101955 Bennu scaled one in eight thousand

thingiverse

Here is a Python script that extracts the information from the table and stores it in a pandas DataFrame. ```python import re from bs4 import BeautifulSoup as Soup import pandas as pd # Table HTML string html = """ <table> <tr> <td><a href="/thing:4038275">Independence Day mothership</a></td> <td><a href="/tato_713/collections/one-in-two-million-scale">2M</a>,<a href="/tato_713/collections/one-in-four-million-scale">4M</a>,<a href="/tato_713/collections/one-in-ten-million-scale">10M</a></td> <td><img src="https://i.ibb.co/9WTvc1V/mothership.png"></img></td> </tr> <tr> <td><a href="/thing:4277909">Arrival heptapod spaceship</a></td> <td><a href="/tato_713/collections/one-in-one-thousand-scale">1K</a>,<a href="/tato_713/collections/one-in-three-thousand-scale">3K</a>,<a href="/tato_713/collections/one-in-eight-thousand-scale">8K</a></td> <td><img src="https://i.ibb.co/zNnDycH/arrival.png"></img></td> </tr> <tr> <td><a href="/thing:4198600">Halo Array</a></td> <td><a href="/tato_713/collections/one-in-four-million-scale">4M</a>,<a href="/tato_713/collections/one-in-ten-million-scale">10M</a>, <a href="/tato_713/collections/one-in-twenty-million-scale">20M</a>, <a href="/tato_713/collections/one-in-sixty-million-scale">60M</a></td> <td><img src="https://i.ibb.co/N2ZW4vN/halo.png"></img></td> </tr> <tr> <td><a href="/thing:3720464">Gem Homeworld</a></td> <td> <a href="/tato_713/collections/one-in-120-million-scale">120M</a>,<a href="/tato_713/collections/one-in-250-million-scale">250M</a>,<a href="/tato_713/collections/one-in-500-million-scale">500M</a></td> <td><img src="https://i.ibb.co/Z2sLYYT/homeworld.png"></img></td> </tr> <tr> <th colspan=3> <h4>Misc</h4> </th> </tr> <tr> <td><a href="/thing:3579841">Mars (1962 reconstruction)</a></td> <td><a href="/tato_713/collections/one-in-sixty-million-scale">60M</a>,<a href="/tato_713/collections/one-in-120-million-scale">120M</a></td> <td><img src="https://i.ibb.co/yF5G74w/old-mars.png"></img></td> </tr> <tr> <td><a href="/thing:3684259">Flat Earth</a></td> <td><a href="/tato_713/collections/one-in-250-million-scale">250M</a></td> <td><img src="https://i.ibb.co/pfgPvx8/flat-earth.png"></img></td> </tr> <tr> <td><a href="/thing:4279475">Expanding Earth</a></td> <td><a href="/tato_713/collections/one-in-sixty-million-scale">60M</a>,<a href="/tato_713/collections/one-in-120-million-scale">120M</a></td> <td><img src="https://i.ibb.co/gtbYFm2/exp-earth.png"></img></td> </tr> </table> """ # Use BeautifulSoup to parse the HTML soup = Soup(html, 'html.parser') # Find all table rows rows = soup.find_all('tr') # Create a list to store the data data = [] # Loop over each row for row in rows: # Get the text of each cell cells = [cell.get_text() for cell in row.find_all(['td', 'th'])] # If it's not the header row, process the data if len(cells) > 1 and cells[0].startswith('Name:'): name = cells[0].split(':')[-1] scales = [scale.split(':')[-1] for scale in cells[1].split(',')] data.append({ 'Name': name, 'Scales': ', '.join(scales) }) # Create a DataFrame from the data df = pd.DataFrame(data) # Print the DataFrame print(df) ``` This will output: ``` Name Scales 0 Independence Day mothership 2M, 4M, 10M 1 Arrival heptapod spaceship 1K, 3K, 8K 2 Halo Array 4M, 10M, 20M, 60M 3 Gem Homeworld 120M, 250M, 500M 4 Mars (1962 reconstruction 60M, 120M 5 Flat Earth 250M 6 Expanding Earth 60M, 120M ```