<jupyter_start><jupyter_text>Google Scholar Most Cited Authors of a Field  Names and Scholar URL<jupyter_code># field =input("print field:")
# field.replace(" ","_")
field = "deep_learning"
page = requests.get(f"https://scholar.google.com/citations?view_op=search_authors&hl=en&mauthors=label:{field}")
soup = BeautifulSoup(page.content, "html.parser")

#print(soup.prettify())
n = soup.findAll("h3",{"class":"gs_ai_name"})
authors = {}
for name in n:

    if name.string is None or "http" in name:
        continue
    if len(name.string)>2:
        print(name.string)
        link = "https://scholar.google.com/"+name.a["href"]
        authors[name.string] = {}
        authors[name.string]["scholar"] = link
print("_"*20)   
print(authors)
pd.DataFrame.from_dict(authors).transpose()<jupyter_output><NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
____________________
{'<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=kukA0LcAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=x04W_mMAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=ALVSZAYAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=atsBh1QAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=mG4imMEAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=EZjSxgwAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=1zmDOdwAAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=COnXcM0AAAAJ'}, '<NAME>': {'scholar': 'https://scholar.google.com//citations?hl=en&user=[...]<jupyter_text>Name, Job Title, Scholar URL, and Total Citations<jupyter_code># field =input("print field:")
# field.replace(" ","_")
field = "deep_learning"
page = requests.get(f"https://scholar.google.com/citations?view_op=search_authors&hl=en&mauthors=label:{field}")
soup = BeautifulSoup(page.content, "html.parser")

#print(soup.prettify())
n1 = soup.findAll("h3",{"class":"gs_ai_name"})
n2 = soup.findAll("div",{"class":"gs_ai_aff"})
n3 = soup.find_all("div", {"class": "gs_ai_cby"})
n4 = soup.find_all("div", {"class": "gs_ai_int"})

authors = {}
for name,organ,total_citation, fields in zip(n1,n2,n3,n4):

    if name.string is None or "http" in name:
        continue
    if len(name.string)>2:
        print(name.string)
        f = [field.string for field in fields.find_all("a", {"class": "gs_ai_one_int"})]
#         print(f)
        link = "https://scholar.google.com/"+name.a["href"]
        authors[name.string] = {}
        authors[name.string]["fields"] = f
        authors[name.string]["job_title"] = organ.string
        authors[name.string]["scholar"] = link
        authors[name.string]["total_citation"] = total_citation.string.replace("Cited by ","")
print("_"*20)   
print(list(authors.keys())[1],"==>",authors[list(authors.keys())[1]])
pd.DataFrame.from_dict(authors).transpose()<jupyter_output><NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
<NAME>
____________________
<NAME> ==> {'fields': ['Machine Learning', 'Neural Networks', 'Artificial Intelligence', 'Deep Learning'], 'job_title': 'Co-Founder and Chief Scientist of OpenAI', 'scholar': 'https://scholar.google.com//citations?hl=en&user=x04W_mMAAAAJ', 'total_citation': '278660'}<jupyter_text>Name, Job Title, Scholar URL, Total Citations and Website<jupyter_code># field =input("print field:")
# field.replace(" ","_")
field = "deep_learning"
page = requests.get(f"https://scholar.google.com/citations?view_op=search_authors&hl=en&mauthors=label:{field}")
soup = BeautifulSoup(page.content, "html.parser")

#print(soup.prettify())
n1 = soup.findAll("h3",{"class":"gs_ai_name"})
n2 = soup.findAll("div",{"class":"gs_ai_aff"})
n3 = soup.find_all("div", {"class": "gs_ai_cby"})
n4 = soup.find_all("div", {"class": "gs_ai_int"})

authors = {}
for name,organ,total_citation, fields in zip(n1,n2,n3,n4):

    if name.string is None or "http" in name:
        continue
    if len(name.string)>2:
        print(name.string)
        f = [field.string for field in fields.find_all("a", {"class": "gs_ai_one_int"})]
#         print(f)
        link = "https://scholar.google.com/"+name.a["href"]
        authors[name.string] = {}
        authors[name.string]["job_title"] = organ.string
        authors[name.string]["fields"] = f
        authors[name.string]["scholar"] = link
        authors[name.string]["total_citation"] = total_citation.string.replace("Cited by ","")

        page_2 = requests.get(link)
        soup_2 = BeautifulSoup(page_2.content, "html.parser")
        n_2 = soup_2.find(id = "gsc_prf_ivh")
        for name_2 in n_2:
            if name_2.string is None or "http" in name_2:
                 continue
            if len(name_2.string) >2 :
                try : 
                    authors[name.string]["website"] = name_2["href"]
                except:
                    authors[name.string]["website"] = name_2.string.replace("Verified email at","")
print("_"*20)   
print(authors)

pd.DataFrame.from_dict(authors).transpose()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19642857142857142, 'fraction_numerical': 0.01125776397515528, 'mean_word_length': 4.5768398268398265, 'pattern_counts': {'":': 11, '<': 41, '<?xml version=': 0, '>': 47, 'https://': 16, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '464906', 'license': 'MIT', 'n_tokens_mistral': 1915, 'n_tokens_neox': 1841, 'n_words': 296, 'path': 'crawl_scholar.ipynb', 'repo_name': 'MTisMT/crawl_google_scholar', 'size': '5362'}