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-dataMain 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-offsetcapability 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
ConnectionErroroccurred in:- Results
- get_results_for_run
- Results
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)
