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https://github.com/fhswf/aki_prj23_transparenzregister.git
synced 2025-04-22 16:12:55 +02:00
refactor: Implement linter feedback
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parent
1c621f46a7
commit
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@ -1,18 +1,27 @@
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"""Unternehmensregister Scraping."""
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import glob
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import logging
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import multiprocessing
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import os
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from pathlib import Path
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from selenium import webdriver
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from selenium.webdriver.common.by import By
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from selenium.webdriver.support import expected_conditions as EC
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from selenium.webdriver.support import expected_conditions as ec
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from selenium.webdriver.support.ui import WebDriverWait
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from tqdm import tqdm
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logger = logging.getLogger()
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def scrape(query: str, download_dir: list[str]):
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"""Fetch results from Unternehmensregister for given query.
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Args:
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query (str): Search Query (RegEx supported)
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download_dir (list[str]): Directory to place output files in
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"""
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download_path = os.path.join(str(Path.cwd()), *download_dir)
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print(download_path)
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options = webdriver.ChromeOptions()
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preferences = {
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"profile.default_content_settings.popups": 0,
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@ -52,7 +61,7 @@ def scrape(query: str, download_dir: list[str]):
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processed_companies = []
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for page_index in tqdm(range(num_pages)):
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for _ in tqdm(range(num_pages)):
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# Find all "Registerinformationen"
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companies_tab = driver.find_elements(
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By.LINK_TEXT, "Registerinformationen des Registergerichts"
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@ -75,7 +84,7 @@ def scrape(query: str, download_dir: list[str]):
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driver.find_element(By.LINK_TEXT, "SI").click()
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# Show shopping cart
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wait.until(
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EC.visibility_of_element_located(
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ec.visibility_of_element_located(
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(By.LINK_TEXT, "Dokumentenkorb ansehen")
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)
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)
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@ -85,12 +94,12 @@ def scrape(query: str, download_dir: list[str]):
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elems[-2].click()
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wait.until(
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EC.visibility_of_element_located((By.ID, "paymentFormOverview:btnNext"))
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ec.visibility_of_element_located((By.ID, "paymentFormOverview:btnNext"))
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)
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driver.find_element(By.ID, "paymentFormOverview:btnNext").click()
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wait.until(
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EC.visibility_of_element_located((By.LINK_TEXT, "Zum Dokumentenkorb"))
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ec.visibility_of_element_located((By.LINK_TEXT, "Zum Dokumentenkorb"))
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)
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driver.find_element(By.LINK_TEXT, "Zum Dokumentenkorb").click()
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@ -98,9 +107,7 @@ def scrape(query: str, download_dir: list[str]):
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driver.find_element(By.CLASS_NAME, "download-wrapper").click()
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try:
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wait.until(
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lambda x: wait_for_download_condition(download_path, num_files)
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)
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wait.until(wait_for_download_condition(download_path, num_files))
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file_name = "".join(e for e in company_name if e.isalnum()) + ".xml"
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rename_latest_file(
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download_path,
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@ -108,9 +115,9 @@ def scrape(query: str, download_dir: list[str]):
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)
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processed_companies.append(company_name)
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except Exception:
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pass
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logger.warning("Exception caught in Scraping")
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finally:
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for click_counter in range(6):
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for _ in range(6):
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driver.back()
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driver.find_element(By.XPATH, '//*[@class="fas fa-angle-right"]').click()
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driver.close()
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@ -119,34 +126,61 @@ def scrape(query: str, download_dir: list[str]):
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def wait_for_download_condition(
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path: str, num_files: int, pattern: str = "*.xml"
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) -> bool:
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"""Selenium wait condition monitoring number of files in a dir.
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Args:
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path (str): Directory path
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num_files (int): Current number of file
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pattern (str, optional): File pattern. Defaults to "*.xml".
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Returns:
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bool: Current num file exceeded
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"""
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return len(glob.glob1(path, pattern)) > num_files
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def get_num_files(path: str, pattern: str = "*.xml") -> int:
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"""Get number of files in directory.
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Args:
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path (str): Directory to scan
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pattern (str, optional): File pattern. Defaults to "*.xml".
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Returns:
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int: Number of files matching pattern
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"""
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return len(glob.glob1(path, pattern))
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def rename_latest_file(path: str, filename: str, pattern: str = "*.xml"):
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"""Rename file in dir with latest change date.
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Args:
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path (str): Dir to check
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filename (str): Name of file
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pattern (str, optional): File pattern. Defaults to "*.xml".
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"""
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list_of_files = [os.path.join(path, file) for file in glob.glob1(path, pattern)]
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latest_download = max(list_of_files, key=os.path.getctime)
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os.rename(latest_download, os.path.join(path, filename))
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if __name__ == "__main__":
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"""Main procedure"""
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import pandas as pd
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df = pd.read_excel(
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df_relevant_companies = pd.read_excel(
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"./data/study_id42887_top-100-unternehmen-deutschland.xlsx",
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sheet_name="Toplist",
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skiprows=1,
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)
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df = df[df["Name"].notna()]
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df_relevant_companies = df_relevant_companies[df_relevant_companies["Name"].notna()]
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batch_size = 5
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pool = multiprocessing.Pool(processes=batch_size)
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params = [
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(query, ["data", "Unternehmensregister", "scraping", query.strip()])
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for query in df.Name
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for query in df_relevant_companies.Name
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]
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# Map the process_handler function to the parameter list using the Pool
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pool.starmap(scrape, params)
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