<jupyter_start><jupyter_text>div.stContainer 자소서 페이지div.viewTitWrap  회사이름div.adviceTotal 자소서 총평dl.qnaLists 질문내용<jupyter_code>soup=BeautifulSoup(browser.page_source,'html.parser')
introduction_page = soup.select('div.stContainer')
browser.find_element_by_css_selector('dt:nth-child(5) > button > span.tx').click()
browser.find_element_by_css_selector('dt:nth-child(7) > button > span.tx').click()
introduction_page[0]
company = introduction_page[0].select('div.viewTitWrap')[0].text.strip()
score = introduction_page[0].select('div.adviceTotal > div > span')[0].text.strip()
group = introduction_page[0].select('div.adviceTotal > p')[0].text.strip()
Q1 = introduction_page[0].select('dt:nth-child(1) > button > span.tx')[0].text.strip()
A1 = introduction_page[0].select('dd:nth-child(2) > div.tx')[0].text.strip()
Q2 = introduction_page[0].select('dt:nth-child(3) > button > span.tx')[0].text.strip()
A2 = introduction_page[0].select('dd:nth-child(4) > div.tx')[0].text.strip()
Q3 = introduction_page[0].select('dt:nth-child(5) > button > span.tx')[0].text.strip()
A3 = introduction_page[0].select('dd:nth-child(6) > div.tx')[0].text.strip()
Q4 = introduction_page[0].select('dt:nth-child(7) > button > span.tx')[0].text.strip()
A4 = introduction_page[0].select('dd:nth-child(8) > div.tx')[0].text.strip()
# clasificar = introduction_page[0].select('dl.qnaLists')[0].text.strip()
contents = []

for scraping in introduction_page:
    company = introduction_page[0].select('div.viewTitWrap')[0].text.strip()
    score = introduction_page[0].select('div.adviceTotal > div > span')[0].text.strip()
    group = introduction_page[0].select('div.adviceTotal > p')[0].text.strip()
    Q1 = introduction_page[0].select('dt:nth-child(1) > button > span.tx')[0].text.strip()
    A1 = introduction_page[0].select('dd:nth-child(2) > div.tx')[0].text.strip()
    Q2 = introduction_page[0].select('dt:nth-child(3) > button > span.tx')[0].text.strip()
    A2 = introduction_page[0].select('dd:nth-child(4) > div.tx')[0].text.strip()
    Q3 = introduction_page[0].select('dt:nth-child(5) > button > span.tx')[0].text.strip()
    A3 = introduction_page[0].select('dd:nth-child(6) > div.tx')[0].text.strip()
    Q4 = introduction_page[0].select('dt:nth-child(7) > button > span.tx')[0].text.strip()
    A4 = introduction_page[0].select('dd:nth-child(8) > div.tx')[0].text.strip()
    result = [company, score, group, Q1, A1, Q2, A2, Q3, A3, Q4, A4 ]
    contents.append(result)

contents
import pandas as pd
df = pd.DataFrame(contents,columns=['company', 'score', 'group', 'Q1', 'A1', 'Q2', 'A2', 'Q3', 'A3', 'Q4', 'A4'])
df
browser.back()
browser.find_element_by_css_selector('li:nth-child(2) > div.txBx > p > a > span').click()
browser.find_element_by_css_selector('dt:nth-child(5) > button > span.tx').click()
browser.find_element_by_css_selector('dt:nth-child(7) > button > span.tx').click()
# contents = []

# for scraping in introduction_page:
#     company = introduction_page[0].select('div.viewTitWrap')[0].text.strip()
#     score = introduction_page[0].select('div.adviceTotal > div > span')[0].text.strip()
#     group = introduction_page[0].select('div.adviceTotal > p')[0].text.strip()
#     Q1 = introduction_page[0].select('dt:nth-child(1) > button > span.tx')[0].text.strip()
#     A1 = introduction_page[0].select('dd:nth-child(2) > div.tx')[0].text.strip()
#     Q2 = introduction_page[0].select('dt:nth-child(3) > button > span.tx')[0].text.strip()
#     A2 = introduction_page[0].select('dd:nth-child(4) > div.tx')[0].text.strip()
#     Q3 = introduction_page[0].select('dt:nth-child(5) > button > span.tx')[0].text.strip()
#     A3 = introduction_page[0].select('dd:nth-child(6) > div.tx')[0].text.strip()
#     Q4 = introduction_page[0].select('dt:nth-child(7) > button > span.tx')[0].text.strip()
#     A4 = introduction_page[0].select('dd:nth-child(8) > div.tx')[0].text.strip()
#     result = [company, score, group, Q1, A1, Q2, A2, Q3, A3, Q4, A4 ]
#     contents.append(result)
    
        
# contents
# df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2125556104794859, 'fraction_numerical': 0.03608502224419179, 'mean_word_length': 5.423809523809524, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 62, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '430633', 'license': 'Apache-2.0', 'n_tokens_mistral': 1596, 'n_tokens_neox': 1605, 'n_words': 197, 'path': 'scraping/Self_introduction_test_copy.ipynb', 'repo_name': 'yoonputer/Team_Project2', 'size': '4046'}