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Analyze Scrapy Cloud data

Project description


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pip install arche

Arche (pronounced as Arkey) helps to verify data using set of defined rules, for example:

  • Validation with JSON schema
  • Coverage
  • Duplicates
  • Garbage symbols
  • Comparison of two jobs

We use it in Scrapinghub to ensure quality of scraped data


Arche requires Jupyter environment, supporting both JupyterLab and Notebook UI

For JupyterLab, you will need to properly install plotly extensions

Then just pip install arche

Use case

  • You need to check the quality of data from Scrapy Cloud jobs continuously.

    Say, you scraped some website and have the data ready in the cloud. A typical approach would be:

  • You want to use it in your application to verify Scrapy Cloud data

Developer Setup

pipenv install --dev
pipenv shell


Any contributions are welcome!

  • Fork or create a new branch
  • Make desired changes
  • Open a pull request


Most recent releases are shown at the top. Each release shows:

  • Added: New classes, methods, functions, etc
  • Changed: Additional parameters, changes to inputs or outputs, etc
  • Fixed: Bug fixes that don't change documented behaviour

Note that the top-most release is changes in the unreleased master branch on Github. Parentheses after an item show the name or github id of the contributor of that change.

Keep a Changelog, Semantic Versioning.

[0.4.0] (Work In Progress)





[0.3.2] (2019-04-18)


  • Allow reading private raw schemas directly from bitbucket, #58


  • Progress widgets are removed before printing graphs
  • New plotly v4 API


  • Failing Compare Prices For Same Urls when url is nan, #67
  • Empty graphs in Jupyter Notebook, #63


  • Scraped Items History graphs

[0.3.1] (2019-04-12)


  • Empty graphs due to lack of plotlyjs, #61

[0.3.0] (2019-04-12)


  • Big notebook size, replaced cufflinks with plotly and ipython, #39


  • Fields Coverage now is printed as a bar plot, #9
  • Fields Counts renamed to Coverage Difference and results in 2 bar plots, #9, #51:
    • Coverage from job stats fields counts which reflects coverage for each field for both jobs
    • Coverage difference more than 5% which prints >5% difference between the coverages (was ratio difference before)
  • Compare Scraped Categories renamed to Category Coverage Difference and results in 2 bar plots for each category, #52:
    • Coverage for field which reflects value counts (categories) coverage for the field for both jobs
    • Coverage difference more than 10% for field which shows >10% differences between the category coverages
  • Boolean Fields plots Coverage for boolean fields graph which reflects normalized value counts for boolean fields for both jobs, #53


  • cufflinks dependency
  • Deprecated category_field tag



  • new arche.rules.duplicates.find_by() to find duplicates by chosen columns
import arche
from arche.readers.items import JobItems
df = JobItems(0, "235801/1/15").df
arche.rules.duplicates.find_by(df, ["title", "category"]).show()
  • basic_json_schema().json() prints a schema in JSON format
  • to print a rule result, e.g.
from arche.rules.garbage_symbols import garbage_symbols
from arche.readers.items import JobItems
items = JobItems(0, "235801/1/15")
  • notebooks to documentation


  • Tags rule returns unused tags, #2
  • basic_json_schema() prints a schema as a python dict


  • Arche().basic_json_schema() deprecated in favor of arche.basic_json_schema()



  • Arche().basic_json_schema() not using items_numbers argument


  • Last release without CHANGES updates

Project details

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