Scrape Google Scholar Metrics Results to CSV with Python

What will be scraped

📌Note: you have an option to save CSV file from public access mandates but there will be no funder link. This blog post shows how to scrape funder link.

If you don't need an explanation:

try the code in the online IDE.

Prerequisites

Basic knowledge scraping with CSS selectors

CSS selectors declare which part of the markup a style applies to thus allowing to extract data from matching tags and attributes.

If you haven't scraped with CSS selectors, there's a dedicated blog post of mine about how to use CSS selectors when web-scraping that covers what it is, pros and cons, and why they're matter from a web-scraping perspective.

Separate virtual environment

It's a thing that creates an independent set of installed libraries including different Python versions that can coexist with each other on the same system prevention libraries or Python version conflicts when working on multiple projects at the same time.

If you didn't work with a virtual environment before, have a look at the dedicated Python virtual environments tutorial using Virtualenv and Poetry blog post of mine to get familiar.

📌Note: using virtual environment is not a strict requirement.

Libraries:

pip install requests lxml beautifulsoup4 pandas

Reducing the chance of being blocked

There's a chance that a request might be blocked. Have a look at how to reduce the chance of being blocked while web-scraping blog of mine, there's eleven methods to bypass blocks from most websites.

Scrape Google Scholar Metrics all Top Publications

import requests, lxml
from bs4 import BeautifulSoup
import pandas as pd


def scrape_all_metrics_top_publications():

    params = {
        "view_op": "top_venues",  # top publications results
        "hl": "en"  # or other lang: pt, sp, de, ru, fr, ja, ko, pl, uk, id
        }

    # https://docs.python-requests.org/en/master/user/quickstart/#custom-headers
    # whatismybrowser.com/detect/what-is-my-user-agent
    headers = {
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.88 Safari/537.36"
        }

    html = requests.get("https://scholar.google.com/citations", params=params, headers=headers, timeout=30)
    soup = BeautifulSoup(html.text, "lxml").find("table")

    df = pd.DataFrame(pd.read_html(str(soup))[0])
    df.drop(df.columns[0], axis=1, inplace=True)
    df.insert(loc=2,
              column="h5-index link",
              value=[f'https://scholar.google.com/{link.a["href"]}' for link in soup.select(".gsc_mvt_t+ td")])

    df.to_csv("google_scholar_metrics_top_publications.csv", index=False)

    # save to csv for specific language
    # df.to_csv(f"google_scholar_metrics_top_publications_lang_{params['hl']}.csv", index=False)

scrape_all_metrics_top_publications()

Create search query parameters:

params = {
    "view_op": "top_venues",  # top publications results
    "hl": "en"                # language:
                              # pt - Portuguese
                              # sp - Spanish
                              # de - German
                              # ru - Russian
                              # fr - French
                              # ja - Japanese
                              # ko - Korean
                              # pl - Polish
                              # uk - Ukrainian
                              # id - Indonesian
}

Pass search query params to request and find() the <table> via BeautifulSoup():

html = requests.get("https://scholar.google.com/citations", params=params)
soup = BeautifulSoup(html.text, "lxml").find("table")

You can scrape table data without scraping with BeautifulSoup() first, but you won't have an option to save links from the table using pandas only.

Scraping table with BeautifulSoup() will allow you to scrape links data as well once passed to pandas read_html().

read_html(), access table data [0] from the soup and create a DataFrame:

df = pd.DataFrame(pd.read_html(str(soup))[0])

Drop unnecessary numeration "Unnamed" column:

df.drop(df.columns[0], axis=1, inplace=True)

Code	Explanation
`df.columns[0]`	first column in the table. In this case it's "Unnamed" column.
`axis=1`	delete column instead of row.
`inplace=True`	allows doing operations on existing `DataFrame` without having to reassign to a new variable.

Insert a new column and add extracted links:

df.insert(loc=2,
          column="h5-index link",
          value=[f'https://scholar.google.com/{link.a["href"]}' for link in soup.select(".gsc_mvt_t+ td")])

Code	Explanation
`loc=2`	location where column will be added.
`columns=`	your column name.
`value=`	your extracted value.

Save to_csv():

df.to_csv("google_scholar_metrics_top_publications.csv", index=False)

index=False to drop default pandas row numbers.

Save to_csv() for a specific language:

df.to_csv(f"google_scholar_metrics_top_publications_lang_{params['hl']}.csv", index=False)

params['hl'] is the language that will be passed to search query params.

Scrape Google Scholar Metrics all Public Access Mandates

import requests, lxml
from bs4 import BeautifulSoup
import pandas as pd


def scrape_all_metrics_public_mandates():
    params = {
        "view_op": "mandates_leaderboard",  # public access mandates results
        "hl": "en"  # or other lang: pt, sp, de, ru, fr, ja, ko, pl, uk, id
        }

    # https://docs.python-requests.org/en/master/user/quickstart/#custom-headers
    # whatismybrowser.com/detect/what-is-my-user-agent
    headers = {
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.88 Safari/537.36"
        }

    html = requests.get("https://scholar.google.com/citations", params=params, headers=headers, timeout=30)
    soup = BeautifulSoup(html.text, "lxml").find("table")

    df = pd.DataFrame(pd.read_html(str(soup))[0])
    df.drop(df.columns[[0, 2]], axis=1, inplace=True)
    df.insert(loc=1, column="Funder Link", value=[link.a["href"] for link in soup.select("td.gsc_mlt_t")])

    df.to_csv("google_scholar_metrics_public_access_mandates.csv", index=False)

    # save to csv for specific language
    # df.to_csv(f"google_scholar_metrics_public_access_mandates_lang_{params['hl']}.csv", index=False)


scrape_all_metrics_public_mandates()

Create search query parameters:

params = {
    "view_op": "mandates_leaderboard",  # public access mandates results
    "hl": "en"  # or other lang: pt, sp, de, ru, fr, ja, ko, pl, uk, id
}

Pass search query params, make a request and find() the <table> via BeautifulSoup():

html = requests.get("https://scholar.google.com/citations", params=params)
soup = BeautifulSoup(html.text, "lxml").find("table")

read_html(),access table data [0] and create a DataFrame:

df = pd.DataFrame(pd.read_html(str(soup))[0])

Drop two unnecessary "Unnamed, Available:" columns:

df.drop(df.columns[[0, 2]], axis=1, inplace=True)

Code	Explanation
`df.columns[[0, 2]]`	first and third columns in the table. In this case it's "Unnamed, Available:" columns.
`axis=1`	delete column instead of row.
`inplace=True`	allows doing operations on existing `DataFrame` without having to reassign to a new variable.

Insert a new column and add extracted links:

df.insert(loc=1, 
          column="Funder Link", 
          value=[link.a["href"] for link in soup.select("td.gsc_mlt_t")])

Code	Explanation
`loc=1`	location where column will be added.
`columns=`	your column name.
`value=`	your extracted value.

Save to_cs():

df.to_csv("google_scholar_metrics_public_access_mandates.csv", index=False)

index=False to drop default pandas row numbers.

Save to_csv() for a specific language:

df.to_csv(f"google_scholar_metrics_public_access_mandates_lang_{params['hl']}.csv", index=False)

params['hl'] is the language that will be passed to search query params.

Outro

If you have anything to share, any questions, suggestions, or something that isn't working correctly, reach out via Twitter at @dimitryzub, or @serp_api.

Yours, Dimitry, and the rest of SerpApi Team.

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