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SerpApi profile image Dmitriy Zub ☀️
Dmitriy Zub ☀️ for SerpApi

Posted on • Updated on • Originally published at serpapi.com

Scrape historic Google Scholar Organic, Cite results using Python

#python #tutorial #webscraping #data

What will be scraped

From Organic results:

what_will_be_scraped_1_01

📌Note: There's a limit of 100 pages on Google Scholar so whenever you see About xxx.xxx results it does not mean that all of these results will be displayed, the same as it on Google Search.

From Cite results:

what_will_be_scraped_3

Prerequisites

Prepare virtual environment and install libraries

If you're on Linux:

python -m venv env && source env/bin/activate
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If you're on Windows and using Git Bash:

python -m venv env && source env/Scripts/activate
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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.

In short, it's a thing that creates an independent set of installed libraries inside different folder including different Python versions that can coexist with each other on the same system thus preventing libraries and Python version conflicts.

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

Install libraries:

pip install google-search-results pandas
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Process

google_scholar_process_1_03_1

If you don't need an explanation:

google_scholar_organic_results_2_1

Scrape Google Scholar Organic Results using Pagination

import os
from serpapi import GoogleSearch
from urllib.parse import urlsplit, parse_qsl


def organic_results():
    print("extracting organic results..")

    params = {
        "api_key": os.getenv("API_KEY"),
        "engine": "google_scholar",
        "q": "minecraft redstone system structure characteristics strength",  # search query
        "hl": "en",        # language
        "as_ylo": "2017",  # from 2017
        "as_yhi": "2021",  # to 2021
        "start": "0"       # first page
    }

    search = GoogleSearch(params)

    organic_results_data = []

    loop_is_true = True

    while loop_is_true:
        results = search.get_dict()

        print(f"Currently extracting page №{results['serpapi_pagination']['current']}..")

        for result in results["organic_results"]:
            position = result["position"]
            title = result["title"]
            publication_info_summary = result["publication_info"]["summary"]
            result_id = result["result_id"]
            link = result.get("link")
            result_type = result.get("type")
            snippet = result.get("snippet")

            try:
              file_title = result["resources"][0]["title"]
            except: file_title = None

            try:
              file_link = result["resources"][0]["link"]
            except: file_link = None

            try:
              file_format = result["resources"][0]["file_format"]
            except: file_format = None

            try:
              cited_by_count = int(result["inline_links"]["cited_by"]["total"])
            except: cited_by_count = None

            cited_by_id = result.get("inline_links", {}).get("cited_by", {}).get("cites_id", {})
            cited_by_link = result.get("inline_links", {}).get("cited_by", {}).get("link", {})

            try:
              total_versions = int(result["inline_links"]["versions"]["total"])
            except: total_versions = None

            all_versions_link = result.get("inline_links", {}).get("versions", {}).get("link", {})
            all_versions_id = result.get("inline_links", {}).get("versions", {}).get("cluster_id", {})

            organic_results_data.append({
              "page_number": results["serpapi_pagination"]["current"],
              "position": position + 1,
              "result_type": result_type,
              "title": title,
              "link": link,
              "result_id": result_id,
              "publication_info_summary": publication_info_summary,
              "snippet": snippet,
              "cited_by_count": cited_by_count,
              "cited_by_link": cited_by_link,
              "cited_by_id": cited_by_id,
              "total_versions": total_versions,
              "all_versions_link": all_versions_link,
              "all_versions_id": all_versions_id,
              "file_format": file_format,
              "file_title": file_title,
              "file_link": file_link,
            })

        if "next" in results.get("serpapi_pagination", {}):
            search.params_dict.update(dict(parse_qsl(urlsplit(results["serpapi_pagination"]["next"]).query)))
        else:
            loop_is_true = False

    return organic_results_data
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Explanation about paginated organic results extraction

Import os, serpapi, urllib libraries:

import os
from serpapi import GoogleSearch
from urllib.parse import urlsplit, parse_qsl
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Create and pass search parameters to GoogleSearch() where all extraction happens on the SerpApi backend:

params = {
    "api_key": os.getenv("API_KEY"),
    "engine": "google_scholar",
    "q": "minecraft redstone system structure characteristics strength",  # search query
    "hl": "en",         # language
    "as_ylo": "2017",   # from 2017
    "as_yhi": "2021",   # to 2021
    "start": "0"
}

search = GoogleSearch(params) # extraction happens here
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Create temporary list() to store the data that later will be used to save to CSV file or passed to cite_results() function:

organic_results_data = []
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Set up a while loop to extract the data from all available pages:

loop_is_true = True

while loop_is_true:
    results = search.get_dict()

    # data extraction code..

    if "next" in results.get("serpapi_pagination", {}):
        search.params_dict.update(dict(parse_qsl(urlsplit(results["serpapi_pagination"]["next"]).query)))
    else:
        loop_is_true = False
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  • If there's no "next" page URL available it will break out of the while loop by setting a loop_is_true to False.
  • if there's a "next" page URL, search.params_dict.update will split and update URL to GoogleSearch(params) for a new page.

Extract data in a for loop:

for result in results["organic_results"]:
    position = result["position"]
    title = result["title"]
    publication_info_summary = result["publication_info"]["summary"]
    result_id = result["result_id"]
    link = result.get("link")
    result_type = result.get("type")
    snippet = result.get("snippet")

    try:
      file_title = result["resources"][0]["title"]
    except: file_title = None

    try:
      file_link = result["resources"][0]["link"]
    except: file_link = None

    try:
      file_format = result["resources"][0]["file_format"]
    except: file_format = None

    try:
      cited_by_count = int(result["inline_links"]["cited_by"]["total"])
    except: cited_by_count = None

    cited_by_id = result.get("inline_links", {}).get("cited_by", {}).get("cites_id", {})
    cited_by_link = result.get("inline_links", {}).get("cited_by", {}).get("link", {})

    try:
      total_versions = int(result["inline_links"]["versions"]["total"])
    except: total_versions = None

    all_versions_link = result.get("inline_links", {}).get("versions", {}).get("link", {})
    all_versions_id = result.get("inline_links", {}).get("versions", {}).get("cluster_id", {})
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  • try/except was used to handle None values when they're not returned from Google backend.

If you try to merge everything in one try block, extracted data will be inaccurate meaning if link or snippet is actually present it will return None sometimes instead, that's why there's a lot of try/except blocks.

Append extracted data to temporary list():

organic_results_data = []

# data extraction and a while loop code... 

organic_results_data.append({
    "page_number": results["serpapi_pagination"]["current"],
    "position": position + 1,
    "result_type": result_type,
    "title": title,
    "link": link,
    "result_id": result_id,
    "publication_info_summary": publication_info_summary,
    "snippet": snippet,
    "cited_by_count": cited_by_count,
    "cited_by_link": cited_by_link,
    "cited_by_id": cited_by_id,
    "total_versions": total_versions,
    "all_versions_link": all_versions_link,
    "all_versions_id": all_versions_id,
    "file_format": file_format,
    "file_title": file_title,
    "file_link": file_link,
})
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Return temporary list() data that will be used later in citation extraction:

return organic_results_data
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google_scholar_cite_05_1

Scrape Google Scholar Cite Results using Pagination

In this section we'll use returned data from organic results and pass result_id to search query in order to extract cite results.

If you already have a list of result id's, you can skip organic results extraction:

# if you already have a list of result id's

result_ids = ["FDc6HiktlqEJ"..."FDc6Hikt21J"]
for citation in result_ids:
    params = {
        "api_key": "API_KEY",             # SerpApi API key
        "engine": "google_scholar_cite",  # cite results extraction
        "q": citation                     # FDc6HiktlqEJ ... FDc6Hikt21J
    }
    search = GoogleSearch(params)
    results = search.get_dict()
    # further extraction code..
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Below the Cite extraction code snippet you'll also find an step-by-step explanation on what is going on.

import os
from serpapi import GoogleSearch
from google_scholar_organic_results import organic_results

def cite_results():

    print("extracting cite results..")

    citation_results = []

    for citation in organic_results():
        params = {
            "api_key": os.getenv("API_KEY"),
            "engine": "google_scholar_cite",
            "q": citation["result_id"]
        }

        search = GoogleSearch(params)
        results = search.get_dict()

        print(f"Currently extracting {citation['result_id']} citation ID.")

        for result in results["citations"]:
            cite_title = result["title"]
            cite_snippet = result["snippet"]

            citation_results.append({
                "organic_result_title": citation["title"],
                "organic_result_link": citation["link"],
                "citation_title": cite_title,
                "citation_snippet": cite_snippet
            })

    return citation_results
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Explanation about Cite results extraction

Create temporary list() to store citation data:

citation_results = []
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Set up a for loop to iterate over organic_results() and pass result_id to "q" search query:

for citation in organic_results():
    params = {
        "api_key": os.getenv("API_KEY"),
        "engine": "google_scholar_cite",
        "q": citation["result_id"]
    }

    search = GoogleSearch(params)  # from where extraction happens on the backend
    results = search.get_dict()    # from where JSON string is coming from
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Set up a second for loop and access data as you would access a dictionary:

for result in results["citations"]:
    cite_title = result["title"]
    cite_snippet = result["snippet"]
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Append extracted data to temporary list() as a dictionary:

citation_results.append({
    "organic_result_title": citation["title"], # to know from where Cite comes
    "organic_result_link": citation["link"],   # to know from where Cite comes
    "citation_title": cite_title,
    "citation_snippet": cite_snippet
})
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Return data from temporary list():

return citation_results
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google_scholar_save_csv_03_1

Save to CSV

We only need to pass returned list of dictionary from organic and cite results to DataFrame data argument and save it to_csv().

import pandas as pd
from google_scholar_organic_results import organic_results
from google_scholar_cite_results import cite_results

print("waiting for organic results to save..")
pd.DataFrame(data=organic_results()) \
  .to_csv("google_scholar_organic_results.csv", encoding="utf-8", index=False)

print("waiting for cite results to save..")
pd.DataFrame(data=cite_results()) \
  .to_csv("google_scholar_citation_results.csv", encoding="utf-8", index=False)
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Explanation about saving results to CSV

Import organic_results() and cite_results() from where data is coming from, and pandas library:

import pandas as pd
from google_scholar_organic_results import organic_results
from google_scholar_cite_results import cite_results
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Save organic results to to_csv():

pd.DataFrame(data=organic_results()) \
    .to_csv("google_scholar_organic_results.csv", encoding="utf-8", index=False)
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Save citation results to to_csv():

pd.DataFrame(data=cite_results()) \
    .to_csv("google_scholar_citation_results.csv", encoding="utf-8", index=False)
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  • data argument inside DataFrame is your data.
  • encoding='utf-8' argument just to make sure everything will be saved correctly. I used it explicitly even thought it's a default value.
  • index=False argument to drop default pandas row numbers.

google_scholar_save_sql_05

Save to SQLite

By the end of this section you'll know how:

  • SQLite database operates,
  • save data to SQLite using pandas,
  • connect and close connection to SQLite database,
  • create and delete tables/columns,
  • add data in a for loop.

An example of how SQLite operates:

1. connection open
    2. transaction started
        3. statement executes
    4. transaction done
5. connection closed
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Save data to SQLite using pandas 

# https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html
import sqlite3
import pandas as pd
from google_scholar_organic_results import organic_results
from google_scholar_cite_results import cite_results

conn = sqlite3.connect("google_scholar_results.db")

# save organic results to SQLite
pd.DataFrame(organic_results()).to_sql(name="google_scholar_organic_results", 
                                       con=conn, 
                                       if_exists="append", 
                                       index=False)

# save cite results to SQLite
pd.DataFrame(cite_results()).to_sql(name="google_scholar_cite_results", 
                                    con=conn, 
                                    if_exists="append", 
                                    index=False)

conn.commit()
conn.close()
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  • name is a name of SQL table.
  • con is a connection to database.
  • if_exists will tell pandas how to behave if the table already exists. By default, it will "fail" and raise a ValueError. In this case pandas will append data.
  • index is to remove DataFrame index column.

Another way of saving data using manual SQLite queries 

import sqlite3

conn = sqlite3.connect("google_scholar_results.db")

conn.execute("""CREATE TABLE google_scholar_organic_results (
                page_number integer,
                position integer,
                result_type text,
                title text, 
                link text,
                snippet text,
                result_id text,
                publication_info_summary text,
                cited_by_count integer,
                cited_by_link text,
                cited_by_id text,
                total_versions integer,
                all_versions_link text,
                all_versions_id text,
                file_format text,
                file_title text,
                file_link text)""")
conn.commit()

 conn.execute("""CREATE TABLE google_scholar_cite_results (
                organic_results_title text, 
                organic_results_link text,
                citation_title text,
                citation_link text)""")
conn.commit()

# store Organic data to database
for item in organic_results():
    conn.execute("""INSERT INTO google_scholar_organic_results
                    VALUES (:page_number,
                            :position,
                            :result_type,
                            :title,
                            :link,
                            :snippet,
                            :result_id,
                            :publication_info_summary,
                            :cited_by_count,
                            :cited_by_link,
                            :cited_by_id,
                            :total_versions,
                            :all_versions_link,
                            :all_versions_id,
                            :file_format,
                            :file_title,
                            :file_link)""",
                     {"page_number": item["page_number"],
                      "position": item["position"],
                      "result_type": item["type"],
                      "title": item["title"],
                      "link": item["link"],
                      "snippet": item["snippet"],
                      "result_id": item["result_id"],
                      "publication_info_summary": item["publication_info_summary"],
                      "cited_by_count": item["cited_by_count"],
                      "cited_by_link": item["cited_by_link"],
                      "cited_by_id": item["cited_by_id"],
                      "total_versions": item["total_versions"],
                      "all_versions_link": item["all_versions_link"],
                      "all_versions_id": item["all_versions_id"],
                      "file_format": item["file_format"],
                      "file_title": item["file_title"],
                      "file_link": item["file_link"]})
conn.commit()

# store Cite data to database
for cite_result in cite_results():
    conn.execute("""INSERT INTO google_scholar_cite_results 
                    VALUES (:organic_result_title,
                    :organic_result_link,
                    :citation_title,
                    :citation_snippet)""",
                 {"organic_result_title": cite_result["organic_result_title"],
                  "organic_result_link": cite_result["organic_result_link"],
                  "citation_title": cite_result["citation_title"],
                  "citation_snippet": cite_result["citation_snippet"]})

conn.commit()
conn.close() # explicit is better than implicit.
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Explanation about saving results using manual SQLite queries

Import sqlite3 library:

import sqlite3
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Connect to existing database or give it a name, and it will be created:

conn = sqlite3.connect("google_scholar_results.db")
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Create Organic results table and commit changes:

conn.execute("""CREATE TABLE google_scholar_organic_results (
                page_number integer,
                position integer,
                result_type text,
                title text, 
                link text,
                snippet text,
                result_id text,
                publication_info_summary text,
                cited_by_count integer,
                cited_by_link text,
                cited_by_id text,
                total_versions integer,
                all_versions_link text,
                all_versions_id text,
                file_format text,
                file_title text,
                file_link text)""")
conn.commit()
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Create Cite results table and commit changes:

conn.execute("""CREATE TABLE google_scholar_cite_results (
            organic_results_title text, 
            organic_results_link text,
            citation_title text,
            citation_link text)""")
conn.commit()
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Add extracted data to Organic table in a loop:

for item in organic_results():
  conn.execute("""INSERT INTO google_scholar_organic_results
                    VALUES (:page_number,
                            :position,
                            :result_type,
                            :title,
                            :link,
                            :snippet,
                            :result_id,
                            :publication_info_summary,
                            :cited_by_count,
                            :cited_by_link,
                            :cited_by_id,
                            :total_versions,
                            :all_versions_link,
                            :all_versions_id,
                            :file_format,
                            :file_title,
                            :file_link)""",
               {"page_number": item["page_number"],
                "position": item["position"],
                "result_type": item["type"],
                "title": item["title"],
                "link": item["link"],
                "snippet": item["snippet"],
                "result_id": item["result_id"],
                "publication_info_summary": item["publication_info_summary"],
                "cited_by_count": item["cited_by_count"],
                "cited_by_link": item["cited_by_link"],
                "cited_by_id": item["cited_by_id"],
                "total_versions": item["total_versions"],
                "all_versions_link": item["all_versions_link"],
                "all_versions_id": item["all_versions_id"],
                "file_format": item["file_format"],
                "file_title": item["file_title"],
                "file_link": item["file_link"]})
conn.commit()
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Add extracted data to Cite table in a loop:

for cite_result in cite_results():
  conn.execute("""INSERT INTO google_scholar_cite_results 
                    VALUES (:organic_result_title,
                    :organic_result_link,
                    :citation_title,
                    :citation_snippet)""",
               {"organic_result_title": cite_result["organic_result_title"],
                "organic_result_link": cite_result["organic_result_link"],
                "citation_title": cite_result["citation_title"],
                "citation_snippet": cite_result["citation_snippet"]})
conn.commit()
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Close database connection:

conn.close()
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Additional useful commands:

# delete all data from the whole table
conn.execute("DELETE FROM google_scholar_organic_results")

# delete table
conn.execute("DROP TABLE google_scholar_organic_results")

# delete column
conn.execute("ALTER TABLE google_scholar_organic_results DROP COLUMN authors")

# add column
conn.execute("ALTER TABLE google_scholar_organic_results ADD COLUMN snippet text")
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Full Extraction Code

import os
from serpapi import GoogleSearch
from urllib.parse import urlsplit, parse_qsl


def organic_results():
  print("extracting organic results..")

  params = {
    "api_key": os.getenv("API_KEY"),
    "engine": "google_scholar",
    "q": "minecraft redstone system structure characteristics strength",  # search query
    "hl": "en",        # language
    "as_ylo": "2017",  # from 2017
    "as_yhi": "2021",  # to 2021
    "start": "0"
  }

  search = GoogleSearch(params)

  organic_results_data = []

  loop_is_true = True

  while loop_is_true:
      results = search.get_dict()

      print(f"Currently extracting page №{results['serpapi_pagination']['current']}..")

      for result in results["organic_results"]:
          position = result["position"]
          title = result["title"]
          publication_info_summary = result["publication_info"]["summary"]
          result_id = result["result_id"]
          link = result.get("link")
          result_type = result.get("type")
          snippet = result.get("snippet")

          try:
            file_title = result["resources"][0]["title"]
          except: file_title = None

          try:
            file_link = result["resources"][0]["link"]
          except: file_link = None

          try:
            file_format = result["resources"][0]["file_format"]
          except: file_format = None

          try:
            cited_by_count = int(result["inline_links"]["cited_by"]["total"])
          except: cited_by_count = None

          cited_by_id = result.get("inline_links", {}).get("cited_by", {}).get("cites_id", {})
          cited_by_link = result.get("inline_links", {}).get("cited_by", {}).get("link", {})

          try:
            total_versions = int(result["inline_links"]["versions"]["total"])
          except: total_versions = None

          all_versions_link = result.get("inline_links", {}).get("versions", {}).get("link", {})
          all_versions_id = result.get("inline_links", {}).get("versions", {}).get("cluster_id", {})

          organic_results_data.append({
            "page_number": results["serpapi_pagination"]["current"],
            "position": position + 1,
            "result_type": result_type,
            "title": title,
            "link": link,
            "result_id": result_id,
            "publication_info_summary": publication_info_summary,
            "snippet": snippet,
            "cited_by_count": cited_by_count,
            "cited_by_link": cited_by_link,
            "cited_by_id": cited_by_id,
            "total_versions": total_versions,
            "all_versions_link": all_versions_link,
            "all_versions_id": all_versions_id,
            "file_format": file_format,
            "file_title": file_title,
            "file_link": file_link,
          })

      if "next" in results.get("serpapi_pagination", {}):
        search.params_dict.update(dict(parse_qsl(urlsplit(results["serpapi_pagination"]["next"]).query)))
      else:
        loop_is_true = False

  return organic_results_data


def cite_results():

  print("extracting cite results..")

  citation_results = []

  for citation in organic_results():
    params = {
      "api_key": os.getenv("API_KEY"),
      "engine": "google_scholar_cite",
      "q": citation["result_id"]
    }

    search = GoogleSearch(params)
    results = search.get_dict()

    print(f"Currently extracting {citation['result_id']} citation ID.")

    for result in results["citations"]:
      cite_title = result["title"]
      cite_snippet = result["snippet"]

      citation_results.append({
        "organic_result_title": citation["title"],
        "organic_result_link": citation["link"],
        "citation_title": cite_title,
        "citation_snippet": cite_snippet
      })

  return citation_results



# example console output when extracting organic results and saving to SQL:
'''
extracting organic results..
Currently extracting page №1..
Currently extracting page №2..
Currently extracting page №3..
Currently extracting page №4..
Currently extracting page №5..
Currently extracting page №6..
Done extracting organic results.
Saved to SQL Lite database.
'''
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Full Saving Code

import pandas as pd
import sqlite3
from google_scholar_organic_results import organic_results
from google_scholar_cite_results import cite_results

# One way of saving to database Pandas
print("waiting for organic results to save..")
organic_df = pd.DataFrame(data=organic_results())
organic_df.to_csv("google_scholar_organic_results.csv", encoding="utf-8", index=False)

print("waiting for cite results to save..")
cite_df = pd.DataFrame(data=cite_results())
cite_df.to_csv("google_scholar_citation_results.csv", encoding="utf-8", index=False)

# ------------------------------

# Another way of saving to database using manual SQLite queries
conn = sqlite3.connect("google_scholar_results.db")

conn.execute("""CREATE TABLE google_scholar_organic_results (
                page_number integer,
                position integer,
                result_type text,
                title text,
                link text,
                snippet text,
                result_id text,
                publication_info_summary text,
                cited_by_count integer,
                cited_by_link text,
                cited_by_id text,
                total_versions integer,
                all_versions_link text,
                all_versions_id text,
                file_format text,
                file_title text,
                file_link text)""")
conn.commit()


conn.execute("""CREATE TABLE google_scholar_cite_results (
            organic_results_title text,
            organic_results_link text,
            citation_title text,
            citation_link text)""")
conn.commit()

for item in organic_results():
  conn.execute("""INSERT INTO google_scholar_organic_results
                    VALUES (:page_number,
                            :position,
                            :result_type,
                            :title,
                            :link,
                            :snippet,
                            :result_id,
                            :publication_info_summary,
                            :cited_by_count,
                            :cited_by_link,
                            :cited_by_id,
                            :total_versions,
                            :all_versions_link,
                            :all_versions_id,
                            :file_format,
                            :file_title,
                            :file_link)""",
                 {"page_number": item["page_number"],
                  "position": item["position"],
                  "result_type": item["type"],
                  "title": item["title"],
                  "link": item["link"],
                  "snippet": item["snippet"],
                  "result_id": item["result_id"],
                  "publication_info_summary": item["publication_info_summary"],
                  "cited_by_count": item["cited_by_count"],
                  "cited_by_link": item["cited_by_link"],
                  "cited_by_id": item["cited_by_id"],
                  "total_versions": item["total_versions"],
                  "all_versions_link": item["all_versions_link"],
                  "all_versions_id": item["all_versions_id"],
                  "file_format": item["file_format"],
                  "file_title": item["file_title"],
                  "file_link": item["file_link"]})
conn.commit()


for cite_result in cite_results():
    conn.execute("""INSERT INTO google_scholar_cite_results
                    VALUES (:organic_result_title,
                    :organic_result_link,
                    :citation_title,
                    :citation_snippet)""",
               {"organic_result_title": cite_result["organic_result_title"],
                "organic_result_link": cite_result["organic_result_link"],
                "citation_title": cite_result["citation_title"],
                "citation_snippet": cite_result["citation_snippet"]})

conn.commit()
conn.close()
print("Saved to SQL Lite database.")


# Example console output:
'''
extracting organic results..
Currently extracting page №1..
...
Currently extracting page №4..
extracting cite results..
extracting organic results..
Currently extracting page №1..
...
Currently extracting page №4..
Currently extracting 60l4wsP6Ps0J citation ID.
Currently extracting 9hkhIFu_BhAJ citation ID.
...
Saved to SQL Lite database.
'''
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What's next

With this data it should be possible to do a research for certain discipline. A great additional feature would be to run script every week, month to get additional data. It can be done using Google Cloud Functions for example.

The follow-up blog post will be about scraping Profile results using pagination as well as Author results.

If your goal is to extract data without the need to write a parser from scratch, figure out how to bypass blocks from search engines, how to scale it or how to extract data from JavaScript - have a try SerpApi or contact SerpApi.

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