This package provides tools for conducting algorithm audits of web search and includes a scraper built on requests with tools for geolocating, conducting, and saving searches. It also includes a modular parser for decomposing a SERP into list of components with categorical classifications and position-based specifications.

• Getting Started
• Usage
  • Prepare a search
  • Conduct a search
  • Save a search
• Localization
  • Obtain location names
  • Conduct a localized search
• Contributing
  • Repair or Enhance a Parser
  • Add a Parser
• Similar Packages
• License

# Install pip version
pip install WebSearcher

# Install Github development version - less stable, more fun!
pip install git+https://github.com/gitronald/WebSearcher@dev

import WebSearcher as ws

# Initialize crawler with defaults (headers, logs, ssh tunnels)
se = ws.SearchEngine()

vars(se)

{'url': 'https://www.google.com/search',
 'params': {},
 'headers': {'Host': 'www.google.com',
  'Referer': 'https://www.google.com/',
  'Accept': '*/*',
  'Accept-Language': 'en-US,en;q=0.5',
  'Accept-Encoding': 'gzip,deflate,br',
  'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:58.0) Gecko/20100101 Firefox/58.0'},
 'ssh_tunnel': None,
 'sesh': <requests.sessions.Session at 0x7f7bad8efba8>,
 'log': <Logger WebSearcher.searchers (DEBUG)>,
 'html': None,
 'results': [],
 'results_html': []}

# Conduct Search
se.search('immigration')

2019-08-14 01:25:38,267 | 2688 | INFO | WebSearcher.searchers | 200 | Searching immigration

# Parse Results
se.parse_results()

2019-08-14 01:25:42,208 | 2688 | INFO | WebSearcher.parsers | Parsing SERP 4d4fe27fe6b6466041e326622719b03ccc6542427c577c69740ae7fc

se.results[0]
{'cite': 'The New York Times',
 'cmpt_rank': 0,
 'details': {'img_url': None, 'live_stamp': False, 'orient': 'h'},
 'lang': 'en',
 'qry': 'immigration',
 'serp_id': '4d4fe27fe6b6466041e326622719b03ccc6542427c577c69740ae7fc',
 'serp_rank': 0,
 'sub_rank': 0,
 'timestamp': '1 day ago',
 'title': 'Trump Policy Favors Wealthier Immigrants for Green Cards',
 'type': 'top_stories',
 'url': 'https://www.nytimes.com/2019/08/12/us/politics/trump-immigration-policy.html'}

Recommended: Append html and meta data as lines to a json file. Useful for larger or ongoing crawls.

se.save_serp(append_to='serps.json')

Alternative: Save individual html files in a directory, named by a provided or (default) generated serp_id. Useful for smaller qualitative explorations where you want to quickly look at what is showing up. No meta data is saved, but timestamps could be recovered from the files themselves.

se.save_serp(save_dir='./serps')

Conduct web searches from a location of choice.

To conduct localized searches, you only need one additional data point: The "Canonical Name" of each location.
These are available online, and can be downloaded using a built in function that checks for the most recent version.

# Set save location  
data_dir = './location_data'

# Download latest data; checks for local version before downloading
ws.download_locations(data_dir)

f  = os.listdir(data_dir)[-1]  # Last file
fp = os.path.join(data_dir, f) # File path
locs = pd.read_csv(fp)         # Read
locs.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 102029 entries, 0 to 102028
Data columns (total 7 columns):
Criteria ID       102029 non-null int64
Name              102029 non-null object
Canonical Name    102029 non-null object
Parent ID         101788 non-null float64
Country Code      102013 non-null object
Target Type       102029 non-null object
Status            102029 non-null object
dtypes: float64(1), int64(1), object(5)
memory usage: 5.4+ MB

# Take a look at the first row
locs.iloc[0]

Criteria ID                       1000002
Name                                Kabul
Canonical Name    Kabul,Kabul,Afghanistan
Parent ID                     9.07539e+06
Country Code                           AF
Target Type                          City
Status                             Active
Name: 0, dtype: object

Looking for Canonical Names. In order to search from a given location, you must find the corresponding canonical name.

# Filter for names containing "Boston" and "Massachusetts"
regex = r'(?=.*Boston)(?=.*Massachusetts)' 
str_mask = locs['Canonical Name'].str.contains(regex)
locs[str_mask]

15849                                Boston,Massachusetts,United States
15908                           East Boston,Massachusetts,United States
66033    Boston Logan International Airport,Massachusetts,United States
84817                        Boston College,Massachusetts,United States
85985                          South Boston,Massachusetts,United States
Name: Canonical Name, dtype: object

After picking one, say 'Boston,Massachusetts,United States', you just add this to your se.search() call:

# Conduct Search
qry = 'pizza'
loc = 'Boston,Massachusetts,United States'
se.search(qry, location=loc)

# Parse Results
se.parse_results()

# Shape as dataframe
results = pd.DataFrame(se.results)

# Show details of local results returned 
results[results.type=='local_results']['details'].tolist()

[{
    'rating': 4.0,
    'n_reviews': 152,
    'sub_type': 'Pizza',
    'contact': '226 N Market St'
},
{
    'rating': 4.6,
    'n_reviews': 752,
    'sub_type': 'Pizza',
    'contact': '69 Salem St'
},
{
    'sub_type': 'Pizza', 
    'contact': 'McCormack Building, 1 Ashburton Pl'
}]

Happy to have help! If you see a component that we aren't covering yet, please add it using the process below. If you have other improvements, feel free to add them any way you can.

Coming next:
- Functions for using multiprocessing to parse SERPs.

1. Examine parser names in /component_parsers/__init__.py
2. Find parser file as /component_parsers/{cmpt_name}.py.

1. Add classifier to component_classifier.py, as 'cmpt_name'
2. Add parser file in /component_parsers as cmpt_name.py, with function parse_cmpt_name.
3. Add import for parse_cmpt_name in /component_parsers/__init__.py

Many other methods for scraping web search in python exist, but many of these projects have been abandoned, and I was not able to find one that provided details on the components (e.g. "Answer Boxes" and "Top Stories") and positioning configurations (e.g. a horizontally or vertically oriented carousel) that compose a modern SERP.

Some of the other projects are still ongoing and very interesting in their own ways. Arranged by increasing URL length. Feel free to add to the list through a pull request if you are aware of others:

• https://github.com/jarun/googler
• http://googolplex.sourceforge.net
• https://github.com/Jayin/google.py
• https://github.com/ecoron/SerpScrap
• https://github.com/henux/cli-google
• https://github.com/Kaiz0r/netcrawler
• https://github.com/nabehide/WebSearch
• https://github.com/NikolaiT/se-scraper
• https://github.com/rrwen/search_google
• https://github.com/howie6879/magic_google
• https://github.com/rohithpr/py-web-search
• https://github.com/MarioVilas/googlesearch
• https://github.com/aviaryan/python-gsearch
• https://github.com/nickmvincent/you-geo-see
• https://github.com/anthonyhseb/googlesearch
• https://github.com/KokocGroup/google-parser
• https://github.com/vijayant123/google-scrap
• https://github.com/BirdAPI/Google-Search-API
• http://googlesystem.blogspot.com/2008/04/google-search-rest-api.html

Copyright (C) 2017-2020 Ronald E. Robertson rer@ronalderobertson.com

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.