Testrail Data: a handy Testrail data analysis tool

What is it?

This is a wrapper of Testrail Api with pandas DataFrame extended. Especially when you are working on huge data-set, say years of results, this is a handly library.

Installation

pip install testrail-data

Main Features

• Transform pulled data into DataFrame object, covering:
  • Case
  • Case Fields
  • Case Type
  • Milestone
  • Plan
  • Priority
  • Results
  • Run
  • Sections
  • Suite
  • Statuses
  • Template
  • Test
• Complete pull with auto-offset capability to walk through all pagination, avalaible to:
  • Run
  • Result
  • Plan
• Meta data filling option to all IDs in:
  • Case
  • Test
  • Result (not in this version)
• Retry pulling when ConnectionError occurred in:
  • Results
    • get_results_for_run

Example usage with DataFrame

from testrail_data import TestRailAPI

api = TestRailAPI("https://example.testrail.com/", "example@mail.com", "password")

# if use environment variables
# TESTRAIL_URL=https://example.testrail.com/
# TESTRAIL_EMAIL=example@mail.com
# TESTRAIL_PASSWORD=password
# api = TestRailAPI()

# if you having a big project with more than 250 runs, 
# this method would help you too pull them down in single call.
df_run = api.runs.to_dataframe(project_id=1)
df_run.info()

# Pulling all Run by Plan
df_run = api.runs.dataframe_from_plan(plan_id=3)

Example usage with Meta data

# continue ...
from testrail_data import TestRailAPI

api = TestRailAPI()
df_case = api.cases.to_dataframe(project_id=1, suite_id=2, with_meta=True)
# Additional name-columns created base on 
# section_id, template_id, type_id, priority_id, suite_id
# all custom_columns are replaced with meta data.

Example query all results from multiple runs

from testrail_data import TestRailAPI

api = TestRailAPI()
run_ids = [1,2,3,4]

df_run = api.results.dataframe_from_runs(*run_ids)