In most cases, processed data is saved as a pdf-file. When extracting data from a pdf-file we use the PyPDF2 library.
import PyPDF2 as pypdf
with open("Cyber-Sicherheit.pdf", "rb") as file:
reader = pypdf.PdfReader(file)
print(reader.metadata) # document-info
print(len(reader.pages)) # count of pages
# {'/Author': 'Rechnungshof', '/CreationDate': "D:20220422095428+02'00'", ...
# read a specific page of file (in this case - first page)
with open("Cyber-Sicherheit.pdf", "rb") as file:
reader = pypdf.PdfReader(file)
print(reader.pages[0].extract_text())
# III–623 der Beilagen zu den Stenographischen Protokollen des Nationalrates XXVII. GP ...
# HTML
When extracting data from the WWW, we need 2 libraries: Beautifulsoup, urllib.
import urllib.request as urllib2
from bs4 import BeautifulSoup# Basics
# loading website
response = urllib2.urlopen("https://htlkrems.ac.at")
html_doc = response.read()
# parsing html-document & output of first 1000 characters
soup = BeautifulSoup(response, "html.parse")
strhtml = soup.prettify()
print(strhtml[:1000])
# navigate in soup-structure
print(soup.title)
# <title>HTL Krems</title>
print(soup.title.string)
# HTL Krems# urllib
response = urllib2.urlopen("https://...")
html_doc = response.read()# BeautifulSoup
# declaration
soup = BeautifulSoup(html_doc, "html.parse")
strhtm = soup.prettify()
# navigation
print(soup.title.string)
# find specific tags/elements
print(soup.find_all("a")) # here, it just finds all "a" tags
for e in elements: # here, we only get the text
print(e.text)