woodbine / sp_E5032_BLBC_gov

Scrapes www.bexley.gov.uk

London Borough of Bexley - Home


This is a scraper that runs on Morph. To get started see the documentation

Contributors woodbine

Last run completed successfully .

Console output of last run

Injecting configuration and compiling... Injecting scraper and running... E5032_BLBC_gov_2017_04 E5032_BLBC_gov_2017_04 E5032_BLBC_gov_2017_12 E5032_BLBC_gov_2017_11 E5032_BLBC_gov_2017_10 E5032_BLBC_gov_2017_09 E5032_BLBC_gov_2017_08 E5032_BLBC_gov_2017_07 E5032_BLBC_gov_2017_06 E5032_BLBC_gov_2017_05 E5032_BLBC_gov_2017_04 E5032_BLBC_gov_2017_Q1 E5032_BLBC_gov_2016_Q4 E5032_BLBC_gov_2016_Q3 E5032_BLBC_gov_2016_Q2 E5032_BLBC_gov_2016_Q1 E5032_BLBC_gov_2015_Q4 E5032_BLBC_gov_2015_Q3 E5032_BLBC_gov_2015_Q2 E5032_BLBC_gov_2014_Y1 E5032_BLBC_gov_2014_04 E5032_BLBC_gov_2013_Y1 E5032_BLBC_gov_2013_04 E5032_BLBC_gov_2012_Y1 E5032_BLBC_gov_2012_04 E5032_BLBC_gov_2011_Y1 E5032_BLBC_gov_2010_Y1

Data

Downloaded 535 times by SimKennedy woodbine MikeRalphson

To download data sign in with GitHub

Download table (as CSV) Download SQLite database (14 KB) Use the API

rows 10 / 36

Statistics

Average successful run time: 9 minutes

Total run time: 6 days

Total cpu time used: 15 minutes

Total disk space used: 45 KB

History

  • Auto ran revision ebfb6b82 and completed successfully .
    27 records added, 27 records removed in the database
    28 pages scraped
  • Auto ran revision ebfb6b82 and completed successfully .
    27 records added, 24 records removed in the database
    28 pages scraped
  • Auto ran revision ebfb6b82 and completed successfully .
    24 records added, 24 records removed in the database
    25 pages scraped
  • Auto ran revision ebfb6b82 and completed successfully .
    24 records added, 24 records removed in the database
    25 pages scraped
  • Auto ran revision ebfb6b82 and completed successfully .
    24 records added, 24 records removed in the database
    25 pages scraped
  • ...
  • Created on morph.io

Show complete history

Scraper code

Python

sp_E5032_BLBC_gov / scraper.py