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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts

---

1. What is a Dataset?

• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.

---

2. Types of Datasets

Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).

Unstructured Data: Images, text, audio, video.

Semi-structured Data: JSON, XML files containing hierarchical data.

---

3. Common Dataset Formats

• CSV (Comma-Separated Values)

• Excel (.xls, .xlsx)

• JSON (JavaScript Object Notation)

• XML (eXtensible Markup Language)

• Images (JPEG, PNG, TIFF)

• Audio (WAV, MP3)

---

4. Loading Datasets in Python

• Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')


• Use libraries like json for JSON files:

import json
with open('data.json') as f:
data = json.load(f)


---

5. Basic Dataset Exploration

• Check shape and size:

print(df.shape)


• Preview data:

print(df.head())


• Check for missing values:

print(df.isnull().sum())


---

6. Summary

• Understanding dataset types is crucial before processing.

• Loading and exploring datasets helps identify cleaning and preprocessing needs.

---

Exercise

• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.

---

#DataScience #Datasets #DataLoading #Python #DataExploration

The rest of the parts 👇
https://t.me/DataScienceM 🌟
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Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide

---

### Objective

In this lesson, we’ll build a Python script that:

• Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123)
• Downloads the HTML content of the chat
• Converts it to a PDF file using pdfkit and wkhtmltopdf

This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format.

---

### 1. Prerequisites

Before starting, you need the following libraries and tools:

#### • Install pdfkit and requests

pip install pdfkit requests


#### • Install wkhtmltopdf

Download from:
https://wkhtmltopdf.org/downloads.html

Make sure to add the path of the installed binary to your system PATH.

---

### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
print("Invalid URL. Must start with https://chat.openai.com/share/")
exit()

try:
# Download HTML content
response = requests.get(chat_url)
if response.status_code != 200:
raise Exception(f"Failed to load the chat: {response.status_code}")

html_content = response.text

# Save HTML to temporary file
with open("temp_chat.html", "w", encoding="utf-8") as f:
f.write(html_content)

# Convert HTML to PDF
pdfkit.from_file("temp_chat.html", output_file)

print(f"\n PDF saved as: {output_file}")

# Optional: remove temp file
os.remove("temp_chat.html")

except Exception as e:
print(f" Error: {e}")


---

### 3. Notes

• This approach works only if the shared page is publicly accessible (which ChatGPT share links are).
• The PDF output will contain the web page version, including theme and layout.
• You can customize the PDF output using pdfkit options (like page size, margins, etc.).

---

### 4. Optional Enhancements

• Add GUI with Tkinter
• Accept multiple URLs
• Add PDF metadata (title, author, etc.)
• Add support for offline rendering using BeautifulSoup to clean content

---

### Exercise

• Try converting multiple ChatGPT share links to PDF
• Customize the styling with your own CSS
• Add a timestamp or watermark to the PDF

---

#Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter

https://t.me/CodeProgrammer
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Python | Machine Learning | Coding | R
Photo
# 📚 Python Tutorial: Convert EPUB to PDF (Preserving Images)
#Python #EPUB #PDF #EbookConversion #Automation

This comprehensive guide will show you how to convert EPUB files (including those with images) to high-quality PDFs using Python.

---

## 🔹 Required Tools & Libraries
We'll use these Python packages:
- ebooklib - For EPUB parsing
- pdfkit (wrapper for wkhtmltopdf) - For PDF generation
- Pillow - For image handling (optional)

pip install ebooklib pdfkit pillow


Also install system dependencies:
# On Ubuntu/Debian
sudo apt-get install wkhtmltopdf

# On MacOS
brew install wkhtmltopdf

# On Windows (download from wkhtmltopdf.org)


---

## 🔹 Step 1: Extract EPUB Contents
First, we'll unpack the EPUB file to access its HTML and images.

from ebooklib import epub
from bs4 import BeautifulSoup
import os

def extract_epub(epub_path, output_dir):
book = epub.read_epub(epub_path)

# Create output directory
os.makedirs(output_dir, exist_ok=True)

# Extract all items (chapters, images, styles)
for item in book.get_items():
if item.get_type() == epub.ITEM_IMAGE:
# Save images
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())
elif item.get_type() == epub.ITEM_DOCUMENT:
# Save HTML chapters
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())

return [item.get_name() for item in book.get_items() if item.get_type() == epub.ITEM_DOCUMENT]


---

## 🔹 Step 2: Convert HTML to PDF
Now we'll convert the extracted HTML files to PDF while preserving images.

import pdfkit
from PIL import Image # For image validation (optional)

def html_to_pdf(html_files, output_pdf, base_dir):
options = {
'encoding': "UTF-8",
'quiet': '',
'enable-local-file-access': '', # Critical for local images
'no-outline': None,
'margin-top': '15mm',
'margin-right': '15mm',
'margin-bottom': '15mm',
'margin-left': '15mm',
}

# Validate images (optional)
for html_file in html_files:
soup = BeautifulSoup(open(os.path.join(base_dir, html_file)), 'html.parser')
for img in soup.find_all('img'):
img_path = os.path.join(base_dir, img['src'])
try:
Image.open(img_path) # Validate image
except Exception as e:
print(f"Image error in {html_file}: {e}")
img.decompose() # Remove broken images

# Convert to PDF
pdfkit.from_file(
[os.path.join(base_dir, f) for f in html_files],
output_pdf,
options=options
)


---

## 🔹 Step 3: Complete Conversion Function
Combine everything into a single workflow.

def epub_to_pdf(epub_path, output_pdf, temp_dir="temp_epub"):
try:
print(f"Converting {epub_path} to PDF...")

# Step 1: Extract EPUB
print("Extracting EPUB contents...")
html_files = extract_epub(epub_path, temp_dir)

# Step 2: Convert to PDF
print("Generating PDF...")
html_to_pdf(html_files, output_pdf, temp_dir)

print(f"Success! PDF saved to {output_pdf}")
return True

except Exception as e:
print(f"Conversion failed: {str(e)}")
return False
finally:
# Clean up temporary files
if os.path.exists(temp_dir):
import shutil
shutil.rmtree(temp_dir)


---

## 🔹 Advanced Options
### 1. Custom Styling
Add CSS to improve PDF appearance:

def html_to_pdf(html_files, output_pdf, base_dir):
options = {
# ... previous options ...
'user-style-sheet': 'styles.css', # Custom CSS
}

# Create CSS file if needed
css = """
body { font-family: "Times New Roman", serif; font-size: 12pt; }
img { max-width: 100%; height: auto; }
"""
with open(os.path.join(base_dir, 'styles.css'), 'w') as f:
f.write(css)

pdfkit.from_file(/* ... */)
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📚 JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.

### 🔍 Key Features:

🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects

### Example Usage:

import easyocr

reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')


### 📌 Ideal For:

Text extraction from photos, scans, and documents
Embedding OCR capabilities in apps (e.g. automated data entry)

🔗 GitHub: https://github.com/JaidedAI/EasyOCR

👉 Follow us for more: @DataScienceN

#Python #OCR #MachineLearning #ComputerVision #EasyOCR
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🧹 ObjectClear — an AI-powered tool for removing objects from images effortlessly.

⚙️ What It Can Do:

🖼️ Upload any image
🎯 Select the object you want to remove
🌟 The model automatically erases the object and intelligently reconstructs the background

⚡️ Under the Hood:

— Uses Segment Anything (SAM) by Meta for object segmentation
— Leverages Inpaint-Anything for realistic background generation
— Works in your browser with an intuitive Gradio UI

✔️ Fully open-source and can be run locally.

📎 GitHub: https://github.com/zjx0101/ObjectClear

#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python


✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🚀 Comprehensive Tutorial: Build a Folder Monitoring & Intruder Detection System in Python

In this comprehensive, step-by-step tutorial, you will learn how to build a real-time folder monitoring and intruder detection system using Python.

🔐 Your Goal:
Create a background program that:
- Monitors a specific folder on your computer.
- Instantly captures a photo using the webcam whenever someone opens that folder.
- Saves the photo with a timestamp in a secure folder.
- Runs automatically when Windows starts.
- Keeps running until you manually stop it (e.g., via Task Manager or a hotkey).

Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring

#Python #Security #FolderMonitoring #IntruderDetection #OpenCV #FaceCapture #Automation #Windows #TaskScheduler #ComputerVision


✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🚀 Comprehensive Guide: How to Prepare for an Image Processing Job Interview – 500 Most Common Interview Questions

Let's start: https://hackmd.io/@husseinsheikho/IP

#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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