- Skills: Data visualization, web development, handling real-time data.
- Example: Developing dashboards for business analytics or monitoring IoT devices [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

5. PySyft for Decentralized Machine Learning:
- Description: PySyft extends PyTorch and TensorFlow to enable secure, privacy-preserving, and decentralized machine learning.
- Skills: Federated learning, data privacy, distributed computing.
- Example: Implementing a federated learning system for collaborative model training across multiple devices [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

6. Real-Time Video Processing with Chromakey:
- Description: Build applications that apply chromakey effects (green screen) to videos in real-time using HTML, CSS, and JavaScript integrated with Python.
- Skills: Video processing, web development, real-time data handling.
- Example: Creating a virtual background application for video conferencing [[❞]](https://dev.to/mukeshkuiry/25-web-development-projects-you-must-work-on-2024-4onl).

7. PyInstaller for Creating Standalone Executables:
- Description: Use PyInstaller to package Python applications into standalone executables, making distribution simpler and more user-friendly.
- Skills: Software packaging, application distribution.
- Example: Packaging a Python script into an executable for Windows, Mac, or Linux systems [[❞]](https://engineersplanet.com/exploring-the-top-10-python-projects-2024/).

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⌨️ Remove the Background of an Image Using Python

# pip install rembg pillow

from rembg import remove
from PIL import Image

input_path = "in.png"
output_path = "out.png"

input = Image.open(input_path)
output = remove(input)
output.save(output_path)

One line printing in python.

We know that the print function prints on each line, and if we use two print functions, they will print on two lines. This example will show how to output on the same line without branching to a new one.

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:

1. Computer vision
2. Natural language processing.

I outlined a roadmap for computer vision I believe many beginners will find helpful.

Artificial Intelligence

Python Code to remove Image Background
—————————————————————-
from rembg import remove
from PIL import Image

image_path = 'Image Name' ## ---> Change to Image name

output_image = 'ImageNew' ## ---> Change to new name your image

input = Image.open(image_path)

output = remove(input)

output.save(output_image)

Print calendar using Python

Web Data Mining with Python (2023)

Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.