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Advertising on the Telegram channel «Python | Machine Learning | Coding»
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Computer science
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This channel is for Programmers, Coders, Software Engineers.
1) Data Science
2) Machine Learning
3) Data viz
4) Artificial Intelligence
5) Quizzes
6) Ebooks
7) Articles
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18.07.2025
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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
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18.07.2025
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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
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20.07.2025
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23.07.2025
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