behave-data¶

Data management for Behave BDD framework.

Typed data tables, readable diffs, dynamic Examples from external sources, reusable fixtures & builders, secret resolution with masking, and declarative tags — all with zero boilerplate in your environment.py.

Built on top of behave-tables for table manipulation.

What you get¶

Typed Tables — Column type annotations (name:str, age:int, active:bool, price:float, created:date) with automatic conversion.
Null Resolution — Empty cells become None, not "". Configurable null markers.
Table Diff — Cucumber-style diff output with row/column mismatch detection.
Raw Tables — Access tables without header assumption.
Dynamic Examples — Load Examples from CSV, JSON, YAML, Excel, SQL, or HTTP APIs.
Fixtures — Reusable data recipes with scoping, nesting, and parametrization.
Builders — Construct test data with derived fields and overrides.
Secrets — Resolve env:VAR, file:path, secret:name placeholders. Multiple backends.
Declarative Tags — @needs_data:name, @with_fixture:name, @cleanup_after.
DataManager — Unified access point for fixtures, builders, and secret resolution.

Install¶

pip install behave-data

Optional dependencies:

pip install behave-data[yaml]    # PyYAML for YAML loader
pip install behave-data[excel]   # openpyxl for Excel loader
pip install behave-data[sql]     # SQLAlchemy for SQL loader
pip install behave-data[http]    # requests for HTTP loader
pip install behave-data[vault]   # hvac for Vault secrets
pip install behave-data[aws]     # boto3 for AWS Secrets Manager
pip install behave-data[all]     # Everything above

Quickstart¶

Add this to features/environment.py:

from behave_data import (
    setup_data,
    before_feature_hook,
    before_scenario_hook,
    before_step_hook,
    after_scenario_hook,
)


def before_all(context):
    setup_data(context)


def before_feature(context, feature):
    before_feature_hook(context, feature)


def before_scenario(context, scenario):
    before_scenario_hook(context, scenario)


def before_step(context, step):
    before_step_hook(context, step)


def after_scenario(context, scenario):
    after_scenario_hook(context, scenario)

Now your .feature files can use typed tables directly:

Feature: Typed users

  Scenario: Users with typed columns
    Given users
      | name:str | age:int | active:bool | created:date |
      | Alice    | 30      | true        | 2024-01-15   |
      | Bob      | 25      | false       | 2024-06-30   |

And in your steps:

from behave import given
from behave_data import typed_wrap

@given("users")
def step_users(context):
    context.users = typed_wrap(context.table).typed_dicts()

context.users[0]["age"] is now an int, active a bool, created a date.

Learn more¶

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