68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
from bs4 import BeautifulSoup
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import pandas as pd
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def strip(string):
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strings = []
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for s in string:
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strings.append(s.text.strip())
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return strings
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def pad_list(lst, length, pad_value='Unknown'):
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return lst + [pad_value] * (length - len(lst))
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# Replace 'your_local_file.html' with the path to your local HTML file
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with open('/Users/sbzuc0a/Downloads/Scrape/index.html', 'r', encoding='utf-8') as html_file:
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content = html_file.read()
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# Parse the HTML content
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soup = BeautifulSoup(content, 'html.parser')
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companies = strip(soup.find_all('h3', class_='media__body__head'))
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locations = strip(soup.find_all('h5'))
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places = strip(soup.find_all('span', class_='link-fix--text'))
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urls_T = soup.find_all('a', class_='media-module__link')
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# companies = []
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# for company in companies_t:
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# companies.append(company.text.strip())
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urls = []
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for url in urls_T:
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urls.append(url.get('href'))
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# print(companies)
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# print(locations)
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# print(places)
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# print(urls)
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# print the length of the lists
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# print(len(companies))
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# print(len(locations))
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# print(len(places))
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# print(len(urls))
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locations = pad_list(locations, len(companies))
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# # Find the data you want to scrape. For example, let's say you want to scrape a table:
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# tables = soup.find_all('table')
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# # Assuming you want to scrape the first table
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# table = tables[0]
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# # Use pandas to read the table
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# data_frame = pd.read_html(str(table))[0]
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df = pd.DataFrame({
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'Company': companies,
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'Location': locations,
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'Place': places,
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'URL': urls
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})
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# # Now you have the data in a pandas DataFrame, you can manipulate it as you wish
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print(df)
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# # Optionally, save the data to a CSV file
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df.to_csv('output_bandage.csv', index=False)
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